Spatial data science for SDG monitoring in an immature data ecosystem

Spatial data science must and is turning its attention to the 2030 Sustainable Development Goals, for which accelerated implementation is urgently needed. Leveraging of Big Data-type developments against small data programs for SDG reporting presents a specific opportunity, particularly so for less developed countries characterised as historically data poor. However, harnessing these emergent possibilities also faces well-known challenges – for example low data literacy, fragmented data collection and coverage gaps, and inadequate governance and sharing. Van den Homberg and Susha (2018) offer a whole-system data ecosystem framework spanning these and other challenges, intended to assist with evaluating data suitability for SDG monitoring. The present paper builds on that framework and retains the same case study focus – namely on SDG indicator 6.1.1 (population proportion using safely managed drinking water sources), for Malawi in central Africa. The paper will provide an overview of a study on access to safe rural drinking water supplies, conducted in partnership with a modest-sized NGO in Malawi’s WASH sector, and with a view towards enhanced indicator monitoring. Adopting a comparative perspective on sectoral data developments, the paper will introduce the NGO’s online database of water point locations, and will discuss work to augment the database with quality measurements. In addition, results will be presented from integrating water point locations with openly-available gridded population datasets produced by different organisations using different methods. Finally, based on the findings and experiences from this project, the paper will elaborate recommendations as to strengthening data ecosystems for SDG monitoring.