Spatial analysis and modelling of drinking water service in low and lower-middle income countries

Abstract

Drinking water is a basic necessity and plays a vital role in improving general health and wellbeing. Following recognition of the essential human right to drinking water, Sustainable Development Goals (SDGs) have included a dedicated Goal 6 (Target 6.1) for drinking water, which addresses a broad range of issues such as availability, accessibility, water quality, and inequalitiesin service. The expanded need for more sophisticated SDG monitoring therefore places high demands on data sources. By combining spatial analysis and modelling techniques with water point data sets, this study proposes several approaches to combine scarce information relating to drinking water services and thereby to facilitate national SDG monitoring. Specifically, spatial integration with water point data was found to be an effective way to add value to conventional data sources such as censuses for monitoring drinking water. In addition, MaxEnt-based predictive modelling method was employed to predict the potential geographical distribution of drinking water supply in the absence of completely surveyed national water point inventories; outputs for Cambodian and Tanzanian examples showed good discriminatory power based on AUCs (0.791 and 0.860 respectively). Although the MaxEnt modelled surface could not replace real water point surveys, it could reasonably give an indication of the potential distribution of water supply and thereby to be used to reveal hidden inequalities in drinking water services, or to investigate surrounding issues by combing with other geospatial data sets.

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ORCID for James Wright: orcid.org/0000-0002-8842-2181

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