Pakistan GIS data

geoBoundaries ADM1 boundaries for Pakistan: 7 units, representing 2019. Source: geoBoundaries, Wikipedia.

geoBoundaries ADM0 boundaries for Pakistan: 1 units, representing 2017. Source: OpenStreetMap, Wambacher. Open Data Commons Open Database License 1.0

geoBoundaries ADM2 boundaries for Pakistan: 126 units, representing 2019. Source: geoBoundaries, Wikipedia.

Open and free data for assessing the human presence on the planet. The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2, ADM3. Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.

The methods developed for Haiti were refined for Pakistan, where a similar correlation was found between built-up areas and 1998 census data. The 100-meter gridded population map was made available to the public during summer 2010’s severe flooding via a viewer and a data download page. The methods used in the Pakistan project are described in a paper published in the journal Remote Sensing of Environment.

**ADAM ID: **FL-20250829-PAK-00 A flood occurred on Aug 29 2025 in Pakistan. It affected an estimated 5949956 people and 892075 ha of cropland. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **FL-20250918-PAK-00 A flood occurred on Sep 18 2025 in Pakistan. It affected an estimated 16793460 people and 2637108 ha of cropland. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **FL-20250911-PAK-00 A flood occurred on Sep 11 2025 in Pakistan. It affected an estimated 13968848 people and 2199880 ha of cropland. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **FL-20250903-PAK-00 A flood occurred on Sep 03 2025 in Pakistan. It affected an estimated 8853542 people and 1383279 ha of cropland. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

Pakistan administrative level 0-3 boundaries (COD-AB) dataset version 01. This dataset is structured into 3 levels: - Admin 1: 7 Province or Territory - Admin 2: 160 District - Admin 3: 577 Tehsil Dates associated with this dataset: - 27 September 2024: dataset reviewed for accuracy and completeness - 09 September 2022: valid for use by the humanitarian community - 02 September 2022: last edit to the dataset before publishing - 02 September 2022: boundaries created by the source Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

# Pakistan Floods: Damage and Flood Proxy Maps, Aug 2022 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage and flood proxy maps for Pakistan Floods (Aug 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: Pakistan, Floods, 1 Sep 2022, v0.8** - **EOS-RS Damage Proxy Map: Pakistan, Floods, 1 Sep 2022, v0.8** - **EOS-RS Damage Proxy Map: Pakistan, Floods, 30 Aug 2022, v0.8** - **EOS-RS Damage Proxy Map: Pakistan, Floods, 30 Aug 2022, v0.8** - **EOS-RS Damage Proxy Map: Pakistan, Floods, 29 Aug 2022, v0.8** - **EOS-RS Damage Proxy Map: Pakistan, Floods, 29 Aug 2022, v0.8** - **EOS-RS Flood Proxy Map: Pakistan, Floods, 21 Aug 2022, v0.3** **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)

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.

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation 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: highway - 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

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

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

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

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

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

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

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

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

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.

This map represents the distribution of sampled households according to their level of food security as measured by Food Insecurity Experience Scale (FIES) at district level of Pakistan. More than half of the households (63.1%) were found to be “food secure”. In urban areas, 68.2% of households were food secure compared to 60% in rural areas. A larger percentage of households were food secure in Gilgit Baltistan (75.6%) and Khyber Pakhtunkhwa (70.9%), with smaller proportions in Balochistan (50.3%) and Khyber Pakhtunkhwa Newly Merged Districts (54.6%). Nationally, 18.3% of households experienced severe food insecurity (urban: 13.9%; rural: 20.9%). Household food security was lowest in the poorest wealth quintile, with 42.1% of these households reporting severe food insecurity. Food insecurity in all districts was also assessed. Districts in Balochistan exhibited the highest degree of food insecurity, with particularly low rates of food security (i.e. high rates of food insecurity) observed in Awaran (0.2%), Jhal Magsi (3.8%) and Dera Bugti (9.0%). Sindh also had low food security (Tando Mohammad Khan: 15.8%; Sujawal: 19.3%; Tharparkar: 21.2%) also exhibited a high prevalence of food insecurity. By comparison, the lowest degree of food insecurity in Punjab was 37.8%, found in Lodhran, although low rates were also observed in Khyber Pakhtunkhwa Newly Merged Districts and Khyber Pakhtunkhwa, including FR Dera Ismail Khan (11.0%) and Mohmand Agency (13.6%). Data Sources: The Ministry of National Services Regulation and Coordination (MoNHSR&C), Government of Pakistan, in collaboration with UNICEF and the United Kingdom's Department for International Development (DFID), developed and carried out the National Nutrition Survey (NNS) 2018. Definition of variables and data sources: Percentage values for Food Insecurity Experience Scale (FIES) is represented by “severe” field. Data Accusation Method: Demographics and Health Survey Methodology to prepare this dataset can be accessed at: https://www.unicef.org/pakistan/media/2836/file/National%20Nutrition%20Survey%202018%20Volume%203.pdf Survey Time Period: The survey was initiated in April 2018 and field activities formally ended in January 2019. **Data creation:** 2021-03-04 **Contact points:** Metadata Contact: [FAOPAK](mailto:mehwish.ali@fao.org) **Data lineage:** Data quality information not available. **Resource constraints:** copyright **Online resources:** National Nutrition Survey District Data Food Insecurity Experience Scale (Pakistan - Admin 3) National Nutrition Survey 2018 Volume 1.pdf National Nutrition Survey 2018 Key Finding Report

