Afghanistan GIS data

# Afghanistan Earthquakes: Damage Proxy Maps, Sep 2025 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Afghanistan Earthquakes (Sep 2025) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Kunar Province, Afghanistan, Earthquakes, 1 Sep 2025, v0.7-full ** (superseded) - **EOS-RS Damage Proxy Map: Kunar Province, Afghanistan, Earthquakes, 1 Sep 2025, v0.7-bldg ** **More map details and files:** **More map details and files:** **Legal Disclaimer:**

# Afghanistan Earthquake: Damage Proxy Maps, Oct 2023 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Afghanistan Earthquake (Oct 2023) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Afghanistan, Earthquake, 13 Oct 2023, v0.5 ** - **EOS-RS Damage Proxy Map: Afghanistan, Earthquake, 13 Oct 2023, v0.5 ** - **EOS-RS Damage Proxy Map: Afghanistan, Earthquake, 8 Oct 2023, v1.2** - **EOS-RS Damage Proxy Map: Afghanistan, Earthquake, 8 Oct 2023, v1.2** - **EOS-RS Damage Proxy Map: Afghanistan, Earthquake, 8 Oct 2023, v0.9** (superseded) - **EOS-RS Damage Proxy Map: Afghanistan, Earthquake, 8 Oct 2023, v0.5** (superseded) - **EOS-RS Damage Proxy Map: Afghanistan, Earthquake, 8 Oct 2023, v0.5** (superseded) - **EOS-RS Damage Proxy Map: Afghanistan, Earthquake, 7 Oct 2023, v0.9** - **EOS-RS Damage Proxy Map: Afghanistan, Earthquake, 7 Oct 2023, v0.5** - **EOS-RS Damage Proxy Map: Afghanistan, Earthquake, 7 Oct 2023, v0.5** (superseded) **More map details and files:** **More map details and files:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Original data ALOS-2 PALSAR-2 Product - JAXA (2022)

# EOS-RS Damage Proxy Map: Afghanistan, Earthquake, 22 Mar 2023, v0.4 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas in Afghanistan where significant changes occurred due to the M6.5 earthquake that occurred on 21 Mar 2023. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (22 Nov 2022 to 10 Mar 2023) and after (22 Mar 2023) the event. The image covers an area indicated by the large white polygon. Each pixel measures about 30 meters across. The colour variation from yellow to red indicates increasingly more significant surface change. Preliminary validation was done by comparing with high-resolution optical imagery and media reports. This map could be used as a guidance to identify damaged areas, and may be less reliable over vegetated areas. Scattered pixels over vegetated areas may be false positives, and a lack of colored pixels over vegetated areas may not mean no damage. The product contains modified Copernicus Sentinel data (2022-2023), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) in collaboration with NASA-JPL and Caltech, using the Advanced Rapid Imaging and Analysis (ARIA) system originally developed at NASA's Jet Propulsion Laboratory, California Institute of Technology, and modified at EOS-RS. **More map details and files:** **More map details and files:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Advanced Rapid Imaging and Analysis (ARIA), NASA-JPL/Caltech, Contains modified Copernicus Sentinel data (2022-2023)

# Afghanistan Earthquake: Damage Proxy Maps, Jun 2022 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Afghanistan Earthquake (Jun 2022) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Afghanistan, Earthquake, 30 Jun 2022, v0.9** - **EOS-RS Damage Proxy Map: Afghanistan, Earthquake, 27 Jun 2022, v0.45** **More map details and files:** **More map details and files:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Advanced Rapid Imaging and Analysis (ARIA), NASA-JPL/Caltech, Contains modified Copernicus Sentinel data (2022), Validated with FirstLook Imagery © 2022 Maxar

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Afghanistan Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2012

This dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.

Points of interest: public-facing amenities, shops, tourist attractions, and landmarks tagged via `amenity`, `shop`, `tourism`, or `man_made`. Useful for general POI reference, urban analysis, and basemap detail. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, shop, tourism, man_made - Part of the country exports on HDX by HOT

Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: railway - Part of the country exports on HDX by HOT

Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: aeroway, building, emergency - Part of the country exports on HDX by HOT

Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: building - Part of the country exports on HDX by HOT

Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, healthcare - Part of the country exports on HDX by HOT

Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: waterway, water, natural - Part of the country exports on HDX by HOT

Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT

Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, building - Part of the country exports on HDX by HOT

Financial services: banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via `amenity`. Useful for financial-inclusion mapping, cash-distribution planning, and economic analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity - Part of the country exports on HDX by HOT

Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: place, landuse - Part of the country exports on HDX by HOT

Forward-looking analysis representing the most likely food security outcomes for the near term (January 2023) and medium term (February 2023 - May 2023) periods.

The Combined Drought Indicator (CDI) dataset identifies different drought classes and their extent, focusing on vegetated areas (at risk, affected, and recovering). It integrates three complementary indicators — Standardized Precipitation Index (SPI), Soil Moisture Index Anomaly (SMA), and FAPAR anomaly — to capture meteorological, agricultural, and vegetation drought conditions. The dataset consists of raster maps with a spatial resolution of ~5 km at the Equator, using WGS84 (EPSG:4326) as the coordinate system. Data are available from 2012 to the present, updated every 10 days, which is suitable for monitoring short-term crop stress. Produced operationally by the European Drought Observatory (EDO), the CDI maps are freely accessible online. Recent updates include: CDIv3 (March 2023): integration of static crop masks to limit analysis to active growing seasons. CDIv4 (June 2024): introduction of dynamic snow masks and a new soil moisture input based on EFAS v5. More: https://drought.emergency.copernicus.eu/data/factsheets/factsheet_combinedDroughtIndicator_v4.pdf

