Bolivia GIS data
＋ Follow new data205 datasets.
uav imagery by Gabriel Mancilla, acquired 2025-08-01 to 2025-08-01, 5.3 cm per pixel, published on OpenAerialMap.
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Bolivia Country Boundary provides a 2021 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2021 boundaries are provided by Michael Bauer Research GmbH. These were published in October 2022. A new layer will be published in 18 months. Other administrative boundaries for this country are also available: Departamento
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Bolivia Departamento Boundaries provides a 2021 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2021 boundaries are provided by Michael Bauer Research GmbH. These were published in October 2022. A new layer will be published in 18 months. Other administrative boundaries for this country are also available: Country
satellite imagery by Sofia Perez, acquired 2025-05-31 to 2025-06-01, 10.0 cm per pixel, published on OpenAerialMap.
geoBoundaries ADM2 boundaries for Bolivia (Plurinational State of): 110 units, representing 2015. Source: GeoBolivia, Derived from ADM3 layer. Public Domain; free use and access to information
geoBoundaries ADM3 boundaries for Bolivia (Plurinational State of): 339 units, representing 2015. Source: GeoBolivia. Public Domain; free use and access to information
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 map illustrates satellite-detected standing water over the El Beni department in Bolivia, as of the 12 February 2018, following heavy rainfalls that caused severe flooding in the area. The analysis was conducted using Radarsat-2 image from 12 February 2018. UNITAR-UNOSAT identified in the analyzed area of the El Beni Department around 52,670 Ha of land that seem to be inundated, most of which are agricultural fields and swamps. The Rio Mamore has expanded along the banks. According to the analysis, the most affected province is Moxos, where more than 35,500 Ha are submerged. In the municipality of Trinidad 6,900 Ha are detected as inundated. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.
This map illustrates satellite-detected standing water over the Cochabamba department in Bolivia, as of the 10 February 2018, following the heavy rainfalls that caused severe flooding in the area. The analysis was conducted using Sentinel -1b and Radarsat-2 images as of 9, 10 and 12 February 2018 respectively. UNITAR-UNOSAT identified in the analyzed area of the Cochabamba Department, around 1,300 Ha of land seem to be inundated, which almost 35% ,450 Ha, are in the province of Punata. It is likely that flood waters have been systematically underestimated within built-up urban areas because of the special characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR UNOSAT.
**ADAM ID: **sm\_us7000qad7 Magnitude 5.4 earthquake at 182.634 depth occurred on Jul 03 2025 99 km SSW of Uyuni, Bolivia. The epicentre of the earthquake was located at latitude -21.2657 longitude -67.2411. It impacted 4261 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.
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 Bolivia (Plurinational State of) **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.4638 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0299 million km (6.4374%) - Unpaved roads: 0.3277 million km (70.6473%) About 0.1063 million km (22.9153%) 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 (0.3365%) 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
No description yet.
No description yet.
No description yet.
No description yet.
No description yet.
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. 10km buffer radius. • Major roads - approx. 2km buffer radius. • Access to IT is characterized by applying the mobile broadband coverage map. The score is achieved by processing sub-model outputs that characterize production, logistical, and socio-economic factors. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. 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:** 2023-01-25 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Maintainer: [Justeen De Ocampo](mailto:justeen.deocampo@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 crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty – El Instituto Nacional de Estadística (INE) 2012. 5. 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:** Download: Crop Storage Final Location: Maize (Bolivia - ~ 500 m)
The raster dataset represents top location score areas suitable for potato 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. 10km buffer radius. • Major roads - approx. 2km buffer radius. • Access to IT is characterized by applying the mobile broadband coverage map. The score is achieved by processing sub-model outputs that characterize production, logistical, and socio-economic factors. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. 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:** 2023-01-25 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Maintainer: [Justeen De Ocampo](mailto:justeen.deocampo@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 crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty – El Instituto Nacional de Estadística (INE) 2012. 5. 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:** Download: Crop Storage Final Location: Potatoes (Bolivia - ~ 500 m)
The raster dataset represents top location score areas suitable for sorghum 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. 10km buffer radius. • Major roads - approx. 2km buffer radius. • Access to IT is characterized by applying the mobile broadband coverage map. The score is achieved by processing sub-model outputs that characterize production, logistical, and socio-economic factors. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. 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:** 2023-01-25 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Maintainer: [Justeen De Ocampo](mailto:justeen.deocampo@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 crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty – El Instituto Nacional de Estadística (INE) 2012. 5. 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:** Download: Crop Storage Final Location: Sorghum (Bolivia - ~ 500 m)
The raster dataset represents top location score areas suitable for soya bean 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. 10km buffer radius. • Major roads - approx. 2km buffer radius. • Access to IT is characterized by applying the mobile broadband coverage map. The score is achieved by processing sub-model outputs that characterize production, logistical, and socio-economic factors. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. 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:** 2023-01-25 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Maintainer: [Justeen De Ocampo](mailto:justeen.deocampo@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 crop distribution within disaggregated units. 3. OpenStreetMap. 4. Poverty – El Instituto Nacional de Estadística (INE) 2012. 5. 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:** Download: Crop Storage Final Location: Soya Bean (Bolivia - ~ 500 m)
The 500 m 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 score is achieved by processing sub-model outputs that characterize production, logistical, and socio-economic factors. The top score 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. 10km buffer radius. • Major roads - approx. 2km buffer radius. Access to IT is characterized by applying the mobile broadband coverage map. **Data publication:** 2023-01-25 **Contact points:** Resource Contact: [FAO-Data](mailto:fao-data@fao.org) Maintainer: [Justeen De Ocampo](mailto:justeen.deocampo@fao.org) **Data lineage:** lineage statement Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2). 2. OpenStreetMap. 3. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. 4. Poverty – El Instituto Nacional de Estadística (INE) 2012. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Download: Dairy Processing Final Location: Cattle (Bolivia - ~ 500 m)
AI-derived building footprint polygons for Bolivia from Microsoft's Global ML Building Footprints, in 238 quadkey tiles of line-delimited GeoJSON, uploaded 2026-02-23.