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We have documented how drones depict insightful aerial perspectives of heritage sites before. Monuments, graves, and other ruins are revealed in ways that allow us to view how ancient civilizations lived but also give archaeologists clues on why some of them ceased to exist. Look no further than the Zamani Project to witness one of the best projects on how drones support these types of efforts. Coincidentally, they visited the fascinating Kua Ruins a few years ago which we also recently surveyed in a training course we facilitated for the Mafia Island Marine Park (MIMP) authorities in Tanzania.
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Click anywhere on earth to see how the water balance is changing over time. This app is based on data from GLDAS version 2.1 , which uses weather observations like temperature, humidity, and rainfall to run the Noah land surface model. This model estimates how much of the rain becomes runoff, how much evaporates, and how much infiltrates into the soil. These output variables, calculated every three hours, are aggregated into monthly averages, giving us a record of the hydrologic cycle going all the way back to January 2000.
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bfa_child_
Population of children less than 5 years old of the administrative area
bfa_access
Proportion of people living in households that have a mosquito net for sleeping in the the administrative area
bfa_bednet
Proportion of people who sleep under an ever-treated or LLIN bed-net in the administrative area
bfa_bedn_1
Proportion of female adults (15-49) who sleep under an ever-treated or LLIN bed-net in the administrative area
bfa_bedn_2
Proportion of children less than 5 years old who sleep under an ever-treated or LLIN bed-net in the administrative area
bfa_incide
Malaria incidence among children less than 5 years old in the administrative area
bfa_acce_1
Number of people living in households that have a mosquito net for sleeping in the the administrative area
bfa_bedn_3
Number of people who sleep under an ever-treated or LLIN bed-net in the administrative area
bfa_bedn_4
Number of female adults (15-49) who sleep under an ever-treated or LLIN bed-net in the administrative area
bfa_bedn_5
Number of children less than 5 years old who sleep under an ever-treated or LLIN bed-net in the administrative area
bfa_inci_1
Malaria incidence in terms of number of children less than 5 years old in the administrative area
Cluster_co
Count of survey clusters within the polygon
Each of the 5 malaria risk elements is included as both an estimated proportion of relevant population and as an estimated count aggregated to the commune level (admin 3). These values are malaria incidence in children under 5; bed net use in children under 5, adult female bed net use, general population bed net use; and general population bed net access. The 1km x 1km Fraym raster data products based on the MIS2017 survey data were aggregated to administrative level 3 (commune). The number of survey clusters in each region from the original MIS2017 survey are also included.
Data Citation : Fraym . 2021.GRID3 Burkina Faso Malaria Risk Indicators by Commune (Admin3). Palisades, NY: Geo-Referenced Infrastructure and Demographic Data for Development (GRID3). URL. Accessed DAY MONTH YEAR
bfa_child_
Population of children less than 5 years old of the administrative area
bfa_access
Proportion of people living in households that have a mosquito net for sleeping in the the administrative area
bfa_bednet
Proportion of people who sleep under an ever-treated or LLIN bed-net in the administrative area
bfa_bedn_1
Proportion of female adults (15-49) who sleep under an ever-treated or LLIN bed-net in the administrative area
bfa_bedn_2
Proportion of children less than 5 years old who sleep under an ever-treated or LLIN bed-net in the administrative area
bfa_incide
Malaria incidence among children less than 5 years old in the administrative area
bfa_acce_1
Number of people living in households that have a mosquito net for sleeping in the the administrative area
bfa_bedn_3
Number of people who sleep under an ever-treated or LLIN bed-net in the administrative area
bfa_bedn_4
Number of female adults (15-49) who sleep under an ever-treated or LLIN bed-net in the administrative area
bfa_bedn_5
Number of children less than 5 years old who sleep under an ever-treated or LLIN bed-net in the administrative area
bfa_inci_1
Malaria incidence in terms of number of children less than 5 years old in the administrative area
Cluster_co
Count of survey clusters within the polygon
This score equally weights two components:
accessibility to health facilities
usage of health facilities
Accessibility is defined as time to travel to health facilities. This approach assumes that individuals will travel via vehicle if there is a road and will walk if there is no road. Vulnerability is ranked into five categories by type of health facility (primary or secondary) and accessibility. Areas less than 30 minutes away from a secondary facility are the least vulnerable and areas further than one hour from either a primary or secondary facility are the most vulnerable. Health facility locations are from a 2017 dataset published by ALHASAN Systems Private Limited through the Humanitarian Data Exchange (HDX). Usage of health facilities is proxied by percentage of women that do not visit health facilities and do not see field workers in a year. Vulnerability from limited usage is ranked into five categories by quintile. Areas in the highest quintile are considered to be the most vulnerable.
This feature layer aggregates scores to the state boundaries from GRID3 GDB v1.69.
Source: Fraym 2020
The Fraym platform weaves together the latest satellite imagery and geostatistical datasets with professionally enumerated household surveys. This allows for the disaggregation and re-aggregation of large datasets to cover any geographically bounded area. Indicators are drawn and harmonized from a wide variety of household surveys and other data sources.
These include the following sources:
USAID: Demographic and health surveys
United Nations: UN population division database
World Bank: Enterprise surveys, living standards, global index surveys, and respective country statistics
National Statistical Offices: National censuses and surveys covering population, businesses, health, housing, agriculture, and other areas
International Monetary Fund: World economic outlook databases and respective country statistics
National Air and Space Administration: Remote sensing satellite data, such as vegetation, temperature, and precipitation
USGS: Landscan, Google Earth, GeoData Institute, OSM
WorldPop: Population density by age groups
Population estimate from WorldPop's Bottom-up gridded population layer. Access for the WorldPop population estimates full methodology visit https://wopr.worldpop.org/
This score equally weights two components:
Accessibility to health facilities
Usage of health facilities
Accessibility is defined as time to travel to health facilities. This approach assumes that individuals will travel via vehicle if there is a road and will walk if there is no road. Vulnerability is ranked into five categories by type of health facility (primary or secondary) and accessibility. Areas less than 30 minutes away from a secondary facility are the least vulnerable and areas further than one hour from either a primary or secondary facility are the most vulnerable. Health facility locations are from a 2017 dataset published by ALHASAN Systems Private Limited through the Humanitarian Data Exchange (HDX). Usage of health facilities is proxied by percentage of women that do not visit health facilities and do not see field workers in a year. Vulnerability from limited usage is ranked into five categories by quintile. Areas in the highest quintile are considered to be the most vulnerable.
This feature layer aggregates the index values by ward boundaries from GRID3 GDB v1.69.
Population estimate from WorldPop's Bottom-up gridded population layer. Access for the WorldPop population estimates full methodology visit https://wopr.worldpop.org/
Source: Fraym 2020
The Fraym platform weaves together the latest satellite imagery and geostatistical datasets with professionally enumerated household surveys. This allows for the disaggregation and re-aggregation of large datasets to cover any geographically bounded area. Indicators are drawn and harmonized from a wide variety of household surveys and other data sources.
These include the following sources:
USAID: Demographic and health surveys
United Nations: UN population division database
World Bank: Enterprise surveys, living standards, global index surveys, and respective country statistics
National Statistical Offices: National censuses and surveys covering population, businesses, health, housing, agriculture, and other areas
International Monetary Fund: World economic outlook databases and respective country statistics
National Air and Space Administration: Remote sensing satellite data, such as vegetation, temperature, and precipitation
USGS: Landscan, Google Earth, GeoData Institute, OSM
WorldPop: Population density by age groups
GRID3 Health Facilities
This score ranks the percentage of the population living in a household that does not have a radio or television and where no adult household members are regular consumers of newspapers, radio, television, or the internet. Regular consumers are defined as adults who use the media at least once a week. Adults are defined as individuals aged 15-49. Ranks are determined by quintiles in the data distribution.
This feature layer aggregates scores to the state boundaries from GRID3 GDB v1.69.
Population estimate from WorldPop's Bottom-up gridded population layer. Access for the WorldPop population estimates full methodology visit https://wopr.worldpop.org/
Source: Fraym 2020
The Fraym platform weaves together the latest satellite imagery and geostatistical datasets with professionally enumerated household surveys. This allows for the disaggregation and re-aggregation of large datasets to cover any geographically bounded area. Indicators are drawn and harmonized from a wide variety of household surveys and other data sources.
These include the following sources:
USAID: Demographic and health surveys
United Nations: UN population division database
World Bank: Enterprise surveys, living standards, global index surveys, and respective country statistics
National Statistical Offices: National censuses and surveys covering population, businesses, health, housing, agriculture, and other areas
International Monetary Fund: World economic outlook databases and respective country statistics
National Air and Space Administration: Remote sensing satellite data, such as vegetation, temperature, and precipitation
USGS: Landscan, Google Earth, GeoData Institute, OSM
WorldPop: Population density by age groups
The exposure risk score equally weights three components:
population density
proximity to others in the household
water, sanitation, and hygiene.
Three indicators are equally weighted within the proximity to others in the household component: intergenerational households (household has a child less than eleven years old and a person above 60 years old), average number of household members per sleeping room, and adults aged 15 to 49 employed in essential occupations (health professions, construction, manufacturing, transport, sales, textiles, protective services). Five indicators are equally weighted within the water, sanitation, and hygiene component: soap for hand-washing inside the home is unavailable, water for hand-washing inside the home is unavailable, household must walk 30 or more minutes roundtrip to collect drinking water, household shares toilet with other households or does not have a toilet, and household does not have piped-in drinking water. Population density is defined as population at the 1km squared level. Indicators within components are combined with z-scores at the 1km squared grid level. This feature layer aggregates scores to the states boundaries from GRID3 GDB v1.69. Population estimate from WorldPop's Bottom-up gridded population layer. Access for the WorldPop population estimates full methodology visit https://wopr.worldpop.org/
The Fraym platform weaves together the latest satellite imagery and geostatistical datasets with professionally enumerated household surveys. This allows for the disaggregation and re-aggregation of large datasets to cover any geographically bounded area. Indicators are drawn and harmonized from a wide variety of household surveys and other data sources.
These include the following sources:
USAID: Demographic and health surveys
United Nations: UN population division database
World Bank: Enterprise surveys, living standards, global index surveys, and respective country statistics
National Statistical Offices: National censuses and surveys covering population, businesses, health, housing, agriculture, and other areas
International Monetary Fund: World economic outlook databases and respective country statistics
National Air and Space Administration: Remote sensing satellite data, such as vegetation, temperature, and precipitation
USGS: Landscan, Google Earth, GeoData Institute, OSM
WorldPop: Population density by age groups
Depuis le 28 décembre 2023, le Congo a connu des précipitations supérieures à la moyenne et des crues soudaines, avec un impact sur l'agriculture et les moyens d’existence. L'équipe Données en situations d’urgence (DIEM) de l'Organisation des Nations Unies pour l'alimentation et l'agriculture (FAO) a étudié les images satellites depuis cette date pour mener une évaluation rapide de l'impact, comprendre les conséquences des inondations et soutenir le pays avec une évaluation sur le terrain. Cette StoryMap présente l'impact potentiel des inondations sur la production agricole et les moyens d'existence au Congo.
Principales observations:
Les inondations ont atteint 513 000 ha; 300 ha de terres cultivées (2 pour cent du total des terres cultivées) ont potentiellement été touchés; Neuf des douze départements du pays ont été concernés; Des infrastructures agricoles ont été détruites. This app shows the location of farmers markets and other stores in the Washington, DC, area where food assistance is accepted.
Click the diamonds on the map to see information about each farmers market, including the types of food assistance it accepts and the demographics of the area it serves. For more reference, turn on the Legend and Layers panels to view contextual data on the map.
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"@id": "https://www.africageoportal.com/maps/africageoportal::usa-current-wildfires-cthomas",
"dct:title": "USA Current Wildfires CThomas",
"dct:description": "This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.
Consumption Best Practices:
\n\n
As a service that is subject to very high usage, ensure \npeak performance and accessibility of your maps and apps by avoiding the\n use of non-cacheable relative Date/Time field filters. To accommodate \nfiltering events by Date/Time, we suggest using the included "Age" \nfields that maintain the number of days or hours since a record was \ncreated or last modified, compared to the last service update. These \nqueries fully support the ability to cache a response, allowing common \nquery results to be efficiently provided to users in a high demand \nservice environment. When ingesting this service in your applications, avoid using POST \n requests whenever possible. These requests can compromise performance \nand scalability during periods of high usage because they too are not \ncacheable.
Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the
NWCG Fire Size Classification applied to the daily acres attribute.
Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the
National Wildfire Coordinating Group Standards for Geospatial Operations .
Events modified in the last 7 days Events that are not given a Fire Out Date Incident Type Kind: Fires Incident Type Category: Prescribed Fire, Wildfire, and Incident Complex \n\nArea Covered: United States
What can I do with this layer?
The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.
Attribute Information
This is a list of attributes that benefit from additional explanation. Not all attributes are listed.
Incident Type Category : This is a breakdown of events into more specific categories.
Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires.Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives.Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities.IrwinID : Unique identifier assigned to each incident record in both point and perimeter layers.
\n\nAcres : these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery : An estimate of acres burning upon the discovery of the fire.Calculated or GIS : A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily : A measure of acres reported for a fire.Final : The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.\n\nDates : the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery : The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes. \n\nContainment : The date and time a wildfire was declared contained. Control : The date and time a wildfire was declared under control.ICS209Report : The date and time of the latest approved ICS-209 report.Current : The date and time a perimeter is last known to be updated.FireOut : The date and time when a fire is declared out.ModifiedOnAge : (Integer) Computed days since event last modified.DiscoveryAge : (Integer) Computed days since event's fire discovery date.CurrentDateAge : (Integer) Computed days since perimeter last modified.CreateDateAge : (Integer) Computed days since perimeter entry created.\n\nGACC : A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.
Fire Mgmt Complexity : The highest management level utilized to manage a wildland fire event.
Incident Management Organization : The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.
Unique Fire Identifier : Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)
Revisions
Jan 4, 2021: Added Integer fields 'Days Since... ' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021 : Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021 : Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory ' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments ', 'Label ', 'ComplexName ', 'ComplexID ', and 'IMTName ' fields will be Null moving forward.Apr 26, 2021 : Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021 : Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021 : Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters.
This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our
Live Feed Status Page!
This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.
Consumption Best Practices:
\n\n
As a service that is subject to very high usage, ensure \npeak performance and accessibility of your maps and apps by avoiding the\n use of non-cacheable relative Date/Time field filters. To accommodate \nfiltering events by Date/Time, we suggest using the included "Age" \nfields that maintain the number of days or hours since a record was \ncreated or last modified, compared to the last service update. These \nqueries fully support the ability to cache a response, allowing common \nquery results to be efficiently provided to users in a high demand \nservice environment. When ingesting this service in your applications, avoid using POST \n requests whenever possible. These requests can compromise performance \nand scalability during periods of high usage because they too are not \ncacheable.
Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the
NWCG Fire Size Classification applied to the daily acres attribute.
Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the
National Wildfire Coordinating Group Standards for Geospatial Operations .
Events modified in the last 7 days Events that are not given a Fire Out Date Incident Type Kind: Fires Incident Type Category: Prescribed Fire, Wildfire, and Incident Complex \n\nArea Covered: United States
What can I do with this layer?
The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.
Attribute Information
This is a list of attributes that benefit from additional explanation. Not all attributes are listed.
Incident Type Category : This is a breakdown of events into more specific categories.
Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires.Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives.Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities.IrwinID : Unique identifier assigned to each incident record in both point and perimeter layers.
\n\nAcres : these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery : An estimate of acres burning upon the discovery of the fire.Calculated or GIS : A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily : A measure of acres reported for a fire.Final : The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.\n\nDates : the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery : The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes. \n\nContainment : The date and time a wildfire was declared contained. Control : The date and time a wildfire was declared under control.ICS209Report : The date and time of the latest approved ICS-209 report.Current : The date and time a perimeter is last known to be updated.FireOut : The date and time when a fire is declared out.ModifiedOnAge : (Integer) Computed days since event last modified.DiscoveryAge : (Integer) Computed days since event's fire discovery date.CurrentDateAge : (Integer) Computed days since perimeter last modified.CreateDateAge : (Integer) Computed days since perimeter entry created.\n\nGACC : A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.
Fire Mgmt Complexity : The highest management level utilized to manage a wildland fire event.
Incident Management Organization : The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.
Unique Fire Identifier : Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)
Revisions
Jan 4, 2021: Added Integer fields 'Days Since... ' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021 : Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021 : Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory ' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments ', 'Label ', 'ComplexName ', 'ComplexID ', and 'IMTName ' fields will be Null moving forward.Apr 26, 2021 : Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021 : Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021 : Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters.
This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our
Live Feed Status Page!
This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.
Consumption Best Practices:
\n\n
As a service that is subject to very high usage, ensure \npeak performance and accessibility of your maps and apps by avoiding the\n use of non-cacheable relative Date/Time field filters. To accommodate \nfiltering events by Date/Time, we suggest using the included "Age" \nfields that maintain the number of days or hours since a record was \ncreated or last modified, compared to the last service update. These \nqueries fully support the ability to cache a response, allowing common \nquery results to be efficiently provided to users in a high demand \nservice environment. When ingesting this service in your applications, avoid using POST \n requests whenever possible. These requests can compromise performance \nand scalability during periods of high usage because they too are not \ncacheable.
Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the
NWCG Fire Size Classification applied to the daily acres attribute.
Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the
National Wildfire Coordinating Group Standards for Geospatial Operations .
Events modified in the last 7 days Events that are not given a Fire Out Date Incident Type Kind: Fires Incident Type Category: Prescribed Fire, Wildfire, and Incident Complex \n\nArea Covered: United States
What can I do with this layer?
The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.
Attribute Information
This is a list of attributes that benefit from additional explanation. Not all attributes are listed.
Incident Type Category : This is a breakdown of events into more specific categories.
Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires.Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives.Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities.IrwinID : Unique identifier assigned to each incident record in both point and perimeter layers.
\n\nAcres : these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery : An estimate of acres burning upon the discovery of the fire.Calculated or GIS : A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily : A measure of acres reported for a fire.Final : The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.\n\nDates : the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery : The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes. \n\nContainment : The date and time a wildfire was declared contained. Control : The date and time a wildfire was declared under control.ICS209Report : The date and time of the latest approved ICS-209 report.Current : The date and time a perimeter is last known to be updated.FireOut : The date and time when a fire is declared out.ModifiedOnAge : (Integer) Computed days since event last modified.DiscoveryAge : (Integer) Computed days since event's fire discovery date.CurrentDateAge : (Integer) Computed days since perimeter last modified.CreateDateAge : (Integer) Computed days since perimeter entry created.\n\nGACC : A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.
Fire Mgmt Complexity : The highest management level utilized to manage a wildland fire event.
Incident Management Organization : The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.
Unique Fire Identifier : Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)
Revisions
Jan 4, 2021: Added Integer fields 'Days Since... ' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021 : Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021 : Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory ' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments ', 'Label ', 'ComplexName ', 'ComplexID ', and 'IMTName ' fields will be Null moving forward.Apr 26, 2021 : Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021 : Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021 : Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters.
This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our
Live Feed Status Page!
This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.
Consumption Best Practices:
\n\n
As a service that is subject to very high usage, ensure \npeak performance and accessibility of your maps and apps by avoiding the\n use of non-cacheable relative Date/Time field filters. To accommodate \nfiltering events by Date/Time, we suggest using the included "Age" \nfields that maintain the number of days or hours since a record was \ncreated or last modified, compared to the last service update. These \nqueries fully support the ability to cache a response, allowing common \nquery results to be efficiently provided to users in a high demand \nservice environment. When ingesting this service in your applications, avoid using POST \n requests whenever possible. These requests can compromise performance \nand scalability during periods of high usage because they too are not \ncacheable.
Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the
NWCG Fire Size Classification applied to the daily acres attribute.
Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the
National Wildfire Coordinating Group Standards for Geospatial Operations .
Events modified in the last 7 days Events that are not given a Fire Out Date Incident Type Kind: Fires Incident Type Category: Prescribed Fire, Wildfire, and Incident Complex \n\nArea Covered: United States
What can I do with this layer?
The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.
Attribute Information
This is a list of attributes that benefit from additional explanation. Not all attributes are listed.
Incident Type Category : This is a breakdown of events into more specific categories.
Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires.Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives.Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities.IrwinID : Unique identifier assigned to each incident record in both point and perimeter layers.
\n\nAcres : these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery : An estimate of acres burning upon the discovery of the fire.Calculated or GIS : A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily : A measure of acres reported for a fire.Final : The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.\n\nDates : the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery : The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes. \n\nContainment : The date and time a wildfire was declared contained. Control : The date and time a wildfire was declared under control.ICS209Report : The date and time of the latest approved ICS-209 report.Current : The date and time a perimeter is last known to be updated.FireOut : The date and time when a fire is declared out.ModifiedOnAge : (Integer) Computed days since event last modified.DiscoveryAge : (Integer) Computed days since event's fire discovery date.CurrentDateAge : (Integer) Computed days since perimeter last modified.CreateDateAge : (Integer) Computed days since perimeter entry created.\n\nGACC : A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.
Fire Mgmt Complexity : The highest management level utilized to manage a wildland fire event.
Incident Management Organization : The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.
Unique Fire Identifier : Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)
Revisions
Jan 4, 2021: Added Integer fields 'Days Since... ' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021 : Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021 : Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory ' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments ', 'Label ', 'ComplexName ', 'ComplexID ', and 'IMTName ' fields will be Null moving forward.Apr 26, 2021 : Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021 : Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021 : Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters.
This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.
If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our
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The following report outlines the workflow used to optimize your Find Hot Spots result: Initial Data Assessment. There were 487 valid input features. There were 4 outlier locations; these will not be used to compute the hexagon size. Incident Aggregation Using a hexagon of width 614.3007 meters and height 532.0000 meters. The aggregation process resulted in 250 weighted areas. Incident Count Properties: Min 1.0000 Max 20.0000 Mean 1.9480 Std. Dev. 1.9311
Scale of Analysis The optimal fixed distance band selected was based on peak clustering found at 2803.5866 meters. Hot Spot Analysis There are 37 output features statistically significant based on a FDR correction for multiple testing and spatial dependence. Output Red output features represent hot spots where high JOIN_COUNT value cluster. Blue output features represent cold spots where low JOIN_COUNT value cluster. This\n app shows evacuation routes and demographic data by census tract for \nHouston, Texas. The darker blue tracts have a higher percentage of \nhouseholds without a vehicle. This pattern helps answer the question: \nWhere in Houston should we provide evacuation assistance during a \nhurricane?
Use\n the map legend and feature pop-ups to learn more about the data shown \nin the map. Click individual census tracts to see pop-up information, \nincluding the percentage of households without a vehicle.
The\n map in this app contains a Living Atlas layer of Houston evacuation \nroutes and a layer of Houston demographic data. The demographic layer is\n styled using an Arcade expression that calculates the percentage of \nhouseholds without a vehicle. This information is also provided in \npop-ups.
Source: https://data.london.gov.uk/dataset/water-quality-london-rivers-other-waterbodies
Date accessed: November 29, 2022
License: https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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Additional Esri Resources:
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please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
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We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Count of population by marital status Count of population by 15-year age increments The source of this data is
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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This map shows the total population in Eswatini in 2023, in a multiscale map (Country and Region). Nationally, there are 1,155,107 people in Eswatini .
The pop-up is configured to show the following information at each geography level:
The source of this data is
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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This map shows the total population in Cameroon in 2023, in a multiscale map (Country, Region, and Department). Nationally, there are 28,647,293 people in Cameroon .
The pop-up is configured to show the following information at each geography level:
The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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This map shows the total population in Angola in 2023, in a multiscale map (Country, Province, and Municipality). Nationally, there are 35,345,973 people in Angola .
The pop-up is configured to show the following information at each geography level:
The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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This map shows the total population in Algeria in 2023, in a multiscale map (Country and Province). Nationally, there are 46,525,636 people in Algeria .
The pop-up is configured to show the following information at each geography level:
Total population Population counts by 15-year age increments Male population counts by 15-year age increments Female population counts by 15-year age increments The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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"dct:description": "\n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This layer shows the purchasing power per capita in Tanzania in 2023, in a multiscale map (Country and Region). Nationally, the purchasing power per capita is 1,557,856 Tanzanian shilling . Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in Tanzanian shilling (TZS) per capita.
The pop-up is configured to show the following information at each geography level:
Purchasing power per capita The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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This layer shows the average household size in Tanzania in 2023, in a multiscale map (Country and Region). Nationally, the average household size is 4.6 people per household . It is calculated by dividing the household population by total households.
The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts of population by marital status The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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This layer shows the purchasing power per capita in Sudan in 2023, in a multiscale map (Country and State). Nationally, the purchasing power per capita is 297,170 Sudanese pound . Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in Sudanese pound (SDG) per capita.
The pop-up is configured to show the following information at each geography level:
Purchasing power per capita The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Count of population by 15-year age increments The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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This layer shows the purchasing power per capita in South Africa in 2023, in a multiscale map (Country, Province, District, Municipality, Main Place, Sub Place, and Small Area). Nationally, the purchasing power per capita is 62,579 South African rand . Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in South African rand (ZAR) per capita.
The pop-up is configured to show the following information at each geography level:
Purchasing power per capita Purchasing power per capita by various categories Count of households by income quintiles The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
Purchasing power per capita The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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This map shows the purchasing power per capita in South Africa in 2023, in a multiscale map (Country, Province, District, Municipality, Main Place, Sub Place, and Small Area). Nationally, the purchasing power per capita is 62,579 South African rand . Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in South African rand (ZAR) per capita.
The pop-up is configured to show the following information at each geography level:
Purchasing power per capita Purchasing power per capita by various categories Count of households by income quintiles The source of this data is
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
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We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
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We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
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We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
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We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
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The source of this data is
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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This layer shows the purchasing power per capita in Nigeria in 2023, in a multiscale map (Country, State, and Local Government Area). Nationally, the purchasing power per capita is 680,581 Nigerian naira . Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in Nigerian naira (NGN) per capita.
The pop-up is configured to show the following information at each geography level:
Purchasing power per capita Purchasing power index per area The Purchasing Power Index compares the demand for a specific purchasing category in an area, with the national demand for that product or service. The index values at the national level are 100, representing average demand for the country. A value of more than 100 represents higher demand than the national average, and a value of less than 100 represents lower demand than the national average. For example, an index of 120 implies that demand in the area is 20 percent higher than the national average; an index of 80 implies that demand is 20 percent lower than the national average.
The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Count of population by 15-year age increments The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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This layer shows the purchasing power per capita in Namibia in 2023, in a multiscale map (Country, Region, and Constituency). Nationally, the purchasing power per capita is 50,756 Namibian dollar . Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in Namibian dollar (NAD) per capita.
The pop-up is configured to show the following information at each geography level:
Purchasing power per capita Purchasing power index per area The Purchasing Power Index compares the demand for a specific purchasing category in an area, with the national demand for that product or service. The index values at the national level are 100, representing average demand for the country. A value of more than 100 represents higher demand than the national average, and a value of less than 100 represents lower demand than the national average. For example, an index of 120 implies that demand in the area is 20 percent higher than the national average; an index of 80 implies that demand is 20 percent lower than the national average.
The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts of population by marital status The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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"dct:description": "\n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
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The pop-up is configured to show the following information at each geography level:
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The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts of population by marital status The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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This layer shows the purchasing power per capita in Morocco in 2023, in a multiscale map (Country, Region, and Province). Nationally, the purchasing power per capita is 21,337 Moroccan dirham . Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in Moroccan dirham (MAD) per capita.
The pop-up is configured to show the following information at each geography level:
Purchasing power per capita Purchasing power per capita index The Purchasing Power Index compares the demand for a specific purchasing category in an area, with the national demand for that product or service. The index values at the national level are 100, representing average demand for the country. A value of more than 100 represents higher demand than the national average, and a value of less than 100 represents lower demand than the national average. For example, an index of 120 implies that demand in the area is 20 percent higher than the national average; an index of 80 implies that demand is 20 percent lower than the national average.
The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts of population by 15-year age increments The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
Purchasing power per capita Purchasing power per capita by various categories The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
",
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts\r\nof population by 15-year age increments The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
Purchasing power per capita The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts of population by marital status The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts of population by 15-year age increments The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
Purchasing power per capita The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts of population by 15-year age increments The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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This layer shows the purchasing power per capita in Ghana in 2023, in a multiscale map (Country, Region, and District). Nationally, the purchasing power per capita is 14,621 Ghanaian cedi . Purchasing Power describes the disposable income (income without taxes and social security contributions, including received transfer payments) of a certain area's population. The figures are in Ghanaian cedi (GHS) per capita.
The pop-up is configured to show the following information at each geography level:
Purchasing power per capita Purchasing power index The Purchasing Power Index compares the demand for a specific purchasing category in an area, with the national demand for that product or service. The index values at the national level are 100, representing average demand for the country. A value of more than 100 represents higher demand than the national average, and a value of less than 100 represents lower demand than the national average. For example, an index of 120 implies that demand in the area is 20 percent higher than the national average; an index of 80 implies that demand is 20 percent lower than the national average.
