BetaBetaDataset
To provide subnational data on WASH insecurity in Niger 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.
The dataset is the 2025 Niger WASH Insecurity Analysis (WIA) produced within the Global WASH Cluster's WIA framework. The WIA is designed to support evidence-based decision-making across the humanitarian–development continuum by bringing together secondary data on WASH services, hazards, and vulnerabilities. The Niger analysis applies this framework at the subnational level to identify geographic concentrations of WASH insecurity and the factors contributing to those conditions.
The workbook includes several analytical sheets that together support the WASH Insecurity Analysis. The Definitions sheet contains the indicator framework and associated data sources, while the Sources sheet provides a review of data availability by data source. The Indicators sheet contains indicator-level data at the administrative level 2 (Adm-2), representing the underlying data used to calculate deprivation for each area. The Deprivations sheet contains binary (0/1) deprivation values calculated from the underlying indicators using country-specific thresholds. These deprivation results are compiled into the WI Matrix, which summarizes deprivation across administrative areas and WIA dimensions. The WI Ranking sheet ranks areas according to their overall WASH Insecurity Score, while 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. A French version can also be found here: https://www.washcluster.net/node/34656
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.