Geostatistical tools are considered to be very imperative in achieving the Sustainable Development Goals (SDGs), most especially in the distribution of facilities. Studies on the application of geostatistics such as Average Nearest Neighbour (ANN) in the spatial distribution of sustainable water supply facilities are often very rare. This study, therefore, explores the critical importance of the ANN analytical tool of ArcGIS to examine the spatial distribution of public water supply facilities in Lapai, Nigeria. The ANN sets the null hypothesis that there is no difference between the random distribution and the distribution of public water facilities in the study area, where the z-score and p-value results are both measures of statistical significance which explains whether the null hypothesis should be accepted or rejected. The results obtained indicate a similar spatial distribution pattern for all water facilities in the study area, as they are dispersedly distributed from the global view. The method will allow more proactive decision making in the provision of sustainable public water supply facilities to better the wellbeing of urban dwellers.

The main objective of this study is to review and evaluate three common interpolation methods namely: Inverse Distance Weighting (IDW), Radial Basis Function (RBF) and Ordinary Kriging (OK), and generate maps of soil pH using these methods. The accuracy and efficiency of the generated maps have been examined as well as the most fitting technique for estimating spatial distribution of soil pH in the study area is identified. Studies were conducted within the limits of land use of RUP “Uchkhoz BGSHA” (Republic of Belarus, Mogilev region, Goretsky district). The total area of the surveyed territory is 3197.89 hectares. For the analysis data is used about pHKCl of soil solution obtained from materials of an agrochemical survey executed in 2014. Forecasting and visualization of the spatial distribution of pH sub(KCl) was carried out using the Geostatistical Analyst module of the ArcGIS software. The experimental anisotropic variograms were calculated to determine the possible spatial structure of soil pH. Based on cross-validation results, a polynomial function was identified as the best variogram model. The model created by the method of radial basis functions turned out to be the most suitable for forecasting purposes (the value of the root-mean-square error was 0.763). In terms of interpolation accuracy, the investigated deterministic and geostatistical methods are located in the next descending row: RBF greater than IDW greater than OK.