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    Combining water point data and machine learning to predict the potential spatial distribution of specific type or group of drinking-water source/service

    by Weiyu Yu 09/25/2017 06:45 AM BST

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      Although SDG monitoring on drinking-water progress is often based on national level indicators, sub-nationally and geospatially disaggregated indicators may become increasingly important as they could effectively reveal inequalities in services between different geographic locations and population groups. As more recent concerns are raised about issues such as water quality, the functionality of facility, and continuity of service, it may also become increasingly important to disaggregate data by specific type of water service. Currently, disadvantaged drinking water services are not often reported at high levels of geospatial disaggregation. Fortunately, as more geospatial data sources become available with the transition from the MDGs to the SDGs, predicting the potential spatial distribution of specific type(s) of drinking-water source using machine learning method becomes possible. DHS modelled surfaces as one of such novel data could potentially be the important sources of predictive covariates for modelling the potential distribution of specific type(s) of drinking-water service.

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      Jim Wright, Nicola Wardrop

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