Bangladesh GIS data
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The raster dataset consists of a 500m score grid for vegetables 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: Crop. • 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) + (”Regional Cities Accessibility” *0.1 ) + (”Port Accessibility” *0. 2) **Data publication:** 2021-10-15 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [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 of crop distribution within disaggregated units. 3. OpenStreetMap. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for vegetable location score (Bangladesh - ~ 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 location score is achieved by processing sub-model outputs characterizing logistical factors dairy processing facilities siting: Supply, demand, Infrastructure/accessibility. 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:** 2021-10-15 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [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 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 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 dairy processing (UHT/powder) final location (Bangladesh - ~ 500m)
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 location score is achieved by processing sub-model outputs characterizing logistical factors dairy processing facilities siting: Supply, demand, Infrastructure/accessibility. 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:** 2021-10-15 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [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 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 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 dairy processing (UHT/powder) final location (Bangladesh - ~ 500m)
The raster dataset consists of a 500 m score grid for dairy processing industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on cattle 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, livestock 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): (”Dairy Intensification” * 0.4) + ("Crop Production" * 0.3) + (“Major Cities Accessibility” * 0.2) + (“Population Density” * 0.1) **Data publication:** 2021-10-15 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [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 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. **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 (UHT/powder) location score (Bangladesh - ~ 500m)
The raster dataset consists of a 500 m score grid for dairy processing industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on 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, livestock 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): (”Dairy Intensification” * 0.4) + ("Crop Production" * 0.3) + (“Major Cities Accessibility” * 0.2) + (“Population Density” * 0.1) **Data publication:** 2021-10-15 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [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 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. **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 (UHT/powder) location score (Bangladesh - ~ 500m)
The raster dataset consists of a 500m score grid for rice 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: Crop. • 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) + (”Regional Cities Accessibility” *0.1 ) + (”Port Accessibility” *0. 2) **Data publication:** 2021-10-15 **Contact points:** Metadata Contact: [FAO-Data](mailto:fao-data@fao.org) Resource Contact: [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 of crop distribution within disaggregated units. 3. OpenStreetMap. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for rice location score (Bangladesh - ~ 500 m)
Natural Earth admin 0 countries bdg at 1:10 million scale: public domain cartographic vector data built to make small-scale world, regional and country maps.
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. Bangladesh Zila Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Bangladesh Geospatial Data. These were published in December 2022. Other administrative boundaries for this country are also available: Country Region UpaZila
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. Bangladesh Region Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Bangladesh Geospatial Data. These were published in December 2022. Other administrative boundaries for this country are also available: Country Zila UpaZila
geoBoundaries ADM4 boundaries for Bangladesh: 5160 units, representing 2020. Source: Bangladesh Bureau of Statistics, OCHA ROAP.
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. Bangladesh Country Boundary provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Bangladesh Geospatial Data. These were published in December 2022. Other administrative boundaries for this country are also available: Region Zila UpaZila
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. Bangladesh UpaZila Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Bangladesh Geospatial Data. These were published in December 2022. Other administrative boundaries for this country are also available: Country Region Zila
uav imagery by GPAD, acquired 2026-09-08 to 2026-09-09, 2.9 cm per pixel, published on OpenAerialMap.
**UNOSAT code: FL20210729BGD** This map illustrates satellite-detected surface waters in Chittagong division, Bangladesh as observed from a Sentinel-1 image acquired on 8 August 2021 at 17:55 local time. Within the analyzed area of about 18,700 km2 and, about 873 km2 of lands appear to be flooded. The water extent appears to have increased of about 21 km2 since 30 July 2021. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Cox's Bazar with ~393,240 people, Chittagong with ~358,320 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
NPM Bangladesh has produced a number of tools based on its regular data collection activities and drone flights. The package of March 2019 is based on NPM Site Assessment 14 (as of 13 February 2019) and NPM UAV imagery (as of 23 January 2019). Here below, the complete package by camp: SW Map package KMZ file Drone image The full image and shapefiles are available at this link.
On January 2018, partners from the Cox Bazar Health Sector conducted a mapping exercise of the Health Facilities in the refugee camp areas.
Satellite and UAV imagery derived roads and footpaths of Rohingya refugee settlements in Cox's Bazar District, Bangladesh. Based on best available imagery acquired in October 2017.
Satellite imagery derived shelter footprints of Rohingya refugee settlements in Cox's Bazar District, Bangladesh. Based on best available imagery acquired in February 2018.