The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts of population by 15-year age increments The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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This layer shows the total population in Eswatini in 2023, in a multiscale map (Country and Region). Nationally, there are 1,155,107 people in Eswatini .
The pop-up is configured to show the following information at each geography level:
The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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"dct:description": "\n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
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The pop-up is configured to show the following information at each geography level:
Purchasing power per capita Purchasing power per capita index The Purchasing Power Index compares the demand for a specific purchasing category in an area, with the national demand for that product or service. The index values at the national level are 100, representing average demand for the country. A value of more than 100 represents higher demand than the national average, and a value of less than 100 represents lower demand than the national average. For example, an index of 120 implies that demand in the area is 20 percent higher than the national average; an index of 80 implies that demand is 20 percent lower than the national average.
The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
",
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts of population by 15-year age increments The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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INTRODUCTION
Western Kenya, has an ecological popularity, including national parks, farmlands, rivers, forests and wetlands.Hequeendo Compassionate Friends CBO program (local steward of the environment ) is not just about the present; it's about securing a sustainable future for the next generations thus portraying an act of humanity . We understand that raising awareness is the first step. We bring local schools, community leaders, and families together to learn about the vital role each person can play in making our ecosystem better for life . The link shows the protected areas in kenya. https://esri-ea.maps.arcgis.com/apps/Shortlist/index.html?appid=194277f97cf5436592654b41ae36397e
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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Estimated population
Number of people per 1x1 square kilometer
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Need more than 30 minutes to get water
Percent of people who live in a household that takes more than 30 minutes to fetch and retrieve water
toilet
Share toilets with others or do not have a toilet
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Self report positive HIV result
Percent of adults (15-49) that self report a positive HIV result
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Household head has no education
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Household head has some or complete primary education
Percent of people living in a household whose head has some or complete primary education
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Household head has at least some secondary education
Percent of people living in a household whose head has at least some secondary education
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tv
Have access to TV or have regular adult TV viewers at home
Percent of people in a household that owns a TV or has at least one adult (15-49) that watches TV at least once per week
news
Regularly read newspaper
Percent of adults (15-49) that reads newspaper at least once per week
pop1549
Estimated adult population (15-49)
Number of adults (15-49) per 1x1 square kilometer
exp_*
Exposure Risk Profile
This profile is an index representing risk of exposure to COVID-19. Columns exp_1 to exp_5 show the number of people in an area at different risk levels. Column exp_s shows the average risk score at the administrative level of interest.
comor_*
Co-morbidities Risk Profile
This profile identifies population that may be at-risk of severe COVID-19 infection or hospitalization. Columns comor_1 to comor_5 show the number of people in an area at different risk levels. Column comor_s shows the average risk score at the administrative level of interest.
socio_*
Socio-economic Vulnerability Risk
This profile is an index of socio-economic vulnerability to COVID-19, highlighting poor populations with limited options to cope with economic shocks. Columns socio_1 to socio_5 show the number of people in an area at different risk levels. Column socio_s shows the average risk score at the administrative level of interest.
com_*
Communication Access Risk Profile
This profile identifies populations with limited access to communications media. Columns com_1 to com_5 show the number of people in an area at different risk levels. Column com_s shows the average risk score at the administrative level of interest.
access_*
Health Facility Access Risk
This profile identifies population facing high risk resulting from limited access to health facilities. Columns access_1 to access_5 show the number of people in an area at different risk levels. Column access_s shows the average risk score at the administrative level of interest.
total_*
Total COVID-19 Risk
This profile is an index of the total COVID-19 Risk combining the comorbidities profile, the health facilities access risk profile, and the exposure risk profile. Columns total_1 to total_5 show the number of people in an area at different risk levels. Column total_s shows the average risk score at the administrative level of interest.
Le CIGEO, la boite à outils géodésiques, est un logiciel gratuit de projection cartographique et de transformation géodésique, capable de réaliser toutes les projections UTM ainsi que la projection TMCI-5.5 (adaptée à la Côte d'Ivoire). Il permet également la transformation entre les systèmes géodésiques ivoiriens ITRF2014 époque 2010.0 et ITRF96 époque 98.2.
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please let us know .
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here Dataset Citation:
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The zipfiles contain the following files:
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Note, these data are operational population estimates and are not official government statistics.
The downloadable Metadata provides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data Cited
Contact release@worldpop.org for more information or gohere .
Data Citation: WorldPop (School of Geography and Environmental Science, University of Southampton). 2020. Bottom-up gridded population estimates for Zambia, version 1.0. https://dx.doi.org/10.5258/SOTON/WP00662 CREDITS: The modelling work was led by Claire A. Dooley with support from Douglas R. Leasure. Geospatial data processing was carried out by Heather R. Chamberlain, Claire A. Dooley and Oliver Pannell. Coordination and stakeholder engagement was led by Heather R. Chamberlain and Claire A. Dooley with support from Polly Marshall. Oversight was provided by Andrew J. Tatem and Attila N. Lazar.
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This map shows the average household size in Tunisia in 2023, in a multiscale map (Country, Region, and Governorate). Nationally, the average household size is 3.9 people per household . It is calculated by dividing the household population by total households.
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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Population estimate from WorldPop's Bottom-up gridded population layer. Access for the WorldPop population estimates full methodology visit https://wopr.worldpop.org/
Source: Fraym 2020
The Fraym platform weaves together the latest satellite imagery and geostatistical datasets with professionally enumerated household surveys. This allows for the disaggregation and re-aggregation of large datasets to cover any geographically bounded area. Indicators are drawn and harmonized from a wide variety of household surveys and other data sources.
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United Nations: UN population division database
World Bank: Enterprise surveys, living standards, global index surveys, and respective country statistics
National Statistical Offices: National censuses and surveys covering population, businesses, health, housing, agriculture, and other areas
International Monetary Fund: World economic outlook databases and respective country statistics
National Air and Space Administration: Remote sensing satellite data, such as vegetation, temperature, and precipitation
USGS: Landscan, Google Earth, GeoData Institute, OSM
WorldPop: Population density by age groups
Active support for this project has concluded \n Retirement Notice: This item is in mature support as of November 2025 and will be retired in December 2026. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.\n
This map shows the average household size in Tanzania in 2023, in a multiscale map (Country and Region). Nationally, the average household size is 4.6 people per household . It is calculated by dividing the household population by total households.
The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts of population by marital status The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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Additional Esri Resources:
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please let us know .
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Additional Esri Resources:
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please let us know .
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Additional Esri Resources:
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please let us know .
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Additional Esri Resources:
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please let us know .
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts of population by 15-year age increments The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts of population by 15-year age increments The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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This map shows the average household size in Egypt in 2023, in a multiscale map (Country and Province). Nationally, the average household size is 3.9 people per household . It is calculated by dividing the household population by total households.
The pop-up is configured to show the following information at each geography level:
Average household size (people per household) Total population Total households Counts of population by 15-year age increments The source of this data is
Michael Bauer Research . The vintage of the data is 2023. This item was last updated in October, 2023 and is updated every 12-18 months as new annual figures are offered.
Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
The source of this data is
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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This layer shows the total population in Algeria in 2023, in a multiscale map (Country and Province). Nationally, there are 46,525,636 people in Algeria .
The pop-up is configured to show the following information at each geography level:
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
please let us know .
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The pop-up is configured to show the following information at each geography level:
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The source of this data is
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Additional Esri Resources:
This item is for visualization purposes only and cannot be exported or used in analysis.
We would love to hear from you. If you have any feedback regarding this item or Esri Demographics,
please let us know .
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These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.
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\n \n \n Year
\n \n \n Resolution
\n \n \n Definition
\n \n \n \n \n Santoro et al. 2018
\n \n \n Global
\n \n \n 2010
\n \n \n 100 m
\n \n \n Above-ground woody biomass for trees\n that are >10 cm diameter-at-breast-height, masked to Landsat-derived\n canopy cover for 2010; biomass is expressed as oven-dry weight of the woody\n parts (stem, bark, branches and twigs) of all living trees excluding stump\n and roots.
\n \n \n \n \n Xia et al. 2014
\n \n \n Global
\n \n \n 1982-2006
\n \n \n 8 km
\n \n \n Above-ground grassland biomass.
\n \n \n \n \n Bouvet et al. 2018
\n \n \n Africa
\n \n \n 2010
\n \n \n 25 m
\n \n \n Above-ground woodland and savannah\n biomass; low woody biomass areas, which therefore exclude dense forests and\n deserts.
\n \n \n \n \n Spawn et al. 2017
\n \n \n Global
\n \n \n 2010
\n \n \n 300 m
\n \n \n Synthetic, global above- and\n below-ground biomass maps that combine recently released satellite-based data\n of standing forest biomass with novel estimates for non-forest biomass\n stocks.
\n \n \n
After aggregating each selected dataset to a nominal scale of 300 m\nresolution, forest categories in the CCI ESA 2010 landcover dataset were used\nto extract above-ground biomass from Santoro et al. 2018 for forest areas.\nWoodland and savanna biomass were then incorporated for Africa from Bouvet et\nal. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside\nof forest. Biomass from croplands, sparse vegetation and grassland landcover\nclasses from CCI ESA, in addition to shrubland areas outside Africa missing\nfrom Santoro et al. 2018, were extracted from were extracted from Xia et al.\n2014. and Spawn et al. 2017 averaged by ecological zone for each landcover\ntype.
\n\nBelow-ground biomass were added using root-to-shoot ratios from the\n2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No\nbelow-ground values were assigned to croplands as ratios were unavailable.\nAbove- and below-ground biomass were then summed together and multiplied by 0.5\nto convert to carbon, generating a single above-and-below-ground biomass carbon\nlayer.
This dataset has not been validated.
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The monitoring system consists of primary data collected from households and key informants (including agricultural inputs vendors, food traders and agriculture extension officers) on a periodic basis (more or less every 3 months). Data are mainly collected through Computer-Assisted Telephone Interviews (CATI). In-person surveys are conducted where the circumstances allow for field access. During the first round of the system, from June to November 2020, more than 40,000 interviews have been completed in more than 20 countries.
Data are used to guide strategic decisions, to design programmes and to inform analytical processes such as the IPC.
The present dashboard shows the portion of data referring to Livestock Production from Colombia, Liberia, DRC, Sierra Leone, Niger, Somalia, Zimbabwe and Mali.
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The two layers contained within the GRID3 AGO - Settlement Extents v3.0 include:
The current version supersedes GRID3 AGO - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive settlement polygons with open data. Estimate a probability value for settlement polygons For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 AGO - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/hmgn-9b91. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
\n
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\nThe zip files contain the following files:
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\n
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MOZ_population_v1_1_gridded.tif
This GeoTIFF raster (.tif), at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimated population counts per grid cell across Mozambique. The projection is the geographic coordinate system WGS84 (World Geodetic System 1984). ‘NoData’ values represent areas that were mapped as unsettled based on building footprints from “Digitize Africa data © 2020 Maxar Technologies, Ecopia.AI”. These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated populations for larger areas.
MOZ_population_v1_1_agesex.zip
This zip file contains 40 rasters in GeoTIFF format. Each raster provides gridded population estimates for an age-sex group. We provide 36 rasters for the commonly reported age-sex groupings of sequential age classes for males and females separately. The files are labelled with either an “m” (male) or an “f” (female) followed by the number of the first year of the age class represented by the data. “f0” and “m0” are population counts of under 1 year olds for females and males, respectively. “f1” and “m1” are population counts of 1 to 4 year olds for females and males, respectively. Over 4 years old, the age groups are in five year bins labelled with a “5”, “10”, etc. Eighty year olds and over are represented in the groups “f80” and “m80”.
We provide an additional four rasters that represent aggregated demographic groups of special interest for development programmes and interventions. These are “under1” (all females and males under the age of 1), “under5” (all females and males under the age of 5), “under15” (all females and males under the age of 15) and “f15_49” (all females between the ages of 15 and 49, inclusive). These data were produced using the peanutButter R package (Leasure et al. 2020b) which multiplied the gridded population estimates (MOZ_population_v1_1_gridded.tif) by gridded age-sex proportions that differ by region (WorldPop et al. 2018, Pezzulo et al. 2017).
While each data file represents population counts, individual values contain decimals, i.e. fractions of people. This is because we do not estimate which grid cell each individual in a given age group occupies. For example, if four grid cells next to each other have values of 0.25 this indicates that there is one person of that age group somewhere in those four grid cells
This work provides an estimate of the geographic distribution of the population of Mozambique in 2017. The outputs are intended as an interim population product to support ongoing development and operations work until such time as the official 2017 Population and Housing Census results are available in a spatial gridded format. At that time, this interim gridded population layer will be superseded and users will be advised to use the official gridded population release from INE.
Data Citation: Bondarenko M, Jones P, Leasure D, Lazar AN, Tatem AJ. 2020. Census disaggregated gridded population estimates for Mozambique (2017), version 1.1. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00672 CREDITS: Patricia Jones and Donna Clarke (WorldPop) supported the generation of inputs for the application of the random forest-based dasymetric mapping approach developed by Stevens et al. (2015). The population disaggregation was done by Maksym Bondarenko (WorldPop), using the random forest population modelling R scripts (Bondarenko et al., 2020). The age-sex rasters, SQL database, and map tileswere created by Doug Leasure (WorldPop). The administrative boundaries and population totals were provided by Arlindo Charles, at INE (National Institute of Statistics) in Mozambique. Claire Dooley and Chris Jochem reviewed the population raster and its documentation, and provided comments and suggestions. Attila N. Lazar (WorldPop) coordinated the work with substantial engagement and translation support from Sandra Baptista (CIESIN). The whole WorldPop group and GRID3 partners are acknowledged for overall project support.
These data were produced by the WorldPop Research Group at the University of Southampton. This work is part of the GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) programme funded by the Bill & Melinda Gates Foundation (BMGF) and the United Kingdom’s Foreign, Commonwealth & Development Office. It is implemented by Columbia University’s Center for International Earth Science Information Network (CIESIN), the United Nations Population Fund (UNFPA), WorldPop at the University of Southampton, and the Flowminder Foundation.
The downloadable Metadata provides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data Cited
Contact release@worldpop.org for more information or go here .
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"dct:title": "GRID3 SEN - Settlement Extents v3.0",
"dct:description": "The two layers contained within the GRID3 SEN - Settlement Extents v3.0 include:
The current version supersedes GRID3 SEN - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 SEN - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/4z4e-8x52. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The zip files contain the following files:
SEN_population_v1_0_gridded.tif
This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total population size per grid cell across Senegal. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.
SEN _population_v1_0_agesex.zip
This zip file contains the following two raster files:
SEN_population_v1_0_gridded_female.tif This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total female population size per grid cell across Senegal. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.SEN_population_v1_0_gridded_male.tif This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total male population size per grid cell across Senegal. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.
Note, these data are operational population estimates and are not official government statistics.
Metadata provides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data Cited.
Contact release@worldpop.org for more information or go here .
Data Citation: Qader S. H., Abbott T., Boytinck, E., Kuepie, M., Lazar A. N., Tatem A. J. 2022. Census disaggregated gridded population estimates for Senegal (2020), version 1.0. University of Southampton. doi:10.5258/SOTON/WP00730
These data were produced by the WorldPop Research Group at the University of Southampton. This work is part of the GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) programme funded by the Bill & Melinda Gates Foundation (BMGF) and the United Kingdom’s Foreign, Commonwealth & Development Office. It is implemented by Columbia University’s Center for International Earth Science Information Network (CIESIN), the United Nations Population Fund (UNFPA), WorldPop at the University of Southampton, and the Flowminder Foundation.
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The zip files contain the following files:
MLI_population_v1_0_gridded.tif
This GeoTIFF raster represents estimates of total population counts within grid cells of approximately 100m across Mali The raster values are the mean of a posterior distribution (calibrated by the census cartographytotals when available) and therefore include decimals (e.g. 0.5 people). An estimate of 0.5 people in twoneighbouring cells indicates that one person lives somewhere within those two cells. NA values representcells where no building footprint is present.
MLI_population_v1_0_agesex.zip
This zip file contains 37 GeoTIFF rasters representing estimated population counts for specific age and sex groups within grid cells of approximately 100m. The rasters were created post-hoc by multiplying the total population counts provided in the MLI_population_v1_0_gridded.tif raster and national age and sex proportions from the United Nations demographic yearbook [7], reproduced in MLI_population_v1_0_agesex_table.csv. The location of the regions of the subnational age-sex pyramids are in MLI_population_v1_0_agesex_regions.tif.
34 rasters represent commonly reported age and sex groups labelled with either an “f” (female) or an “m” (male) followed by the number of the first year of the corresponding age class. The age groups consist of five-year bins labelled with a “0”, “5”, “10”, etc. Eighty-year-olds and over are represented in the groups “f80” and “m80”. Three additional rasters represent demographic groups frequently targeted in public health campaigns. These groups are labelled as “under5” (all females and males under the age of five), “under15” (all females and males under the age of 15) and “f15_49” (all females between the ages of 15 and 49, inclusive).
Data Citation:
WorldPop and Institut National de la Statistique du Mali. 2022. Census-cartography-based gridded population estimates for Mali (2020), version 1.0. WorldPop, University of Southampton. doi: 10.5258/SOTON/WP00745.
These data were produced by the WorldPop Research Group at the University of Southampton as part of the GRID3 Project, GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) programme funded by the Bill and Melinda Gates Foundation (BMGF) and the United Kingdom’s Foreign, Commonwealth & Development Office (INV 009579, formerly OPP 1182425). The study was approved by the Faculty Ethics Committee of the University of Southampton (ERGO II 64957). The project was led by the Center for International Earth Science Information Network (CIESIN) at Columbia University, in collaboration with the WorldPop Research Group at the University of Southampton, the United Nations Fund for Population (UNFPA) and the Malian Institut National de la Statistique (INSTAT).
The downloadable
Metadata provides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data Cited.
Contact release@worldpop.org for more information or go here .
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The zip files contain the following files:
KEN_population_v1_0_gridded.tif
This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total population size per grid cell across Kenya. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.
KEN_population_v1_0_agesex.zip
This zip file contains 40 GeoTIFF rasters representing estimated population counts for specific age and sex groups within grid cells of approximately 100m. We provide 36 rasters for the commonly reported age-sex groupings of sequential age classes for males and females separately. These are labelled with either an “m” (male) or an “f” (female) followed by the number of the first year of the age class represented by the data. “f0” and “m0” are population counts of under 1-year olds for females and males, respectively. “f1” and “m1” are population counts of 1 to 4 year olds for females and males, respectively. Over 4 years old, the age groups are in five year bins labelled with a “5”, “10”, etc. Eighty year olds and over are represented in the groups “f80” and “m80”. We provide four additional rasters that represent demographic groups often targeted by programmes and interventions. These are “under1” (all females and males under the age of 1), “under5” (all females and males under the age of 5), “under15” (all females and males under the age of 15) and “f1549” (all females between the ages of 15 and 49, inclusive).
These data were produced post-hoc by multiplying the total population counts provided in the KEN_population_v1_0_gridded.tif raster and age and sex proportions derived the US Census bureau age-sex projections for each sub-county. While this data represents population counts, values contain decimals, i.e. fractions of people. This is because both the input population data and age-sex proportions contain decimals. For this reason, it is advised to aggregate the rasters at a coarser scale. For example, if four grid cells next to each other have values of 0.25 this indicates that there is 1 person of that age group somewhere in those four grid cells.
Data Citation
Gadiaga A. N., Abbott T. J., Chamberlain H., Lloyd C. T., Lazar A. N., Darin E., Tatem A. J. 2022. Census-disaggregated gridded population estimates for Kenya (2021), version 1.0. University of Southampton. doi:10.5258/SOTON/WP00747.
The downloadableMetadata provides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data Cited.
Contact release@worldpop.org for more information or gohere .
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The zip files contain the following files:
BFA_population_v1_1_gridded.tif
This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of total population size per grid cell across Burkina Faso. The projection is the geographic coordinate system WGS84 (World Geodetic System 1984). NA values represent areas that were mapped as unsettled based on building footprints provided by the Digitize Africa project of Ecopia.AI and Maxar Technologies [2]. These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated populations for larger areas.
BFA_population_v1_1_agesex.zip
This zip folder contains 39 population rasters in geotiff format. Each raster provides gridded population estimates for an age-sex group. Files are labelled with either an “m” (male) or an “f” (female) followed by the number of the first year of the age within the age group represented by the data. The age groups are in five-year bins labelled with a “5”, “10”, etc. For instance, “f0” and “m0” are population counts of under 5 year olds for females and males, respectively. Eighty-five year olds and over are represented in the groups “f85” and “m85”. We provide three additional rasters that represent demographic groups often targeted by programmes and interventions. These are “under5” (all females and males under the age of 5), “under15” (all females and males under the age of 15) and “f15_49” (all females between the ages of 15 and 49, inclusive). These data were produced using age-sex proportions from the 2019 census as provided in the age-sex proportion table (BFA_population_v1_1_agesex.csv) with corresponding region raster (BFA_population_v1_1_agesex_regions.tif). The age-sex proportions were applied to the gridded population estimates (BFA_population_v1_1_gridded.tif) to allocate the population to the different age-sex classes. While this data represents population counts, values contain decimals, i.e. fractions of people. This is because we do not estimate which grid cell each individual in a given age group occupies. For this reason, it is advised to aggregate the rasters at a coarser scale. For example, if four grid cells next to each other have values of 0.25 this indicates that there is 1 person of that age group somewhere in those four grid cells. Note: f0 and m0 in this application represent under 5 population, contrary to other datasets on the WorldPop Open Population Repository where f0 and m0 represent population under the age of 1.
Data Citation:
WorldPop and Institut National de la Statistique et de la Démographie du Burkina Faso. 2022. Censusbased gridded population estimates for Burkina Faso (2019), version 1.1. WorldPop, University of Southampton. doi: 10.5258/SOTON/WP00736.
These data were produced by the WorldPop Research Group at the University of Southampton. This work is part of the GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) project funded by the Bill and Melinda Gates Foundation (BMGF) and the United Kingdom Foreign, Commonwealth & Development Office (OPP1182425). Project partners include WorldPop at the University of Southampton, the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network in the Columbia Climate School at Columbia University, and the Flowminder Foundation. The Burkina Faso Institut National de la Statistique et de la Démographie supported, facilitated this work, reviewed the results and provided the census database. The modelling work, geospatial data processing, and stakeholder engagement was led by Edith Darin. Support for the engagement work and review of the methods was offered by Mathias Kuépié (UNFPA) and Antoinette Wannebo (CIESIN). Oversight was done by Andrew J.Tatem and Attila N. Lazar.
The downloadable Metadata provides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data Cited
Contact release@worldpop.org for more information or go here .
Church locations and names in Nigeria. Released in September 2020. Dataset is incomplete for the country.
Farm locations and names in Nigeria. Released in September 2020. Dataset is incomplete for the country.
Energy and electricity substation locations and names in Nigeria. Released in September 2020. Dataset is incomplete for the country.
Factory and industrial site locations and names in Nigeria. Released in September 2020. Dataset is incomplete for the country.
Filling station locations and names in Nigeria. Released in September 2020. Dataset is incomplete for the country.
Government building locations and names in Nigeria. Released in September 2020. Dataset is incomplete for the country.
Fire station locations and names in Nigeria. Released in September 2020. Dataset is incomplete for the country.
Internally Displaced Persons (IDP) sites and names in Nigeria. IDP sites are makeshift shelter provided for people who have been forced to leave their native homes and still within their respective country geographical boundary to find temporary refuge till their homes are safe to return.
Downloaded from geodatabase (GDB) version 1.75 (released in March 2021). Dataset is incomplete for the country.
Nigeria country-wide market locations and names. Released in September 2020.
Mosque locations and names in Nigeria. Released in September 2020. Dataset is incomplete for the country.
Police station locations and names in Nigeria. Released in September 2020. Dataset is incomplete for the country.
Nigeria country-wide primary, secondary, tertiary, private, public, and religious school locations and names. Released in September 2020.
Nigeria country-wide settlement points and names. Released in March 2021.
Nigeria country-wide operational LGA boundaries (administrative level 2). The LGA boundaries dataset was released in March 2021.
The index equally weights three risk profiles ( co-morbidities, health facilities access, and exposure) at a 1km squared resolution, which are then aggregated to the ward boundary level.
Source: Fraym 2020
The Fraym platform weaves together the latest satellite imagery and geostatistical datasets with professionally enumerated household surveys. This allows for the disaggregation and re-aggregation of large datasets to cover any geographically bounded area. Indicators are drawn and harmonized from a wide variety of household surveys and other data sources.
These include the following sources:
USAID: Demographic and health surveys
United Nations: UN population division database
World Bank: Enterprise surveys, living standards, global index surveys, and respective country statistics
National Statistical Offices: National censuses and surveys covering population, businesses, health, housing, agriculture, and other areas
International Monetary Fund: World economic outlook databases and respective country statistics
National Air and Space Administration: Remote sensing satellite data, such as vegetation, temperature, and precipitation
USGS: Landscan, Google Earth, GeoData Institute, OSM
WorldPop: Population density by age groups
Water points and names in Nigeria. Released in September 2020. Dataset is incomplete for the country.
Post office locations and names in Nigeria. Released in September 2020. Dataset is incomplete for the country.
The dataset is available for download in shapefile format and can be viewed in Geographic Information Systems(GIS) such as ArcMap, QGIS, or Google Earth (Pro). The data is available for all of Zambia. It has the following attributes: Name, Original Name (from original dataset), Type, Sub Type, Source, Province (Code), District (Code), Ward (Code), Constituency (Code), Global ID.
The “NSDI Zambia Operational School Points and Names, Version 01 (Beta)” provides school and school affiliated point locations, names, types, and source of the data. The dataset was developed by compiling and standardizing existing government data sources of school point locations and names data from the 2010 census cartography from Zambia Statistics Agency (ZamStats), the Ministry of General Education (MoGE) and the Ministry of Water Development Sanitation and Environmental Protection (MWDSEP).
The dataset is available for download in shapefile format and can be viewed in Geographic Information Systems(GIS) such as ArcMap, QGIS, or Google Earth (Pro). The data is available for all of Zambia. It has the following attributes: Name, Original Name (from original dataset), Type, Sub Type, Source, Province (Code), District (Code), Ward (Code), Constituency (Code), Global ID
For more information about the settlement data and methodology, see the data release notes here .
The " NSDI Zambia Operational Health Facility Layer, Version 01 (Beta)" provides health facility and health affiliated infrastructure point locations, names, types, and source of the data. The dataset was developed by compiling and standardizing existing government data sources of health facility and affiliated infrastructure locations and names data from the Ministry of Health (MoH) obtained in 2019 and from the 2010 census cartography from Zambia Statistics Agency (ZamStats).
The dataset is available for download in shapefile format and can be viewed in Geographic Information Systems(GIS) such as ArcMap, QGIS, or Google Earth (Pro). The data is available for all of Zambia. It has the following attributes: Name, Original Name (from original dataset), Type, Sub Type, Source, Province (Code), District (Code), Global ID. The dataset also includes a Label column which can be used for labeling.
For more information about the settlement data and methodology, see the data release notes here .
The administrative district boundaries were generated at a scale of 1:10,000 and validated against official district narratives provided by the Government of the Republic of Zambia, using high resolution imagery and 1:50,000 topographic maps. The shapefile covers the National, 10 Provincial and 116 District Boundaries.
This data provides gridded population estimates (at spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Nigeria, along with the estimates of the number of people belonging to various age and sex groups. Using robust Bayesian statistical hierarchical modelling framework, population modelling and estimation experts from WorldPop (www.worldpop.org) at the University of Southampton combined ‘headcount’ (input population) datasets obtained from the 2022-23 National Malaria Elimination Program (NMEP) with settlement footprint and geospatial covariates to estimate population numbers at high-resolution grid cells. The approach facilitated accounting for the multiple levels of variability within the data, while simultaneously quantifying uncertainties in the parameter estimates. After capturing the spatial variability of population, the modelled estimates were scaled based on the UN WPP July 2025 median national population projections.
This data also contains mastergrid for gridded population estimates.
Population totals for administrative units are also available.
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This map is for use in the " What is the spatial pattern of demographic variables around the world?" activity in Section 1 of the Going Places with Spatial Analysis course. The map contains population characteristics by country for 2013.
These data come from the Population Reference Bureau's 2014 World Population Data Sheet .
The Population Reference Bureau (PRB) informs people around the world about population, health, and the environment, empowering them to use that information to advance the well-being of current and future generations.
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Zoom in to large scales (e.g. City level or 1:80k scale ) to see the leisure area features display. You can click on a feature to get the name of the leisure area (if available). The name of the leisure area will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer.
Create New Layer
If you would like to create a more focused version of this leisure areas layer displaying just one or two leisure types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. leisure is stadium), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. parks) that are ready to use, but not for every type of leisure area .
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Create New Layer
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Important Note
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Zoom in to large scales (e.g. City level or 1:80k scale) to see the POI features display. You can click on the feature to get the name of the POI. The name of the POI will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.
Create New Layer
If you would like to create a more focused version of this POI layer displaying just one or two types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. amenity is bar or shop is alcohol), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers that are ready to use, but not for every type of amenity.
Important Note
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This layer contains WorldPop's 100-meter resolution annual estimates of total population from the year 2000 to 2020.
Usage notes:
This layer is configured to be viewed only at a scale range for large-scale maps, i.e., zoomed into small areas of the world. Because the underlying data for this layer is relatively large and because raster pyramids cannot accurately represent aggregated population counts, there are no pyramids. Thus, this layer may at times require 10 to 15 seconds to draw. We recommend using this layer in in conjunction with WorldPop's 1-km resolution Total Population layer to create web maps that allow users to pan and zoom to wider areas ; this web map contains an example of this combination. WorldPop estimated the total population for each cell in this data using a random forest-based dasymetric redistribution model (Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data . PloS one, 10(2), e0107042 ). The estimates in this layer use WorldPop's Top-down unconstrained method which estimates the total population for each cell with a Random Forest-based dasymetric model. This diagram visually describes this model that uses known populated locations to analyze imagery to find similarly populated locations.
The DOI for the original WorldPop.org total population data is 10.5258/SOTON/WP00645 .
Recommended Citation: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. Accessed from https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Total_Population_1km/ImageServer, which was acquired from https://www.worldpop.org/doi/10.5258/SOTON/WP00645 on 15 Sep, 2021.
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The GEOGloWS ECMWF Streamflow System represents a daily 51-member ensemble streamflow forecast for over 1 million reaches across the globe. Gridded surface runoff, provided by the European Centre for Medium-range Weather Forecasting (ECMWF), is downscaled and routed to the streams using the Routing Application for Parallel computation of Discharge (RAPID). This animation layer shows the first 6-days of a 15-day forecast at 3-hr intervals. Additionally, a 40-yr historical simulation was produced based on ECMWF’s ERA5 dataset. From this historical simulation , return periods for each reach are calculated and used to color the stream segments when/where events exceed these thresholds.
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What can you do with this layer?
This map service is designed for fast data visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the forecast data sequentially using the time slider, which is set to three hour intervals by default by Enabling Time Animation. This layer type is not recommended for use in analysis.
Revisions:
Dec 15, 2020 : Updated 'flowstyle ' field values from color names: 'blue', 'yellow', 'purple', ... to Return Period values. This now reports '0', '2', '5', '10', '25', '50', and '100' indicating the forecasted return period. The symbology has also been extended to better depict the relevant state of each reach using these periods.Retirement Notice: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.
WorldCover provides a new baseline global land cover product at 10 m resolution for 2020 based on Sentinel-1 and 2 data. It was developed and validated in almost near-real time and at the same time maximizes the impact and uptake for the end users.
Variable mapped: 11 land cover classes
Data Projection: WGS84 (WKID 4326)
Mosaic Projection: Web Mercator (WKID3857)
Extent: World
Cell Size: 8.33333333333333E-05 degrees (10m)
Source Type: 8 bit unsigned
Visible Scale: All scales are visible
Source: ESA (European Space Agency)
Publication Date: October 20, 2021
More Details from the WorldCover consortium:
https://esa-worldcover.org/en
Classes
10. Tree Cover 20. Shrubland 30. Grassland 40. Cropland 50. Built-up 60. Bare or sparse vegetation 70. Snow and Ice 80. Permanent water bodies 90. Herbaceous wetland 95. Mangroves 100. Moss and lichen
Retirement Notice: This item is in mature support as of June 2025 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.
This tile layer is designed to provide a a hydrologically oriented set of features to use with the World Terrain Base Layer or other simple base maps. The map features a hydro-centric design based on the amount of water flowing within the drainage network such that symbols of the same size and color represent roughly the same amount of water.
This map shows surface water flow as a linear phenomenon even over and through bodies of water. Using the best available data we show relative flow accurately, so that if one river carries more water downstream than another river, the result will be that the river will have a thicker symbol on the map.
This map is used as an overlay for content such as elevation from the World Terrain Base service or thematic services such as soil units, vegetation, or ecoregions. Combined with a basemap and your map services, this map provides a frame of reference for showing regional, national, and continental hydrologic phenomena such as drought, runoff, river level monitoring and flood forecasting.
River names are collected in the UTF8 character set so river names are collected in their original language but are written in the Roman alphabet. Sources for all river names are from the open source geonames.org project so they are international by nature.
The map is compiled from several sources. The global scales (very small scales through 1:2,300,000) include content from: HydroSHEDS, GTOPO30 Global Topographic Data, SRTM, GLWD, WorldClim, GRDC, and WWF Global 200 Terrestrial Eco Regions, with the latter three providing the inputs and basis for calculating flow. At medium scales (1:36,000 to 1:2,000,000) this service currently contains only U.S. data from the NHDPlusV2 that was jointly produced by the USGS and EPA.
Ile-Ife is considered the ancestral and spiritual home of the Yoruba people, with a history stretching back centuries. Ile-Ife, known as the cradle of the Yoruba people, is an ancient city with a rich cultural and historical heritage. It is renowned for its art, particularly its bronze, stone, and terracotta sculptures, which reached their peak between the 12th and 15th centuries. Modakeke, a town with origins rooted in the aftermath of the Oyo Empire's collapse, was initially a refugee settlement within Ile-Ife.
Political Power: Ile-Ife was the capital of the Yoruba kingdom of Ife, a powerful entity with extensive trade networks. Urban Development: Archaeological evidence suggests a well-planned city with sophisticated infrastructure, including paved roads and drainage systems. Mature Support Notice: This item is in mature support as of February 2025. A new version of this item is available for your use.
This web application highlights some of the capabilities for accessing Sentinel-2 imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Sentinel-2 images on a daily basis.
Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Sentinel-2 Explorer app provides the power of Sentinel-2 satellites, which gather data beyond what the eye can see. Use this app to draw on Sentinel's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the Sentinel-2 archive to visualize how the Earth's surface has changed over the last fourteen months
Quick access to the following band combinations and indices is provided:
Band
Description
Wavelength (µm)
Resolution (m)
1
Coastal aerosol
0.433 - 0.453
60
2
Blue
0.458 - 0.523
10
3
Green
0.543 - 0.578
10
4
Red
0.650 - 0.680
10
5
Vegetation Red Edge
0.698 - 0.713
20
6
Vegetation Red Edge
0.733 - 0.748
20
7
Vegetation Red Edge
0.773 - 0.793
20
8
NIR
0.785 - 0.900
10
8A
Narrow NIR
0.855 - 0.875
20
9
Water vapour
0.935 - 0.955
60
10
SWIR – Cirrus
1.365 - 1.385
60
11
SWIR-1
1.565 - 1.655
20
12
SWIR-2
2.100 - 2.280
20
Agriculture : Highlights vigorous vegetation in bright green, stressed vegetation dull green and bare areas brown; Bands 11, 8, 2Natural Color : Bands 4, 3, 2Color Infrared : Healthy vegetation is bright red while stressed vegetation is dull red; Bands 8, 4 ,3 SWIR (Short-wave Infrared) : Highlights rock formations; Bands 12, 11, 4Geology : Highlights geologic features; Bands 12, 11, 2Bathymetric : Highlights underwater features; Bands 4, 3, 1Vegetation Index : Normalized Difference Vegetation Index(NDVI) with Colormap ; (Band 8 - Band 4)/(Band 8 + Band 4)Moisture Index : Normalized Difference Moisture Index (NDMI); (Band 8 - Band 11)/(Band 8 + Band 11)Normalized Burn Ratio : (Band 8 - Band 12)/(Band 8 + Band 12)Built-Up Index : (Band 11 - Band 8)/(Band 11 + Band 8)NDVI Raw : Normalized Difference Vegetation Index(NDVI); (Band 8 - Band 4)/(Band 8 + Band 4)NDVI - VRE only Raw : NDVI with VRE bands only; (Band 6 - Band 5)/(Band 6 + Band 5)NDVI - VRE only Colorized : NDVI with VRE bands only with Colormap; (Band 6 - Band 5)/(Band 6 + Band 5)NDVI - with VRE Raw : Also known as NDRE. NDVI with VRE band 5 and NIR band 8; (Band 8 - Band 5)/(Band 8 + Band 5)NDVI - with VRE Colorized : Also known as NDRE with Colormap; (Band 8 - Band 5)/(Band 8 + Band 5)NDWI Raw : Normalized Difference Water index with Green band and NIR band; (Band 3 - Band 8)/(Band 3 + Band 8)NDWI - with VRE Raw : Normalized Difference Water index with VRE band 5 and Green band 3; (Band 3 - Band 5)/(Band 3 + Band 5)NDWI - with VRE Colorized : NDWI index with VRE band 5 and Green band 3 with Colormap; (Band 3 - Band 5)/(Band 3 + Band 5)Custom SAVI : (Soil Adjusted Veg. Index); Offset + Scale*(1.5*(Band 8 - Band 4)/(Band 8 + Band 4 + 0.5))Custom Water Index : Offset + Scale*(Band 3 - Band 12)/(Band 3 + Band 12)Custom Burn Index : Offset + Scale*(Band 8 - Band 13)/(Band 8 + Band 13)Urban Index : Offset + Scale*(Band 8 - Band 12)/(Band 8 + Band 12)Optionally, you can also choose the \"Custom Bands\" or \"Custom Index\" option to create your own band combinations
The Time tool enables access to a temporal time slider and a temporal profile of different indices for a selected point. The Time tool is only accessible at larger zoom scales. It provides temporal profiles for indices like NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index) and Urban Index. The Identify tool enables access to information on the images, and can also provide a spectral profile for a selected point. The Bookmark tool will direct you to pre-selected interesting locations.
NOTE: Using the Time tool to access imagery in the Sentinel-2 archive requires an ArcGIS account.
The following Imagery Layer are being accessed :
Sentinel-2 - Provides access to 10, 20, and 60m 13-band multispectral imagery and a range of functions that provide different band combinations and indices.
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Variable mapped: Projected land cover in 2050.
Data Projection: Cylindrical Equal Area
Mosaic Projection : Cylindrical Equal Area
Extent: Global
Cell Size: 300m
Source Type: Thematic
Visible Scale: 1:50,000 and smaller
Source: Clark University
Publication date: April 2021
What you can do with this layer?
This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.”
This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.
Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:
There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest).
What these layers model (and what they don’t model)
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The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.
Quantitative Variables used to create Models
Biomass Crop Suitability Distance to Airports Distance to Cropland 2010 Distance to Primary Roads Distance to Railroads Distance to Secondary Roads Distance to Settled Areas Distance to Urban 2010 Elevation GDP Human Influence Index Population Density Precipitation Regions Slope Temperature
Qualitative Variables used to create Models
Biomes Ecoregions Irrigated Crops Protected Areas Provinces Rainfed Crops Soil Classification Soil Depth Soil Drainage Soil pH Soil Texture Were small countries modeled?
Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.
As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.
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39 Small Countries Not Modeled
There were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:
Andorra Antigua and Barbuda Barbados Cape Verde Comoros Cook Islands Djibouti Dominica Faroe Islands French Guyana French Polynesia Gibraltar Grenada Guam Guyana Iceland Jan Mayen Kiribati Liechtenstein Luxembourg Maldives Malta Marshall Islands Micronesia, Federated States of Moldova Monaco Nauru Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Seychelles Suriname Svalbard The Bahamas Tonga Tuvalu Vatican City
Index to land cover values in this dataset:
The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover.
1 Mostly Cropland
2 Grassland, Scrub, or Shrub
3 Mostly Deciduous Forest
4 Mostly Needleleaf/Evergreen Forest
5 Sparse Vegetation
6 Bare Area
7 Swampy or Often Flooded Vegetation
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9 Surface Water
10 Permanent Snow and Ice
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Mosaic Projection : Cylindrical Equal Area
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Source Type: Thematic
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Source: Clark University
Publication date: April 2021
What you can do with this layer?
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This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.
Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:
There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest).
What these layers model (and what they don’t model)
The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.
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Quantitative Variables used to create Models
Biomass Crop Suitability Distance to Airports Distance to Cropland 2010 Distance to Primary Roads Distance to Railroads Distance to Secondary Roads Distance to Settled Areas Distance to Urban 2010 Elevation GDP Human Influence Index Population Density Precipitation Regions Slope Temperature
Qualitative Variables used to create Models
Biomes Ecoregions Irrigated Crops Protected Areas Provinces Rainfed Crops Soil Classification Soil Depth Soil Drainage Soil pH Soil Texture Were small countries modeled?
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As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.
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39 Small Countries Not Modeled
There were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:
Andorra Antigua and Barbuda Barbados Cape Verde Comoros Cook Islands Djibouti Dominica Faroe Islands French Guyana French Polynesia Gibraltar Grenada Guam Guyana Iceland Jan Mayen Kiribati Liechtenstein Luxembourg Maldives Malta Marshall Islands Micronesia, Federated States of Moldova Monaco Nauru Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Seychelles Suriname Svalbard The Bahamas Tonga Tuvalu Vatican City
Index to land cover values in this dataset:
The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover.
1 Mostly Cropland
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5 Sparse Vegetation
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Among the most affected are small species that are often overlooked, creatures that don't attract the same attention as elephants or lions but are just as important to the web of life. One such species is the pancake tortoise , a soft-shelled reptile so rare and unique that few people have ever seen one, let alone heard its story.
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EGYPT @ CROCODILE DELIGHT IN BIODIVERSITY HERITAGE SITE OF AFRICA IS AFRICAN SAFARI DELIGHT
Jewel and Jenita mol Mango plant for this International Day of Biological & Biodiversity on May 22 ,
THE KING CROCODILE OF EGYPT LIVED IN THE NILE RIVER BIODIVERSITY SITES
EGYPT IS CROCODILE GOD'S OWN COUNTRY
EGYPT BIODIVERSITY AND ECOSYSTEM IS MAKING THE COUNTRY A CROCODILE GOD'S OWN COUNTRY, NILE CROCODILE SITES & CROCODILE SPACE ECOSYSTEM IS THE E@ CROCODILE IN THE BIODIVERSITY HERITAGE SITE OF AFRICAN CONTINENT WHERE E IS EGYPT COUNTRY
THE E@ CROCODILE IN THE BIODIVERSITY HERITAGE SITE OF AFRICAN CONTINENT WHERE E IS EGYPT COUNTRY
EGYPT IS CROCODILE GOD'S OWN COUNTRY IS THE STORY - E@ CROCODILE IN THE BIODIVERSITY HERITAGE SITE & ECOSYSTEM WHERE E @ IS EGYPT COUNTRY
SOBEK CROCODILE THE KING OF NILE RIVER & NILE RIVER THE GIFT OF EGYPT BIODIVERSITY & ECOSYSTEM HAD KOM OMBO TEMPLE, CROCODILE CITY ARISINOE
The NUBIANS , NILOTIC, FELLAH, URBAN People in the Egyptian Community Worshiped Crocodile in the the city of Faiyum in Egypt is known as the "Crocodile City" because it was the center of the ancient Egyptian crocodile god, Sobek. The city is located in the Faiyum Oasis, which was a major center for the worship of Sobek and in temple. The crocodile cult at Fayoum in Egypt, centered around the city of Crocodilopolis (Arsinoe), further emphasizes the cultural importance of these creatures in ancient Egypt. Crocodiles were regarded as sacred beings, with temples and rituals dedicated to their worship .
E @ Crocodile Biodiversity & Ecosystem where E is ENTRY OF TWO CROCODILE TRESPASSERS IN CROCODILE TERRITORY
The Temple of Kom Ombo in Egypt is known as the Crocodile Temple because it's dedicated to Sobek, the crocodile god. The temple is also dedicated to Haroeris, the falcon god. Crocodile mummies are found in many Egyptian tombs, and were an important part of ancient Egyptian religion and rituals. The mummies were created by crocodile priests who wrapped the crocodiles in the same material and care as human mummies. These ancient crocodile mummies are so well-preserved, they almost look alive. Archaeologists have unearthed the remains of 10 mummified crocodiles from an Egyptian tomb that are so well-preserved, one scientist said it's "like almost a real crocodile just lying there in front of you is making EGYPT BIODIVERSITY AND ECOSYSTEM A CROCODILE GOD'S COUNTRY AND THIS CROCODILE FAMILY WILL OVERCOME HUMANS LIKE THEY HAD OVERCOME DINOSAURS IN FUTURE DEPEND ON THE CONSERVATION & PROTECTION OF CROCODILE NOW DONE BY COUNTRIES ACROSS THE WORLD
The Divine God Animals of Egypt are Sobek, crocodile-headed god of the Nile; Sekhmet, leonine goddess of war; Anubis, jackal god of the underworld; and Hathor, mother goddess with a cow's horns , Bastet was the goddess of protection, pleasure, and the bringer of good health. She had the head of a cat and a slender female body, Seth beast not a animals . Where Sobek was a crocodile-headed god with several important connotations, including his association with the colour green. The worship of Sobek peaked in the Middle Kingdom (c. 2055 -1650 BC), whose name is seen lent to several Twelfth and Thirteenth Dynasty Pharaohs such as Sobeknefru and Sobekhotep I –IV. Nile crocodiles is associated with the sacred and Nile crocodiles and is often represented as a crocodile-headed humanoid
SUMMARY- EGYPT IS CROCODILE GOD'S OWN COUNTRY BIODIVERSITY & ECOSYSTEM
E @ BIODIVERSITY HERITAGE SITE IN AFRICAN CONTINENT
THE E@ CROCODILE IN THE BIODIVERSITY HERITAGE SITE OF AFRICAN CONTINENT WHERE E IS EAST AFRICA THE BEST
More International travel to east African safari than the south Africa and west Africa even though Africa is the best for safari Tourist . The National park & animals conservation projects are less in west Africa compared to South Africa and East Africa where the largest number of national parks are in East Africa compared to south Africa and west Africa . In Kenya there is 24 National park , Tanzania there is 22 national parks and in west Africa Zambia there is 21 national park and south Africa there is 20 national parks , in west Africa Ivory coast and Nigeria there is 8 national park each making east Africa the best for safari tourist . In east Africa the infrastructure for tourist is far more better than west and south Africa making east Africa the best as they advertise more than the west . Compared to the land area , the land areas used for conservation is more in east Africa than in west so this make East Africa the best than south and west making East or West East Africa the best where E @Stands for east African tourist safari
E @ BIODIVERSITY HERITAGE SITE IN AFRICAN CONTINENT WHERE E IS EAST AFRICAN NILE CROCODILE
E @ BIODIVERSITY HERITAGE SITE IN AFRICAN CONTINENT WHERE E IS EAST AFRICAN NILE CROCODILE & SOBEK
EGYPT CROCODILE GOD'S OWN COUNTRY BIODIVERSITY & ECOSYSTEM
RESEARCHING SPACE ECOSYSTEM FOR E @ BIODIVERSITY HERITAGE SITE (BHS) FOR E @ CROCODILE DIVERSITY , E @ HABITAT DIVERSITY & E @ ECOSYSTEM DIVERSITY IN AFRICA. THE SUSTAINABLE DEVELOPMENT OF SPACE ECOSYSTEM FOR SPACE DATABASE AND TECHNOLOGY WILL DEVELOP THE E @ AFRICAN BIODIVERSITY DATA BASE THAT MONITORS & CONTROL THE E @ BIODIVERSITY HERITAGE SITE (BHS) FOR E @ CROCODILE DIVERSITY , E @ HABITAT DIVERSITY & E @ ECOSYSTEM DIVERSITY IN AFRICA
SPACE ECOSYSTEM & SPACE DATABASE TECHNOLOGY DEVELOPING THE E @ BIODIVERSITY HERITAGE SITE IS THE CONVERSION OF AFRICA DIVERSE DIGITAL DATA BASE TO AFRICA SPACIAL BIODIVERSITY DATA BASE ECOSYSTEM .
The Conversion of Digital to Spacial Maps connecting the Space Ecosystem is a vision of Future like Vision 2050 Developing African countries from Developing to a Developed Country Achieving sustainable development in the field of Climatic change, Energy for all, Food for all, etc .The conversion will shape up the economy of African countries in large scale cultivation, Cultivation of Bare land, Automated Machinery & technology , Saving large quantity of energy and fuel for the future . The Conversion to Spacial ecosystem 3 D maps will develop remote area of land like forest, water ways, water falls where large number of species inhabitant like crocodile and the conservation and reproduction of the habitat is the main aim of many organization like World Wildlife organization is due to Crocodile are important to the ecosystem
The World Wildlife organization had many project and one such is E @ Crocodile in the Biodiversity Heritage site where E @ Stands for Infinite Number of Crocodile Projects as e ∞ is equal to infinity in which e is constant considering a crocodile and ∞ is variable considering the project . This is because e is a number greater than 1, and when multiplied by itself an infinite number of times, it results in a number that is extremely large. so E @ Crocodile s infinity e ∞ , Absolute infinity Ω ω & Infinity series( Σ ) like Crocodile is the beginning and the end in the book of revelation as Crocodile is the earth for million of years from Dinosaurs period to the ancient world
E @ Crocodile Biodiversity & Ecosystem where E is Infinity Projects
INFINITY OF E @CROCODILE IN THE BHS IS THE BEGINNING & END OF THE THINGS WHERE E IS INFINITY OF CROCODILE PROJECT ALL ALONG AFRICA
Secret of Art is Infinity - Aaron’s crocodile eat the Egyptian magicians Pharaoh crocodile" make the God the Great by the Crocodile
The theme for World Wildlife Day (WWD) 2024 was "Connecting People and Planet: Exploring Digital Innovation in Wildlife Conservation". The day was celebrated on March 3, 2024 . The theme for World Wildlife Day (WWD) 2025 is "Wildlife Conservation Finance: Investing in People and Planet". The theme emphasizes the importance of investing in wildlife to ensure a resilient future for people and the planet.
The March 3 and 4 day highlighted the loss of biodiversity and the need for digital innovation in wildlife conservation and that digital conservation and in future this digital conservation is used for raising funds in the projects in 2025 in the Wildlife Conservation Finance: Investing in People and Planet and E @ Crocodile in the Biodiversity Heritage site in the African continent is given below is the digital conservation of crocodile & Planet for year 2024 and 2025
E @ Crocodile Biodiversity & Ecosystem where E is Electronic governance of Crocodile Site in Egypt & Africa
E @ Crocodile in the Biodiversity heritage site where E @ means electronic governance ' of African crocodile site is using information and communication technologies (ICTs) (such as Wide Area Networks, the Internet, and mobile computing) at various levels of the government and the public sector and beyond, for the purpose of enhancing governance.
MASSACRED IN CROCODILE ISLANDS RAMREE - INDONESIA WORLD WAR 2
THE ART OF WORLD BIODIVERSITY IS PEACE AND NOT CONFLICT - THE ART OF PEACE IS UNITY & NOT SEPARATION ,THE ART OF WAR IS THE WAR THAT IS LAST RESORT FOR PEACE WHEN DIALOGUE & DIPLOMACY FAIL. WAR IS DESTRUCTION, BIODIVERSITY LOSS IS THE HISTORICAL CROCODILES WAR IN THE MANGROVE SWAMPS OF RAMREE ISLANDS. MY HEART WILL GO ON FOR NILE CROCODILE HABITAT- The Message of My Hear will Go on is the is that - I Know my heart will go on" uses the metaphor of the heart continuing to beat in response to strong emotions, like love or fear convey the idea that Emotions remains strong and persistent, even through challenges and separation when in a crocodile encounter In Wild is fear and when in Swimming with Crocodile In 'Cage of Death' In Australian is fun and joy . "We'll stay forever in many way" & reassures that Human and crocodile bond is unbreakable even in the fear emotion while in a crocodile territory . it is one of hope and resilience in the face of loss and adversity. it is the ability to withstand adversity and bounce back from difficult life events by wisely Maintaining a space , distance & gap for the crocodile family from human Inhabited area & there by succeed in building relationships overcoming the negative experiences , feelings & learn how to get attached to crocodile and their victims . we also need to learn when and the ways to go ahead a crocodile & Human conflict that is terror , attack and murder and reduce human interference due to Infrastructure development works , fishing and tourism, floods & Disasters in crocodile insisted place and their territory . Alligator and crocodile bites are usually serious injuries. Doctors must stop bleeding. Then wounds are cleaned, and severely damaged tissue is removed. Because bites from alligators and crocodiles are very likely to become infected, people are usually given antibiotics is making the heart beat in a crocodile encounter .
CROCODILE THE HOLY HOST AS THEY PREY ON ALL HABITAT IN AS A LEADER
crocodile is often considered a "host" in an ecological context because it occupies a position at the top of the food chain in its aquatic environment , meaning it is typically the largest predator in its habitat and preys on other animals, making it the primary "host" for parasites or smaller creatures within its ecosystem; essentially, it is the dominant organism that other animals rely on, either as prey or as a source of food for scavengers after it has eaten.
Crocodiles are considered attractive for a number of reasons, including their muscular build, unique body form, and intelligent eyes makes a love emotions and at the same the Crocodiles are considered not attractive is due the larger species of crocodiles can be very dangerous to humans making a fear or threat emotions making the heart to beat for life . The Saltwater and Nile crocodiles are the most dangerous, killing hundreds of people each year in parts of South-East Asia and Africa. American alligators, Mugger crocodiles and possibly the endangered Black Caiman, are also very dangerous to humans. The crocodile are on the two sides of the coin where few crocodile are attractive and other not attractive . When there is Multiple group Crocodile in attractive and non attractive category there are multiple organizations that work on crocodile conservation
Crocodile whisper, Rangers Research teams Organization
Crocodile Conservation & rescue Organization of Rangers are IUCN SSC Crocodile Specialist Group ( CSG), International Crocodilian Farmers' Association ( ICFA) , The Crocodile Foundation and consist of a team of worldwide network of biologists, wildlife managers, government officials, independent researchers, non - government (NGO) representatives, farmers, traders, tanners, fashion leaders, and private companies actively involved in the conservation of the world's 23 living species of alligators, crocodiles, caimans and gharial in the wild. Crocodiles are primarily solitary animals & generally prefer to be left alone, and they can live for up to two years without food or water., Crocodiles are more social than other reptiles and will come together for important activities like hunting and raising their young. Crocodiles are considered dangerous to humans for a number of reasons, including: They are hungry, They are defending their territory , They are defending their young , and They are attacking another species that is near a person. Professional People & Activist are those who work with crocodiles professionally are called crocodile specialists, and they may be biologists, wildlife managers, government officials, or other professionals like crocodile whisper, Rangers & and research team s who tracks the crocodile using devices . Crocodile whisperer may refer to someone who has devoted their life to rescuing crocodiles , feeding them, Conserve them & does not harm crocodiles like Steve Irwin was known as the "Crocodile Hunter" because of his expertise with crocodiles and other wildlife and Rangers , Scientist and research teams are groups of people who catch crocodiles in traps and sometimes harm crocodile as rangers have killed crocodiles in various locations, for a number of reasons, including to protect people and livestock, and to ensure public safety as these crocodile flesh is sold as meat and skin for leather use .
AT THE END OF THE DAY CROCODILE IS HONORED AS A SURVIVING DINOSAURS
AT THE END OF THE DAY The Crocodile is honored on its traditional and historical background across the world and is a major attraction of the public is mentioned as follow s " . Crocodile Sobek is Honored as the GOD of Nile in Egypt . Honoring Crocodile as the Carnivores and scavenger , Honoring Crocodile for its long historical background of 2000 years backs , Honoring the crocodile family chain in different countries across the world in 27 species of Crocodiles , Honoring crocodile as the king of waters , Honoring crocodile for the Existence of Crocodile habitat even after 2000 years . Honoring crocodile as a sacred and worshiped in Egypt and in many monuments , temples across many countries in festivals , Honoring crocodile as the savior of water ways by controlling the population of fishes , Honoring Crocodile as a symbol in Roman coin called the "As de Nîmes" features a crocodile chained to a palm, a symbol of victory for the Romans, Honoring in the National flag Jamaica, Honoring creator of Islands Cayman, Honoring Crocodile as a National animal of the country , Honoring Crocodile the emblem of Surabaya City, Indonesia , Honoring Nile crocodile for defeating the Alexander the great army when crossing Nile river , Honoring crocodile in the Crocodile poem . Honoring Sarcosuchus as a Super croc , Honoring Gustava as the king of Nile Crocodile , Honoring Steve Irwin the crocodile Hunter , Honoring the crocodile whispers, rangers , appeaser , Honoring and portrating the Crocodile in many events, and operation of Army as Crocodile smile & Tears , Creek Crocodile and featuring crocodile as a main icon based on the Methodology , Mind ethics , Myth , ideologies in many religion and society of people in Egypt, Africa and in other countries is connected to the ecosystem and BHS (Biodiversity heritage sites) of Nile valley and the Aquatic reptile the Nile crocodile .
E @ Crocodile Biodiversity & Ecosystem where E is ENEMY & ENCOUNTER
ITS ALL IN THE GAME OF THE MAN - CROCODILE CONFLICT & ATTACKS
This all shows " ITS ALL IN THE GAME" and there is nothing to be surprised or upset in the Human -crocodile conflict because it is a normal way of reacting a situation according to the behavior pattern of a crocodile - human conflict in the Biodiversity heritage site ecosystem (BHS) . This become important & necessary for the countries to overcome the malpractices , Manipulation , corruption , pollution , that destroy Biodiversity heritage site of the places and effort had to made in every country for the sustainable development of the Biodiversity heritage sites so that the changes in Biodiversity of he place should not be by war or theft , but by the peace , security and development . To win or lose the toss is all the part of the game . It gives the winning team a chance to win the match by winning the the toss to start the game.
The Biodiversity changes made Crocodile to Survive and Dinosaurs to die
Crocodiles and dinosaurs lived during different periods of Earth's history, but there is evidence that crocodiles attacked dinosaurs. A fossil from the Cretaceous period in Queensland, Australia, revealed that a crocodile ate a dinosaur as its last meal. A fossilized neck bone from Alberta, Canada, shows a puncture wound that appears to be a bite mark from a crocodile. The bite mark was on a young Cryodrakon, a type of flying reptile called a pterosaur.
Crocodile survived the extinction of Dinosaurs is due Crocodile can survive lon period of time with out food , Crocodile have body that uses less energy , crocodile lived in places that is less affected by the asteroid attacks that killed dinosaurs . This is all due to biodiversity loss that happened due to biodiversity changes .This Biodiversity changes had made Crocodile -Human conflicts as in future when humans dies like dinosaurs ,these crocodile may survive . Human-crocodile conflict (HCC) is any negative interaction between humans and crocodiles that impacts social, economic, or cultural life. HCC can occur when crocodiles injure or kill people, or when humans enter crocodile territory. Crocodile attacks are at record high showing that crocodiles killed 10 persons in 14 months and 26 human deaths in 30 months. The war on biodiversity in Africa is a contemporary issue that involves the militarization of wildlife conservation and the notion that biodiversity protection is a war. This has led to the transformation of national parks into violent spaces. Inorder to save the biodiversity Since the 1980s, several African governments have responded to declining wildlife populations by issuing shoot-on-sight orders for “poachers” found within national parks. War is now a common model and metaphor for conceptualizing and planning biodiversity protection in Africa. Human war affect biodiversity by destroying the Protected areas, Wildlife populations, Human population density rise in protected areas , Starving populations killing crocodile for meat ,skin and money .Human conflict and armed conflict can have a significant negative impact on biodiversity in Environmental degradation, Damage to ecosystems,
The Biodiversity war that made Ghost town and Biodiversity loss
The Biodiversity war d ue to Human or Arms conflict is having the significant negative impact on the country's biodiversity like Habitat destruction, Wildlife displacement, Pollution, Marine life loss, risk in Landmines and explosive ordnance. Military activities like training & Land use , Biodiversity loss.The diverse relationships between conflict and biodiversity. (a) Conflicts take place as a result of disagreements and disputes over access to, control over and use of diverse natural resources. Sometimes, alternative uses are in direct opposition with each other, often referred to as 'conflict of use'. Humans depend upon biodiversity for survival, such as for the foods we eat, medicines we use to stay healthy, and materials we wear or use to build our homes. These services are the tangible products or items that we and other species consume for survival.Human-wildlife conflict is when encounters between humans and wildlife lead to negative results, such as loss of property, livelihoods, and even life. Defensive and retaliatory killing may eventually drive these Crocodile that had a long history in the biodiversity & other species to extinction . It is a fact is true that Many countries spend large amounts of money on their military, while failing to address climate change and biodiversity. Colombia’s new president Gustavo Petro took to the podium and asked: “What is the use of war if what we need is to save the human species?” Int he Past the War had ruined the Biodiversity in Vietnamese as the war destroyed large forest land , Congolese civil wars kills lot animals species , Iraq war reduced the Marshes and oil well on fires , Afghanistan’s conflict destroy the forest lands , Nepal conflict destroyed the Nepal’s ecosystems, Colombia mining and logging destroyed the ecosystem . it is necessary for countries to effectively protect biodiversity during armed conflict .
WORLD EARTH DAY CRISIS WITH OVER DUMPING OF WASTE IN PUBLIC PLACES
“Mother Nature will tell one day” is a story that conveys the message that humans must repair the damage in biodiversity heritage site & ecosystem they have caused to the planet. The story highlights the importance of caring for nature & natural assets, such as forests, oceans, rivers, and soils, provide us with food, water, air, and other goods and services, Restoring damaged ecosystems like A forestation , land use changes can help combat climate change poverty , mass extinction , and how humans can do so by planting trees, feeding animals, and loving the natural world as Being in nature can provide a sense of calm & peace , especially for people who live in urban areas other Mother nature will tell one day I had nothing more to give.
Peace and Biodiversity Dialogue Initiative (PBDI)
Peace and Biodiversity are related as in Peace and Biodiversity Dialogue Initiative (PBDI) is the challenge of managing (protecting) biodiversity-rich ecosystems in sociopolitical environments of increasing violence, as well as persistent poverty and inequality, is a top priority: The PBDI is a program that promotes cooperation between protected areas across national boundaries. The Republic of Korea launched the PBDI in 2014 to support the Convention on Biological Diversity (CBD) in its goal to establish and strengthen trans boundary protected areas by means of new Peace Parks which is the trans boundary protected areas (TBPAs) that are formally dedicated to the protection and maintenance of biological diversity, and of natural and associated cultural resources, and to the promotion of peace and co-operation., convention for resolve conflict & Peace with Nature , it is necessary to make peace with earth for the development of biodiversity heritage site so that the earth is not destroyed .
BIODIVERSITY WAR DESTROY THE EARTH NATURAL RESOURCES & ANIMALS
5 June is World Environment Day . .The theme for 2024, "Our Land, Our Future. We are #Generation Restoration," means We must restore ecosystems to tackle climate change, save species from extinction and secure our future.is against the BIODIVERSITY WARS SAYS ARMS TRADING IS A ILLEGAL DOG BUSINESS as war against Biodiversity is destruction because war and crises destroyed food chain and agriculture in Countries had triggered an urban agriculture boom or Peri urban cultivation and this led to a total disruption of the flow of food and agricultural supplies, cash, medicine, fuel, and more destroying the forest and ecosystem of the country .
Peri-urban agriculture on One side and War & Urbanization on other side
CROCODILE SITES OF AFRICA IN THE RACE TO PROTECT BIODIVERSITY
Biodiversity Races are held to protect the planet from war and destruction . There is Biodiversity Race for war and other is Biodiversity Race for Peace . The Biodiversity race for development by means of destruction and military is total loss and Biodiversity race for peace is conservation that is giving sustainable development for a better future in Africa . The Biodiversity Race is an event where participants record the number of species of plants and animals they can see within a time limit. The event is organized by environmental research organizations and NGOs. is to protect the planet and save the animal life from human conflict and war .Biodiversity Races for peace programs and events are done for the conservation of Animals from extinction like in the Fortress conservation, convivial conservation and in Community conservation . The World Coalition for Peace with Nature calls for a balanced relationship with nature, including protecting, conserving, restoring, and sustainably using biodiversity making peace with nature is biodiversity peace building by the biodiversity race . Biodiversity Races is for peace Global conservation is by Tree planting like Afforestation , No war & conflict as Biodiversity loss beyond the war zone like Loss of arable land: Wars can cause a loss of arable land, Stopping Environmental degradation by as Wars can damage critical ecosystems, pollute land and rivers, and lead to deforestation .Biodiversity is a social justice issue, and the effects of war on biodiversity can be injustice . Biodiversity and conflict are linked in both directions, with war negatively impacting biodiversity and biodiversity protection potentially contributing to peace building. The Biodiversity Race for war is loss is based on the Damage to ecosystems, Damage critical ecosystem, Devastate wildlife populations. Increase the risk of zoonotic diseases, Disrupting wildlife and livelihoods. Climate change, Pollution, Destruction of habitats, Land use change, Over exploitation of earth resources Environmental degradation, extinction: ,food shortage, Social disruption, carbon emission , Radiation & loss of space in Military training. The term "war to save biodiversity" refers to the idea that conservationists are engaged in a battle to protect species from poachers and armies, November 6 is the International Day for Preventing the Exploitation of the Environment in War and Armed Conflict . Biodiversity is in free fall globally, declining faster than at any other moment in human history. Current global efforts to prevent and reduce biodiversity loss focus on times of peace .
CROCODILE TROPHY IN RESCUING NOT HUNTING PROTECT BIODIVERSITY
BIODIVERSITY TROPHY IS HUNTING OR CONSERVATION should reduce Man- Animal conflict , giving trophy is hunting is a injustice to Biodiversity conservation act world wide
CROCODILE BIODIVERSITY CENSUS
Spatial Biodiversity Census. The Present Status of the BIODIVERSITY CENSUS had declined because Biodiversity loss had negatively impact the earth as per the reports . T he average size of global wildlife populations have declined by 73% in 50 years, The Biodiversity census gives biodiversity loss statics based on the wildlife faces threats from habitat loss, invasive species, disease and climate change, is giving policymakers need clear, accurate information to make sound conservation decisions. Like Sampling in Census survey , the bio-cube is manageable way of exploring the biodiversity in the world . The cube helps us keep everything centralized . "You can 'roll' the cube to look at spatial patterns of where plants and animals are found or 'roll' it to reveal species' change through time. and make Global Biodiversity survey is Combining historical records of biodiversity with modern surveys and data from remote-sensing satellites can provide a clearer picture of life on Earth and how it has changed over time. A team of international environmentalists and data scientists have developed a plan for tracing the numbers and positions of our planet's millions of plants and animal species – calling it as a biodiversity census . The Census is developing the Biodiversity and Ecosystem for Peace and security of the planet to reduce Man and Animal & conflict , Biodiversity war , Man Made destruction , Act of God Destruction ,Mass destruction and extinction in period of time as Biodiversity Cli matic changes or loss are not always the Act of God it can be by Conflict, war or man made
COP SUMMIT ON CLIMATIC CHANGE & CRIME IS PROTECTING CROCODILE HABITAT
The Conference of Parties COP for climatic change and other in Biodiversity change Meeting held every yearly is to Save the Biodiversity of the Earth . The Conference of the Parties for climatic changes is held yearly and the Conference of the Parties for biodiversity change is held once in two years . The Conference of the Parties for climatic changes last held is COP 28 in Dubai at the end of 2023 and The Conference of the Parties for biodiversity change last held is COP 15 in Montreal, Canada at the end of 2022 & COP 16 in 2024 at Cali Colombia . The Conference of the Parties (COP) is a series of meetings held under the United Nations Convention on Biological Diversity (CBD) to determine global efforts for nature. The COP's objectives include: Overseeing the implementation and future development of the CBD , Achieving a sustainable future for biodiversity , Approving global plans for protecting and restoring nature , Reversing nature loss , aim of limiting global warming to as close as possible to 1.5 °C above pre-industrial levels.The COP Truce is an initiative that calls for a suspension of conflicts during the United Nations Climate Change Conference (COP29) to send a message that peace and climate action are interconnected. COP stands for Conference of the Parties, and it's the main decision-making body of the United Nations Framework Convention on Climate Change (UNFCCC). At COP meetings, governments assess efforts to limit global warming and make decisions on: Cutting greenhouse gas emissions, Adapting to climate change impacts, Funding vulnerable countries hit by climate disasters, and Helping countries green their economies. The United Nations Climate Change Conferences (COP) have addressed solar and wind energy, as well as fossil fuels, at multiple meetings for saving the Biodiversity . ANIMALS SAVES THE BIODIVERSITY BUT HUMANS DESTROYED . Animal only fight and kill each other , but humans fight that not only kill each other but also the world by war,deforestation,pollution, global warming etc. In UN Summit on Biodiversity target on saving biodiversity by planing task by nations to save the biodiversity by saying A "people's COP" to heal the planet is about rethinking how people live together in a way that doesn't make nature a victim of development in U.N Summit on Biodiversity and Climatic change in COP 16 Colombia .
ICC DECLARING WAR CRIME IS THE INHUMANITY ACT & VIOLATION OF INTERNATIONAL HUMANITARIAN LAW
In the Earth Biodiversity ,lions are traditionally known as the king of the land, and crocodiles are known as the king of the rivers. Biodiversity May be good at all times as Biodiversity is essential for the health of the planet and all life on Earth, including humans. It supports healthy ecosystems that provide and Biodiversity is not good at all times because humans are the main driver of biodiversity loss. Biodiversity loss is the decline or disappearance of the variety of life on Earth, which can lead to the breakdown of ecosystems and the end of species. E @ Biodiversity Heritage site is essential for the processes that support all life on Earth, including humans. E @ Biodiversity Heritage site develop a wide range of animals, plants and microorganisms, we cannot have the healthy ecosystems that we rely on to provide us with the air we breathe and the food we eat. And people also value nature of itself. E @ Biodiversity heritage site is able positively clarify the ICC arrest warrant issued to Israeli Prime Minister Netanyahu and Russian president Putin for ‘war crimes’ as war can be an aggression on biodiversity and ecosystems in many ways like Resource extraction , Pollution, Greenhouse gas emissions, Deforestation:, Wildlife harm, Loss of natural resources, Ecosystem disruption, Mass population movement:, Loss of wildlife had been in Gaza Israeli bombing , in South Lebanon & Beirut by Israeli force and in Ukraine province of Kharkiv, Kyiv, Odesa, Zaporizhzhia, Dnipro, Sumy, and Chernihiv regions by Russian Army made them a war criminal by the International Criminal Court (ICC) .
BECOME FRIEND OF CROCODILE FROM E@ CROCODILE IN BIODIVERSITY HERITAGE SITE (BHS) PACKAGE IN FUND RAISING & CONSERVATION
THE ART OF BIODIVERSITY IS PEACE AND NOT CONFLICT - THE ART OF PEACE IS UNITY & NOT SEPARATION ,THE ART OF WAR IS WAR THE LAST RESORT FOR PEACE WHEN DIALOGUE & DIPLOMACY FAIL. WAR OF MASS DEATH , DESTRUCTION & BIODIVERSITY LOSS IS HISTORICAL CROCODILES WAR IN THE MANGROVE SWAMPS OF RAMREE ISLANDS OR AS GORILLA WAR / GUERRILLA WAR....SPACE ECOSYSTEM DEVELOP ,MONITOR ,PUBLISH AND CREATE SPACE FOR E @ SITE ,E @ CLIENTS , E @ BIODIVERSITY & E @ CROCODILE IS COMMUNAL SERVICE RATHER THAN GOVERNMENT ,SOCIETY AS COMMUNITY OWNED E @ NETWORK IS DEMOCRATIC AND PRIORITIZES PEOPLE RIGHT AND PRIVACY THAN GOVERNMENT THAT CENSORS & MAKE INTERNET RESTRICTION COMMUNAL SERVICE IN E @ OPERATION MAKES ANIMAL TO PROTECT THERE TERRITORY AND HUMANS & LOCAL COMMUNITY PROTECTING THEIR TERRITORY IS COMMUNITY BASED CONSERVATION (CBC) IN E @ CROCODILE IS NOT TARGETING CROCODILE IN HUMAN - CROCODILE CONFLICT HCC OTHERWISE THE CONFLICT IS TARGETING THE CROCODILE ONLY AS A DRAGON FORCING THEM TO EXTINCTION . THIS IS ONE OF MANY WAYS TO STOP TARGETING CROCODILE BY PUBLISHING MANY CONSERVATION JOURNAL AND RESEARCH WORKS, PORTRAYING IMAGES OF CROCODILE , SONGS,VEDIEOS , ONLINE ZOO , CROCODILE PLANETARIUM & TRAINING PEOPLE TO COEXIST WITH CROCODILES MAKING THEM A FRIEND OF HUMANS AND MAKING SPACE TO GRADUALLY STOP ECO TERRORISM , ENVIRONMENTAL TERRORISM , CROCODILE RIGHT EXTREMISM , CROCODILE AGRO CRIME & AGRO TERRORISM & GRADUALLY REDUCE DISCRIMINATION AGAINST CROCODILE SPECIES IN MANY PARTS OF AFRICA AND REST OF THE WORLD AND NOT TO DECIDE THE FUTURE OF CROCODILE ON THEIR APPEARANCE ,SIZE , WEIGHT AS THERE WON'T BE ANY OTHER GUSTAVE OR SARCOSUCHUS CROCODILE AS THESE ARE CONSIDERED NOT AS A CROCODILE BUT TRADITIONALLY A DRAGON SALT WATER OR FRESH WATER CROCODILE OF BIGGER SIZE OF THE RIVER & SALT WATER SEA LIKE THE DRAGON / DINOSAURS IN BIG SIZE THAT ARE DEADLY AND IS NOT FALLiNG IN E @ CROCODILE ENVIRONMENT AS ALL CROCODILE ARE NOT BIGGER LIKE THE GUSTAVE OR SARCOSUCHUS MAN-EATERS MALE DRAGON CROCODILES WHICH IS A AQUATIC OR SEMI AQUATIC SIMILAR TO LIKE A CROCODILE HAVING SOME RELATION WITH CROCODILE SPECIES IN OTHER SIZES .
E PACKAGE FOR CROCODILE IS E @ CROCODILE IN BIODIVERSITY HERITAGE SITE
E @ CROCODILE IS NOT THE DEATH OR END OF CROCODILE ,BUT IT IS THE DAY OF THE CROCODILE AND THE HABITAT CONSERVATION AS WELL AS E @ BIODIVERSITY IS NOT THE BIODIVERSITY LOSS BUT IS IT BIODIVERSITY GAIN . THERE MIGHT BE PEOPLE WHO DEDICATED THE LIFE WITH CROCODILE FOR YEARS AS RANGERS OR FIELD RESEARCHERS , BUT THIS IS E @ IS DIGITAL PLATFORM PUBLICATION OF E @ BLOGS , E @ SOCIAL MEDIA , E @ EDITORIAL , & STORY MAPS ANALYZING THE CROCODILE IN E @ CROCODILE & IN E @BIODIVERSITY THAT REQUIRED LOT FOLLOW UPS AND UPDATION AS THIS KIND OF E @ PLATFORM IS MORE EFFICIENT , TRANSPARENT , ACCOUNTABLE AND ALLOW DATA COMMUNICATION AND A ELECTRONIC OFFICE AT HOME, OFFICE, REMOTE VILLAGE, REMOTE OFFICES , RESEARCH CENTERS ETC . E @ CROCODILE IS A E - PACKAGE THAT GIVES IMPORTANCE FOR CROCODILE THAT CONSIST OF THE SPACE ECOSYSTEM , EARTH ECOSYSTEM , FOREST & CROCODILE ECOSYSTEM IS MAKING CROCODILE A PART OF ANCIENT CIVILIZATION , MODERN LIFE & FUTURE GENERATION , NATURE & ECOLOGY , SCIENCE & TECHNOLOGY WITH VARIETY OF SPECIES , ORGANISMS , DEVICES INCLUDING HUMANS & THEIR PHYSICAL ENVIRONMENT ,
E @ CROCODILE IS A MASTER PLAN THAT IS DONE TO MONITOR & CONTROL HABITAT LOSS BY CONSERVATION THAT CAN BE DONE BY MEANS OF SATELLITE REMOTE SENSING A ADVANTAGED TECHNOLOGY , 3D IMAGING , EVEN IN TIME OF WORST CLIMATE , MONITORING ISOLATED PLACES SEPARATED FROM MAIN LANDS AND EVEN IN DISASTER . E @ CROCODILE WILL BENEFIT THE END USER WHO IS A LOCAL COMMUNITY AND CROCODILE BY MEANS TRACKING SIGNALS SO THAT ALL CROCODILE , VILLAGES ARE GPS LOCATED AND NOT ALLOWING HUMAN ACTIVITY IN CROCODILE GPS LOCATION SO THAT HUMAN CROCODILE COEXISTENCE CAN BE DEVELOPED BY NULLIFYING THE CONFLICT SO THAT THIS WILL NOT ALLOW COMPLICATING, MANIPULATING , DESTROYING THE CROCODILE GPS PLACES WITH ISSUES OF ATTACKS AND NOT HAVING LOSS OF LIFE IS THE MOST IMPORTANCE OF E @ CROCODILE THAT IS A MAJOR DEVELOPMENT BY MEANS OF SPACE ECOSYSTEM AND E @ SERVICE OF CROCODILE MASTER PLAN .
EGYPT AND THE NILE CROCODILE IN THE NILE VALLEY
The Civilization is in the Nile valley of Africa passing through Egypt Where People inhabited 2000 Year Before . The People in the Valley Followed the tradition and history of the people and had constructed tombs where Mummies of Human and Crocodile are Found . The Tomb of Crocodile and he fossils are found in many areas of North Sahara Countries especially in Egypt where God and his holy disciple converted people to Christianity from the old traditions and belief. These Crocodile Tombs in the valley show that Crocodile are important part of the ecosystem and considered them as God like the other Gods and saints . This belief had made more investigation and research to find out the crocodile Biodiversity and ecosystem in Africa staring from the Ancient civilization to the national parks of Africa & heritage site .These are the Major Content of the E@Crocodile in the biodiversity heritage site of the African continent
THE E@ CROCODILE IN THE BIODIVERSITY HERITAGE SITE OF AFRICAN CONTINENT WHERE E IS END OF THE STORY
In the End Crocodile iogo of the French luxury fashion brand Lactose, Creator of Islands, National Animals of Pakistan and East Timor ., . crocodile appears on the national flag of Lesotho . Crocodile are economically important because their skins ,meat and other body parts are used to make luxury goods ,jewelry, medicine , Important animals is the ecology of the earth & Nature . . In the end Honoring Nile crocodile for defeating the Alexander the great army when crossing Nile river , Honoring crocodile in the Crocodile poem . Honoring Sarcophagus as a Super crocodile , Honoring Gustav as the king of Nile Crocodile , Honoring Steve Irwin the crocodile Hunter , Honoring the crocodile whispers, rangers , appeaser , Honoring and portraying the Crocodile in many events, and operation of Army as Crocodile smile & Tears , Creek Crocodile and featuring crocodile as a main icon based on the Methodology , Mind ethics , Myth , ideologies in many religion and society of people in Egypt, Africa and in other countries is connected to the ecosystem and BHS (Biodiversity heritage sites) of Nile valley and the Aquatic reptile the Nile crocodile .
Crocodile in Near future will come up with many other stories like a crocodile with human head or a fish with crocodile head and they live for generation to come . In near future crocodile may appear like fish or human heads and live for generation
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More details on this dataset can be found here . The coastlines hotspots of Africa layer is part of the Digital Earth Africa coastlines dataset, representing the changes of coastlines across the continent as a summary layer producing moving window hotspots of coastal change at three differing scales of 15 km , 5 km, and 1 km. This layer is useful for visualizing regional or continental-scale patterns of coastal change.
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Temporal Coverage: 2000 to Present
Spatial Resolution: 30 x 30 meter
Update Frequency: Annual from 2000 - Present; 6 months from end of previous year
Source Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
More details on this dataset can be found here . The coastlines hotspots of Africa layer is part of the Digital Earth Africa coastlines dataset, representing the changes of coastlines across the continent as a summary layer producing moving window hotspots of coastal change at three differing scales of 15 km , 5 km, and 1 km. This layer is useful for visualizing regional or continental-scale patterns of coastal change.
Key Properties:
Geographic Coverage: Continental Africa - approximately 37° North to 35° South
Temporal Coverage: 2000 to Present
Spatial Resolution: 30 x 30 meter
Update Frequency: Annual from 2000 - Present; 6 months from end of previous year
Source Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
More details on this dataset can be found here . The coastlines hotspots of Africa layer is part of the Digital Earth Africa coastlines dataset, representing the changes of coastlines across the continent as a summary layer producing moving window hotspots of coastal change at three differing scales of 15 km , 5 km, and 1 km. This layer is useful for visualizing regional or continental-scale patterns of coastal change.
Key Properties:
Geographic Coverage: Continental Africa - approximately 37° North to 35° South
Temporal Coverage: 2000 to Present
Spatial Resolution: 30 x 30 meter
Update Frequency: Annual from 2000 - Present; 6 months from end of previous year
Source Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
More details on this dataset can be found here . The Digital Earth Africa shorelines layer is a continental dataset that includes annual shorelines along the entire African coastline that has been generated for 2000 to Present.
The product combines satellite data from the Digital Earth Africa program with tidal modelling to map the typical location of the coastline at mean sea level each year. The product enables trends of coastal erosion and growth to be examined annually at both a local and continental scale, and for patterns of coastal change to be mapped historically and updated regularly as data continues to be acquired. This allows current rates of coastal change to be compared with that observed in previous years or decades.
The ability to map shoreline positions for each year provides valuable insights into whether changes to the coastline are the result of particular events or actions, or a process of more gradual change over time. This information can enable scientists, managers, and policy makers to assess impact from the range of drivers impacting the coastlines and potentially assist planning and forecasting for future scenarios.
Annual shoreline vectors from 2000 that represent the median or ‘most representative’ position of the shoreline at approximately mean sea level tide (0 m Above Mean Sea Level) for each year. Light-coloured lines (e.g. yellow) in the plot represent recent shorelines (e.g. 2024), while dark-coloured lines represent older shorelines (e.g. 2000).
Key Properties
Geographic Coverage: Continental Africa - approximately 37° North to 35° South
Temporal Coverage: 2000 to Present
Spatial Resolution: 30 x 30 meter
Update Frequency: Annual from 2000 - Present; 6 months from end of previous year
Parent Dataset: Landsat Collection 2 Surface Reflectance
Source Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
More details on this dataset can be found
here .
dataset providing robust rates of coastal change (in metres per year) for every 30 m along Africa’s non-rocky (e.g. sandy and muddy) coastlines. These rates are calculated by linearly regressing annual shoreline positions against time, using the most recent shoreline as a baseline.
Negative values (red points) indicate retreat (e.g. erosion), and positive values indicate growth (e.g. progradation) over time. By default, rates of change are shown for points with a statistically significant trend over time only.
Key Properties
Geographic Coverage: Continental Africa - approximately 37° North to 35° South
Temporal Coverage: 2000 to Present
Spatial Resolution: 30 x 30 meter
Update Frequency: Annual from 200 - Present; 6 months from end of previous year
Source Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Rates of change statistics attributes:
\n \n Attribute
\n \n \n \n rate_time
\n \n \n Annual rates of change (in metres per year)\n calculated by linearly regressing annual shoreline distances against time\n (excluding outliers). Negative values indicate retreat and positive values\n indicate growth.
\n \n \n \n \n sig_time
\n \n \n Significance (p-value) of the linear relationship\n between annual shoreline distances and time. Small values (e.g. p-value <\n 0.01) may indicate a coastline is undergoing consistent coastal change\n through time.
\n \n \n \n \n se_time
\n \n \n Standard error (in metres) of the linear\n relationship between annual shoreline distances and time. This can be used to\n generate confidence intervals around the rate of change given by rate_time\n (e.g. 95% confidence interval = se_time * 1.96).
\n \n \n \n \n outl_time
\n \n \n Individual annual shoreline are noisy estimators\n of coastline position that can be influenced by environmental conditions\n (e.g. clouds, breaking waves, sea spray) or modelling issues (e.g. poor tidal\n modelling results or limited clear satellite observations). To obtain\n reliable rates of change, outlier shorelines are excluded using a robust\n Median Absolute Deviation outlier detection algorithm, and recorded in this\n column.
\n \n \n \n \n sce
\n \n \n Shoreline Change Envelope (SCE). A measure of the\n maximum change or variability across all annual shorelines, calculated by\n computing the maximum distance between any two annual shoreline (excluding\n outliers). This statistic excludes sub-annual shoreline variability.
\n \n \n \n \n nsm
\n \n \n Net Shoreline Movement (NSM). The distance\n between the oldest (2000) and most recent annual shoreline (excluding\n outliers). Negative values indicate the coastline retreated between the\n oldest and most recent shoreline; positive values indicate growth. This\n statistic does not reflect sub-annual shoreline variability, so will\n underestimate the full extent of variability at any given location.
\n \n \n \n \n max_year, min_year
\n \n \n The year that annual shorelines were at their\n maximum (i.e. located furthest towards the ocean) and their minimum (i.e.\n located furthest inland) respectively (excluding outliers). This statistic\n excludes sub-annual shoreline variability.
\n \n \n \n \n outl_time
\n \n \n Individual annual shoreline are noisy estimators\n of coastline position that can be influenced by environmental conditions\n (e.g. clouds, breaking waves, sea spray) or modelling issues (e.g. poor tidal\n modelling results or limited clear satellite observations). To obtain\n reliable rates of change, outlier shorelines are excluded using a robust\n Median Absolute Deviation outlier detection algorithm, and recorded in this\n column.
\n \n \n
More details on this dataset can be found
here .
The timing of this expansion of the Friday mosque coincides with the construction of the new Grand Mosque in Gedi. This synchronicity suggests a possible connection, possibly related to a wider religious or political shift in the region around the mouth of the Tana River and Mida Bay. In particular, the addition of the third column opposite the mihrab may not have been purely functional but may reflect the influence of another Muslim sect within the community.
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Variable mapped: Projected land cover in 2050.
Data Projection: Cylindrical Equal Area
Mosaic Projection : Cylindrical Equal Area
Extent: Global
Cell Size: 300m
Source Type: Thematic
Visible Scale: 1:50,000 and smaller
Source: Clark University
Publication date: April 2021
What you can do with this layer?
This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.”
This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.
Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:
There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest).
What these layers model (and what they don’t model)
The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.
The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.
Quantitative Variables used to create Models
Biomass Crop Suitability Distance to Airports Distance to Cropland 2010 Distance to Primary Roads Distance to Railroads Distance to Secondary Roads Distance to Settled Areas Distance to Urban 2010 Elevation GDP Human Influence Index Population Density Precipitation Regions Slope Temperature
Qualitative Variables used to create Models
Biomes Ecoregions Irrigated Crops Protected Areas Provinces Rainfed Crops Soil Classification Soil Depth Soil Drainage Soil pH Soil Texture Were small countries modeled?
Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.
As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.
Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.
39 Small Countries Not Modeled
There were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:
Andorra Antigua and Barbuda Barbados Cape Verde Comoros Cook Islands Djibouti Dominica Faroe Islands French Guyana French Polynesia Gibraltar Grenada Guam Guyana Iceland Jan Mayen Kiribati Liechtenstein Luxembourg Maldives Malta Marshall Islands Micronesia, Federated States of Moldova Monaco Nauru Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Seychelles Suriname Svalbard The Bahamas Tonga Tuvalu Vatican City
Index to land cover values in this dataset:
The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover.
1 Mostly Cropland
2 Grassland, Scrub, or Shrub
3 Mostly Deciduous Forest
4 Mostly Needleleaf/Evergreen Forest
5 Sparse Vegetation
6 Bare Area
7 Swampy or Often Flooded Vegetation
8 Artificial Surface or Urban Area
9 Surface Water
10 Permanent Snow and Ice
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Phenomenon Mapped: Precipitation
Units: Millimeters
Time Interval: Monthly
Time Extent: 2000/01/01 to present
Cell Size: 28 km
Source Type: Scientific
Pixel Type: Signed Integer
Data Projection: GCS WGS84
Mosaic Projection: Web Mercator Auxiliary Sphere
Extent: Global Land Surface
Source: NASA
Update Cycle: Sporadic
What can you do with this layer?
By applying the "Calculate Anomaly" processing template, it is also possible to view these data in terms of deviation from the mean, instead of total evapotranspiration. Mean evapotranspiration for a given month is calculated over the entire period of record - 2000 to present.
Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is eight years.
Variables: This layer has two variables: rainfall and snowfall. By default the two are summed, but you can view either by itself using the multidimensional filter, or by applying the relevant raster function. You must disable time animation on the layer before using its multidimensional filter.
Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.
Proportion of people who live in a household that does not have water for handwashing in the home. The higher the value, the greater proportion of people that live in a household with no water for handwashing per 1x1 square kilometer. Grids with less than 30 people are replaced with a value of NA.
Source: Fraym; Base Survey: Zambia DHS 2018
Population estimate from WorldPop's Bottom-up gridded population layer. Access for the WorldPop population estimates full methodology visit https://wopr.worldpop.org/
Proportion of people who live in a household that takes more than 30 minutes to fetch and retrieve water. The higher the value, the greater proportion of people that live in a household that takes more than 30 minutes to fetch and retrieve water per 1x1 square kilometer. Grids with less than 30 people are replaced with a value of NA.
Source: Fraym; Base Survey: Zambia DHS 2018
Population estimate from WorldPop's Bottom-up gridded population layer. Access for the WorldPop population estimates full methodology visit https://wopr.worldpop.org/
Proportion of people who live in a household that does not have piped-in drinking water in the home. The higher the value, the greater proportion of people that live in a household with no piped-in drinking water per 1x1 square kilometer. Grids with less than 30 people are replaced with a value of NA.
Source: Fraym; Base Survey: Zambia DHS 2018
Population estimate from WorldPop's Bottom-up gridded population layer. Access for the WorldPop population estimates full methodology visit https://wopr.worldpop.org/
Proportion of adult men (15-49) that are literate in Zambia. The higher the value, the greater percentage of adult men (15-49) that are able to read at least parts of sentence per 1x1 square kilometer. Grids with less than 30 population are replaced with a value of NA.
Source: Fraym; Base Survey: Zambia DHS 2018
Population estimate from WorldPop's Bottom-up gridded population layer. Access for the WorldPop population estimates full methodology visit https://wopr.worldpop.org/
Point stéréoscopique au sol, occupant une position stratégique visant à
compléter la description altimétrique du terrain (relief) fournie par les courbes de niveau.
Sources: Fraym; Base Survey: Zambia DHS 2018
Population estimate from WorldPop's Bottom-up gridded population layer. Access for the WorldPop population estimates full methodology visit https://wopr.worldpop.org/
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Adeegan waxa loguterigali inlaguxabiyo xoog kusabsan Korantada DEPCO
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Grand Canyon, Arizona, USA
Golden Gate, California, USA
Taj Mahal, Agra, India
Vatican City
Bronze age white horse, Uffington, UK
Uluru (Ayres Rock), Australia
Machu Picchu, Cusco, Peru
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In ArcGIS Pro
Parameters
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Living Atlas . Discover more 3D rules to help you perform your work.Learn more about ArcGIS Pro in the Getting to Know ArcGIS Pro lesson
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Moi University was first established in 1984. In its first year of existence, there was only one department - the Department of Forestry.
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Stay with me and I promise more Dopamine as you read on... What is dopamine?
The image on the right should give you an idea, but if you want to know more about Dopamine then ask Google! However, for my sake and for the sake of the story I'm trying to tell - do that later. Suffice it to say that if you're still with me and reading this - then whatever Dopamine might be - you're getting a healthy dose of it and you'll want to continue reading on.
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"dct:description": "This historical map from the David Rumsey Map Collection is a Composite of) The World on Mercator's projection. Drawn under the direction of Mr. Pinkerton by L. Hebert. Neele sculpt. 352 Strand. London: published ... 1812, by Cadell & Davies, Strand & Longman, Hurst, Rees, Orme, & Brown, Paternoster Row.
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More information on this historical map... Water bodies are a key element in the landscape. This layer provides a global map of large water bodies for use in landscape-scale analysis.
Dataset Summary
This layer provides access to a 250m cell-sized raster of surface water created by extracting pixels coded as water in the
Global Lithological Map and the
Global Landcover Map . The layer was created by Esri in 2014. What can you do with this layer?
This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.
This layer has
query ,
identify , and
export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe.
This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
The
Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.
The
Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.
Most of us understand the hydrologic cycle in terms of the visible paths that water can take such as rainstorms, rivers, waterfalls and lakes. However, an even larger volume of water flows through the air all around us in two invisible paths: evaporation and transpiration. These two paths together are referred to as evapotranpsiration (ET), and claim 61% of all terrestrial precipitation. Solar radiation, air temperature, wind speed, soil moisture, and land cover all affect the rate of evapotranspiration, which is a major driver of the global water cycle, and key component of most catchments' water budget. This map contains a historical record showing the volume of water lost to evapotranspiration during each month from March 2000 to the present (NASA sometimes has a lag of several months in generating the data).
Dataset Summary
The GLDAS Evapotranspiration layer is a time-enabled image service that shows total actual evapotranspiration monthly from 2000 to the present, measured in millimeters of water loss. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages.
What can you do with this layer?
Time
Important
When precipitation falls on the surface of the Earth, much of it is captured in storage (e.g. lakes, aquifers, soil moisture, snowpack, and vegetation). Precipitation that exceeds the storage capacity of the landscape becomes runoff, which flows into river systems. Overland flow is the most visible form of runoff, causing erosion and flash floods, but subsurface flow is the larger contributor in many watersheds. Subsurface flow can emerge on the surface through springs, or more commonly, seep into rivers and lakes through their banks.
In urban areas, impervious land cover drastically increases the amount of surface runoff generated, which sweeps trash and urban debris into waterways and increases the likelihood and severity of flash floods. In agricultural areas, surface or subsurface runoff can carry excess salts and nutrients, especially nitrogen and phosphorus. This map contains a historical record showing the amount of runoff generated each month from March 2000 to present. It is reported in millimeters, so multiply by a surface area to calculate the total volume of runoff.
Dataset Summary
The GLDAS Runoff layer is a time-enabled image service that shows average monthly runoff from 2000 to the present measured in millimeters. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages.
What can you do with this layer?
Time
Variables:
Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.
Precipitation is water released from clouds in the form of rain, sleet, snow, or hail. It is the primary source of recharge to the planet's fresh water supplies. This map contains a historical record showing the volume of precipitation that fell during each month from March 2000 to the present. Snow and hail are reported in terms of snow water equivalent - the amount of water that will be produced when they melt.
Dataset Summary
The GLDAS Precipitation layer is a time-enabled image service that shows average monthly precipitation from 2000 to the present, measured in millimeters. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages.
What can you do with this layer?
Time:
Variables:
Important:
Wayback imagery is a digital archive of the World Imagery basemap, enabling users to access more than 100 different versions of World Imagery archived over the past 10 years. Each record in the archive represents a version of World Imagery as it existed on the date it was published.
This app offers a dynamic Wayback browsing and discovery experience where previous versions of the World Imagery basemap are presented within the map, along a timeline, and as a list. Versions that resulted in local changes are dynamically presented to the user based on location and scale. Preview changes by hovering over and/or selecting individual layers. When ready, one or more Wayback layers can be added to an export queue and pushed to a new ArcGIS Online web map. Browse, preview, select, and create, it’s all there!
Kempinski is the world’s oldest luxury hotel management company. Founded in Berlin in 1897, the brand is headquartered in Geneva, Switzerland, and operates over 80 five-star hotels and residences across Europe, the Middle East, Africa, Asia, and the Americas.
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"dct:description": "Landsat 4, 5, 7, 8, and 9 multispectral and multitemporal analysis ready imagery with on-the-fly renderings and indices for visualization and analysis. This layer is time enabled and includes a number band combinations and indices rendered on demand. See available spectral bands in the table below. The imagery in this layer is sourced from the Microsoft Planetary Computer
Landsat Collection 2 Level-2 data catalog on Azure.
Geographic Coverage
Temporal Coverage
This layer includes imagery from Landsat missions 4, 5, 7, 8, and 9, with a time span of 1982 to present Landsat 8 and 9, are actively working in tandem, revisiting and capturing each point on Earth's land surface every 8 days Product Level
Landsat Collection 2 Level-2 Science Products NOTE: The appropriate scale factors are dynamically applied to this imagery. The pixel values provided by the service are provided as scientific floating point values for Surface Reflectance and Surface Temperature. More information on Level-2 Science Products Image Selection/Filtering
A number of fields are available for filtering, including Acquisition Date, Estimated Cloud Cover, and Product ID To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images Visual Rendering
The default rendering is Natural Color (bands 4,3,2) with Dynamic Range Adjustment (DRA) The DRA version of each layer enables visualization of the full dynamic range of the images Rendering (or display) of band combinations and calculated indices is done on-the-fly from the source images via Raster Functions Various pre-defined Raster Functions can be selected or custom functions can be created Pre-defined functions: Natural Color with DRA, Agriculture with DRA, Geology with DRA, Color Infrared with DRA, Bathymetric with DRA, Short-wave Infrared with DRA, Normalized Difference Moisture Index Colorized, NDVI Raw, NDVI Colorized, NBR Raw Multispectral Bands
Band
Description
Wavelength* (µm)
Spatial Resolution (m)
1
Coastal aerosol**
0.43 - 0.45
30
2
Blue
0.45 - 0.52
30
3
Green
0.52 - 0.60
30
4
Red
0.63 - 0.69
30
5
NIR
0.76 - 0.90
30
6
SWIR 1
1.55 - 1.75
30
7
SWIR 2
2.08 - 2.35
30
8
Pixel QA
NA
30
9
Surface Temperature (Kelvin)
10.4-12.5
30***
10
Surface Temperature QA
NA
30
*This is the max range for each band based on the combined missions. For reference to the distinct ranges for each mission see this document .
**Coastal Aerosol is only available from Landsat 8 and 9. This band is simply a place holder and does not contain data for the other missions.
***The thermal band is acquired at 100 or 120 meter resolution and resampled to 30 meters.
Learn more about the Quality Assessment (QA) Bands
Data Source
Landsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). The imagery in this layer is hosted in Azure as part of the Microsoft Planetary Computer Data Catalog .
Land cover data represent a descriptive thematic surface for characteristics of the land's surface such as densities or types of developed areas, agricultural lands, and natural vegetation regimes. Land cover data are the result of a model, so a good way to think of the values in each cell are as the predominating value rather than the only characteristic in that cell.
Land use and land cover data are critical and fundamental for environmental monitoring, planning, and assessment.
Dataset Summary
BaseVue 2013 is a commercial global, land use / land cover (LULC) product developed by MDA . BaseVue covers the Earth’s entire land area, excluding Antarctica. BaseVue is independently derived from roughly 9,200 Landsat 8 images and is the highest spatial resolution (30m), most current LULC product available. The capture dates for the Landsat 8 imagery range from April 11, 2013 to June 29, 2014. The following 16 classes of land use / land cover are listed by their cell value in this layer: Deciduous Forest: Trees > 3 meters in height, canopy closure >35% (<25% inter-mixture with evergreen species) that seasonally lose their leaves, except Larch. Evergreen Forest: Trees >3 meters in height, canopy closure >35% (<25% inter-mixture with deciduous species), of species that do not lose leaves. (will include coniferous Larch regardless of deciduous nature). Shrub/Scrub: Woody vegetation <3 meters in height, > 10% ground cover. Only collect >30% ground cover. Grassland: Herbaceous grasses, > 10% cover, including pasture lands. Only collect >30% cover. Barren or Minimal Vegetation: Land with minimal vegetation (<10%) including rock, sand, clay, beaches, quarries, strip mines, and gravel pits. Salt flats, playas, and non-tidal mud flats are also included when not inundated with water. Not Used (in other MDA products 6 represents urban areas or built up areas, which have been split here in into values 20 and 21). Agriculture, General: Cultivated crop lands Agriculture, Paddy: Crop lands characterized by inundation for a substantial portion of the growing season Wetland: Areas where the water table is at or near the surface for a substantial portion of the growing season, including herbaceous and woody species (except mangrove species) Mangrove: Coastal (tropical wetlands) dominated by Mangrove species Water: All water bodies greater than 0.08 hectares (1 LS pixel) including oceans, lakes, ponds, rivers, and streams Ice / Snow: Land areas covered permanently or nearly permanent with ice or snow Clouds: Areas where no land cover interpretation is possible due to obstruction from clouds, cloud shadows, smoke, haze, or satellite malfunction Woody Wetlands: Areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or substrate periodically is saturated with, or covered by water. Only used within the continental U.S. Mixed Forest: Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover. Only used within the continental U.S. Not Used Not Used Not Used Not Used High Density Urban: Areas with over 70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation where constructed materials account for >60%. Commercial, industrial, and transportation i.e., Train stations, airports, etc. Medium-Low Density Urban: Areas with 30%-70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation, where constructed materials account for greater than 40%. Commercial, industrial, and transportation i.e., Train stations, airports, etc. MDA updated the underlying data in late 2016 and this service was updated in February 2017. An improved selection of cloud-free images was used to produce the update, resulting in improvement of classification quality to 80% of the tiles for this service.
This layer can be used to create maps and to visualize the underlying data across the ArcGIS platform. It can also be used as an analytic input in ArcMap and ArcGIS Pro.
This layer has query, identify, and export image services available. The layer is restricted to an 16,000 x 16,000 pixel limit, which represents an area of nearly 300 miles on a side. This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
This vector layer provides a detailed shaded relief basemap. The map is designed to be used as a backdrop for topographical, soil, hydro, landcover or other outdoor recreational maps. The layer currently includes hillshade to the largest map scales globally (Greenland coverage to ~1:72K). This hillshade is included in the
Outdoor and
World Topographic Map (with Contours and Hillshade) tile layers.
Use this Map
You can add this layer to a web map and save as your own map. You can also combine this layer's JSON with the JSON of another vector tile layer. This is presented as a multisource tile layer that can be viewed in Map Viewer, or the latest versions of ArcGIS Pro or Runtime.
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Dataset Summary
What can you do with this layer?
This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services, which represents an area roughly 2,400 kilometers on a side. The source data for this layer are available
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This layer is part of a larger collection of
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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Organisms and nonliving elements of the environment such as climate, soil, and water are connected through the movement of nutrients and energy in
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disturbance and
succession .
Dataset Summary
This layer provides access to a 100m cell sized raster of Ecosystems of Africa. The layer’s attributes include macrogroup, division, formation, subclass, and class.
Layers that were used to create the ecosystems map are available on ArcGIS Online:
What can you do with this layer?
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This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
Created as part of the USGS’s Africa Ecosystems Mapping project , the Africa Surficial Lithology layer maps the geology of Africa into 20 classes based bedrock type and the distribution of unconsolidated surface material. The data available through this layer map the key geological features that affect the distribution of plants and ecosystems in Africa.
Dataset Summary
This layer provides access to a 100m cell sized raster compiled from multiple sources. The data covers Africa, Madagascar, and other Islands near Africa. It was published by the U.S. Geological Survey and The Nature Conservancy in 2009.
What can you do with this layer?
This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services, which represents an area roughly 2,400 kilometers on a side. The source data are available
here .
This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and\nanalysis tasks Africa Ecosystems Mapping project , the Africa Land Surface Forms layer classifies the landscape of Africa into seven classes: Smooth plains, irregular plains, escarpments, hills, breaks, low mountains, and high mountains/deep canyons.Dataset Summary
This layer provides access to a 100m cell size raster derived from
SRTM and other data that divides the African landscape into seven classes based on land form. The data covers Africa, Madagascar, and other coastal islands near Africa. It was published in 2009 by the
USGS Rocky Mountain Geographic Science Center .
What can you do with this layer?
This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services, which represents an area roughly 2,400 kilometers on a side. The source data for this layer are available
here .
This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
Africa Ecosystems Mapping project , the Africa Isobioclimates layer classifies the landscape of Africa into 157 climate regions based on temperature and moisture patterns.
Dataset Summary
This layer provides access to a 100m cell sized raster with climate region information for Africa, Madagascar, and other islands near Africa. It was published by the U.S. Geological Survey and The Nature Conservancy in 2009.
Link to source metadata What can you do with this layer?
This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services, which represents an area roughly 2,400 kilometers on a side. The source data for this layer are available
here .
This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
The annual infant mortality rate in Africa ranges from 99 to 2031 deaths of children less than one-year-old per 10,000 live births.
Dataset Summary
Link to source metadata What can you do with this layer? This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services. The source data for this layer are available
here .
This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
\n\n Plants play a central role in the carbon cycle by absorbing carbon dioxide from the atmosphere and incorporating it in the structure of the plant. Globally living plants contain 500 billion metric tons of carbon, more than 60 times the amount of carbon released to the atmosphere by humans each year. Understanding the distribution of the carbon stored in living plants, known as biomass , is key to estimating the effects of land use change on the climate.
Dataset Summary
This layer provides access to a 1 km cell sized raster with data on the density of carbon stored in living plants in metric tons per hectare for the year 2000. The data covers Africa, Madagascar, and other islands near Africa. It was published by the Oak Ridge National Laboratory Carbon Dioxide Information Analysis Center in 2008.
Link to source metadata
What can you do with this layer?
This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services. The source data for this layer are available
here .
This\n layer is part of a larger collection of
landscape layers that you can \nuse to perform a wide variety of mapping and analysis tasks.
Variable: SD_TY_SN_PPC_s
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Key Properties
Geographic Coverage: Continental Africa - approximately North to 35° South
Temporal Coverage: 2007-2010, 2015 - 2021*
Spatial Resolution: 25 x 25 meter
Update frequency : Annual from 2007 to 2010 & 2015 to 2021
Product Type: Radar backscatter
Product Level: Radiometric Terrain Corrected (RTC)
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Source Data Coordinate System: WGS 84 (World Geodetic System 1984) (EPSG:4326)
Service Coordinate System: WGS 84 (World Geodetic System 1984) (EPSG:4326)
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HV backscatter
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date
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Local incidence angle
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"dct:title": "Digital Earth Africa's Sentinel-2 Annual GeoMAD",
"dct:description": "GeoMAD is\nthe Digital Earth Africa (DE Africa) surface reflectance geomedian and triple\nMedian Absolute Deviation data service. It is a cloud-free composite of\nsatellite data compiled over specific timeframes. This service is ideal for longer-term\ntime series analysis, cloudless imagery and statistical accuracy.
\n\nGeoMAD has\ntwo main components: Geomedian and Median Absolute Deviations (MADs)
\n\nThe\ngeomedian component combines measurements collected over the specified\ntimeframe to produce one representative, multispectral measurement for every\npixel unit of the African continent. The end result is a comprehensive dataset\nthat can be used to generate true-colour images for visual inspection of\nanthropogenic or natural landmarks. The full spectral dataset can be used to\ndevelop more complex algorithms.
\n\nFor each\npixel, invalid data is discarded, and remaining observations are mathematically\nsummarised using the geomedian statistic. Flyover coverage provided by\ncollecting data over a period of time also helps scope intermittently cloudy\nareas.
\n\nVariations\nbetween the geomedian and the individual measurements are captured by the three\nMedian Absolute Deviation (MAD) layers. These are higher-order statistical\nmeasurements calculating variation relative to the geomedian. The MAD layers\ncan be used on their own or together with geomedian to gain insights about the\nland surface and understand change over time.
Key Properties
Geographic Coverage: Continental Africa - approximately North to 35° South
Temporal Coverage: 2017 – 2022*
Spatial Resolution: 10 x 10 meter
Update Frequency : Annual from 2017 - 2022
Product Type: Surface Reflectance (SR)
Product Level: Analysis Ready (ARD)
Number of Bands: 14 Bands
Source Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
*Time is enabled on this service using UTC – Coordinated Universal Time. To assure you are seeing the correct year for each annual slice of data, the time zone must be set specifically to UTC in the Map Viewer settings each time this layer is opened in a new map. More information on this setting can be found here: Set the map time zone .
Applications
GeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. This service is ideal for:
Longer-term time series analysis Cloud-free imagery Statistical accuracy
Available Bands
Band ID
Description
Value range
Data type
No data value
B02
Geomedian B02 (Blue)
1 - 10000
uint16
0
B03
Geomedian B03 (Green)
1 - 10000
uint16
0
B04
Geomedian B04 (Red)
1 - 10000
uint16
0
B05
Geomedian B05 (Red edge 1)
1 - 10000
uint16
0
B06
Geomedian B06 (Red edge 2)
1 - 10000
uint16
0
B07
Geomedian B07 (Red edge 3)
1 - 10000
uint16
0
B08
Geomedian B08 (Near infrared (NIR) 1)
1 - 10000
uint16
0
B8A
Geomedian B8A (NIR 2)
1 - 10000
uint16
0
B11
Geomedian B11 (Short-wave infrared (SWIR) 1)
1 - 10000
uint16
0
B12
Geomedian B12 (SWIR 2)
1 - 10000
uint16
0
SMAD
Spectral Median Absolute Deviation
0 - 1
float32
NaN
EMAD
Euclidean Median Absolute Deviation
0 - 31623
float32
NaN
BCMAD
Bray-Curtis Median Absolute Deviation
0 - 1
float32
NaN
COUNT
Number of clear observations
1 - 65535
uint16
0
Bands can be subdivided as follows:
\n\n
\n Geomedian — 10 bands: The geomedian is calculated using\n the spectral bands of data collected during the specified time period.\n Surface reflectance values have been scaled between 1 and 10000 to\n allow for more efficient data storage as unsigned 16-bit integers (uint16).\n Note parent datasets often contain more bands, some of which are not used\n in GeoMAD. The geomedian band IDs correspond to bands\n in the parent Sentinel-2 Level-2A data. For example, the Annual GeoMAD\n band B02 contains the annual geomedian of the Sentinel-2 B02 band. \n Median Absolute Deviations (MADs) — 3\n bands: Deviations\n from the geomedian are quantified through median absolute deviation\n calculations. The GeoMAD service utilises three MADs, each stored in a\n separate band: Euclidean MAD (EMAD), spectral MAD (SMAD), and Bray-Curtis\n MAD (BCMAD). Each MAD is calculated using the same ten bands as in the\n geomedian. SMAD and BCMAD are normalised ratios, therefore they are\n unitless and their values always fall between 0 and 1. EMAD\n is a function of surface reflectance but is neither a ratio nor\n normalised, therefore its valid value range depends on the number of bands\n used in the geomedian calculation. Count — 1 band: The number of clear satellite measurements\nof a pixel for that calendar year. This is around 60 annually, but doubles at\nareas of overlap between scenes. “Count” is not incorporated in either the\ngeomedian or MADs calculations. It is intended for metadata analysis and data\nvalidation. Processing
All clear observations for the given time period are collated from the parent dataset. Cloudy pixels are identified and excluded. The geomedian and MADs calculations are then performed by the hdstats package. Annual GeoMAD datasets for the period use hdstats version 0.2.
More details on this dataset can be found here .
GeoMAD is the\nDigital Earth Africa (DE Africa) surface reflectance geomedian and triple\nMedian Absolute Deviation data service. It is a cloud-free composite of\nsatellite data compiled over specific timeframes. This service is ideal for\nlonger-term time series analysis, cloudless imagery and statistical accuracy.
\n\nGeoMAD has two\nmain components: Geomedian and Median Absolute Deviations (MADs).
\n\nThe geomedian component combines measurements\ncollected over the specified timeframe to produce one representative,\nmultispectral measurement for every pixel unit of the African continent. The\nend result is a comprehensive dataset that can be used to generate true-colour\nimages for visual inspection of anthropogenic or natural landmarks. The full\nspectral dataset can be used to develop more complex algorithms.
\n\nFor each pixel, invalid data is discarded, and\nremaining observations are mathematically summarised using the geomedian\nstatistic. Flyover coverage provided by collecting data over a period of time\nalso helps scope intermittently cloudy areas.
\n\nVariations between the geomedian and the\nindividual measurements are captured by the three Median Absolute Deviation\n(MAD) layers. These are higher-order statistical measurements calculating\nvariation relative to the geomedian. The MAD layers can be used on their own or\ntogether with geomedian to gain insights about the land surface and understand\nchange over time.
Key Properties
Geographic Coverage: Continental Africa - approximately North to 35° South
Temporal Coverage: 2013 – 2020*
Spatial Resolution: 30 x 30 meter
Update frequency : Annual from 2013 - 2020
Product Type: Surface Reflectance (SR)
Product Level: Analysis Ready (ARD)
Number of Bands: 10 Bands
Source Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
*Time is enabled on this service using UTC – Coordinated Universal Time. To assure you are seeing the correct year for each annual slice of data, the time zone must be set specifically to UTC in the Map Viewer settings each time this layer is opened in a new map. More information on this setting can be found here: Set the map time zone .
Applications
GeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. This service is ideal for:
Longer-term time series analysis Cloud-free imagery Statistical accuracy
Available Bands
Band ID
Description
Value range
Data type
No data value
SR_B2
Geomedian SR_B2 (Blue)
1 - 10000
uint16
0
SR_B3
Geomedian SR_B3 (Green)
1 - 10000
uint16
0
SR_B4
Geomedian SR_B4 (Red)
1 - 10000
uint16
0
SR_B5
Geomedian SR_B5 (NIR)
1 - 10000
uint16
0
SR_B6
Geomedian SR_B6 (SWIR 1)
1 - 10000
uint16
0
SR_B7
Geomedian SR_B7 (SWIR 2)
1 - 10000
uint16
0
SMAD
Spectral Median Absolute Deviation
0 - 1
float32
NaN
EMAD
Euclidean Median Absolute Deviation
0 - 31623
float32
NaN
BCMAD
Bray-Curtis Median Absolute Deviation
0 - 1
float32
NaN
COUNT
Number of clear observations
1 - 65535
uint16
0
Bands have been subdivided as follows:
Geomedian - 6 bands: The geomedian is calculated using the\nspectral bands of data collected during the specified time period. Surface\nreflectance values have been scaled between 1 and 10000 to allow for more\nefficient data storage as unsigned 16-bit integers ( uint16 ). Note parent datasets\noften contain more bands, some of which are not used in GeoMAD.Median Absolute Deviations\n(MADs) - 3 bands: Deviations from the geomedian are quantified through median\nabsolute deviation calculations. The GeoMAD service utilises three MADs, each\nstored in a separate band: Euclidean MAD (EMAD), spectral MAD (SMAD), and\nBray-Curtis MAD (BCMAD). Each MAD is calculated using the same ten bands as in\nthe geomedian. SMAD and BCMAD are normalized ratios, therefore they are\nunitless and their values always fall between 0 and 1 Count - 1 band: The number of clear\nsatellite measurements of a pixel for that calendar year. This is around 20 for Landsat 8 annually, but doubles at areas of overlap\nbetween scenes. “Count” is not incorporated in either the geomedian or MADs\ncalculations. It is intended for metadata analysis and data validation.Processing
All clear observations for the given time period\nare collated from the parent dataset. Cloudy pixels are identified and\nexcluded. The geomedian and MADs calculations are then performed by the hdstats package. Annual GeoMAD datasets for the period use hdstats version\n0.2.
