Improved monitoring framework for local planning in the water, sanitation and hygiene sector: From data to decision-making

A decision support tool to place drinking water sources in rural communities

Installing more drinking water sources is a promising way to achieve the 6th sustainable development goal "Clean water and sanitation" in rural communities. A key parameter for the installation of new water pumps is geographical position, because the number of people who could gain access to drinking water depends on the location of the pump. To improve the choice of the most appropriate location, we propose a decision support tool to place a new drinking water source in a rural community. This tool relies on four complementary maps, which are obtained from GPS data, survey data, and a water source choice model. The first map shows the spatial distribution of the households and of the existing water sources in the village. The three remaining maps present the following quantities as a function of the position of a new drinking water source in the village: the number of users of the new drinking water source, the improvement of drinking water access, and the daily water demand per capita at the new drinking water source. The decision support tool is applied to a village in Burkina Faso. Results indicate that using the proposed method could allow eight times more people to gain access to drinking water in comparison to a random positioning of the new drinking water source. The original contribution of this work is, first, the consideration of existing water sources in the village, as well as seasonality. Second, we base our analysis on a water source choice model, which accounts for water quality in addition to the distance to the water source. Third, we consider the variability of the water volume collected by the households throughout the village. The developed tool is generic, transferable to other villages and useful for various decision-making entities (e.g. local authorities and non-governmental organizations).

Preprocessing alternatives for compositional data related to water, sanitation and hygiene

The Sustainable Development Goals (SDGs) 6.1 and 6.2 measure the progress of urban and rural populations in their access to different levels of water, sanitation and hygiene (WASH) services, based on multiple sources of information. Service levels add up to 100%; therefore, they are compositional data (CoDa). Despite evidence of zero value, missing data and outliers in the sources of information, the treatment of these irregularities with different statistical techniques has not yet been analyzed for CoDa in the WASH sector. Thus, the results may present biased estimates, and the decisions based on these results will not necessarily be appropriate. In this article, we therefore: i) evaluate methodological imputation alternatives that address the problem of having either zero values or missing values, or both simultaneously; and ii) propose the need to complement the point-to-point identification of the WHO/UNICEF Joint Monitoring Program (JMP) with other robust alternatives, to deal with outliers depending on the number of data points. These suggestions have been considered here using statistics for CoDa with isometric log-ratio (ilr) transformation. A selection of illustrative cases is presented to compare performance of different alternatives.