Accessibility to major cities dataset is modelled as raster-based travel time/cost analysis, computed for the 26 largest cities (>250k habitants) in the country. The following cities are included: City - Population Chiniot 278,528 Nawabshah 279,338 Mingora 331,377 Okara 358,146 Kasur 358,296 Mardan 359,024 Wah Cantonment 379,534 Sahiwal 388,795 Gujrat 390,758 Dera Ghazi Khan 397,362 Rahimyar Khan 420,963 Sheikhūpura 473,269 Larkana 488,006 Sukkur 500,401 Sialkot 656,730 Sargodha 658,208 Bahawalpur 762,774 Quetta 999,385 Hyderabad 1,733,622 Multan 1,872,641 Peshawar 1,969,823 Gujranwala 2,028,421 Rawalpindi Islamabad 3,106,827 Faisalabad 3,210,158 Lahore 11,119,985 Karachi 14,884,402 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:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for roads, railways, rivers; UN Map country border; The HydroSHEDS 15' resolution GRID for the DEM, GHSL - Global Human Settlement Layer. For the city population **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 file for Accessibility: Travel Time-Cost to Major Cities (Pakistan - ~ 500m)

Accessibility to ports dataset is modeled as raster-based travel time/cost analysis. The model travel time/cost from/to ports is defined for major maritime ports only. 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 (optimal location). **Data publication:** 2021-11-01 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for road, railways, rivers; UN Map country border. **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 file for Accessibility: Travel Time-Cost to Major Maritime Ports (Pakistan - ~ 500m)

The regional cities accessibility dataset is modelled as raster-based travel time/cost analysis. Individual cumulative travel time/cost maps were produced for major regional cities (>850k habitants) less than 500 km from the border. The following values were assumed: City - Country Delhi 16,349,831  Ahmedabad 5,633,927  Surat 4,591,246 Kabul 4,434,550 Jaipur 3,046,163 Vadodara 1,752,371  Ludhiana 1,618,879 Meerut 1,571,434 Rajkot 1,390,640 Srinagar 1,264,202 Amritsar 1,183,549  Jodhpur 1,138,300 Chandigarh 1,026,459  Kota 1,001,694 Moradabad 889,810 Aligarh 874,408 Dushanbe 863,400 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 (optimal location). **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for roads, railways, rivers; UN Map country border; The HydroSHEDS 15' resolution GRID for the DEM, GHSL - Global Human Settlement Layer. **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 file for Accessibility: Travel Time-Cost to Major Regional Cities (Pakistan- ~ 500m)

The raster dataset represents top location score areas suitable for maize storage filtered by exclusive criteria: access to finance, distance to major roads and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 20km (0.18 degree) buffer radius. • Major roads - approx. 2km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. The top 99th percentile is selected/clipped using the exclusive criteria. 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 (optimal location). **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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: VMPI source: UNDP (United Nations Development Programme). 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2020. **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 file for Crop Storage Final Location: Maize (Pakistan - ~ 500m)

The raster dataset represents top location score areas suitable for rice storage filtered by exclusive criteria: access to finance, distance to major roads and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 20km (0.18 degree) buffer radius • Major roads - approx. 2km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. The top 99th percentile is selected/clipped using the exclusive criteria. 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 (optimal location). **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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: VMPI source: UNDP (United Nations Development Programme 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2020 **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 file for Crop Storage Final Location: Rice (Pakistan - ~ 500m)

The raster dataset represents top location score areas suitable for temperate fruits storage filtered by exclusive criteria: access to finance, distance to major roads and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 20km (0.18 degree) buffer radius • Major roads - approx. 2km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. The top 99th percentile is selected/clipped using the exclusive criteria. 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 (optimal location). **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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: VMPI source: UNDP (United Nations Development Programme). 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2020 **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 file for Crop Storage Final Location: Tropical Fruits (Pakistan - ~ 500m)

The raster dataset represents top location score areas suitable for wheat storage filtered by exclusive criteria: access to finance, distance to major roads and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 20km (0.18 degree) buffer radius. • Major roads - approx. 2km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. The top 99th percentile is selected/clipped using the exclusive criteria. 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 (optimal location). **Data publication:** 2021-10-18 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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: VMPI source: UNDP (United Nations Development Programme 5. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2020 **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 file for Crop Storage Final Location: Wheat (Pakistan - ~ 500m)

The raster dataset consists of a 500m score grid for temperate fruits storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Temperate fruits. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.1) + (“Poverty” * 0.1) + ("Major Ports Accessibility" * 0.1)+("Major Regional Cities Accessibility" * 0.1). 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 (optimal location). **Data publication:** 2021-11-17 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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: VMPI source: UNDP (United Nations Development Programme **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 file for Crop Storage Location Score: Temperate Fruits (Pakistan - ~ 500m)

The raster dataset consists of a 500m score grid for tropical fruits storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Tropical fruits. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.1) + (“Poverty” * 0.1) + ("Major Ports Accessibility" * 0.1)+("Major Regional Cities Accessibility" * 0.1). 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 (optimal location). **Data publication:** 2021-11-17 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Dariia Nesterenko](mailto:dariia.nesterenko@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: VMPI source: UNDP (United Nations Development Programme **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 file for Crop Storage Location Score: Tropical Fruits (Pakistan - ~ 500m)