Fine scale mapping of water sources in low-income settings: A comparative study in Misungwi, Tanzania

TL;DR

This study compares two methods for mapping water sources in a low-income area of Tanzania to improve access to safe water.

Contribution

The study introduces a comparative analysis of community mapping and drone imagery for fine-scale water source mapping in low-income settings.

Findings

Drone imagery identified more unimproved water sources (225) compared to community mapping (90).

Community mapping provided insights into how water sources are used by local residents.

Drone-based mapping was more time-consuming, costly, and required advanced skills compared to community mapping.

Abstract

Access to safe water, sanitation, and hygiene is a basic human need for health and well-being. Yet, 2.2 billion people globally in 2022 did not have access to safely managed drinking water. Presently there are no publicly available methods for monitoring and measuring access to water sources in low-income settings at a fine spatial scale. The objective of this study was to map and identify areas with improved and unimproved water points in Misungwi, Tanzania using two different methods: 1) community mapping with direct field observations, and 2) drone imagery. We quantified and summarized the number of improved and unimproved water sources, as defined by the WHO/UNICEF Joint Monitoring Programme core questions and noted their specific uses where applicable. We also compared the results of both data collection methods outlining their respective advantages and limitations. The community maps and direct field observations not only served as a method to identify water sources, but also provided insights into how community members used and interacted with each water source. In contrast, the drone imagery only served as a method to systematically identify water sources in the study area. A notable advantage of the drone imagery, however, was its ability to identify more unimproved water sources (225 vs 90) compared to the direct field observations. Both methods were effective in identifying water sources at a fine scale, but the drone imagery involved a more time-intensive process, demanded advanced skills, and incurred a higher cost compared to the community mapping with direct field observations. This study highlights the need for accurate and readily accessible data on water sources which is imperative for planning, developing, and managing improved water sources, especially in underserved areas such as Misungwi, Tanzania.

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