Malawi GIS data
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**UNOSAT code TC20230314MWI, GDACS Id: 1000961** This map illustrates cumulative satellite-detected surface waters in Southern Region, Malawi between 17 to 21 March 2023 as observed from: VIIRS imagery from 17 to 21 March 2023; Sentinel-1 imagery acquired on 17 Mar. 2023 and Sentinel-2 imagery acquired on 21 Mar. 2023. Within the analyzed area of about 30,000 km2, about 2,100 km2 of land appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, ~280,000 people are potentially exposed or living close to flooded areas. 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 the backscattering properties of the radar signal.
**UNOSAT code TC20230314MWI, GDACS Id: 1000961** This map illustrates satellite-detected surface waters in Southern Region, Malawi as observed from a RCM-2 image acquired on 14 Mar. 2023 at 05:10 local time. Within the analyzed area of about 7,200 km2, about 430 km2 of land appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, ~75,000 people are potentially exposed or living close to flooded areas. 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 the backscattering properties of the radar signal.
The ICA is a process of consultations supported by mapped-out data that produces a strategic plan describing where different combinations of programme themes are appropriate to achieve goals of reducing food insecurity and climate related shock risk. The ICA combines multi-year food security trends with natural shock risk data to highlight sub-national areas where different programme strategies make sense. Food security trend maps shows areas where safety nets can address regular food insecurity, and others where shocks make recovery more important. Climate-related natural shock risk maps show where DRR, preparedness and early warning efforts can complement food-security objectives. Atop this core foundation, mapped data on subjects including nutrition, gender, livelihoods and resilience can enrich theme-level strategic planning in which all pieces work together. The full group of ICA partners discuss these analytical results to arrive at strategic programmatic directions.
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.
Malawi Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2022
**UNOSAT code: TC20220126MWI** This map illustrates satellite-detected surface waters in Balaka, Blantyre, Neno, and Zomba districts, Southern region, Malawi as observed from a Sentinel-1 image acquired on 26 January 2021 at 05:16 local time and using an automated analysis with Artificial Intelligence based methods. Within the analyzed area of about 1,000 km2, about 28 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, about 5,400 people are potentially exposed or living close to flooded areas. 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.
**UNOSAT code: TC20220126MWI** This map illustrates the floods (cumulative) aggregated using Sentinel-1 images acquired between 26 January and 2 February 2022 about 05:00 local time. The flood analysis were carried out by using automated analysis with an Artificial Intelligence based method, and Worldpop spatial demographic data. About 102,000 people were exposed or living close to flooded areas. The population potentially exposed to floods are mainly located in the districts of Chikwawa with ~ 33,700 people and Nsanje with ~ 33,700 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.
The main purpose of the Malawi Health Facility Registry is to provide an easily accessible national online database containing a comprehensive list of health facilities in the country for purposes of facilitating planning for service delivery and performance accountability. The Ministry of Health and Population maintains a comprehensive list of all health facilities in Malawi with a purpose of strengthening health service delivery. The list was previously maintained through a spreadsheet file which made it difficult to update and to access especially by the decision makers. Additionally, various sections of the Ministry have been maintaining parallel health facility lists making it harder to share and triangulate data across systems. To address these challenges, the Ministry has implemented an online Master Health Facility Registry (MHFR). The MHFR is a web service with a user interface to allow health workers get access to the full list of facilities and view summaries through the dashboard in addition to performing custom queries depending of specific information Please consult the official website to get the most updated database
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 Malawi **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.2216 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0069 million km (3.1158%) - Unpaved roads: 0.0435 million km (19.613%) About 0.1712 million km (77.2712%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0003 million km (0.1706%) 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
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The ICA is a process of consultations supported by mapped-out data that produces a strategic plan describing where different combinations of programme themes are appropriate to achieve goals of reducing food insecurity and climate related shock risk. The ICA combines multi-year food security trends with natural shock risk data to highlight sub-national areas where different programme strategies make sense. Food security trend maps shows areas where safety nets can address regular food insecurity, and others where shocks make recovery more important. Climate-related natural shock risk maps show where DRR, preparedness and early warning efforts can complement food-security objectives. Atop this core foundation, mapped data on subjects including nutrition, gender, livelihoods and resilience can enrich theme-level strategic planning in which all pieces work together. The full group of ICA partners discuss these analytical results to arrive at strategic programmatic directions.
