Malawi GIS data

uav imagery by Clinton Nkolokosa, acquired 2026-10-04 to 2026-10-04, 5.0 cm per pixel, published on OpenAerialMap.

geoBoundaries ADM3 boundaries for Malawi: 245 units, representing 2019. Source: OpenStreetMap, MASDAP (Malawi Spatial Data Platform).

geoBoundaries ADM1 boundaries for Malawi: 3 units, representing 2017. Source: OpenStreetMap, Wambacher. Open Data Commons Open Database License 1.0

geoBoundaries ADM2 boundaries for Malawi: 28 units, representing 2020. Source: National Statistics Office of Malawi, OCHA ROSEA.

geoBoundaries ADM0 boundaries for Malawi: 1 units, representing 2017. Source: OpenStreetMap, Wambacher. Open Data Commons Open Database License 1.0

uav imagery by Clinton Nkolokosa, acquired 2026-10-03 to 2026-10-04, 5.0 cm per pixel, published on OpenAerialMap.

uav imagery by Clinton Nkolokosa, acquired 2026-08-13 to 2026-08-14, 3.2 cm per pixel, published on OpenAerialMap.

uav imagery by Clinton Nkolokosa, acquired 2026-08-13 to 2026-08-14, 5.0 cm per pixel, published on OpenAerialMap.

For the floods in Southern Malawi of March 2019, we have combined flood extent maps (Sentinel) with HRSL settlement/population grid. This results in a calculation of # of affected buildings/people per district. The results is shared through maps and in a shapefile. ### 1. Data sources ## Sentinel 1 Imagery from 7th of March 2017 ## Sentinel 2 Imagery from 10th/12th/14th of March 2017 ## HRSL population data Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 9 March 2019. ### 2. Good to know The flood extent for Nsanje district was separately added on March 14th, to the existing flood extent for the main area from March 12th. ### 3. Methodology ## A. Flood Extent Based on SAR The following steps were used to detect flood extent(water/no water). In SNAP tool the raw data downloaded from sci-hub Copernicus was processed to calibrate image for atmospheric correction, spike filter and terrain correction(This is mainly for Sentinel 1 data). Finally defining water no water based on a threshold applied on the corrected image. Defining a threshold is always a challenge in SAR image analysis for flood detection, we collected data from the field to define this threshold. For Sentinel 2 as a first step cloud filter was calculated by applying a combined threshold on Band 2 and Band 10. The cloud mask shown in the figure below didn’t capture shadows of clouds, these were miss interpreted by the flood algorithm as water/flood. To correct this areas with more cloud cover were clipped out with a polygon. To define water no water based on sentinel data we used NDWI index, the treshold is adjusted based on data collected from the field Validation points were collected by Field team tested different values and check if the threshold identified fits with observation. The complete methodology how to detect flooding based on Sentinel 1 data and SNAP toolbox is documented in ESA website. ## B. Affected People To calculate number of affected people per each admin level, flood extent map is combined with HRSL population data. This is done in two steps: First, in step 1, we calculate a raster, which multiplies the population grid with the flood grid, such that we are left with only "population in flooded area". This is done using raster calculator where population density raster was multiplied by flood extent raster, which has a value of 0 for no flood and 1 for flood areas. Note that the flood extent grid was first resampled to match it to the population grid. This whole exercise is repeated for settlement/buildings instead of population. Step 2: We apply zonal statistics per TA to calculate total number of buildings/people affected in each admin level. For each Admin level2 estimated number of affected people and affected houses are plotted in the map. The zonal statistics data used for plotting can be found in the shape file.

**ADAM ID: FL-20230217-MWI-01** Flood covering 106653.0 sq m on Feb 17 2023 in Malawi. It impacted 182158 people.

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2011

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2, ADM3. 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.

V1.5 The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Malawi: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Malawi. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming walking speeds on all roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

A 100 m spatial resolution geotiff of motorised travel time in seconds to nearest health facility in Malawi. The data was generated using the Child Poverty and Access to Services (CPAS) software (10.5281/zenodo.4638563) and was created as part of the CPAS project within the Data for Children Collaborative. The travel time is calculated assuming driving speeds of local public transport options on all-weather roads/asphalt roads and walking speeds on all other roads, tracks, paths and land cover types. A full description is available here a video description of the data is also available here Projection system is: GCS_WGS_1984 EPSG 4326 for all

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2013

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2017

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2012

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2018

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2015

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2019

**ADAM ID: **FL-20250314-MWI-00 A flood occurred on Mar 14 2025 in Malawi. It affected an estimated 262271 people and 41646 ha of cropland. 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.

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 Web: 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/

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 Web: 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/

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 Web: 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/

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 Web: 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/

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2011

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2013

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2015

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2016

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2015

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2018

Malawi Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2022

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2012

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2016

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Malawi Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2018