Recognizing an information gap within existing road network datasets in the Southern Teknaf camps of Coxs Bazar District (Camps 24-27 and Nayapara Registered Camp), REACH dispached field teams to the area for a roads and paths mapping exercise from November 14-15, 2018. Areas within and on the edges of the camps were divided into grid cells, through which REACH field teams systematically traversed all paths while carrying GPS tracking devices. This dataset was then merged with the existing OpenStreetMap (OSM) roads and paths dataset for the area to provide a comprehensive map of roads and footpaths in and around the camps for mapping and spatial analysis purposes. When present in this dataset, OpenStreetMap attributes and identifiers have been preserved. This exercise was carried out with the support of European Civil Protection and Humanitarian Aid Operations (ECHO)
In this analysis we have combined several data sources around the floods in Bangladesh in August 2017. ## Visualization * See attached map for a map visualization of this analysis. * See http://bit.ly/2uFezkY for a more interactive visualization in Carto. ## Situation Currently, in Bangladesh many water level measuring stations measure water levels that are above danger levels. This sets in triggers in motion for the partnership of the 510 Data Intitiative and the Red Cross Climate Centre to get into action. ## Indicators and sources In the attached map, we combined several sources: * Locations of waterlevel stations and their respective excess water levels (cms above danger level) at 14/08/2017 (Source: http://www.ffwc.gov.bd/index.php/googlemap?id=20) * Population density in Bangladesh to quickly see where many people live in comaprison to these higher water-level stations. (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00018 >> the People per hectare 2015 UN-adjusted totals file is used.) * Vulnerability Index: we constructed a Vulnerability Index (0-10) based on two sources. First poverty incidence was collected from Worldpop (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00020 >> The estimated likelihood of living below $2.50/day). Second, we used a Deprivation Index which is estimated in the report Lagging District Reports 2015 (Source: http://www.plancomm.gov.bd/wp-content/uploads/2015/02/15_Lagging-Regions-Study.pdf > Appendices > Table 20), which combines many socio-economic variables into one Deprivation Index through PCA analysis. ## Detailed methodology Vulnerability * The above-mentioned poverty source file is on a raster level. This raster level poverty was transformed to admin-4 level geographic areas (source: https://data.humdata.org/dataset/bangladesh-admin-level-4-boundaries), by taking a population-weighted average. (Source population also Worldpop). * The district-level PCA components from abovementioned reports were matched to the geodata based on district names, and thus joined to the admin-4 level areas, which now contain a poverty value as well as Deprivation Index value. Note that all admin-4 areas within one district (admin-2) obviously all have the same value. The poverty rates do differ between all admin-4 areas. * Lastly, both variables were transformed to a 0-10 score (linearly), and a geomean was taken to calculate the final index of the two. A geomean (as opposed to an arithmetic mean) is often used in calculating composite risk indices, for example in the widely used INFORM-framework (www.inform-index.org).
NPM Bangladesh has produced a number of tools based on its regular data collection activities and drone flights. The package of July 2018 is based on NPM Site Assessment 11 (as of 22 July) and NPM most updated drone imagery (as of 2 August). Here below, the complete package by camp: - SW Map package - KMZ file - Drone image The full image and shapefiles are available at this link.
NPM Bangladesh has produced a number of tools based on its regular data collection activities and drone flights. The package of November 2018 is based on NPM Site Assessment 13 (as of 20 November) and NPM most updated drone imagery (as of 26 September). Here below, the complete package by camp: SW Map package KMZ file Drone image The full image and shapefiles are available at this link.
NPM Bangladesh has produced a number of tools based on its regular data collection activities and drone flights. The package of August 2018 is based on NPM Site Assessment 12 (as of 4 September) and NPM most updated drone imagery (as of 11 September). Here below, the complete package by camp: - SW Map package - KMZ file - Drone image The full image and shapefiles are available at this link.
This shapefile contains the bridges of Kutupalong-Balukhali Rohingya refugee sites including existing and Planned bridges.
In early 2019, UNICEF and the Cox's Bazar WASH sector identified a need to comprehensively map key WASH infrastructure using physical unique identifier (UUID) tags, in order to produce a database containing information about each piece of infrastructure across the camps. The objectives of this effort were to facilitate accurate, consistent spatial identification of unique infrastructure by all interested actors, to provide a baseline ‘snapshot’ of WASH infrastructure functionality at the time of coding, and to establish a common dataset to be used by all partners. The core infrastructure being covered by this exercise includes tubewells, latrines, and bathing facilities.