Known Limitations
The Landsat 8 (& 9) GeoMAD has a known issue with data quality over marine regions. The GeoMAD algorithm uses pixel quality information from the input data to identify and mask pixels with poor quality obervations. Landsat 8 & 9 analysis ready satellite images over the ocean often contain negative surface reflectance values, and the GeoMAD masking procedures remove pixels where any negative values occur. Thus, in regions where pixels are persistently negative throughout the year, the GeoMAD product will contain a no-data value. An example of this can be seen in Image 7 below where a shallow marine system contains no-data values in the GeoMAD because the NIR band values in the input data are persistently negative.
More details on this dataset can be found here .
"togo-healthsites-shp" . The file is available in SHP format , compressed as a ZIP file with a total size of 62.7 KB . It was created on April 10, 2025, at 17:24:03 UTC and last updated on the same day at 17:24:02 UTC . The metadata was subsequently updated on April 15, 2025, at 17:39:44 UTC . The dataset has a unique resource ID of 13c5deae-f725-463c-81c8-0efbfd9b4c31 , and it belongs to the dataset with ID bfd67f3e-7b03-42c2-a1ce-abb5a229c5d9 . It is categorized as a file upload resource, with a MIME type of application/zip . Microdata is marked as FALSE , and the data can be accessed or downloaded via the following URL: https://data.humdata.org/dataset/bfd67f3e-7b03-42c2-a1ce-abb5a229c5d9/resource/13c5deae-f725-463c-81c8-0efbfd9b4c31/download/togo-shapefiles.zip .",
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Key Properties
Geographic Coverage: Continental Africa - approximately 37° North to 35° South
Temporal Coverage: 1984 - 2022
Spatial Resolution: 30 x 30 meter
Update frequency : Annual from 1984 - 2022
Number of Bands: 3 Bands
Source Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
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Value range
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int16
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frequency
Frequency of water detection at a location
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\n\nInterpreting WOfS
The WOfS service should be interpreted with caveats in the following situations:
Mixed pixels : Discretion should be used where a single pixel covers both water and land. These areas tend to occur on the edges of lakes, and in wetlands where there is a mix of water and vegetation.Turbid or dark water: The WOfS algorithm is developed to identify a diverse range of waterbodies. However, the classifier may miss dark water surfaces or water with high concentration of sediments. In some cases, the impact can be mitigated by using a temporal summary of WOfS, such as the Annual Summary or All-Time Summary. A waterbody may be missed in a single observation, but over the course of the year it is mapped as water in other dates and therefore mapped as a waterbody in the summary products.Other environmental factors : Sediment, floating vegetation and similar obstructions change the colour of water and can obfuscate water detection by WOfS.Inaccurate input data: Inaccurate input surface reflectance may lead to false classification in WOfS. To maximize coverage, all pixels within a valid surface reflectance range (0-1) from Landsat Collection 2 are used to generate the WOFLs. When creating WOfS summaries, only WOFLs processed from Landsat Tier 1 data with good geometric accuracy are used.
Note that WOfS is not intended for studying ocean. Validation has been centred around inland and near-coastal waterbodies.
\n\nMore details on this dataset can be found here .
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Data accessed from here: https://www-udc.ig.utexas.edu/external/plates/data.htm
The surface of the Earth is broken up into large plates. There are seven major plates: North America, South America, Eurasia, Africa, India, the Pacific, and Antarctica. There are also numerous microplates. The number and shapes of the plates change over geologic time. Plates are divided by boundaries that are seismically active. The different plate boundaries can be described by the type of motion that is occurring between the plates at specific locations.
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Data accessed from here: https://www-udc.ig.utexas.edu/external/plates/data.htm
The surface of the Earth is broken up into large plates. There are seven major plates: North America, South America, Eurasia, Africa, India, the Pacific, and Antarctica. There are also numerous microplates. The number and shapes of the plates change over geologic time. Plates are divided by boundaries that are seismically active. The different plate boundaries can be described by the type of motion that is occurring between the plates at specific locations.
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Data accessed from here: https://www-udc.ig.utexas.edu/external/plates/data.htm
This\n scene highlights layers for Johannesburg, South Africa available in ArcGIS to \nsupport your work in 3D. Use these layers in conjunction with your own \nlayers to create new scenes focused on a specific topic or area of \ninterest to you. What's in this scene? Terrain: Includes a global 3D terrain layer to provide elevation context. Your layers are placed in relationship to this terrain Basemap: Includes one of the ArcGIS Basemaps regularly used in in your mapping work Scene Layers: Includes a layer of 3D buildings to help understand your data within the context of the built environment. The layer is a file type optimized for rendering in 3D.
Create your own scene Open this item using the Open in Scene Viewer button Choose basemap: Select one of the ArcGIS basemaps from the Basemap Gallery Add your own unique layers Create slides to direct users to interesting places in your scene - See More Save and share the results of your work with others in your organization and the public For more see these helpful videos Volta River basin on a seasonal scale. The components include averaged precipitation, evapotranspiration, blue ET, green ET, water yield, and surface runoff.",
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"vcard:hasEmail": "mailto: africageoportal@esri.com"
},
"dct:publisher": {
"@type": "foaf:Agent",
"foaf:name": "Africa GeoPortal"
},
"dcat:theme": {
"@type": "skos:Concept",
"skos:prefLabel": "Geospatial"
},
"dct:accessRights": {
"@type": "dct:RightsStatement",
"@label": {
"@value": "public"
}
},
"dct:identifier": "https://www.arcgis.com/home/item.html?id=a5d17ffc31a5431ca995e92c1d65f156",
"dcat:keyword": [
"tp"
],
"dct:provenance": {
"@type": "dct:ProvenanceStatement",
"@label": {
"@value": ""
}
},
"dct:issued": "2025-03-17T20:04:23.000Z",
"dct:modified": "2025-03-17T20:04:23.000Z",
"dct:language": {
"@id": "lang/FRA"
},
"dct:spatial": {
"@type": "dct:Location",
"dct:bbox": [
{
"@value": "",
"@type": "opengis:wktLiteral"
}
]
},
"dcat:distribution": [
{
"@type": "dcat:Distribution",
"dcat:accessURL": {
"@id": "https://www.africageoportal.com/datasets/africageoportal::dep-congo"
},
"dct:format": {
"@id": "ftype/HTML"
},
"dct:description": "Web Page",
"dct:title": "ArcGIS Hub Dataset"
}
]
},
{
"@type": "dcat:Dataset",
"@id": "https://www.africageoportal.com/datasets/africageoportal::borehole-cape-coast-1",
"dct:title": "Borehole Cape Coast",
"dct:description": "{{description}}",
"dcat:contactPoint": {
"@id": "https://africa.maps.arcgis.com/sharing/rest/community/users/Kwabena_Danso?f=json",
"@type": "vcard:Kind",
"vcard:fn": "Kwabena_Danso",
"vcard:hasEmail": "mailto: africageoportal@esri.com"
},
"dct:publisher": {
"@type": "foaf:Agent",
"foaf:name": "Africa GeoPortal"
},
"dcat:theme": {
"@type": "skos:Concept",
"skos:prefLabel": "Geospatial"
},
"dct:accessRights": {
"@type": "dct:RightsStatement",
"@label": {
"@value": "public"
}
},
"dct:identifier": "https://www.arcgis.com/home/item.html?id=bfa262d87751457c9e507f3a6413085e",
"dcat:keyword": [
"Survey123"
],
"dct:provenance": {
"@type": "dct:ProvenanceStatement",
"@label": {
"@value": ""
}
},
"dct:issued": "2025-03-17T00:00:37.000Z",
"dct:modified": "2025-03-17T00:00:50.000Z",
"dct:language": {
"@id": "lang/"
},
"dct:spatial": {
"@type": "dct:Location",
"dct:bbox": [
{
"@value": "POLYGON((-180,90,180,90,180,-90,-180,-90,-180,90))",
"@type": "opengis:wktLiteral"
}
]
},
"dcat:distribution": [
{
"@type": "dcat:Distribution",
"dcat:accessURL": {
"@id": "https://www.africageoportal.com/datasets/africageoportal::borehole-cape-coast-1"
},
"dct:format": {
"@id": "ftype/HTML"
},
"dct:description": "Web Page",
"dct:title": "ArcGIS Hub Dataset"
}
]
},
{
"@type": "dcat:Dataset",
"@id": "https://www.africageoportal.com/datasets/GRID3::grid3-zwe-settlement-extents-v3-0",
"dct:title": "GRID3 ZWE - Settlement Extents v3.0",
"dct:description": "The two layers contained within the GRID3 ZWE - Settlement Extents v3.0 include:
The current version supersedes GRID3 ZWE - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 ZWE - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/ws6e-pb92. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 ZMB - Settlement Extents v3.0 include:
The current version supersedes GRID3 ZMB - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 ZMB - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/0pet-p051. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 ZAF - Settlement Extents v3.0 include:
The current version supersedes GRID3 ZAF- Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 ZAF - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/tzre-w692. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 UGA - Settlement Extents v3.0 include:
The current version supersedes GRID3 UGA - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 UGA - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/zgzd-6t74. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 TZA - Settlement Extents v3.0 include:
The current version supersedes GRID3 TZA - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive settlement polygons with open data. Estimate a probability value for settlement polygons For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 TZA - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/asej-zz08. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 TGO - Settlement Extents v3.0 include:
The current version supersedes GRID3 TGO - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 TGO - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/yz03-8697. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 TCD - Settlement Extents v3.0 include:
The current version supersedes GRID3 TCD - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 TCD - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/e6j5-2141. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 SYC - Settlement Extents v3.0 include:
The current version supersedes GRID3 SYC - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 SYC - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/pfbj-h371. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 STP - Settlement Extents v3.0 include:
This is the settlement extents layer.
The current version supersedes GRID3 STP - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 STP - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/zfp5-pv42. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 SSD - Settlement Extents v3.0 include:
The current version supersedes GRID3 SSD - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 SSD - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/2nth-xx70. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 SOM - Settlement Extents v3.0 include:
The current version supersedes GRID3 SOM - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 SOM - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/3yy0-tt77. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 SLE - Settlement Extents v3.0 include:
The current version supersedes GRID3 SLE - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 SLE - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/p7mq-nr26. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 RWA - Settlement Extents v3.0 include:
The current version supersedes GRID3 RWA - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 RWA - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/fs4k-jr23. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 REU - Settlement Extents v3.0 include:
The current version supersedes GRID3 REU - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data.
Estimation of a probability value for settlement polygons
Addition of a building-area measure
Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets
Elimination of building-count ranges
Elimination of variable comparing to previous versions
For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 REU - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/k4z8-ha20. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 NER - Settlement Extents v3.0 include:
The current version supersedes GRID3 NER - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 NER - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/9w2b-5m53. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 NAM - Settlement Extents v3.0 include:
The current version supersedes GRID3 NAM - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 NAM - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/tpag-pg11. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 MWI - Settlement Extents v3.0 include:
The current version supersedes GRID3 MWI - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 MWI - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/n8ge-tc24. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 MUS - Settlement Extents v3.0 include:
The current version supersedes GRID3 MUS - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 MUS - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/bhc9-9j70. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 MRT - Settlement Extents v3.0 include:
The current version supersedes GRID3 MRT - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 MRT - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/e4n2-8443. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 MOZ - Settlement Extents v3.0 include:
The current version supersedes GRID3 MOZ - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 MOZ - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/d3vh-qz17. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 MLI - Settlement Extents v3.0 include:
The current version supersedes GRID3 MLI - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 MLI - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/8hjv-tj96. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
Variable: SD_VARDIV
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"dcat:contactPoint": {
"@id": "https://africa.maps.arcgis.com/sharing/rest/community/users/silviae-castano?f=json",
"@type": "vcard:Kind",
"vcard:fn": "silviae-castano",
"vcard:hasEmail": "mailto:csi@cgiar.org"
},
"dct:publisher": {
"@type": "foaf:Agent",
"foaf:name": "Africa GeoPortal"
},
"dcat:theme": {
"@type": "skos:Concept",
"skos:prefLabel": "Geospatial"
},
"dct:accessRights": {
"@type": "dct:RightsStatement",
"@label": {
"@value": "public"
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},
"dct:identifier": "https://www.arcgis.com/home/item.html?id=4a3eb1ba28ac4ff39038898cbb329347",
"dcat:keyword": [
"Bean",
"Bean Atlas",
"Bean production",
"Africa",
"PABRA"
],
"dct:provenance": {
"@type": "dct:ProvenanceStatement",
"@label": {
"@value": "Mukankusi, C., Chirwa R., Muthoni-Andriatsitohaina, R. & Ojara, M. (2019). Bean grain types and varieties. In: Farrow, A. & Muthoni-Andriatsitohaina, R. (eds.). Atlas of common bean production in Africa (2ª ed.). Pan-Africa Bean Research Alliance Better Beans for Africa PABRA. pp 67-91"
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"dct:issued": "2020-01-23T16:27:18.000Z",
"dct:modified": "2020-04-08T23:10:42.000Z",
"dct:language": {
"@id": "lang/ENG"
},
"dct:spatial": {
"@type": "dct:Location",
"dct:bbox": [
{
"@value": "POLYGON((-17.1748,19.7162,49.3115,19.7162,49.3115,-34.7937,-17.1748,-34.7937,-17.1748,19.7162))",
"@type": "opengis:wktLiteral"
}
]
},
"dcat:distribution": [
{
"@type": "dcat:Distribution",
"dcat:accessURL": {
"@id": "https://www.africageoportal.com/maps/CSI::sd-vardiv-2000-f38-2000"
},
"dct:format": {
"@id": "ftype/HTML"
},
"dct:description": "Web Page",
"dct:title": "ArcGIS Hub Dataset"
},
{
"@type": "dcat:Distribution",
"dcat:accessURL": {
"@id": "https://services.arcgis.com/P8Cok4qAP1sTVE59/arcgis/rest/services/SD_VARDIV_2000_F38_2000/FeatureServer"
},
"dct:format": {
"@id": "ftype/JSON"
},
"dct:description": "Esri REST",
"dct:title": "ArcGIS GeoService"
}
]
},
{
"@type": "dcat:Dataset",
"@id": "https://www.africageoportal.com/datasets/africageoportal::etat-2",
"dct:title": "Etat",
"dct:description": "{{description}}",
"dcat:contactPoint": {
"@id": "https://africa.maps.arcgis.com/sharing/rest/community/users/Moulady?f=json",
"@type": "vcard:Kind",
"vcard:fn": "Moulady",
"vcard:hasEmail": "mailto: africageoportal@esri.com"
},
"dct:publisher": {
"@type": "foaf:Agent",
"foaf:name": "Africa GeoPortal"
},
"dcat:theme": {
"@type": "skos:Concept",
"skos:prefLabel": "Geospatial"
},
"dct:accessRights": {
"@type": "dct:RightsStatement",
"@label": {
"@value": "public"
}
},
"dct:identifier": "https://www.arcgis.com/home/item.html?id=3184981c0dc343c7976e242582d51481",
"dcat:keyword": [],
"dct:provenance": {
"@type": "dct:ProvenanceStatement",
"@label": {
"@value": ""
}
},
"dct:issued": "2026-05-20T17:24:12.000Z",
"dct:modified": "2026-05-20T17:24:12.000Z",
"dct:language": {
"@id": "lang/FRA"
},
"dct:spatial": {
"@type": "dct:Location",
"dct:bbox": [
{
"@value": "",
"@type": "opengis:wktLiteral"
}
]
},
"dcat:distribution": [
{
"@type": "dcat:Distribution",
"dcat:accessURL": {
"@id": "https://www.africageoportal.com/datasets/africageoportal::etat-2"
},
"dct:format": {
"@id": "ftype/HTML"
},
"dct:description": "Web Page",
"dct:title": "ArcGIS Hub Dataset"
}
]
},
{
"@type": "dcat:Dataset",
"@id": "https://www.africageoportal.com/maps/africageoportal::wetlands-for-pla-sources-sustainable-raw-materials-from-organic-wastes-for-pvc-pipe-production3",
"dct:title": "Wetlands for PLA Sources; Sustainable Raw Materials from Organic Wastes for PVC Pipe Production3",
"dct:description": "{{description}}",
"dcat:contactPoint": {
"@id": "https://africa.maps.arcgis.com/sharing/rest/community/users/fatayoopeyemi_GIS?f=json",
"@type": "vcard:Kind",
"vcard:fn": "fatayoopeyemi_GIS",
"vcard:hasEmail": "mailto: africageoportal@esri.com"
},
"dct:publisher": {
"@type": "foaf:Agent",
"foaf:name": "Africa GeoPortal"
},
"dcat:theme": {
"@type": "skos:Concept",
"skos:prefLabel": "Geospatial"
},
"dct:accessRights": {
"@type": "dct:RightsStatement",
"@label": {
"@value": "public"
}
},
"dct:identifier": "https://www.arcgis.com/home/item.html?id=51f30e0226a44f4e9b0cd7eae6da8cad",
"dcat:keyword": [
"GIS",
"Wetlands",
"PLA",
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],
"dct:provenance": {
"@type": "dct:ProvenanceStatement",
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"dct:issued": "2022-09-11T17:32:40.000Z",
"dct:modified": "2022-09-11T17:32:48.000Z",
"dct:language": {
"@id": "lang/ENG"
},
"dct:spatial": {
"@type": "dct:Location",
"dct:bbox": [
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"@type": "opengis:wktLiteral"
}
]
},
"dcat:distribution": [
{
"@type": "dcat:Distribution",
"dcat:accessURL": {
"@id": "https://www.africageoportal.com/maps/africageoportal::wetlands-for-pla-sources-sustainable-raw-materials-from-organic-wastes-for-pvc-pipe-production3"
},
"dct:format": {
"@id": "ftype/HTML"
},
"dct:description": "Web Page",
"dct:title": "ArcGIS Hub Dataset"
}
]
},
{
"@type": "dcat:Dataset",
"@id": "https://www.africageoportal.com/datasets/GRID3::grid3-mdg-settlement-extents-v3-0",
"dct:title": "GRID3 MDG - Settlement Extents v3.0",
"dct:description": "The two layers contained within the GRID3 MDG - Settlement Extents v3.0 include:
The current version supersedes GRID3 MDG - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 MDG - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/q6ye-pn21. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 LSO - Settlement Extents v3.0 include:
The current version supersedes GRID3 LSO - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 LSO - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/rxdw-w473. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 LBR - Settlement Extents v3.0 include:
The current version supersedes GRID3 LBR - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 LBR - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/12ar-zg98. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 KEN - Settlement Extents v3.0 include:
The current version supersedes GRID3 KEN - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 KEN - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/6hnz-fr54. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 GNQ - Settlement Extents v3.0 include:
The current version supersedes GRID3 GNQ - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 GNQ- Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/wr0f-sp79. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 GNB - Settlement Extents v3.0 include:
The current version supersedes GRID3 GNB - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 GNB - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/vp9v-f744. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
Variable: SD_VARDIV
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Variable: SD_VARDIV
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"dct:title": "Facts About Kenya Counties\n\nNB: Elected Officials served between year 2013 and August 2017",
"dct:description": "This app showcases the 47 Kenya counties giving a summary description as to what each county is all about.
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The ACLED dataset is made up of thousands of individual data points, each referring to an individual event (a battle, an attack, a riot, etc.). Each event contains the following information:
The date – the day on which the event took place; The type of violence – what kind of violence was involved; The actors involved – violent actors identified by group name or type (for example, the LRA or Protesters);The type of group – actors identified by a numeric code, indicating whether the actors are part of state forces (military or police), rebels, militias, communal/ethnic militias, etc.The country – the state in which the event took place;The location – several columns detailing the administrative zone and town / village location at which the event took place;Latitude and longitude coordinates – the geo-reference for the individual event;The source of the report – the source in which the description of the event was found;Event notes – a brief description of the event;Fatalities – the reported number of fatalities For more information on using ACLED for Humanitarian purposes, please see this
report .
Supporting documentation, including the project Codebook (detailing the data collection and recording process), a general user guide, and working papers which explore sources and methodology in greater detail are all available online at
http://www.acleddata.com/ .
This dataset is updated once a week in following with the information detailed here,
http://www.acleddata.com/data/realtime-data . It is updated using a script that runs on AWS Lambda. The source code for that script can be found on GitHub,
here .
L'Africa ha bisogno di essere amata, subito, nel concreto!
L'Europa è chiamata in prima linea a questa condivisione... Noi tutti, che pur vivendo in un precario benessere, mettiamo ogni giorno sulla tavola il nostro pane quotidiano.
Paola Garbini Siani
In questo viaggio mostro alcuni dei luoghi dove Paola ha portato amore, un amore concreto fatto di scuole, orfanotrofi, pozzi e centri specializzati.
Le mappe mi hanno aiutato a dare una connotazione ai luoghi visitati rendendo la mia storia dinamica, interattiva, "mapposa".
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"dct:description": "Tanzania is a country with many tourist attractions. Approximately 38 percent of\n Tanzania's land area is set aside in protected areas for conservation.There are 16 national parks, 29 game reserves, 40 controlled conservation areas (including the Ngorongoro Conservation) and marine parks. Tanzania is also home to Mount kilimanjaro, the highest point in Africa.Tanzania has almost 38% of its land reserved as protected areas, one of the world's highest percentage
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What other places do you find interesting? You can create a new scene with your own favorites places. Feel free to add comments below regarding your favorite places and I will check them out and perhaps add them to the scene.
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"dct:description": "This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes.
Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:
Green (fast): 85 - 100% of free flow speeds Yellow (moderate): 65 - 85% Orange (slow); 45 - 65% Red (stop and go): 0 - 45% Esri's historical, live, and predictive traffic feeds come directly from HERE (
www.HERE.com ). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. The real-time and predictive traffic data is updated every five minutes through traffic feeds.
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The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the
service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the
directions and routing documentation and
ArcGIS Help .
This map features the World Imagery map, focused on the continent of Africa. World Imagery provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map includes 15m TerraColor imagery at small and mid-scales (~1:591M down to ~1:72k) and 2.5m SPOT Imagery (~1:288k to ~1:72k) for the world. DigitalGlobe sub-meter imagery is featured in many parts of the world, including Africa. Sub-meter Pléiades imagery is available in select urban areas. Additionally, imagery at different resolutions has been contributed by the GIS User Community.
For more information on this map, view the World Imagery item description .
Metadata: This service is metadata-enabled. With the Identify tool in ArcMap or the World Imagery with Metadata web map , you can see the resolution, collection date, and source of the imagery at the location you click. Values of "99999" mean that metadata is not available for that field. The metadata applies only to the best available imagery at that location. You may need to zoom in to view the best available imagery.
Feedback: Have you ever seen a problem in the Esri World Imagery Map that you wanted to see fixed? You can use the Imagery Map Feedback web map to provide feedback on issues or errors that you see. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.
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This custom web map was generated from Wayback layers selected in the World Imagery Wayback app . Wayback imagery is a digital archive of the World Imagery basemap, enabling users to access different versions of World Imagery captured over the years. Each Wayback layer in this web map represents World Imagery as it existed on the date specified. ",
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The countries of Africa are a filtered view of the
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The
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This digital map comprised of four types of data: (A) Distribution of marine protected areas, (B) seven layers biodiversity features, (C) two types of threats, and (D) 16 environmental variables.
(A). Marine protected areas:
Distribution of Marine protected areas in the Coral Triangle. This dataset consists of 678 MPAs polygon boundaries, derived from the World Database of Protected Areas-WDPA (www.protectedplanet.net), Coral Triangle Atlas (ctatlas.reefbase.org) and Indonesian datasets of marine protected areas (MoF-MoMAF, 2010; MoMAF, 2016). The dataset consists of 35% of the total 1,972 MPAs in the region (White et al., 2014). Around 95% of the missing MPAs’ boundaries were associated with small marine managed areas.
The layers' attribute table provides detailed information following its native sources (WDPA, CTAtlas) (e.g. information of Name, Local Name, Designation Type, IUCN Category, coverage etc) with amendment and adjustment from local sources (Indonesian database).
To allow a simple indexing, a new CT MPAs ID format (MPA_Id) introduced. This new ID consists of 10 digits: "A BC DEFG HIJ"
where:
A = Country; 1 = Indonesia, 2 = Malaysia, 3 = Philippines, 4 = Papua New Guinea, 5 = Solomon Islands, and 6 = Timor Leste
BC = IUCN MPAs Category; Strict Nature Reserve (1a = 11, 1b = 12), National Park (20), Habitat and Species Management Areas (40), Protected Landscape/Seascape (50) and Managed Resources Protected Areas (60)
DEFG = Establishment year (e.g., 1980)
HIJ = Number; ordered based on their establishment year
Example : Taka Bonerate National Park ; ID = 1201985035. It means that Taka Bonerate is Indonesian MPAs (1), is a national park (20), established in 1985, and MPAs number 35.
(B). Biodiversity Features:
Sensitive habitat derived from the distribution of three biogenic habitats (coral reefs, seagrass and mangrove forest). The datasets is mapped in grid square cells with spatial resolution of 5 km. Habitat richness calculated based on the number of habitat occurs in each cell. The cells value ranged from 1 - 3. The higher the value, the higher its biodiversity importance. Data were collated from the Ocean Data Viewer (http://data.unep-wcmc.org/), developed by UNEP-WCMC (UNEP World Conservation Monitoring Centre) and were clipped to the Coral Triangle Region. Species richness (species ranges) developed from a modeled geographic distribution of 10,672 species ranges, extracted from AquaMaps ( www.aquampas.org ) . The richness is calculated based on the number of predicted species in each cells. Within the Coral Triangle, the number of predicted species per cells ranged from 0 to 5,509. This datasets is classified into 10 equal interval classes. and Species richness (species occurrence) generated from the occurrence records of 19,251 species, retrieved from the Ocean Biogegraphic Informatian System (OBIS) datasets ( www.iobis.org ). Species Richness was analyzed using rarefaction index of ES50 (estimated species in random 50 samples) based on the Hulbert’s Index of expected species richness (Hurlbert, 1971) and Hulbert’s Standard Errors (Heck et al. , 1975). This dataset is mapped grid square cells with spatial resolution of 50 km. The richness index is classified into 10 equal interval classes. The distribution of 834 species of special conservation concern, mapped in grid square cells with spatial resolution of 50 km. The richness was classified into 10 equal interval classes. Point occurrence records were extracted from OBIS (www.iobis.org) and FishBase (www.fishbase.org). Species richness was analyzed using rarefaction index of ES35 (estimated species in random 35 samples) The distribution of 373 restricted-range reef fish species, mapped in grid square cells with spatial resolution of 5 km. The richness index was calculated based on the number of species in each cells. The number of species per cells range from 1 to 101 species. This dataset is classified into 10 equal interval classes. This datasets were extracted from a database of the distribution of nearly 4,000 species of Indo-Pacific reef fish es (Allen, 2008; Allen & Erdmann, 2013) . Within this datasets: (a) restricted-range reef fishes is defined as a reef fish species with a spatial distribution of less than 5 million km2 and whose known range is only within the Coral Triangle, (b) reef fishes is defined as as fish species that live on shallow water coral reefs and associated substrata (i.e., sand or rubble patches, seagrass beds, etc.) less than 60 m deep. Important areas for sea turtles, derived from nesting sites and migratory reoutes of 6 species. The occurrence record were interpolated into grid square cells, with spatial resolution of 50 km. Richness was calculated based on the number of sea turtle species that fell in each cell (i.e., 1, 2,3). No cells had more than three species of turtle present. , and Habitat rugosity, derived a vector of ruggedness measures of benthic terrain. This index were generated from bathymetry data that was extracted GMED (Global Marine Environment Datasets) (gmed.auckland.ac.nz) and analyzed using the Benthic Terrain Modeller (BTM) 3.0 of ArcGIS 10.5 . This dataset is mapped in grid square cells with spatial resolution of 50 km. The VRMs index is ranged from 0.1 (areas with low terrain variations to 0.9 (areas with high terrain variations). This dataset is classified into 10 equal interval classes. (C). Threat to marine environment:
Spatial distribution of anthropogenic pressure (AP) to marine environments. This dataset is mapped in grid square cells with a spatial resolution of 5 km. The anthropogenic pressure (AP) value ranged from 0 – 15.4, indicating areas from low to high human-induced pressure. AP Index is classified into 10 class of equal interval. This dataset is retrieved from the database of cumulative human impacts on the world’s oceans developed by Halpern et al., (2008;2015). The impact was assessed from 19 different types of anthropogenic stressors: land-based drivers (nutrient inputs, organic/inorganic pollution, and population density), ocean-based drivers (commercial fishing, artisanal fishing, benthic structures, shipping lanes, invasive species, and ocean pollution), climate change (SLR, SST, UV and acidification). Spatial distribution of climate change pressure index, generated from the datasets of the anomaly of sea surface thermal stress developed by Hooidonk et al. (2016). Spatial distribution of sea surface thermal stress level based on the average of Degree Heating Weeks (DHWs) from 2006 to 2099. This dataset is mapped in grid square cells with a spatial resolution of 5 km. The projected thermal stress index ranged from 5.6 – 20.2, indicating areas from low to high vulnerability to climate change effect and classified into 10 classes of equal interval. (D). Environmental Variables
Spatial distribution of 16 environmental variables, i.e., depth, slope, land distance, temperature, surface current, salinity, wind speed, tide, primary productivity, photosynthetically active radiation (PAR), chlorophyll-a, pH, dissolved oxygen, nitrate, silicate, and calcite. The dataset is mapped to a grid square of 50 km and represented in a point layer. Each environmental variable's layers are classified into 10 equal interval classes.
