Lao People S Democratic Republic GIS data

UAV imagery by admin, acquired 2026-07-11 to 2026-07-11, 5.0 cm per pixel, published on OpenAerialMap.

geoBoundaries ADM0 boundaries for Lao People's Democratic Republic: 1 units, representing 2017. Source: OpenStreetMap, Wambacher. Open Data Commons Open Database License 1.0

geoBoundaries ADM2 boundaries for Lao People's Democratic Republic: 148 units, representing 2019. Source: National Geographic Department, OCHA ROAP.

UAV imagery by admin, acquired 2025-04-23 to 2025-04-23, 2.0 cm per pixel, published on OpenAerialMap.

geoBoundaries ADM1 boundaries for Lao People's Democratic Republic: 18 units, representing 2017. Source: OpenStreetMap, Wambacher. Open Data Commons Open Database License 1.0

uav imagery by Central Institute of Forensic Science, acquired 2026-01-13 to 2026-01-13, 5.0 cm per pixel, published on OpenAerialMap.

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2. 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.

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.

The Gender Equality Monitoring (GEM) platform offers open access to officially published data and periodically updated sex disaggregated data repository. It visualizes gender gaps at subnational level in various sectors such as education, health, employment, access to information, intra-household decision making, political participation, as well as gender inequality index (GII). These are starting points to further explore the causes and more complex dynamics of gender inequality. For more information, visit the ADPC Gender Equality Monitor.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Lao People's Democratic Republic **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.1092 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0081 million km (7.4564%) - Unpaved roads: 0.0378 million km (34.6378%) About 0.0632 million km (57.9058%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0004 million km (0.6281%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

Lao People's Democratic Republic administrative level 0-2 boundaries (COD-AB) dataset version 01. This dataset is structured into 2 levels: - Admin 1: 18 province (khoueng) or prefecture (kampheng nakhon) - Admin 2: 148 district (muang) Dates associated with this dataset: - 30 October 2025: dataset reviewed for accuracy and completeness - 12 November 2019: valid for use by the humanitarian community - 01 January 2019: last edit to the dataset before publishing - 01 January 2019: boundaries created by the source Contributed by OCHA Regional Office for Asia and the Pacific (ROAP). Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

# EOS-RS Flood Proxy Map: Central Laos, Floods, 23 July 2025, v0.8 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Flood Proxy Map (FPM) depicting areas that are likely flooded in Central Laos, due to tropical storm Wipha. 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 (19 Nov 2024 to 11 Jul 2025) and after (23 Jul 2025) the event. The map covers an area indicated by the white polygon. The preliminary map shows areas which are likely flooded (shown by light blue pixels of approximately 30m in size) due to heavy rainfall. Preliminary validation was conducted using news reports. This map should be used as a guide to identify areas that are likely flooded, and may be less reliable over urban and vegetated areas. This product contains modified Copernicus Sentinel data (2024-2025), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2024-2025)

# Laos Storm Koguma: Flood Proxy Maps, Jun 2021 The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and Caltech, in collaboration with the Earth Observatory of Singapore (EOS), produced these flood proxy maps for Laos Storm Koguma (Jun 2021) 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 - **ARIA-SG Flood Proxy Map: Laos, Tropical Storm Koguma, 16 Jun 2021, v0.3 ** - **ARIA-SG Flood Proxy Map: Laos, Tropical Storm Koguma, 14 Jun 2021, v0.3 ** **More map details and files:**

# ARIA-SG Flood Proxy Map: Laos Floods, 6 Sep 2019, v0.1 Derived from synthetic aperture radar data acquired by the Copernicus Sentinel-1 satellites operated by the European Space Agency (ESA) before (3 Apr) and during (6 Sep) the event. Analysed by the ARIA-SG team at the Earth Observatory of Singapore (EOS), using the ARIA-SG system originally developed by NASA-JPL and Caltech. Data processing used an AWS Open Dataset of Copernicus Sentinel-1 data for the Asia region (https://registry.opendata.aws/sentinel1-slc-seasia-pds). **More map details and files:**

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

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

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

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

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

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

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

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

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

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

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

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 modelled as raster-based travel time/cost analysis, computed for the largest cities (>15k habitants) in the country. This 1km 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 creation:** 2022-04-22 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [Maribel Elias](mailto:maribel.elias@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for roads, railways, waterways, the HydroSHEDS 15' resolution GRID for the DEM, citypopulation, The Hand-in-Hand Initiative, 2020 **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Accessibility: Travel time-cost to major cities

The raster dataset consists of a 1000 m score grid for crops 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 logistical factors for selected crop warehouse locations: • Supply: Cassava. • 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" * 0.35) + (“Railway Accessibility” * 0.25) + (“Major Cities Accessibility” * 0.2) + (”Poverty” * 0.1) + ("Human Population Density" * 0.1) **Data creation:** 2022-05-03 **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 – **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: cassava (Lao PDR - ~ 1000m)

The raster dataset consists of a 1000 m score grid for crops 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 logistical factors for selected crop warehouse locations: • Supply: Maize and 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" * 0.35) + (“Railway Accessibility” * 0.25) + (“Major Cities Accessibility” * 0.2) + (”Poverty” * 0.1) + ("Human Population Density" * 0.1) **Data creation:** 2022-05-03 **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 – **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: Maize and rice (Lao PDR - ~ 1000m)

The raster dataset consists of a 1000 m score grid for crops 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 logistical factors for selected crop warehouse locations: • Supply: Tropical fruit. • 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" * 0.35) + (“Railway Accessibility” * 0.25) + (“Major Cities Accessibility” * 0.2) + (”Poverty” * 0.1) + ("Human Population Density" * 0.1) **Data creation:** 2022-05-03 **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 – **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: Tropical fruit (Lao PDR - ~ 1000m)

The raster dataset consists of a 1000 m score grid for crops 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 logistical factors for selected crop warehouse locations: • 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" * 0.35) + (“Railway Accessibility” * 0.25) + (“Major Cities Accessibility” * 0.2) + (”Poverty” * 0.1) + ("Human Population Density" * 0.1) **Data creation:** 2022-05-03 **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 – **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: Vegetables (Lao PDR - ~ 1000m)

The raster dataset consists of a 1000 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, population density, poverty and density of cattle, goats, sheep or pigs. 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 data. It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”Livestock Density” * 0.35) + ("Railway Accessibility" * 0.25) + (“Major Cities Accessibility” * 0.2) + ("Poverty" * 0.1) + ("Population density" * 0.1) **Data creation:** 2022-05-03 **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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 3. OpenStreetMap. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Slaughterhouse Location Score - Cattle (Lao PDR - ~ 1000 m)

The raster dataset consists of a 1000 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, population density, poverty and density of cattle, goats, sheep or pigs. 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 data. It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”Livestock Density” * 0.35) + ("Railway Accessibility" * 0.25) + (“Major Cities Accessibility” * 0.2) + ("Poverty" * 0.1) + ("Population density" * 0.1) **Data creation:** 2022-05-03 **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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 3. OpenStreetMap. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Slaughterhouse Location Score - Pigs (Lao PDR - ~ 1000 m)

The raster dataset consists of a 1000 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, population density, poverty and animal density. 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 data. It consists of an arithmetic weighted sum of normalized grids (0 to 100): (”PoultryDensity” * 0.35) + ("Railway Accessibility" * 0.25) + (“Major Cities Accessibility” * 0.2) + ("Poverty" * 0.1) + ("Population density" * 0.1) **Data creation:** 2022-05-03 **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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 3. OpenStreetMap. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Slaughterhouse Location Score - Poultry (Lao PDR - ~ 1000 m)