Pakistan GIS data
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The raster dataset consists of a 500m score grid for vegetables storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Vegetables. • 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 Pakistan Crop Storage Location Score: Vegetables (Pakistan - ~ 500m).
The raster dataset consists of a 500m score grid for wheat storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Wheat. • 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: Wheat (Pakistan - ~ 500m)
The raster dataset consists of a 500m score grid for cotton storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Cotton. • 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 Crop Storage Location Score: Сotton (Pakistan - ~ 500m)
The 500m raster dataset represents selected top location score areas filtered by exclusive criteria: access to finance, distance to major roads, access to IT and distance to urban areas. The layer was 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 characterizing logistical factors for dairy processing (UHT/milk powder) facilities siting: Supply, demand, Infrastructure/accessibility. The top 99th percentile is selected/clipped using the exclusive criteria. Access to finance, distance to roads and urban areas 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. **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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. 6. Poverty: VMPI source: UNDP (United Nations Development Programme). 7. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. **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 Dairy Processing Final Location: Cattle (Pakistan - ~ 500m)
The raster dataset consists of a 500m 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 buffalo dairy production intensification potential defined using crop production, livestock production systems, and buffalo distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, buffalo distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility) 4. Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.25) + ("Human Population Density" * 0.1) + (“Major Cities Accessibility” * 0.1) + (“ Accessibility to ports” * 0.1) + ( "Poverty" * 0.1) + (”Dairy Intensification” * 0.35). **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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. 6. 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 Dairy Processing Location Score: Buffalo (Pakistan - ~ 500m)
The raster dataset consists of a 500m score grid for dairy processing industry (UHT and milk powder) 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 dairy production intensification potential defined using crop production, livestock production systems, and cattle distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, cattle distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility) 4. Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.25) + ("Human Population Density" * 0.1) + (“Major Cities Accessibility” * 0.1) + (“ Accessibility to ports” * 0.1) + ( "Poverty" * 0.1) + (”Dairy Intensification” * 0.35). **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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. 6. 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 Dairy Processing Location Score: Cattle (Pakistan - ~ 500m)
The raster dataset consists of a 500m 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 goat dairy production intensification potential defined using crop production, livestock production systems, and goat distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, goat distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility) 4. Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.25) + ("Human Population Density" * 0.1) + (“Major Cities Accessibility” * 0.1) + (“ Accessibility to ports” * 0.1) + ( "Poverty" * 0.1) + (”Dairy Intensification” * 0.35). **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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. 6. 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 Dairy Processing Location Score: Goat (Pakistan - ~ 500m)
The raster dataset consists of a 500m 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 sheep dairy production intensification potential defined using crop production, livestock production systems, and sheep distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, sheep distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility) 4. Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.25) + ("Human Population Density" * 0.1) + (“Major Cities Accessibility” * 0.1) + (“ Accessibility to ports” * 0.1) + ( "Poverty" * 0.1) + (”Dairy Intensification” * 0.35). **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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. 6. 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 Dairy Processing Location Score: Sheep (Pakistan - ~ 500m)
The 500m raster dataset represents selected top location score areas filtered by exclusive criteria: access to finance, distance to major roads, access to IT, and distance to urban areas. The layer was 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 characterizing logistical factors for the slaughterhouse facilities siting: Demand, Supply, Infrastructure/accessibility. The top 99th percentile is selected/clipped using the exclusive criteria. Access to finance, distance to roads and urban areas 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 by applying the mobile broadband coverage map. **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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. 6. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. 7. 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 Slaughterhouse Final Location: Cattle (Pakistan - ~ 500m)
The raster dataset consists of a 500m score grid for slaughterhouse 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 buffalo production intensification potential defined using crop production, livestock production systems, and buffalo distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, buffalo distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility) It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.2) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.2) + (”Buffalo Intensification” * 0.3) + (“Poverty” * 0.1) **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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. 6. 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 Slaughterhouse Location Score: Buffalo (Pakistan - ~ 500m)
The raster dataset consists of a 500m score grid for slaughterhouse 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 intensification potential defined using crop production, livestock production systems, and cattle distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, cattle distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility). It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.2) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.2) + (”Cattle intensification” * 0.3) + (“Poverty” * 0.1). **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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. 6. 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 Slaughterhouse Location Score: Cattle (Pakistan - ~ 500m)
The raster dataset consists of a 500m score grid for slaughterhouse 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 goat production intensification potential defined using crop production, livestock production systems, and goat distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, goat distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility). It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.2) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.2) + (”Goat intensification” * 0.3) + (“Poverty” * 0.1). **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 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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. 6. 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 Slaughterhouse Location Score: Goat (Pakistan - ~ 500m)
The raster dataset consists of a 500m score grid for slaughterhouse 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 sheep production intensification potential defined using crop production, livestock production systems, and sheep distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, sheep distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility). It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.2) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.2) + (”Sheep intensification” * 0.3) + (“Poverty” * 0.1). **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 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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. 6. 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 Slaughterhouse Location Score: Sheep (Pakistan - ~ 500m)
The raster dataset consists of a 500m score grid for rice storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Rice. • 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: Rice (Pakistan - ~ 500m)
Natural Earth admin 0 countries pak at 1:10 million scale: public domain cartographic vector data built to make small-scale world, regional and country maps.