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"@id": "https://www.africageoportal.com/datasets/esri::africa-crop-wheat-harvested-area",
"dct:title": "Africa Crop Wheat - Harvested Area",
"dct:description": "Wheat (Triticum spp.) is one of the world's most important crops. In the developing world only rice is more important as a food source. In Africa, Ethiopia and South Africa are important producers.
Dataset Summary
This layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of wheat harvested in Africa. The data are in units of hectares/grid cell.
For more information about this dataset and the importance of wheat as a staple food see the
Harvest Choice webpage .
For data on other agricultural species in Africa see these layers:
Data for important agricultural crops in South America are available here . What can you do with this layer?
This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.
This layer has
query ,
identify , and
export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels
which allows access to the full dataset.
The source data for this layer are available
here .
This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
The
Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.
Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:
Sorghum ( Sorghum bicolor ) is an important grass species used for human and animal food. It was first cultivated in Africa and currently 53% of the world's production is in sub-Saharan Africa. Millions of farmers in arid regions rely on this crop due to its drought tolerant qualities.
Dataset Summary
This layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of sorghum harvested in Africa. The data are in units of hectares/grid cell.
For more information about this dataset and the importance of sorghum as a staple food see the
Harvest Choice webpage .
For data on other agricultural species in Africa see these layers:
Data for important agricultural crops in South America are available here . What can you do with this layer?
This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.
This layer has
query ,
identify , and
export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels
which allows access to the full dataset.
The source data for this layer are available
here .
This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
The
Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.
Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:
Rice (Oryza sativa and O. glaberrima ) is one of the world's most important staple food crops. Over half of the world's population relies on rice. The people in some parts of Africa have been cultivating rice for over 3,500 years.
Dataset Summary
This layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of rice harvested in Africa. The data are in units of hectares/grid cell.
For more information about this dataset and the importance of rice as a staple food see the
Harvest Choice webpage .
For data on other agricultural species in Africa see these layers:
Data for important agricultural crops in South America are available here .
What can you do with this layer?
This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.
This layer has
query ,
identify , and
export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels
which allows access to the full dataset.
The source data for this layer are available
here .
This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
The
Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.
Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:
Potato (Solanum tuberosum ) a native of South America was first domesticated between 8000 and 5000 BC. In the middle of the 16th century it was introduced to Europe, Asia and Africa. Africa produces about 5% of the world's potato crop.
Dataset Summary
This layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of potato harvested in Africa. The data are in units of hectares/grid cell.
For more information about this dataset and the importance of potato as a staple food see the
Harvest Choice webpage .
For data on other agricultural species in Africa see these layers:
Data for important agricultural crops in South America are available here .
What can you do with this layer?
This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.
This layer has
query ,
identify , and
export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels
which allows access to the full dataset.
The source data for this layer are available here .
This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
The
Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.
Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:
Millet is a group of grass species that produce small-seeded grains used for food and animal feed around the world. Common species include finger millet (Eleusine coracana ), pPanicum miliaceum ), pPennisetum glaucum ), and fSetaria italica ). Africa is a major producer growing 56% of the worlds millet. Pearl millet is an important food source in Africa and is known for its tolerance of hot and dry climates.
Dataset Summary
This layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of millet harvested in Africa. The data are in units of hectares/grid cell.
For more information about this dataset and the importance of millet as a staple food see the
Harvest Choice webpage .
For data on other agricultural species in Africa see these layers:
Data for important agricultural crops in South America are available here .
What can you do with this layer?
This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.
This layer has
query ,
identify , and
export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels
which allows access to the full dataset.
The source data for this layer are available
here .
This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
The
Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.
Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:
Maize (Zea mays ), also known as corn, is a crop of world wide importance. Originally domesticated in what is now Mexico, its tolerance of diverse climates has lead to its widespread cultivation. Globally, it is tied with rice as the second most widely grown crop. Only wheat is more widely grown. In Africa it is grown throughout the agricultural regions of the continent from the Nile Delta in the north to the country of South Africa in the south. In sub-Saharan Africa it is relied on as a staple crop for 50% of the population.
Dataset Summary
This layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of maize harvested in Africa. The data are in units of hectares/grid cell.
For more information about this dataset and the importance of maize as a staple food see the
Harvest Choice webpage .
For data on other agricultural species in Africa see these layers:
Data for important agricultural crops in South America are available here .
What can you do with this layer?
This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.
This layer has
query ,
identify , and
export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels
which allows access to the full dataset.
The source data for this layer are available
here .
This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
The
Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.
Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:
Groundnut (Arachis hypogaea
Dataset Summary
This layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of groundnut harvested in Africa. The data are in units of hectares/grid cell.
For more information about this dataset and the importance of casava as a staple food see the
Harvest Choice webpage .
For data on other agricultural species in Africa see these layers:
Data for important agricultural crops in South America are available
here .
What can you do with this layer?
This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.
This layer has
query ,
identify , and
export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels
which allows access to the full dataset.
The source data for this layer are available
here .
This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
The
Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.
Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:
Cassava (Manihot esculenta ) also known as manioc in South America, is grown world-wide in tropical and sub-tropical regions providing an important staple for the diet of over half a billion people. It is drought tolerant and grows well in marginal soils. More than half of the world's cassava production is from Africa and Nigeria is the world's largest producer. In Ghana, cassava accounts for roughly 30% of the calories eaten. The root of the cassava plant must be prepared to remove harmful compounds prior to eating.
Dataset Summary
This layer provides access to a 5 arc-minute (approximately 10 km at the equator) cell-sized raster of the 1999-2001 annual average area of cassava harvested in Africa. The data are in units of hectares/grid cell.
For more information about this dataset and the importance of casava as a staple food see the
Harvest Choice webpage .
For data on other agricultural species in Africa see these layers:
Data for important agricultural crops in South America are available
here .
What can you do with this layer?
This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.
This layer has
query ,
identify , and
export image services available. This layer is restricted to a maximum area of 24,000 x 24,000 pixels
which allows access to the full dataset.
The source data for this layer are available
here .
This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
The
Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.
The combined processes of evaporation and transpiration, known as evapotranspiration (ET), plays a key role in the
water cycle . Precipitation that falls on land can either run off in streams and rivers, soak into the ground, or return to the atmosphere through evapotranspiration. Water that evaporates returns directly to the atmosphere while water that is transpired is taken up by plant roots and lost to the atmosphere through the leaves.
Evapotranspiration data can be used to calculate regional water and energy balance and soil water status and provides key information for water resource management. Potential evapotranspiration, the amount of ET that would occur if soil moisture were not limited, is a purely meteorological characteristic, based on air temperature, solar radiation, and wind speed.
Actual evapotranspiration also depends on water availability, so it might occur at very close to the potential rate in a rainforest, but be much lower in a desert despite the higher potential there.
Dataset Summary
Phenomenon Mapped: Evapotranspiration
Units: Millimeters per year
Cell Size: 927.6623821756539 meters
Source Type: Continuous
Pixel Type: 16-bit unsigned integer
Data Coordinate System: Web Mercator Auxiliary Sphere
Extent: Global
Source: University of Montana Numerical Terradynamic Simulation Group
Publication Date: March 10, 2015
ArcGIS Server URL: https://landscape6.arcgis.com/arcgis/
This layer provides access to a 1km cell sized raster of average annual evaporative loss from the land surface, measured in mm/year. Data are from the
MOD16 Global Evapotranspiration Product , which is derived from MODIS imagery by a team of researchers at the University of Montana. This algorithm, which involves estimating land surface temperature and albedo and using them to solve the
Penman-Monteith equation, is not valid over urban or barren land so these are shown as NoData, as is any open water. For all other pixels, the algorithm was used to estimate evapotranspiration for every 8-day period from 2000 to 2014 and these estimates have been averaged together to come up with the annual normal. You can also get access to the monthly totals using the
MODIS Toolbox .
What can you do with this Layer?
This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the
ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.
In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers . A window will open. Type "evapotranspiration" in the search box and browse to the layer. Select the layer then click Add to Map . In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas . Type "evapotranspiration" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in
raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.
Training data was collected and used to classify the generated cloud free composite images into various classification categories while considering the IPCC classes and sugarcane as one of the classes. The feature classes considered to generate the LULC product included; Built up, Forest land, Grassland, Bareland, Wetland, Other cropland and Sugarcane. The exercise was conducted as per the sugarcane growing zone. The resulting product from the LULC classification was then subjected to a single feature class categorization of sugarcane, the land cover feature of interest to generate sugarcane and non-sugarcane products.
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Zoom in to large scales (e.g. City level or 1:160k scale ) to see the amenity features display. You can click on a feature to get the name of the feature. The name of the feature will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.
This feature layer provides access to OpenStreetMap (OSM) educational establishment point data for Africa, which is updated every 15 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes amenity features defined as a query against the hosted feature layer where the a menity value is any of ' school ', 'university ', 'college ', or 'kindergarten '.In OSM, amenities are useful and important facilities for visitors and residents, such as schools and universities. These features are identified with an amenity tag . In OSM, amenity=school is used to identify a place where pupils, normally between the ages of about 6 and 18 are taught under the supervision of teachers. This includes primary and secondary schools. See amenity=college , amenity=university , and
amenity=kindergarten for other educational establishments.
Zoom in to large scales (e.g. Cities level or 1:160k scale ) to see the educational features display. You can click on a feature to get the name and any other available attributes of the feature. The name of the feature will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.
This feature layer provides access to OpenStreetMap (OSM) shops data for Africa, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes shop features defined as a query against the hosted feature layer (i.e. shop is not blank).
In OSM, a shop is a place selling retail products or services , such as a supermarket, bakery, or florist. These features are identified with a shop tag . There are thousands of different tag values for shop used in the OSM database. In this feature layer, unique symbols are used for several of the most popular shop types, while lesser used types are grouped in an "other" category.
Zoom in to large scales (e.g. Neighborhood level or 1:80k scale) to see the shop features display. You can click on the feature to get the name of the shop. The name of the shop will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.
Create New Layer
If you would like to create a more focused version of this shop layer displaying just one or two shop types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. shop is jewelry), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. supermarket or convenience shop) that are ready to use, but not for every type of shop.
Important Note
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Zoom in to large scales (e.g. Neighborhood level or 1:20k scale ) to see the amenity features display. You can click on a feature to get the name of the amenity. The name of the amenity will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.
Create New Layer
If you would like to create a more focused version of this medical facilities layer displaying just one or two amenity types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. amenity is hospital), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri will publish a few such layers (e.g. Places of Worship, Schools, and Parking) that are ready to use, but not for every type of amenity.
Important Note
This feature layer provides access to OpenStreetMap (OSM) landuse areas data for Africa, which is updated every 15 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM polygon (closed way) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes landuse features defined as a query against the hosted feature layer (i.e. landuse is not blank).
In OSM, landuse describes the primary use of land by humans . These features are identified with a landuse tag . There are thousands of different tag values for landuse used in the OSM database. In this feature layer, unique symbols are used for several of the most popular landuse types, while lesser used types are grouped in an "other" category.
Zoom in to large scales (e.g. City level or 1:80k scale) to see the landuse area features display. You can click on a feature to get the name of the landuse area (if available). The name of the landuse area will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer.
Create New Layer
If you would like to create a more focused version of this landuse areas layer displaying just one or two landuse types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. landuse is commercial), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. residential, forest) that are ready to use, but not for every type of landuse area .
Important Note
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Zoom in to large scales (e.g. Streets level or 1:20k scale) to see the highway features display. You can click on a feature to get the name of the highway (if available). The name of the highway will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer.
Create New Layer
If you would like to create a more focused version of this highway layer displaying just one or two highway types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. highway is path), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. cycleway and pedestrian) that are ready to use, but not for every type of highway .
Important Note
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Zoom in to large scales (e.g. City level or 1:80k scale ) to see the waterway features display. You can click on a feature to get the name of the waterway (if available). The name of the waterway will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer.
Create New Layer
If you would like to create a more focused version of this waterway layer displaying just one or two waterway types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. waterway is dam), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri may publish a few such layers (e.g. streams and rivers) that are ready to use, but not for every type of waterway.
Important Note
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Create New Layer
If you would like to create a more focused version of this amenity layer displaying just one or two amenity types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. amenity is fire station), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri will publish a few such layers (e.g. Schools, Medical Facilities , and Places of Worship) that are ready to use, but not for every type of amenity.
Important Note
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This model present an integrated flood risk analysis for Ona river basin in the western part of Ibadan City, Nigeria. It shows the magnitude of risk and the validation data using past flood inventory layer.
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This model present an integrated flood risk analysis for Ona river basin in the western part of Ibadan City, Nigeria. It shows the magnitude of risk and the validation data using past flood inventory layer.
This model present an integrated flood risk analysis for Ona river basin in the western part of Ibadan City, Nigeria. It shows the magnitude of risk and the validation data using past flood inventory layer.
This model present an integrated flood risk analysis for Ona river basin in the western part of Ibadan City, Nigeria. It shows the magnitude of risk and the validation data using past flood inventory layer.
This model present an integrated flood risk analysis for Ona river basin in the western part of Ibadan City, Nigeria. It shows the magnitude of risk and the validation data using past flood inventory layer.
This model present an integrated flood risk analysis for Ona river basin in the western part of Ibadan City, Nigeria. It shows the magnitude of risk and the validation data using past flood inventory layer.
This model present an integrated flood risk analysis for Ona river basin in the western part of Ibadan City, Nigeria. It shows the magnitude of risk and the validation data using past flood inventory layer.
This model present an integrated flood risk analysis for Ona river basin in the western part of Ibadan City, Nigeria. It shows the magnitude of risk and the validation data using past flood inventory layer.
This model present an integrated flood risk analysis for Ona river basin in the western part of Ibadan City, Nigeria. It shows the magnitude of risk and the validation data using past flood inventory layer.
This model present an integrated flood risk analysis for Ona river basin in the western part of Ibadan City, Nigeria. It shows the magnitude of risk and the validation data using past flood inventory layer.
This model present an integrated flood risk analysis for Ona river basin in the western part of Ibadan City, Nigeria. It shows the magnitude of risk and the validation data using past flood inventory layer.
Information about patient charges for each hospital is included in a table. To open the table, click the Show Table button underneath Clark County Hospitals in the Contents pane.
The data comes from Personal Health Choices, a joint publication of the Center for Health Information Analysis (CHIA) and the Nevada Department of Health and Human Services, Division of Health Care Financing and Policy.
Maasai Mara is one of Africa’s Greatest Wildlife Reserves situated in south-west Kenya.It forms Africa’s most diverse, incredible and most spectacular eco-systems and possibly the world’s top safari big game viewing eco-system together with the Serengeti National Park
Fairmont Mara Safari Club
Tucked peacefully in the north-western corner of the renowned Maasai Mara Game Reserve - home to the great wildebeest migration - and surrounded on three sides by the Mara River, Fairmont Mara Safari Club is your ideal destination for a meeting or event in the wild.
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The current version supersedes GRID3 GMB - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 GMB - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/yyyg-g140. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 GIN - Settlement Extents v3.0 include:
The current version supersedes GRID3 GIN - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 GIN - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/7nqp-pj27. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 GHA - Settlement Extents v3.0 include:
The current version supersedes GRID3 GHA - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 GHA - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/9scv-2v68. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 GAB - Settlement Extents v3.0 include:
The current version supersedes GRID3 GAB - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 GAB - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/zmc2-r660. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 ETH - Settlement Extents v3.0 include:
The current version supersedes GRID3 ETH - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 ETH - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/2hhy-yd24. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 ERI - Settlement Extents v3.0 include:
The current version supersedes GRID3 ERI - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 ERI - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/1r2n-0j10. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 DJI - Settlement Extents v3.0 include:
The current version supersedes GRID3 DJI - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 DJI - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/hwps-jn87. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 CPV - Settlement Extents v3.0 include:
The current version supersedes GRID3 CPV - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data.
Estimation of a probability value for settlement polygons
Addition of a building-area measure
Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets
Elimination of building-count ranges
Elimination of variable comparing to previous versions
For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 CPV - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/nrt8-am44. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 COM - Settlement Extents v3.0 include:
The current version supersedes GRID3 COM - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 COM - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/5v9f-tm89. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 COG - Settlement Extents v3.0 include:
The current version supersedes GRID3 COG - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 COG - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/pt06-ef20. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 CMR - Settlement Extents v3.0 include:
The current version supersedes GRID3 CMR - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 CMR - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/hsw1-e629. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 CIV - Settlement Extents v3.0 include:
The current version supersedes GRID3 CIV - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 CIV - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/5f1x-1356. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 CAF - Settlement Extents v3.0 include:
The current version supersedes GRID3 CAF - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 CAF - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/b3h7-2k33. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 BWA - Settlement Extents v3.0 include:
This is the settlement extents layer.
The current version supersedes GRID3 BWA - Settlement Extents v2. 0; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 BWA - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/yk00-pw68. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 BFA - Settlement Extents v3.0 include:
The current version supersedes GRID3 BFA - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 BFA - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/4z03-2v66. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 BEN - Settlement Extents v3.0 include:
The current version supersedes GRID3 BEN - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive settlement polygons with open data. Estimate a probability value for settlement polygons For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 BEN- Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/yen9-aj46. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
The two layers contained within the GRID3 BDI - Settlement Extents v3.0 include:
The current version supersedes GRID3 BDI - Settlement Extents v2.0 ; the following changes were made:
Development of a new methodology to derive building counts and settlement polygons with open data. Estimation of a probability value for settlement polygons Addition of a building-area measure Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets Elimination of building-count ranges Elimination of variable comparing to previous versions For more information on data inputs, methodology, and codebooks please see the Data Release Notes .
Recommended Citation:
Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 BDI - Settlement Extents v3.0. New York: GRID3. https://doi.org/doi:10.7916/hgbb-ec60. Accessed [DAY MONTH YEAR].
Terms of use:
Users are free to download, store, access, use, copy, adapt, transform, alter, arrange, build upon, distribute and transmit this work and any derivative works. Attribution of the source must be provided, and further distribution of this work or derived work must maintain the same terms of data use and license as set forth in this Terms of Use.
Copyright 2024. The Trustees of Columbia University in the City of New York.
Data license:
The data and accompanying document are licensed under a Creative Commons Attribution-ShareAlike 4.0 International, CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0) and specified in legal code (https://creativecommons.org/licenses/by-sa/4.0/legalcode).
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
This product is one of the indicator layers selected in assessing vulnerability during Wami-Ruvu-Rufiji mapping.
Land Use Land Cover change shows the changes in land use and land cover between the years 2000 and 2010 within the basin region. Decreasing landscape capacity to regulate water quality and availability poses high senstivity of the communities and water resources,this has an direct relationship to the overall vulnerability component. The landuse landcover dataset is obtained from Regional Centre for Mapping of Resources for Development(2010) .
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This map was designed and created by Cindy Prostak.
Après le lancement des deux réseaux TramWay de Rabat Salé et de Casablanca, le Maroc a connu un saut qualitatif dans le domaine des transports collectifs urbains.
Le TramWay de Rabat Salé a été réalisé avec un budget de plus de EUR 303 millions .Pour l'instant,deux lignes d’une longueur totale de 20 km et 31 stations sont en service.
Concernant le TramWay de Casablanca, La première ligne du Tramway de Casablanca est opérationnelle depuis le 12 Décembre 2012. D’une longueur de 31 km, la ligne T1 part ainsi de la périphérie de la ville, pour traverser de nombreux points névralgiques : les gares de Casa Voyageur (future gare du TGV) et de l’Oasis, le centre-ville, les quartiers d’affaires, les quartiers commerçants, les universités et les zones de loisirs et plages. Des travaux sont prévus pour l'élaboration des lignes T2. Cette carte représente la position des stations et lignes Tramway se trouvant dans les villes marocaines Rabat-Salé et Casablanca.
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7 ports de plaisance SAIDIA, KABILA, MARINA SMIR, BOUREGREG, SABLES D’OR ,MARINA D’AGADIR, LAGUNE DE MARCHICA
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Key Properties
Geographic Coverage: Continental Africa - approximately 37° North to 35 SouthTemporal Coverage: 2019Spatial Resolution: 10 x 10 meterUpdate Frequency: TBDNumber of Bands: 3 BandsParent Dataset: Digital Earth Africa's Sentinel-2 Semiannual GeoMAD Source Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)
\n\nDigital\nEarth Africa’s cropland extent maps for Eastern, Western, and Northern Africa\nshow the estimated location of croplands in these countries for the period of\nJanuary to December 2019:
\n\nEastern: Tanzania, Kenya, Uganda, Ethiopia, Rwanda and Burundi Western: Nigeria, Benin, Togo, Ghana, Cote d'Ivoire, Liberia, Sierra Leone, Guinea and Guinea-Bissau Northern: Morocco, Algeria, Tunisia, Libya and Egypt Sahel: Mauritania, Senegal, Gambia, Mali, Burkina Faso, Niger, Chad, Sudan, South Sudan, Somalia and Djibouti Southern: South Africa, Namibia, Botswana, Lesotho and Eswatini Cropland is\ndefined as:
\n\n"a\npiece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is\nsowed/planted and harvestable at least once within the 12 months after the\nsowing/planting date."
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\n\nThis\ndefinition will exclude non-planted grazing lands and perennial crops which can\nbe difficult for satellite imagery to differentiate from natural vegetation.
\n\nThe\nprovisional cropland extent maps have a resolution of 10 metres and were built\nusing Copernicus Sentinel-2 satellite images from 2019. The cropland extent\nmaps were built separately using extensive training data from Eastern, Western,\nand Northern Africa, coupled with a Random Forest machine learning model. A\ndetailed exploration of the methods used to produce the cropland extent map can\nbe found in the Jupyter Notebooks in DE Africa’s crop-mask
\n\nIndependent\nvalidation datasets suggest the following accuracies:
\n\n\n The Eastern Africa cropland extent map has\n an overall accuracy of 90.3 %, and an f-score of 0.85 The Western Africa cropland extent map has\n an overall accuracy of 83.6 %, and an f-score of 0.75 The Northern Africa cropland extent map has\n an overall accuracy of 94.0 %, and an f-score of 0.91 The Sahel Africa cropland extent map has an overall\n accuracy of 87.9 %, and an f-score of 0.78 The Southern Africa cropland extent map has an overall\n accuracy of 86.4 %, and an f-score of 0.75 \n\nThe algorithms\nfor all regions tend to report more omission errors (labelling actual crops as\nnon-crops) than commission errors (labelling non-crops as crops). Where\ncommission errors occur, they tend to be focussed around wetlands and seasonal\ngrasslands which spectrally resemble some kinds of cropping.
\n\nAvailable Bands
Band ID
Description
Value range
Data type
NoData/Fill value
mask
crop extent (pixel)
0 - 1
uint8
0
prob
crop probability (pixel)
0 - 100
uint8
0
filtered
crop extent (object-based)
0 - 1
uint8
0
\n\nmask : This band\ndisplays cropped regions as a binary map. Values of 1 indicate the\npresence of crops, while a value of 0 indicates the absence of\ncropping. This band is a pixel-based cropland extent map, meaning the\nmap displays the raw output of the pixel-based Random Forest classification.
\n\nprob : This band\ndisplays the prediction probabilities for the ‘crop’ class. As this service\nuses a random forest classifier, the prediction probabilities refer to the\npercentage of trees that voted for the random forest classification. For\nexample, if the model had 200 decision trees in the random forest, and 150 of\nthe trees voted ‘crop’, the prediction probability is 150 / 200 x 100 = 75\n%. Thresholding this band at > 50 % will produce a map identical\nto mask.
\n\nfiltered : This band displays cropped regions as a\nbinary map. Values of 1 indicate the presence of crops, while a value\nof 0 indicates the absence of cropping. This band is an object-based cropland extent map where the mask band has\nbeen filtered using an image segmentation algorithm (see this paper
\n\nhere .
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distribution of humans . This layer provides a landscape-scale estimate of the distance from large water bodies.
Dataset Summary
This layer provides access to a 250m cell-sized raster of distance to surface water. To facilitate mapping, the values are in units of pixels. To convert this value to meters multiply by 250. The layer was created by extracting surface water values from the
World Lithology and
World Land Cover layers to produce a
surface water layer . The distance from water was calculated using the ArcGIS
Euclidian Distance Tool . The layer was created by Esri in 2014.
W hat can you do with this layer?
This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.
This layer has
query ,
identify , and
export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe.
This layer is part of a larger collection of
landscape layers that you can use to perform a wide variety of mapping and analysis tasks.
The
Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.
The
Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.
The GRID3 NGA - Travel Time Friction Surface v1.0 dataset is a spatial data layer in tiff format. The datasets contain 2 layers:
The resolution is 30 m. The unit of measurement in both surfaces is minutes per meter.
For more information on the methodology and data sources used during the production of this data, click here .
Dataset citation:
Center for Integrated Earth System Information (CIESIN), Columbia University. 2025. GRID3 NGA - Travel Time Friction Surface v1.0. New York: Columbia University. https://doi.org/10.7916/tw5k-ys98. Accessed <DAY MONTH YEAR>
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences. For dataset-related questions, please send an email to: info@ciesin.columbia.edu
For more information on the methodology and data sources used during the production of this data, click here .
Dataset citation:
Center for Integrated Earth System Information (CIESIN), Columbia University. 2025. GRID3 NGA - Roads v1.0.New York: Columbia University. https://doi.org/10.7916/b42r-6w17. Accessed <DAY MONTH YEAR>.
Contacts and data queries:
The authors of this dataset appreciate feedback regarding the data, including suggestions, discovery of errors, difficulties in using the data, and format preferences.
For dataset-related questions, please send an email to: info@ciesin.columbia.edu
This app incorporates the GEOGLOWS Streamflow forecast service , a 10-day river forecast from the European Centre for Medium-range Weather Forecasting (ECMWF). This service is updated daily. The app includes an interactive graph showing streamflow forecasts for the next 10 days and allows users to anticipate stream flow events. The app is set to the extent of Kenya.
This app can provide situational awareness for water. It is used by local to national governments and other organizations to improve water sustainability and to mitigate water-related risks, such as the flooding. It is also useful in irrigation planning for agriculture, hydroelectric dam management, transportation logistics and commerce for cargo barges and ferries, and recreation planning for tour boats, paddle sports, and fishing.
To create your own app for your country please see this blog
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This layer supports analysis but, if needed, a direct download of the data can be accessed here .
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\n \n \n Year
\n \n \n Resolution
\n \n \n Definition
\n \n \n \n \n Santoro et al. 2018
\n \n \n Global
\n \n \n 2010
\n \n \n 100 m
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\n \n \n \n \n Xia et al. 2014
\n \n \n Global
\n \n \n 1982-2006
\n \n \n 8 km
\n \n \n Above-ground grassland biomass.
\n \n \n \n \n Bouvet et al. 2018
\n \n \n Africa
\n \n \n 2010
\n \n \n 25 m
\n \n \n Above-ground woodland and savannah\n biomass; low woody biomass areas, which therefore exclude dense forests and\n deserts.
\n \n \n \n \n Spawn et al. 2017
\n \n \n Global
\n \n \n 2010
\n \n \n 300 m
\n \n \n Synthetic, global above- and\n below-ground biomass maps that combine recently released satellite-based data\n of standing forest biomass with novel estimates for non-forest biomass\n stocks.
\n \n \n
After aggregating each selected dataset to a nominal scale of 300 m\nresolution, forest categories in the CCI ESA 2010 landcover dataset were used\nto extract above-ground biomass from Santoro et al. 2018 for forest areas.\nWoodland and savanna biomass were then incorporated for Africa from Bouvet et\nal. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside\nof forest. Biomass from croplands, sparse vegetation and grassland landcover\nclasses from CCI ESA, in addition to shrubland areas outside Africa missing\nfrom Santoro et al. 2018, were extracted from were extracted from Xia et al.\n2014. and Spawn et al. 2017 averaged by ecological zone for each landcover\ntype.
