South Sudan GIS data

geoBoundaries ADM2 boundaries for South Sudan: 78 units, representing 2020. Source: South Sudan Information Management Working Group (IMWG), National Bureau of Statistics (NBS), and OCHA, OCHA South Sudan.

geoBoundaries ADM1 boundaries for South Sudan: 10 units, representing 2020. Source: South Sudan Information Management Working Group (IMWG), National Bureau of Statistics (NBS), and OCHA, OCHA South Sudan.

geoBoundaries ADM0 boundaries for South Sudan: 1 units, representing 2020. Source: South Sudan Information Management Working Group (IMWG), National Bureau of Statistics (NBS), and OCHA, OCHA South Sudan.

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 Datasets on HDX that refer to the Data Warehouse have been archived. South Sudan Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2016

The Census Bureau mapped South Sudan, a country with a substantial rural population. Satellite imagery was combined with 2008 census data and 2017 population estimates to produce gridded population layers.

These data were produced by the WorldPop Research Group at the University of Southampton. This work was funded by the Bill and Melinda Gates Foundation (BMGF) and the United Kingdom's Department for International Development (OPP1182408). The primary intended use of these data was aiding the BMGF field teams. The modelling work was led by Claire Dooley with support from Chris Jochem and oversight by WorldPop director Andy Tatem and GRID3 lead Attila Lazar. The support of the whole WorldPop group is acknowledged, as well as the our GRID3 partners (UNFPA, Columbia University and Flowminder). We thank the teams at IOM and ACLED for their excellent work in collecting data and making it freely available. This work was supported with funding from the Bill & Melinda Gates Foundation (BMGF) and the United Kingdom’s Department for International Development (DFID). These data may be distributed using a Creative Commons Attribution Share-Alike 4.0 License. __DATA DESCRIPTION__ This dataset provides population estimates for each settled 100m grid square in South Sudan. The grid square values were derived using the National Bureau of Statistics’ 2019 population projection estimates that were adjusted to account for displacement of people. The locations people have been displaced to were directly obtained from IOM’s Displacement Tracking Matrix (DTM). The locations people have been displaced from were derived using DTM and the Armed Conflict Locations and Events Database (ACLED). Numbers of displaced people per location were calculated using recorded numbers of international refugees and internally displaced persons. For further details, please, read SSD_population_v1_0_README.pdf __Recommended citation__ _WorldPop (School of Geography and Environmental Science, University of Southampton). 2020. South Sudan 2019 gridded population estimates from census projections adjusted for displacement, version 1.0. https://dx.doi.org/10.5258/SOTON/WP00659_

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

South Sudan 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. South Sudan 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. South Sudan 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. South Sudan Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2012

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 South Sudan **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.1612 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0005 million km (0.3146%) - Unpaved roads: 0.0617 million km (38.2843%) About 0.099 million km (61.4011%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0 million km (0.0015%) 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

South Sudan administrative level 0-3 boundaries (COD-AB) dataset version 03. This dataset is structured into 3 levels: - Admin 1: 10 State - Admin 2: 79 County - Admin 3: 512 Payam Dates associated with this dataset: - 09 October 2024: dataset reviewed for accuracy and completeness - 19 December 2022: valid for use by the humanitarian community - 01 November 2020: last edit to the dataset before publishing - 01 November 2020: boundaries created by the source Contributed by OCHA South Sudan. 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. South Sudan 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. South Sudan 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. South Sudan 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. South Sudan 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. South Sudan Near 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. South Sudan 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. South Sudan 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. South Sudan Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2017

South Sudan 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. South Sudan Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2011

Metadata from source: https://data.humdata.org/dataset/cod-ab-ssd South Sudan administrative level 0-3 boundaries (COD-AB) dataset. These administrative boundaries were established in: 2020 NOTE: Ages disaggregated into only four cohorts. No COD-PS record for Abyei Region. See COD-PS caveat about data rounding. The geoservice layer and the pak_admbndl_admALL_ocha_itos_20230308 lines shapefile contains the following special 'admLevel' codes: 76 = Northern boundary of the disputed area known as the Ilemi Triangle between South Sudan and Kenya (not a boundary fo any administrative polygon) 77 = Remainder of the boundary between South Sudan and Sudan 78 = That part of the Abyei region bouhndary that borders Sudan 79 = That part of the Abyei region boundary that borders the rest of South Sudan (not included in the polygons layers) This COD-AB was most recently reviewed for accuracy and necessary changes in October 2024. The COD-AB requires improvements. Sourced from South Sudan Information Management Working Group (IMWG), National Bureau of Statistics (NBS), International Organization for Migration (IOM) and OCHA Vetting by Information Technology Outreach Services (ITOS) with funding from USAID. This COD-AB is suitable for database or GIS linkage to the South Sudan COD-PS. No edge-matched (COD-EM) version of this COD-AB has yet been prepared. Please see the COD Portal. Administrative level 1 contains 10 feature(s). The normal administrative level 1 feature type is ""State"". Administrative level 2 contains 79 feature(s). The normal administrative level 2 feature type is ""County"". Administrative level 3 contains 512 feature(s). The normal administrative level 3 feature type is ""Payam"". Recommended cartographic projection: UTM zone 36 (mainly) but partly zone 35 This metadata was last updated on January 9, 2025.

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