Data analysis

The Global Moran’s I and spatial scan statistical tests were done to explore the presence of clustering in the study area and local spatial clusters (hotspots) of unimproved sources of drinking water using ArcGIS version 10.3 [17] and Kuldorff’s SaTScan version 9.4 [18] softwares, respectively. Global Moran’s I statistic measures whether the patterns of unimproved sources of drinking water are dispersed, clustered or randomly distributed in the study area. Moran’s I values close to − 1 indicate unimproved sources of drinking water (UISDW) dispersed, whereas, Moran’s I close to + 1 and 0 indicate UISDW is clustered and distributed randomly, respectively. A statistically significant Moran’s I (p < 0.05) value leads to the rejection of the null hypothesis and indicates clustering of unimproved sources of drinking water [15, 19].

The spatial scan statistic uses a scanning window that moves across the study area. Households using unimproved sources of drinking water were taken as “Cases” and households using improved sources of drinking water were taken as “Controls” to fit the Bernoulli model. The maximum spatial cluster size of < 50% of the population at risk with a circle radius of 100 km was used, as an upper limit, which allowed both small and large clusters to be detected and ignored clusters that contained more than the maximum limit. For each potential cluster, a likelihood ratio test statistic was used to determine if the number of observed cases within the cluster were significantly higher than expected or not. The primary and secondary clusters were identified and assigned p-values and ranked based on their likelihood ratio test, on the basis of 999 Monte Carlo replications [15, 20, 21].