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

Accessibility to major cities dataset is modeled as raster-based travel time/cost analysis, computed for the largest cities (>50k habitants) in the country. This 500m resolution raster dataset is part of FAO's Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location). **Data publication:** 2022-12-06 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@fao.org) **Data lineage:** lineage statement lineage statement Produced using 1. OpenStreetMap data for roads, railways, and rivers; 2. UN Map country border. 3. The HydroSHEDS 15' resolution GRID for the DEM. 4. GHSL - Global Human Settlement Layer. 5. Citypopulation.de **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). **Online resources:** Zipped raster TIF for accessibility to major cities (Afghanistan- ~ 500 m)

The raster dataset consists of a 500 m score grid for the crop storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize considered factors for crop warehouse location: • Supply: Cereal. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Socio-economic/poverty It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (”Poverty” * 0.1) + (“Major Cities Accessibility” * 0.3) **Data publication:** 2022-12-07 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty – Study by Figueroa, W., Marsicovetere, P., & Peñate, M. (2020) based on the results of the XII National Population Census and VII Housing Census of 2018. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). **Online resources:** Zip raster TIF for crop Storage Location Score: Cereal (Afghanistan- ~ 500m)

The raster dataset consists of a 500 m score grid for the crop storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize considered factors for crop warehouse location: • Supply: Potato. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Socio-economic/poverty It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (”Poverty” * 0.1) + (“Major Cities Accessibility” * 0.3) **Data publication:** 2022-12-07 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty – Study by Figueroa, W., Marsicovetere, P., & Peñate, M. (2020) based on the results of the XII National Population Census and VII Housing Census of 2018. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). **Online resources:** Crop Storage Location Score: Potato (Afghanistan- ~ 500m) Zip raster TIF for crop Storage Location Score: Potato (Afghanistan- ~ 500m)

The raster dataset consists of a 500 m score grid for the crop storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize considered factors for crop warehouse location: • Supply: Tropical fruit. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Socio-economic/poverty It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (”Poverty” * 0.1) + (“Major Cities Accessibility” * 0.3) **Data publication:** 2022-12-07 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty – Study by Figueroa, W., Marsicovetere, P., & Peñate, M. (2020) based on the results of the XII National Population Census and VII Housing Census of 2018. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). **Online resources:** Zip raster TIF for crop Storage Location Score: Tropical fruit (Afghanistan- ~ 500m)

The raster dataset consists of a 500 m score grid for the crop storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize considered factors for crop warehouse location: • Supply: Vegetables. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Socio-economic/poverty It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (”Poverty” * 0.1) + (“Major Cities Accessibility” * 0.3) **Data publication:** 2022-12-07 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty – Study by Figueroa, W., Marsicovetere, P., & Peñate, M. (2020) based on the results of the XII National Population Census and VII Housing Census of 2018. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). **Online resources:** Zip raster TIF for crop Storage Location Score: Vegetables (Afghanistan- ~ 500m)

The raster dataset consists of a 500 m score grid for the crop storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize considered factors for crop warehouse location: • Supply: Wheat. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. • Socio-economic/poverty It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (”Poverty” * 0.1) + (“Major Cities Accessibility” * 0.3) **Data publication:** 2022-12-07 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty – Study by Figueroa, W., Marsicovetere, P., & Peñate, M. (2020) based on the results of the XII National Population Census and VII Housing Census of 2018. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). **Online resources:** Zip raster for crop Storage Location Score: Wheat (Afghanistan- ~ 500m)

The raster dataset consists of a 500 m score grid for dairy processing industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on cattle production potential using proximity to cities and cattle density. The score is achieved by processing sub-model outputs that characterize production, logistical, and socio-economic factors. 1. Supply - Feed and livestock distribution. 2. Infrastructure - Transportation network (accessibility). 3. Socio-economic/poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”Cattle Density” * 0.4) + (“Major Cities Accessibility” * 0.4) + (”Poverty” *0.2 ) **Data publication:** 2023-01-30 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Maintainer: [Maribel Elias](mailto:maribel.elias@fao.org) **Data lineage:** lineage statement FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2). 2. OpenStreetMap. 3. Poverty rate. World Bank 2019. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). **Online resources:** Zipped TIF raster file: Dairy Processing Location Score: Cattle (Afghanistan - ~ 500 m)

The raster dataset consists of a 500 m score grid for slaughterhouse industry facilities, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on livestock production potential using proximity to cities, crop production, and aggregated density of cattle, goats, sheep and pig. Aggregated density layer produced using weighting by the average live weight: Goat 30kg (X0.11); Sheep 30kg (X0.11); Cattle 210kg (X0.78). The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed and livestock distribution. 2. Infrastructure - Transportation network (accessibility) 3. Poverty rate. It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”Livestock Density” * 0.4) + ("Asset Wealth" * 0.3) + (“Major Cities Accessibility” * 0.2) + ("Crop Production" * 0.1). **Data publication:** 2023-01-27 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2). 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty rate. World bank 2019 **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). **Online resources:** Zipped TIF raster file for Slaughterhouse Location Score (Afghanistan- ~ 500 m)

Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027.  A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Pakistan Country Boundary provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. These were published in December 2022. Other administrative boundaries for this country are also available: Province Zillah

Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027.  A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Pakistan Province Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. These were published in December 2022. Other administrative boundaries for this country are also available: Country Zillah

Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027.  A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Pakistan Zillah Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. These were published in December 2022. Other administrative boundaries for this country are also available: Country Province

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Afghanistan Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2017

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Afghanistan Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2014

geoBoundaries ADM2 boundaries for Afghanistan: 398 units, representing 2014. Source: Afghanistan Information Management Services, Esri Disaster Response Program. license was officially a custom license simply described as open on ArcGIS Hub. No other restictions were included so collector decided on public domain as the official license.

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Afghanistan Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2012