Includes a binary GeoTIFF where 0 values are non-flooded and 1 values are flooded. Included a metadata file that describes a summary and methods for the data.
The Site Management sector, with the support of SM partners (IOM, UNHCR, ADRA and Solidarités International) conducted a mapping exercise during the months of April and May 2018 in the areas of Teknaf upazila currently hosting Rohingya refugees. The purpose of the exercise was to identify and map the boundaries of local paras, namely group of houses. The mapping exercise aimed to better define the areas of responsibility of the newly established Para Development Commitees, and to support RRRC’s CiCs in the process of defining the new camp boundaries in Teknaf. IOM Needs and Population Monitoring (NPM) provided technical support to better identify the para names and demarcation. The para mapping exercise was conducted in two rounds. The first exercise took place from 1 to 3 of April 2018 in the areas of Alikhali, Leda, Nayapara, Muchoni, Jadimura and Domdumia. A second exercise took place on 21 and 22 April 2018 covering the areas of Hakimpara, Jamtoli, Putibonia, Shamlapur and Unchiprang. This dataset presents the figures of the Rohingya refugee population in Teknaf, by para. These data were collected during NPM's regular data collection exercises (Baseline and Site Assessment), which usually capture information on a majhee block level. The two systems are currently coexisting and overlapping. In order to better visualize how the two systems interact, NPM produced a set of maps available at this link.
Kutupalong Rohingya Refugee Camp, Bangladesh. The structure footprint covers the camps directly affected by the fire of the 22nd March 2021 (Camp 8E, Camp 8W and Camp 9). The footprint is based on high resolution drone imagery from March 2020 (IOM) and has been processed by REACH/UNOSAT. The footprint includes all digitised structures and so includes bridges, shelters and smaller structures, such as latrines etc. This footprint is an early release of the full structure/shelter footprint for all camps and has not been QCed. Users should use accordingly. The full, QCed, post-processed structure/shelter footprint for all camps will be released in mid-April.
Summary: This file represents digitization work carried out by UNOSAT and REACH using IOM-NPM drone imagery from April 2021. All fields should be indicative, the 'Type' field indicates whether the structure was thought to be fully build at the time imagery was taken. Limitations: Digitised areas show covered spaces within the camps. These may be latrines, tubewells, bathing stations, shelters or larger structures (many of which are multiple shelter joined under one continuous roof). This is also the case in previous 'shelter' footprints. Bridges are not included in this dataset, but many can be found in the previous 2019 footprint. Imagery is known to have horizontal offset of up to a few meters in some areas but is generally accurate. Work is ongoing by REACH to manually update the footprints to ensure that they accurately represent features in the field. As future iterations and improvements to the footprints are made, additional versions will be released. The work has not yet been fully ground truthed and is based on expert interpretation of UAV imagery. In addition to relying on imagery interpretation, the footprints are bound by the limitations present in the UAV images that were utilized. Credits: Processing UNOSAT / REACH (2021) Drone Imagery Inputs, IOM-NPM (April 2021)
NPM Bangladesh has produced a number of tools based on its regular data collection activities. The package of November 2019 is based on NPM Site Assessment 16 (during the months of 21st August – 10th October) and NPM most updated drone imagery (as of 23 January 2019). Here below, the complete package by camp: SW Map package KMZ file Drone image The full image and shapefiles are available at this link.
**UNOSAT code TC20230510BGD, GDACS Id: 1000970** This map illustrates satellite-based damage assessment in Saint Martin’s Island Cox’Bazar District, Chattogram Division detected by using a Pleiades very high-resolution satellite image acquired on 15 May 2023. Within the analyzed area, UNOSAT identified 211 affected and potentially affected structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 17% of the total number of structures affected in Saint Martin’s Island. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).
**UNOSAT code: FL20200713BGD** This map illustrates satellite-detected surface waters over Mymensingh, Rajshahi and Rangpur Division of Bangladesh as observed from a TerraSAR-X image acquired on 18 July 2020. Within the analyzed area of about 9,000 km2, a total of about 2,860 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please sendground feedback to UNITAR - UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
**UNOSAT code: FL20200713BGD** This map illustrates satellite-detected surface waters over Bangladesh as observed from a TanDEM-X image acquired on 19 July 2020 due to the current monsoon rains. Within the analyzed area of about 14,000 km2, a total of about 6,800 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.