geoBoundaries ADM3 boundaries for Pakistan: 554 units, representing 2017. Source: Pathways Data Pvt. Ltd., https//www.pbs.gov.pk/content/population-census/. Open Data Commons Open Database License 1.0
**ADAM ID: **sm\_us6000u2y1 Magnitude 5.7 earthquake at 10.0 depth occurred on Oct 17 2026 44km W of Kuzdar Tehsil. The epicentre of the earthquake was located at latitude 28.0179 longitude 66.1708. It impacted 114531 people within 50km. 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.
National Constituency Boundaries of Pakistan compiled by ALHASAN SYSTEMS Private Limited as one of its R&D projects and in the public interest. ALHASAN SYSTEMS made these boundaries public before Pakistan’s General Election 2013 in its Open Data/ Open Access [OD/OA] Seminar on May 5, 2013. This data is used thoroughly in Pakistan by many stakeholders and researchers including UN and other donor agencies. For further details on the use of this data please download Alhasan Systems monthly Election Bulletins from [http://www.alhasan.com/bulletins/election].
Polling Station of Pakistan compiled by ALHASAN SYSTEMS Private Limited as one of its R&D projects and in the public interest. ALHASAN SYSTEMS made these Polling Stationpublic before Pakistan’s General Election 2018in its Open Data/ Open Access [OD/OA] Seminar on May 5, 2013. This data is used thoroughly in Pakistan by many stakeholders and researchers including UN and other donor agencies. For further details on the use of this data please download Alhasan Systems monthly Election Bulletins from [http://www.alhasan.com/bulletins/election]. ALHASAN SYSTEMS is a privately owned development company with a history that goes back to 1990. At present is registered in Pakistan and Canada. This hi-tech knowledge management, business psychology modeling, and publishing company is constantly contributing its data and services to both humanitarian and developmental causes through its Public/ Private Partnership [PPP] SKIM and ODOA initiatives. ALHASAN SYSTEMS strives to provide the most cost effective solutions and services, which not only serve its clients’ immediate requirements but also contribute to the much larger cause of community welfare and development. Its area of professional services spreads from environment, energy, health, natural resources, critical infrastructure, utilities management, tourism, and investments, to community development and crisis management. ALHASAN SYSTEMS corporate roadmap focuses on new trends in the field of Geomatics Engineering, Geo-engineering, Data Management, Bio Interfacing, Business Psychology Modeling, Hi-tech publishing, e-Learning, and Smart Power Gridding and Engineering Services. This is possible when fairly serious ecological, political, and moral ramifications are addressed strategically. That’s why social awareness, advocacy, and capacity building remain at the heart of ALHASAN SYSTEMS. ALHASAN SYSTEMS constantly update its data in relation to its projects and also as a service to larger public bodies as well as research community to promote its pioneering 100% self-financed Open Data/ Open Access [OD/OA] initiative in Pakistan. We also share a number of additional layers and thousands of maps each year free-of-cost in the larger public interest. All our data are made available through Pakistan’s only Metadata portal [www.geopakistan.pk] launched in 2012 by iMMAP in collaboration with its partners under USAID funding. This portal is now hosted and maintained at NED University of Engineering & Technology with hundreds of registered researchers in Pakistan. Alhasan Systems is also a United Nations Humanitarian Data Exchange Portal [https://data.hdx.rwlabs.org/] member agency and share its resources through HDX for larger humanitarian benefits.
Pakistan Presidential Disaster Declarations
Electricity Consumption Trends in Punjab
District Swat Terrain Modelling & Land Cover Database
Pakistan administrative level 0-3 COD-EM edge-matched gazetteer, shapefiles, geodatabase, and geoservice. COD-EM datasets do not replace the authoritative COD-AB available here; however COD-EM datasets may be preferred for cartographic purposes. See caveats. Note: these boundaries and P-codes are not compatible with the Pakistan COD-PS. Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.