Drone orthomosaic. Post Tropical Storm Chalane Mapping, Malawi conducted with Crowddroning by GLOBHE. More maps and data available on demand upon request from locations globally at https://globhe.com/ MORE CROWDDRONING BY GLOBHE Webb: https://globhe.com/ Facebook: https://www.facebook.com/Crowddroning Twitter: https://twitter.com/globhedrones Instagram: https://www.instagram.com/globhedrones/ LinkedIn: https://www.linkedin.com/company/globhedrones/
**UNOSAT code: TC20220126MWI** This map illustrates population potentially exposed to floods (cumulative) aggregated by district in Malawi from Sentinel-1 images acquired between 26 January and 2 February 2022 about 05:00 local time. The flood analysis was carried out by using automated analysis with an Artificial Intelligence based method, and Worldpop spatial demographic data. About 102,000 people were exposed or living close to flooded areas. The population potentially exposed to floods are mainly located in the districts of Chikwawa with ~ 33,700 people and Nsanje with ~ 33,700 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.
Malawi administrative level 0-3 boundaries (COD-AB) dataset version 02. This dataset is structured into 3 levels: - Admin 1: 3 - Admin 2: 32 - Admin 3: 433 Dates associated with this dataset: - 04 October 2024: dataset reviewed for accuracy and completeness - 05 April 2023: valid for use by the humanitarian community - 22 October 2018: last edit to the dataset before publishing - 22 October 2018: boundaries created by the source Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries
Malawi Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2021
Malawi Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2020
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
The raster dataset represents top location score areas suitable for cassava 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 location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-10-15 **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. 4. Altas AI - Asset Wealth Index 2020. 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:** Zipped TIF raster file for cassava final location (Malawi - ~ 500 m)
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 location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-10-15 **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. 4. Altas AI - Asset Wealth Index 2020. 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:** Zipped TIF raster file for maize final location (Malawi - ~ 500 m)
The raster dataset represents top location score areas suitable for sweet 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 location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-10-15 **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. 4. Altas AI - Asset Wealth Index 2020. 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:** Zipped TIF raster file for sweet potato final location (Malawi - ~ 500 m)
The raster dataset represents top location score areas suitable for tropical fruits storage, filtered by exclusive criteria: access to finance, distance to major roads, and access to IT (mobile broadband connection). Access to finance and roads are defined using a linear distance threshold: • Banks - approx. 10km buffer radius. • Major roads - approx. 2km buffer radius. Access to IT and electricity is characterized by applying the mobile broadband coverage map and the Atlas AI Electrification map. The location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-10-15 **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. 4. Altas AI - Asset Wealth Index 2020 and Electrification 2021. 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:** Zipped TIF raster file for tropical fruits final location (Malawi - ~ 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 and electricity is characterized by applying the mobile broadband coverage map and the Atlas AI Electrification 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. 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. Atlas AI - Asset Wealth Index 2020 and Electrification 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 (UHT/powder) final location (Malawi - ~ 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 and electricity is characterized by applying the mobile broadband coverage map and the Atlas AI Electrification 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. 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. Atlas AI - Asset Wealth Index 2020 and Electrification 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 (UHT/powder) final location (Malawi - ~ 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 for the slaughterhouse facilities siting: Demand, Supply, Infrastructure/accessibility. Access to finance, distance to roads, and urban areas are defined using a linear distance threshold: • Banks - approx. 10km • Major roads - approx. 2km Access to IT and electricity is characterized by applying the mobile broadband coverage map and the Atlas AI Electrification 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. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. OpenStreetMap. 4. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021. 5. Atlas AI - Asset Wealth Index 2020 and Electrification 2021. **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 slaughterhouse final location (Malawi - ~ 500m)
The raster dataset represents top location score areas suitable for sugar cane 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 location score is achieved by processing sub-model outputs characterizing logistical factors for crop warehouse siting: Supply, demand, Infrastructure/accessibility. The location score from 0 to 100 is then obtained through a simple arithmetic weighted sum of the normalized/scaled grids. This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location). **Data publication:** 2021-10-15 **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. 4. Altas AI - Asset Wealth Index 2020. 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:** Zipped TIF raster file for sugar cane final location (Malawi - ~ 500 m)
The Soil and Terrain database for Malawi (version 1.0), at scale 1:1 million, was compiled based on the soil map of Malawi at scale 1:250,000 (compiled by the Land Resources Evaluation Project) that was complemented with soil boundary information from the provisional soil map at scale 1:1 million. The 90m SRTM-DEM was used to define the various landform types of the SOTER units and also to adjust their boundaries. The SOTER map units were attributed with soil information obtained from the Africa Soil Profiles Database. The SOTER compilation followed the methodology described in the SOTER Procedures Manual Version 2. SOTER forms a part of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts. DOI for dataset (submitted): 10.17027/isric.wdcsoils.20160002