BetaBetaDataset
To provide subnational data on WASH insecurity in South Sudan by combining indicators of WASH service access, hazard impacts, hazard exposure, and WASH-related vulnerability to identify and rank areas facing the greatest WASH-related risks
This dataset is the 2025 South Sudan WASH Insecurity Analysis (WIA) developed under the Global WASH Cluster's WIA framework. The WIA provides a standardized approach for identifying areas within a country where WASH insecurity is greatest and supports evidence-based prioritization, resource allocation, humanitarian planning, and coordination. The South Sudan analysis provides a county-level (Admin-2) assessment of relative WASH insecurity. It is particularly relevant in the South Sudan context, where conflict, displacement, recurrent flooding, and chronic underfunding contribute to substantial differences in WASH conditions between areas.
The workbook includes the indicator framework and associated data sources in the Definitions sheet, a review of data availability by data source in the Sources sheet, and indicator-level data at Adm-2 level in the Indicators sheet. The Indicators data provide the underlying information used to calculate deprivations for each area. The Deprivations sheet contains binary (0/1) deprivation values calculated from the indicator data using country-specific thresholds, which are then compiled to calculate overall WASH insecurity scores. The WI Matrix summarizes deprivations across administrative areas and WIA dimensions, while the WI Ranking sheet ranks areas according to their WASH Insecurity Score. The Top 10 sheet identifies the ten areas with the highest WASH insecurity and provides a breakdown of the contribution of each dimension to the overall score.
The WIA uses a common analytical methodology based primarily on secondary data to assess WASH insecurity at subnational level. The framework considers WASH service levels, exposure to hazards, and WASH-related vulnerabilities. Indicator-level data are first compiled for administrative areas and then assessed against country-specific thresholds to identify individual deprivations. These are represented as binary values (0 = no deprivation and 1 = deprivation). The resulting deprivations are combined across the WIA dimensions to generate an overall WASH Insecurity Score, which is then used to rank administrative areas and identify areas with the highest levels of WASH insecurity.