This map shows estimates of wealth and poverty around the world. The estimates are computed from satellite imagery, internet data, and other non-traditional sources of data.
The height and color of each grid cell help visualize micro-regional wealth. By default, cell height indicates absolute wealth (relative to the world, and labeled "AWE"), and cell color indicates relative wealth (relative to other regions of the country, and labeled "RWI").
You can choose different metrics to display by clicking on the settings icon. The default view simultaneously displays the Absolute Wealth Estimate (using cell height) and the Relative Wealth Index (using cell color) of each location. This view (AWE+confidence) instead uses cell color to indicate the confidence of the estimate at each grid cell.
Explore the map yourself by clicking and dragging, by using the scroll wheel to zoom, and by using the right mouse button (or holding CTRL) to tilt or rotate the point of view.
Absolute Wealth
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AWE+RWI simultaneously displays the Absolute Wealth Estimate (using cell height) and the Relative Wealth Index (using cell color) of each location. Refer to our paper to learn more about what these numbers mean.
AWE+Confidence simultaneously displays the Absolute Wealth Estimate (using cell height) and the confidence of the estimate (using cell color) of each location. Refer to our paper to learn more about what these numbers mean.
RWI+Confidence simultaneously displays the Relative Wealth Index (using cell color) and the confidence of the estimate (using transparency) of each location. Refer to our paper to learn more about what these numbers mean.
By default, the height of the bars auto-adjusts to the region being displayed. By setting the height manually, you can compare the height of two different locations using the same scale.
Absolute Wealth
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Absolute Wealth
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Confidence
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The mean confidence index indicates the average confidence of the wealth estimates, averaged over the entire region shown in the current view.
In each country, surveyors go to hundreds of villages. In each village, dozens of households are surveyed in person. Each survey takes 2-4 hours, and is designed to measure the socioeconomic status of the household.
The data sources include satellites, mobile phone networks, social media, and other geospatial data. These “big” data sources are matched to the “traditional” survey data using geographic markers present in both datasets.
These algorithms “learn” which characteristics of the big data are useful in predicting wealth. For instance, villages with good cell phone coverage, and with lots of paved surface, tend to be wealthier.
Once the machine learning model is trained and validated using ground truth data (i.e., once we ensure that the model can accurately predict the wealth of villages where we know the wealth values), the model is used to predict the wealth of regions where there is no survey data.
Absolute Wealth
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Mean Confidence: