Zimbabwe GIS data

geoBoundaries ADM1 boundaries for Zimbabwe: 10 units, representing 2020. Source: Zimbabwe National Statistics Agency (ZIMSTAT www.zimstat.co.zw) Central Statistics Office, OCHA ROSEA.

geoBoundaries ADM0 boundaries for Zimbabwe: 1 units, representing 2020. Source: Zimbabwe National Statistics Agency (ZIMSTAT www.zimstat.co.zw) Central Statistics Office, OCHA ROSEA.

geoBoundaries ADM2 boundaries for Zimbabwe: 91 units, representing 2020. Source: Zimbabwe National Statistics Agency (ZIMSTAT www.zimstat.co.zw) Central Statistics Office, OCHA ROSEA.

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 Datasets on HDX that refer to the Data Warehouse have been archived.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 Datasets on HDX that refer to the Data Warehouse have been archived.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

A 100 m spatial resolution geotiff of walking travel time in seconds to nearest health facility in Zimbabwe. 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 Zimbabwe. 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. Zimbabwe Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2018

Zimbabwe Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

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 Datasets on HDX that refer to the Data Warehouse have been archived.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

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 Zimbabwe **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.2945 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.016 million km (5.4466%) - Unpaved roads: 0.0403 million km (13.6891%) About 0.2381 million km (80.8643%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0001 million km (0.0321%) 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

Zimbabwe administrative level 0-3 boundaries (COD-AB) dataset version 01. This dataset is structured into 3 levels: - Admin 1: 10 Province - Admin 2: 91 District - Admin 3: 1961 Ward Dates associated with this dataset: - 28 January 2025: dataset reviewed for accuracy and completeness - 11 September 2018: valid for use by the humanitarian community - 31 August 2018: last edit to the dataset before publishing - 31 August 2018: boundaries created by the source Contributed by OCHA Regional Office for Southern and Eastern Africa (ROSEA). Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

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

Zimbabwe Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Zimbabwe Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2014

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Zimbabwe 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. Zimbabwe Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2014

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

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

The FEWS NET Data Warehouse is **currently unavailable**. Datasets on HDX that refer to the Data Warehouse have been archived. Zimbabwe Current Situation FEWS NET Acute Food Insecurity Classifications Geographic data for 2014

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

Vegetation of the Zambezi area as used in the SAFARI 2000 project assessing the contribution of burning woody African vegetation south of the Equator to atmospheric aerosols. Vegetation of the East Africa as used in the SAFARI 2000 project assessing the contribution of burning woody African vegetation south of the Equator to atmospheric aerosols. Project abstract: The objective of the National Botanical Institute's (NBI) vegetation mapping project was to map woody plant species to provide estimates of the fraction of individual species contributing to the peak leaf area index for designated vegetation types in southern Africa. The target was to account for 80% of the woody vegetation leaf area in terms of named species, for 80% of the surface area of Africa south of the equator. The data sources are both published and unpublished species lists for vegetation types and individual sample plots, with the species contribution estimated by local experts in terms of dominants and subdominants. Source maps include: Low and Rebelo (1998) covering South Africa, Lesotho, and Swaziland; Giess (1971) covering Namibia; Wild and Barbosa (1968) covering Botswana, Zimbabwe, Zambia, Malawi, and Mozambique; Barbosa (1970) covering Angola; White (1983) covering Tanzania, Kenya, Uganda, and Somalia; and White (1983) covering Ruanda, Burundi, Congo, and Gabon. Each source map delineates of a wide variety of land cover categories that differ from region to region. Because vegetation discontinuities exist along some of the regional borders and a perfectly continuous regional map could not be achieved within the timeframe and budget of the project, the final regional map is made up of six independent sub-regional maps. The data set also includes a cross-referenced database of woody plant species, in order of species dominance, associated with all mapped units. The following areas and vegetation types were excluded according to the sub-project objectives, appearing as transparent in the map : areas north of the Equator, evergreen moist forests, pure grasslands, desert and arid regions, Fynbos. These areas are indicated as such on the final product map. Main water bodies are also mapped specifically.

Zimbabwe Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

Zimbabwe Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

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

Zimbabwe Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

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