South Sudan

Years of insecurity, famine and disease have made South Sudan one of the most challenging areas of the world for development.

Human Alteration of the Area in and Around South Sudan

Image, above: Human alteration of South Sudan. Brighter colors correspond to higher levels of anthropogenic impact.

Landcover

South Sudan is technically sustainable but food insecurity remains an issue through the country.

Juba Landcover and Shelters: Juba is one of the more populated places in South Sudan. Note the 2014 construction of UN shelters.

Aid Access: The HIU map links the issues of food insecurity and conflict, particularly in the north-central and south-central areas of the country.

Livelihood Zones: The western groundnut livelihood zones correlate with the forest landcover while the eastern livestock and sorghum zones correlate with the shrub and grassland.

Population: refugees from the 2000-2018 conflicts resulted fled to neighboring countries.

Nighttime Lights: South Sudan exhibits a comparative absence of nighttime lights, as seen from space, indicative of lack of developed areas.

Destroyed Structures, 2016: The year of 2016 saw extensive conflict in South Sudan. This dataset is likely not comprehensive but nevertheless depicts broad conflict.

Juba Infrastructure: This data set is an example of how infrastructure data can be collected into a GIS but may not reflect data about who has access to the infrastructure.

High Resolution Image of UN Compound, Juba

Accessibility of Project Counties: The counties in red will require additional support personnel and/or transport. Maps such as these will help the project prioritize its data collection efforts.

Randomized Survey Sites: The map is an example of a 2023 Q1 report of surveys conducted.

Household Resiliency Index by County: Using data modeled from the preceding map, it was possible to aggregate survey information and generate county-scale resiliency measures.

Predicted Resiliency: Simulated HRI values were applied to the survey points in Wau county. Using Kriging, a predicative surface was generated and HRI values can now be estimated for regions that were not directly surveyed.

Data sources include: NASA, NOAA, Dept. of State, USAID, Mercy Corps