Estimating clean water availability seems surprisingly difficult given lack of coordinated national data collection.
”...surveys are labor-intensive and expensive, so information is only gathered once every five to 10 years. Anything that affects water use at a shorter timescale, from livestock farming to seasonal changes in rainfall, won’t be captured. And until recently, surveys didn’t ask about water quality at all, Greenwood added. For most regions, only one survey’s worth of data on drinking water contamination exists so far, which makes it difficult to assess trends over time.
Greenwood’s team incorporated 39 different sources of geospatial data in their study, gathered on land and via satellite, in addition to survey data from over 64,000 households across 27 countries between 2016 and 2020. They used all of this information to train machine learning models to estimate whether the water in a given place met four safety criteria from the WHO/UNICEF Joint Monitoring Programme (JMP), which collects data on water supply, sanitation, and hygiene: improved (as in, from a source that could be safe, like pipes, rather than an unprotected well), and whether it was available when needed, accessible without a commute, and free from fecal contamination.”
Over 4 billion people don’t have access to clean drinking water at home, more than 2x previous estimates
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Estimating clean water availability seems surprisingly difficult given lack of coordinated national data collection.
”...surveys are labor-intensive and expensive, so information is only gathered once every five to 10 years. Anything that affects water use at a shorter timescale, from livestock farming to seasonal changes in rainfall, won’t be captured. And until recently, surveys didn’t ask about water quality at all, Greenwood added. For most regions, only one survey’s worth of data on drinking water contamination exists so far, which makes it difficult to assess trends over time.
Greenwood’s team incorporated 39 different sources of geospatial data in their study, gathered on land and via satellite, in addition to survey data from over 64,000 households across 27 countries between 2016 and 2020. They used all of this information to train machine learning models to estimate whether the water in a given place met four safety criteria from the WHO/UNICEF Joint Monitoring Programme (JMP), which collects data on water supply, sanitation, and hygiene: improved (as in, from a source that could be safe, like pipes, rather than an unprotected well), and whether it was available when needed, accessible without a commute, and free from fecal contamination.”