1. Geospatial data can predict women’s well-being.

Researchers at the Flowminder Foundation and WorldPop project used satellite imagery to improve existing data on women and girls from demographic and health surveys in Bangladesh, Haiti, Kenya, Nigeria and Tanzania.

Why? Many types of social and health data — such as child stunting, literacy and access to modern contraception, which are the indicators Data2X focused on in this case study — are correlated with geospatial factors such as accessibility, elevation and aridity that can be mapped across entire countries using satellite imagery.

When you combine well-being indicators available in regular demographic and health surveys, then look at different aspects of geospatial data, you’ll notice a pattern where geospatial variables are correlated with DHS variables.

Huh? Geospatial data is freely available almost everywhere, while DHS data isn’t — so using the information where both are available allows you to model out what the indicators will be in the areas where you only have satellite data. In short, geospatial modeling can transform a limited number of survey data points distributed unevenly across the country into a continuous landscape of information.

The challenge? The granularity of this data could allow for more localized decision-making, but it is not always certain that one geospatial variable is always relevant for predicting one of the DHS well-being outcomes, as it varies greatly by country: “Like anything else in development, the context matters for everything,” Furst-Nichols said.

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