Linking poverty with water and sanitation in targeting households for achieving sustainable development
By Novlloyd E. Celeste
This blog post provides an introduction to the recently published paper from the Journal of Water, Sanitation & Hygiene for Development, and highlights some of the key features of the research.
The complex decision-making process among policy makers in the water and sanitation sector is easier if backed up with data. As such, data analysis can provide enough evidence to support strategic interventions for implementation in water and sanitation in the Philippines. As shown in a recent study, there is a linkage between poverty and access to water and sanitation in the Philippines. Accordingly, the poorest households have limited access to safe water and sanitation facilities and the type of toilet facility among households is predicted. This approach is essential in targeting aid to these households for sustainable development.
The research findings are particularly relevant given the growing population and increasing demand for safe water and basic sanitation facilities in the Philippines. Access to these services is not equal between urban and rural households, with urban areas having higher access. The utilization of data mining and classification algorithms is an innovative approach to aid in providing better intervention and aid to communities living in poverty. This approach is one of the best data-driven decision-making tools applicable to policy making.
The study utilized data from the Department of Science and Technology Food and Nutrition Research Institute consisting of 39,771 respondents. Statistical tools were used, such as Cramer’s V and multinomial logistic regression, to determine the association of toilet facilities versus access to safe water, water source, and whether the toilet is shared or not, and to predict the type of toilet facility with other household characteristics. To classify different types of toilet facilities, the researchers utilized the Classification and Regression Tree algorithm, taking into account access to safe water, water sources, and the wealth quintile of households.
The research highlights the significant link between poverty and access to water and sanitation in the Philippines. The findings demonstrate the need for better sanitation interventions among the poorest households, and that the utilization of innovative approaches such as data mining and classification algorithms can aid in addressing the current challenges. With the available data from the community, policy makers can target who will be the project beneficiaries eliminating biases in selection.
Apparently, there is a big potential of data mining and classification algorithms in identifying households in need of specific types of sanitation interventions. This is to provide relevant and specific solution to issues in the water and sanitation sector. This approach is not only applicable in identifying specific community interventions but also how to make it more sustainable in the long run.
To top it all, the study highlights the importance of identifying communities in need of sanitation interventions, particularly among the poorest households, and highlights the potential of innovative approaches such as data mining and classification algorithms in aiding this effort. With this, policymakers can provide relevant and effective interventions based on the need of the community.