\n\nBelow-ground biomass were added using root-to-shoot ratios from the\n2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No\nbelow-ground values were assigned to croplands as ratios were unavailable.\nAbove- and below-ground biomass were then summed together and multiplied by 0.5\nto convert to carbon, generating a single above-and-below-ground biomass carbon\nlayer.
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This layer shows species rarity patterns for restio species of southern Africa based on data availability. Species rarity is the average range-restrictedness of all species expected to occur in each grid cell. Here rarity is expressed as a raw value from low (purple) to high (yellow). NOTE: raw rarity values are very small (<1).
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Biodiversity is critical for maintaining the function of ecosystems and their services to humans. Using different biodiversity measures can shed light on which areas should be prioritized for biodiversity conservation. Beyond the number of species occurring in an area, rare species that have small range extents should be prioritized in conservation planning, as the conservation opportunities are limited for these range-restricted species especially when comparing them to wide-ranging species. Patterns of species richness and range rarity provide insights about the biogeography of taxa and offer an initial basis for global biodiversity conservation efforts.
Data source used to predict species ranges
Occurrences: GBIF (2017). Global Biodiversity Information Facility Data Portal. Accessed on 5th July 2017.
Expert maps: IUCN (2017). International Union for Conservation of Nature - Red List of Threatened Species. Accessed on January 2017.
Overview
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Data source used to predict species ranges
Occurrences: GBIF (2017). Global Biodiversity Information Facility Data Portal. Accessed on 5th July 2017.
Expert maps: IUCN (2017). International Union for Conservation of Nature - Red List of Threatened Species. Accessed on January 2017.
Overview
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Data source used to predict species ranges
Occurrences: GBIF (2017). Global Biodiversity Information Facility Data Portal. Accessed on 5th July 2017.
Expert maps: IUCN (2017). International Union for Conservation of Nature - Red List of Threatened Species. Accessed on January 2017.
Overview
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Data source used to predict species ranges
Occurrences: GBIF (2017). Global Biodiversity Information Facility Data Portal. Accessed on 5th July 2017.
Expert maps: IUCN (2017). International Union for Conservation of Nature - Red List of Threatened Species. Accessed on January 2017.
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Project name: Improving Ecological Stability of Dijo and Bilate Watershed
Coordinator: People in Need
Funding: The field mapping campaign had been funded by the Czech Republic Development Cooperation Agency.
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This layer shows species rarity patterns for mammal species of southern Africa based on data availability. Species rarity is the average range-restrictedness of all species expected to occur in each grid cell. Here rarity is expressed as a raw value from low (purple) to high (yellow). NOTE: raw rarity values are very small (<1).
Species ranges were modeled at a 1-km resolution, integrating occurrence data and expert maps, where each distribution was predicted across Africa, and limited to the southern Africa extent. This extent represents the area for which there was complete spatial and taxonomic data coverage for this species group.
Biodiversity is critical for maintaining the function of ecosystems and their services to humans. Using different biodiversity measures can shed light on which areas should be prioritized for biodiversity conservation. Beyond the number of species occurring in an area, rare species that have small range extents should be prioritized in conservation planning, as the conservation opportunities are limited for these range-restricted species especially when comparing them to wide-ranging species. Patterns of species richness and range rarity provide insights about the biogeography of taxa and offer an initial basis for global biodiversity conservation efforts.
Data source used to predict species ranges
Occurrences: GBIF (2017). Global Biodiversity Information Facility Data Portal. Accessed on 5th July 2017.
Expert maps: IUCN (2017). International Union for Conservation of Nature - Red List of Threatened Species. Accessed on January 2017.
Biodiversity is critical for maintaining the function of ecosystems and their services to humans. Using different biodiversity measures can shed light on which areas should be prioritized for biodiversity conservation. Beyond the number of species occurring in an area, rare species that have small range extents should be prioritized in conservation planning, as the conservation opportunities are limited for these range-restricted species especially when comparing them to wide-ranging species. Patterns of species richness and range rarity provide insights about the biogeography of taxa and offer an initial basis for global biodiversity conservation efforts.
Species richness is the number of species ranges estimated to overlap in each cell. The data presented in this layer reflect the number of species occurring in each grid cell expressed as a raw value from low (dark blue) to high (yellow).
Species ranges were modeled at a 1-km resolution, integrating occurrence data and expert maps, where each distribution was predicted across Africa, and limited to the southern Africa extent. This extent represents the area for which there was complete spatial and taxonomic data coverage for this species group.
Biodiversity is critical for maintaining the function of ecosystems and their services to humans. Using different biodiversity measures can shed light on which areas should be prioritized for biodiversity conservation. Beyond the number of species occurring in an area, rare species that have small range extents should be prioritized in conservation planning, as the conservation opportunities are limited for these range-restricted species especially when comparing them to wide-ranging species. Patterns of species richness and range rarity provide insights about the biogeography of taxa and offer an initial basis for global biodiversity conservation efforts.
Species rarity in this case refers to the “average range-size rarity”, also called “range size rarity.” It is the average of the inverse global range sizes of species predicted to occur in a grid cell. Here rarity is expressed as a raw value from low (dark blue) to high (yellow).
Species ranges were modeled at a 1-km resolution, integrating occurrence data and expert maps, where each distribution was predicted across Africa, and limited to the southern Africa extent. This extent represents the area for which there was complete spatial and taxonomic data coverage for this species group.
Biodiversity is critical for maintaining the function of ecosystems and their services to humans. Using different biodiversity measures can shed light on which areas should be prioritized for biodiversity conservation. Beyond the number of species occurring in an area, rare species that have small range extents should be prioritized in conservation planning, as the conservation opportunities are limited for these range-restricted species especially when comparing them to wide-ranging species. Patterns of species richness and range rarity provide insights about the biogeography of taxa and offer an initial basis for global biodiversity conservation efforts.
Species richness is the number of species ranges estimated to overlap in each cell. The data presented in this layer reflect the number of species occurring in each grid cell expressed as a raw value from low (dark blue) to high (yellow).
Species ranges were modeled at a 1-km resolution, integrating occurrence data and expert maps, where each distribution was predicted across Africa, and limited to the southern Africa extent. This extent represents the area for which there was complete spatial and taxonomic data coverage for this species group.
Biodiversity is critical for maintaining the function of ecosystems and their services to humans. Using different biodiversity measures can shed light on which areas should be prioritized for biodiversity conservation. Beyond the number of species occurring in an area, rare species that have small range extents should be prioritized in conservation planning, as the conservation opportunities are limited for these range-restricted species especially when comparing them to wide-ranging species. Patterns of species richness and range rarity provide insights about the biogeography of taxa and offer an initial basis for global biodiversity conservation efforts.
Species richness is the number of species ranges estimated to overlap in each cell. The data presented in this layer reflect the number of species occurring in each grid cell expressed as a raw value from low (dark blue) to high (yellow).
Species ranges were modeled at a 1-km resolution, integrating occurrence data and expert maps, where each distribution was predicted across Africa, and limited to the southern Africa extent. This extent represents the area for which there was complete spatial and taxonomic data coverage for this species group.
Biodiversity is critical for maintaining the function of ecosystems and their services to humans. Using different biodiversity measures can shed light on which areas should be prioritized for biodiversity conservation. Beyond the number of species occurring in an area, rare species that have small range extents should be prioritized in conservation planning, as the conservation opportunities are limited for these range-restricted species especially when comparing them to wide-ranging species. Patterns of species richness and range rarity provide insights about the biogeography of taxa and offer an initial basis for global biodiversity conservation efforts.
Species richness is the number of species ranges estimated to overlap in each cell. The data presented in this layer reflect the number of species occurring in each grid cell expressed as a raw value from low (dark blue) to high (yellow).
Species ranges were modeled at a 1-km resolution, integrating occurrence data and expert maps, where each distribution was predicted across Africa, and limited to the southern Africa extent. This extent represents the area for which there was complete spatial and taxonomic data coverage for this species group.
Biodiversity is critical for maintaining the function of ecosystems and their services to humans. Using different biodiversity measures can shed light on which areas should be prioritized for biodiversity conservation. Beyond the number of species occurring in an area, rare species that have small range extents should be prioritized in conservation planning, as the conservation opportunities are limited for these range-restricted species especially when comparing them to wide-ranging species. Patterns of species richness and range rarity provide insights about the biogeography of taxa and offer an initial basis for global biodiversity conservation efforts.
Species richness is the number of species ranges estimated to overlap in each cell. The data presented in this layer reflect the number of species occurring in each grid cell expressed as a raw value from low (dark blue) to high (yellow).
Species ranges were modeled at a 1-km resolution, integrating occurrence data and expert maps, where each distribution was predicted across Africa, and limited to the southern Africa extent. This extent represents the area for which there was complete spatial and taxonomic data coverage for this species group.
Biodiversity is critical for maintaining the function of ecosystems and their services to humans. Using different biodiversity measures can shed light on which areas should be prioritized for biodiversity conservation. Beyond the number of species occurring in an area, rare species that have small range extents should be prioritized in conservation planning, as the conservation opportunities are limited for these range-restricted species especially when comparing them to wide-ranging species. Patterns of species richness and range rarity provide insights about the biogeography of taxa and offer an initial basis for global biodiversity conservation efforts.
Species richness is the number of species ranges estimated to overlap in each cell. The data presented in this layer reflect the number of species occurring in each grid cell expressed as a raw value from low (dark blue) to high (yellow).
Species ranges were modeled at a 1-km resolution, integrating occurrence data and expert maps, where each distribution was predicted across Africa, and limited to the southern Africa extent. This extent represents the area for which there was complete spatial and taxonomic data coverage for this species group.
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This map was designed and created by Wesley Jones.
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"dct:description": "This web map provides a customized vector basemap for the world symbolized with a unique "newspaper" styled map, with a focus on Africa. It has a black & white appearance with select features highlighted in red. Many of the area fills have halftone patterns commonly found in traditional newspaper printing.
This vector tile layer is built using the same data sources used for the World Topographic Map and other Esri basemaps. The comprehensive map data includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority.
This map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map.
Customize this Map
Because this map includes a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers, switch to alternate local language (in some areas), and refine the treatment of disputed boundaries. For details on how to customize this map, please refer to these articles on the ArcGIS Online Blog .
This map was designed and created by Cindy Prostak.
This vector tile layer is built using the same data sources used for
other Esri vector tile layers and web maps . The map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, trees, and administrative boundaries. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority.
Use this Map
This map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the Colored Pencil tile layer items referenced in this map.
Customize this Map
Because this map includes a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers, switch to alternate local language (in some areas), and refine the treatment of disputed boundaries. For details on how to customize this map, please refer to these articles on the ArcGIS Online Blog .
This map was designed and created by Cindy Prostak.
This web map provides a customized vector layer for the world symbolized with a unique antique styled map, with a modern flair -- including the benefit of multi-scale mapping. This map is focused on Africa, with a mask layer in the basemap.
This web map is built using the same data sources used for the World Topographic Map and other Esri basemaps. The comprehensive map data includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority.
This map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map.
Customize this Map
Because this map contains a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers, switch to alternate local language (in some areas), and refine the treatment of disputed boundaries. For details on how to customize this map, please refer to these articles on the ArcGIS Online Blog .
This map was designed and created by Cindy Prostak.
This vector tile layer provides a buffered mask around an area of interest (AOI), in this case Africa. The layer can be overlayed on other basemaps and operational layers to call attention to the AOI within the mask. This layer is designed for a dark basemap.
The layer is a vector tile layer so it is designed to (a) perform well at multiple scale levels and (b) be customized as needed to work well with other basemaps or layers. The vector tile layer can be added to the basemap layer of a web map and set as a reference layer, if needed, to mask other reference layers in the map.
This vector tile layer provides a buffered mask around an area of interest (AOI), in this case Africa. The layer can be overlayed on other basemaps and operational layers to call attention to the AOI within the mask. This layer is opaque and designed for light basemap.
The layer is a vector tile layer so it is designed to (a) perform well at multiple scale levels and (b) be customized as needed to work well with other basemaps or layers. The vector tile layer can be added to the basemap layer of a web map and set as a reference layer, if needed, to mask other reference layers in the map.
This vector tile layer provides a buffered mask around an area of interest (AOI), in this case Africa. The layer can be overlayed on other basemaps and operational layers to call attention to the AOI within the mask. This layer is designed for light basemap.
The layer is a vector tile layer so it is designed to (a) perform well at multiple scale levels and (b) be customized as needed to work well with other basemaps or layers. The vector tile layer can be added to the basemap layer of a web map and set as a reference layer, if needed, to mask other reference layers in the map.
This map contains a number of world-wide dynamic image services providing\naccess to various Landsat scenes covering the landmass of the World for\nvisual interpretation. Landsat 8 collects new scenes for each location on Earth every 16 days, assuming limited cloud coverage. Newest and near cloud-free scenes are displayed by default on top. Most scenes collected since 1st January 2015 are included. The service also includes scenes from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).
The service contains a range of different predefined renderers for Multispectral, Panchromatic as well as Pansharpened scenes. The layers in the service can be time-enabled\nso that the applications can restrict the displayed scenes to a specific date range.
This ArcGIS Server dynamic service can be used in Web Maps and ArcGIS Desktop, Web and Mobile applications using the REST based image services API. Users can also export images, but the exported area is limited to maximum of 2,000 columns x 2,000 rows per request.
Data Source: The imagery in these services is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). The data for these services reside on the Landsat Public Datasets hosted on the Amazon Web Service cloud. Users can access full scenes from https://github.com/landsat-pds/landsat_ingestor/wiki/Accessing-Landsat-on-AWS , or alternatively access http://landsatlook.usgs.gov to review and download full scenes from the complete USGS archive .
For more information on Landsat 8 images, see http://landsat.usgs.gov/landsat8.php .
*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit http://landsat.usgs.gov/science_GLS.php .
For more information on each of the individual layers, see
http://www.arcgis.com/home/item.html?id=d9b466d6a9e647ce8d1dd5fe12eb434b ;
http://www.arcgis.com/home/item.html?id=6b003010cbe64d5d8fd3ce00332593bf ;
http://www.arcgis.com/home/item.html?id=a7412d0c33be4de698ad981c8ba471e6
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About openAFRICA
openAFRICA aims to be largest independent repository of open data on the African continent.
openAFRICA is not a government portal. It is instead a grassroots initiative, maintained by Code for Africa as a public service. The platform is therefore available as a free resource for ordinary citizens, civil society organisations, civic activists, the media, and government agencies.
You can upload your data to openAFRICA to either store it, or to make it available to a wider public. You can also download data already on openAFRICA for re-use in your own projects. We are happy to help you both upload / re-use data.
Much of the data on openAFRICA has been 'liberated' by pioneering organisations, volunteers or activists. It is therefore an eclectic collection that is far from comprehensive. You will, however, find datasets here that are difficult to access elsewhere. If there is crucial data that you can't find here or elsewhere, ask and we might be able to help you liberate it.
All of the data on openAFRICA is intended to be 'actionable'. The data is meant to help you change the world. We therefore want you to analyse it, visualise it, share it, and build tools or services with it.
openAFRICA runs with support from Code for Africa, the World Bank and Google.
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"dct:title": "AfricaMap (Harvard University)",
"dct:description": "AfricaMap is housed at the Center for Geographic Analysis at Harvard University with an initial grant from the Harvard Provosts Fund for Innovative Computing and ongoing support from the W. E. B. Du Bois Institute, the Department of African and African American Studies and the Committee for African Studies at Harvard University. AfricaMap grew out of a project, called Baobab, funded by the Seaver Institute.
AfricaMap is powered by World Map. WorldMap allows anyone to:
Upload layers overlay them with thousands of other layers Create and edit maps and link map features to media content Publish data to the world or to just a few collaborators Export data to standard interoperable formats Make use of online cartographic tools for symbolizing layers Georeference paper maps online using the Map Warper Tool World Cities layer , with just the cities intersecting with the continent of Africa.
The popup for the layer includes a dynamic link to
Wikipedia , using an Arcade expression.
In the 1980s, charcoal and fuel wood met more than 95 percent of Uganda's energy needs.In 2005 and 2006, low water levels of Lake Victoria, the main source of the country's electricity generation potential, led to a generation shortage and an energy crisis. ",
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From the first cautious steps in the Great Rift Valley, humans have adapted to the challenges of Africa. Today it lies divided into more nations than any other continent. But as shown by this satellite-derived map, landscapes that ignore national borders form a mosaic of ecosystems that subtly blend one into the other -- from the temperate Mediterranean through desert and scattered croplands to forest, swamp, and savanna -- each with its own rules for life. Published in September 2005.
>> Order print map <<
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Jewel and Jenita mol Mango plant for this International Day of Biological & Biodiversity on May 22 ,
EGYPT IS CROCODILE GOD'S OWN COUNTRY
SUMMARY- EGYPT IS CROCODILE GOD'S OWN COUNTRY BIODIVERSITY & ECOSYSTEM
E @ BIODIVERSITY HERITAGE SITE IN AFRICAN CONTINENT WHERE E IS EAST AFRICAN NILE CROCODILE 
SPACE ECOSYSTEM & DATABASE TECHNOLOGY ANALYSE AFRICA BIODIVERSITY BY THE E @ NILE CROCODILE HABITAT & THE E @ BIODIVERSITY HERITAGE SITE
RESEARCHING SPACE ECOSYSTEM FOR E @ BIODIVERSITY HERITAGE SITE (BHS) FOR E @ CROCODILE DIVERSITY , E @ HABITAT DIVERSITY & E @ ECOSYSTEM DIVERSITY IN AFRICA. THE SUSTAINABLE DEVELOPMENT OF SPACE ECOSYSTEM FOR SPACE DATABASE AND TECHNOLOGY WILL DEVELOP THE E @ AFRICAN BIODIVERSITY DATA BASE THAT MONITORS & CONTROL THE E @ BIODIVERSITY HERITAGE SITE (BHS) FOR E @ CROCODILE DIVERSITY , E @ HABITAT DIVERSITY & E @ ECOSYSTEM DIVERSITY IN AFRICA
AT THE END OF THE DAY "The Crocodile is honored on its traditional and historical background across the world and is a major attraction of the public is mentioned as follows" . Crocodile Sobek is Honored as the GOD of Nile in Egypt . Honoring Crocodile as the Carnivores and scavenger , Honoring Crocodile for its long historical background of 2000 years backs , Honoring the crocodile family chain in different countries across the world in 27 species of Crocodiles , Honoring crocodile as the king of waters , Honoring crocodile for the Existence of Crocodile habitat even after 2000 years . Honoring crocodile as a sacred and worshiped in Egypt and in many monuments , temples across many countries in festivals , Honoring crocodile as the savior of water ways by controlling the population of fishes , Honoring Crocodile as a symbol in Roman coin called the "As de Nîmes" features a crocodile chained to a palm, a symbol of victory for the Romans, Honoring in the National flag Jamaica, Honoring creator of Islands Cayman, Honoring Crocodile as a National animal of the country , Honoring Crocodile the emblem of Surabaya City, Indonesia , Honoring Nile crocodile for defeating the Alexander the great army when crossing Nile river , Honoring crocodile in the Crocodile poem . Honoring Sarcosuchus as a Super croc , Honoring Gustava as the king of Nile Crocodile , Honoring Steve Irwin the crocodile Hunter , Honoring the crocodile whispers, rangers , appeaser , Honoring and portrating the Crocodile in many events, and operation of Army as Crocodile smile & Tears , Creek Crocodile and featuring crocodile as a main icon based on the Methodology , Mind ethics , Myth , ideologies in many religion and society of people in Egypt, Africa and in other countries is connected to the ecosystem and BHS (Biodiversity heritage sites) of Nile valley and the Aquatic reptile the Nile crocodile . 
The Biodiversity war due to Human or Arms conflict is having the significant negative impact on the country's biodiversity like Habitat destruction, Wildlife displacement, Pollution, Marine life loss, risk in Landmines and explosive ordnance. Military activities like training & Land use , Biodiversity loss.The diverse relationships between conflict and biodiversity. (a) Conflicts take place as a result of disagreements and disputes over access to, control over and use of diverse natural resources. Sometimes, alternative uses are in direct opposition with each other, often referred to as 'conflict of use'. Humans depend upon biodiversity for survival, such as for the foods we eat, medicines we use to stay healthy, and materials we wear or use to build our homes. These services are the tangible products or items that we and other species consume for survival.Human-wildlife conflict is when encounters between humans and wildlife lead to negative results, such as loss of property, livelihoods, and even life. Defensive and retaliatory killing may eventually drive these Crocodile that had a long history in the biodiversity & other species to extinction . It is a fact is true that Many countries spend large amounts of money on their military, while failing to address climate change and biodiversity. Colombia’s new president Gustavo Petro took to the podium and asked: “What is the use of war if what we need is to save the human species?” Int he Past the War had ruined the Biodiversity in Vietnamese as the war destroyed large forest land , Congolese civil wars kills lot animals species , Iraq war reduced the Marshes and oil well on fires , Afghanistan’s conflict destroy the forest lands , Nepal conflict destroyed the Nepal’s ecosystems, Colombia mining and logging destroyed the ecosystem . it is necessary for countries to effectively protect biodiversity during armed conflict . 
5 June is World Environment Day . .The theme for 2024, "Our Land, Our Future. We are #Generation Restoration," means We must restore ecosystems to tackle climate change, save species from extinction and secure our future.is against the BIODIVERSITY WARS SAYS ARMS TRADING IS A ILLEGAL DOG BUSINESS as war against Biodiversity is destruction because war and crises destroyed food chain and agriculture in Countries had triggered an urban agriculture boom or Peri urban cultivation and this led to a total disruption of the flow of food and agricultural supplies, cash, medicine, fuel, and more destroying the forest and ecosystem of the country . 
Peri-urban agriculture is the cultivation of crops and rearing of animals in the areas around cities. to meet the demand of foods in the city It can include a variety of activities, such as growing herbs, medicinal and ornamental plants, and raising livestock. Peri-urban agriculture can be a sustainable way to provide food and support the local economy, and can also help to create a more resilient urban environment. Peri-urban agriculture can support biodiversity by providing a variety of habitats for plants and animals. Peri-urban agriculture has helped in diversifying the food sources, thereby reducing energy footprints. This has also led to diversi- fication in income opportunities which helped in times of economic crisis and in alleviating poverty..Urban and p Biodiversity eri-urban agriculture zones are key drivers for sustainability and urban biodiversity. Biodiversity favors resilience by supporting and mitigating the negative impacts of the built environment by hosting a diversity of fauna and flora in the cities peri urban cultivation . Wars also remembers us the Crocodile war in Ramsree islands and make Crocodile is a icon in many places and had a major attraction making every year on June 17th, World Crocodile Day is celebrated as a global awareness initiative to highlight the endangered status of crocodiles and alligators worldwide is not only saving crocodile but also stopping biodiversity wars in the world Environment day November 6 . 
Biodiversity Census is the Process of tracing the numbers and positions of our planet's millions of plants and animal species and Combining historical records of biodiversity with modern surveys and data from remote-sensing satellites can provide a clearer picture of life on Earth and how it has changed over time is Spatial Biodiversity Census. The Present Status of the BIODIVERSITY CENSUS had declined because Biodiversity loss had negatively impact the earth as per the reports . The average size of global wildlife populations have declined by 73% in 50 years, The Biodiversity census gives biodiversity loss statics based on the wildlife faces threats from habitat loss, invasive species, disease and climate change, is giving policymakers need clear, accurate information to make sound conservation decisions. Like Sampling in Census survey , the bio-cube is manageable way of exploring the biodiversity in the world . The cube helps us keep everything centralized . "You can 'roll' the cube to look at spatial patterns of where plants and animals are found or 'roll' it to reveal species' change through time. and make Global Biodiversity survey is Combining historical records of biodiversity with modern surveys and data from remote-sensing satellites can provide a clearer picture of life on Earth and how it has changed over time. A team of international environmentalists and data scientists have developed a plan for tracing the numbers and positions of our planet's millions of plants and animal species – calling it as a biodiversity census . The Census is developing the Biodiversity and Ecosystem for Peace and security of the planet to reduce Man and Animal & conflict , Biodiversity war , Man Made destruction , Act of God Destruction ,Mass destruction and extinction in period of time as Biodiversity Cli matic changes or loss are not always the Act of God it can be by Conflict, war or man made 
In the Earth Biodiversity ,lions are traditionally known as the king of the land, and crocodiles are known as the king of the rivers. Biodiversity May be good at all times as Biodiversity is essential for the health of the planet and all life on Earth, including humans. It supports healthy ecosystems that provide and Biodiversity is not good at all times because humans are the main driver of biodiversity loss. Biodiversity loss is the decline or disappearance of the variety of life on Earth, which can lead to the breakdown of ecosystems and the end of species. E @ Biodiversity Heritage site is essential for the processes that support all life on Earth, including humans. E @ Biodiversity Heritage site develop a wide range of animals, plants and microorganisms, we cannot have the healthy ecosystems that we rely on to provide us with the air we breathe and the food we eat. And people also value nature of itself. E @ Biodiversity heritage site is able positively clarify the ICC arrest warrant issued to Israeli Prime Minister Netanyahu and Russian president Putin for ‘war crimes’ as war can be an aggression on biodiversity and ecosystems in many ways like Resource extraction , Pollution, Greenhouse gas emissions, Deforestation:, Wildlife harm, Loss of natural resources, Ecosystem disruption, Mass population movement:, Loss of wildlife had been in Gaza Israeli bombing , in South Lebanon & Beirut by Israeli force and in Ukraine province of Kharkiv, Kyiv, Odesa, Zaporizhzhia, Dnipro, Sumy, and Chernihiv regions by Russian Army made them a war criminal by the International Criminal Court (ICC) . 
E @ CROCODILE IS NOT THE DEATH OR END OF CROCODILE ,BUT IT IS THE DAY OF THE CROCODILE AND THE HABITAT CONSERVATION AS WELL AS E @ BIODIVERSITY IS NOT THE BIODIVERSITY LOSS BUT IS IT BIODIVERSITY GAIN . THERE MIGHT BE PEOPLE WHO DEDICATED THE LIFE WITH CROCODILE FOR YEARS AS RANGERS OR FIELD RESEARCHERS , BUT THIS IS E @ IS DIGITAL PLATFORM PUBLICATION OF E @ BLOGS , E @ SOCIAL MEDIA , E @ EDITORIAL , & STORY MAPS ANALYZING THE CROCODILE IN E @ CROCODILE & IN E @BIODIVERSITY THAT REQUIRED LOT FOLLOW UPS AND UPDATION AS THIS KIND OF E @ PLATFORM IS MORE EFFICIENT , TRANSPARENT , ACCOUNTABLE AND ALLOW DATA COMMUNICATION AND A ELECTRONIC OFFICE AT HOME, OFFICE, REMOTE VILLAGE, REMOTE OFFICES , RESEARCH CENTERS ETC . E @ CROCODILE IS A E - PACKAGE THAT GIVES IMPORTANCE FOR CROCODILE THAT CONSIST OF THE SPACE ECOSYSTEM , EARTH ECOSYSTEM , FOREST & CROCODILE ECOSYSTEM IS MAKING CROCODILE A PART OF ANCIENT CIVILIZATION , MODERN LIFE & FUTURE GENERATION , NATURE & ECOLOGY , SCIENCE & TECHNOLOGY WITH VARIETY OF SPECIES , ORGANISMS , DEVICES INCLUDING HUMANS & THEIR PHYSICAL ENVIRONMENT , 
E @ CROCODILE IS A MASTER PLAN THAT IS DONE TO MONITOR & CONTROL HABITAT LOSS BY CONSERVATION THAT CAN BE DONE BY MEANS OF SATELLITE REMOTE SENSING A ADVANTAGED TECHNOLOGY , 3D IMAGING , EVEN IN TIME OF WORST CLIMATE , MONITORING ISOLATED PLACES SEPARATED FROM MAIN LANDS AND EVEN IN DISASTER . E @ CROCODILE WILL BENEFIT THE END USER WHO IS A LOCAL COMMUNITY AND CROCODILE BY MEANS TRACKING SIGNALS SO THAT ALL CROCODILE , VILLAGES ARE GPS LOCATED AND NOT ALLOWING HUMAN ACTIVITY IN CROCODILE GPS LOCATION SO THAT HUMAN CROCODILE COEXISTENCE CAN BE DEVELOPED BY NULLIFYING THE CONFLICT SO THAT THIS WILL NOT ALLOW COMPLICATING, MANIPULATING , DESTROYING THE CROCODILE GPS PLACES WITH ISSUES OF ATTACKS AND NOT HAVING LOSS OF LIFE IS THE MOST IMPORTANCE OF E @ CROCODILE THAT IS A MAJOR DEVELOPMENT BY MEANS OF SPACE ECOSYSTEM AND E @ SERVICE OF CROCODILE MASTER PLAN . 
Routes Nationales : 10.203 km.