**UNOSAT code FL20221121PAK** Status: Overall decrease in the observed surface waters Further action(s): continue monitoring Evolution of Cumulative Surface Waters over I.R. of Pakistan (21 to 25 Apr. 2023) Vs. (26 to 30 Apr. 2023) Between 26 to 30 April 2023 approximately 1 million people are potentially exposed or living close to maximum floodwaters areas(*); Approximately 230,000 people are potentially exposed or living close to minimum floodwaters areas(**) between 26 to 30 April 2023; Based on satellite observations between 21 to 25 April 2023 and compared with observations between 26 to 30 April 2023, the maximum flood water extent appears to continue to retract with approximately ~ -3,700 km² in Sindh, ~ - 1,500 km² in Punjab, ~ -160 km² in Balochistan and ~ - 180 km² in Khyber Pakhtunkhwa.
**UNOSAT code FL20221121PAK** Status: Overall decrease of flood waters observed Further action(s): continue monitoring Evolution of Cumulative Flood Waters over I.R. of Pakistan (01-07 January 2023 Vs 09-15 January 2023): Between 09 and 15 January 2023 approximately 4.5 million people remain potentially exposed or living close to maximum floodwaters areas(*); Approximately 1.3 million people are potentially exposed or living close to minimum floodwaters areas(**) between 09 and 15 January 2023; Based on satellite observations between 01 and 07 January 2023 and compared with observations between 09 and 15 January 2023 , the maximum flood water extent appears to continue to retract with approximately ~ -1,800 km² in Sindh , ~ - 250 km² in Balochistan, ~ -200 km² in Khyber Pakhtunkhwa and ~ -200 km² in Gilgit Baltistan.
**UNOSAT code FL20221121PAK** Status: Overall decrease of flood waters observed Further action(s): continue monitoring Summary of findings; Evolution of Cumulative Flood Waters over I.R. of Pakistan (14-20 November 2022 Vs 25-31 December 2022): - Between 25 and 31 December 2022 approximately 5 million people remain potentially exposed or living close to flooded areas; - Based on satellite observations between 14 and 20 November 2022 and compared with observations between 25 and 31 November 2022, the overall flood water extent appears to continue to decrease with approximately ~ -6,000 km² in Sindh, ~ -1,300 km² in Punjab and ~ - 500 km² in Balochistan.
**UNOSAT code FL20220808PAK, GDACS Id: 1101522** Status: Overall decrease of flood waters observed Further action(s): continue monitoring Summary of findings; Evolution of Cumulative Flood Waters over I.R. of Pakistan (03-09 October 2022 Vs 11-17 October 2022): Between 11 and 17 October 2022 approximately 11 million people remain potentially exposed or living close to flooded areas; Based on satellite observations between 03 and 09 October 2022 and compared with observations between 11 and 17 October 2022, the overall flood water extent appears to continue to decrease with approximately 200 km² in Balochistan, ~Punjab 100 km² and ~2,700 km² in Sindh.
**UNOSAT code FL20220808PAK, GDACS Id: 1101522** Status: Overall decrease of flood waters observed Further action(s): continue monitoring Summary of findings; Evolution of Cumulative Flood Waters over I.R. of Pakistan (15 - 21 September Vs 26 September-2 October 2022): Between 26 September and 2 October 2022 approximately 15 million people remain potentially exposed or living close to flooded areas; Based on satellite observations between 15 to 21 September 2022 and compared with observations between 26 September and 2 October 2022, the overall flood water extents appear to continue to decrease with approximately 600 km² in Balochistan, ~Punjab 500 km² and ~2,600 km² in Sindh.
**UNOSAT code FL20221121PAK** Status: Overall decrease of flood waters observed Further action(s): continue monitoring Summary of findings; Evolution of Cumulative Flood Waters over I.R. of Pakistan (11-17 October 2022 Vs 14-20 November 2022): Between 14 and 20 November 2022 approximately 8 million people remain potentially exposed or living close to flooded areas; Based on satellite observations between 11 and 17 October 2022 and compared with observations between 14 and 20 November 2022 , the overall flood water extent appears to continue to decrease with approximately ~ -9,000 km² in Sindh ,-400 km² in Balochistan, and ~ +500 km² in Punjab.
**UNOSAT code FL20220808PAK, GDACS Id: 1101522** Status: Overall decreasing flood waters observed Further action(s): continue monitoring Evolution of Cumulative Flood Impact Assessment (08 - 14 September Vs 15-21 September 2022): From 15 to 21 September 2022 approximately 17 million people remain potentially exposed or living close to flooded areas; Based on satellite observations between 08 to 14 September 2022 and compared with observations between 15 and 21 September 2022, the overall flood waters extents appears to continue to decrease with approximately 600 km² in Balochistan, ~Punjab 1,100 km² and ~5,000 km² in Sindh.