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 population, and cell color indicates relative wealth (relative to other regions of the country).
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.
RWI: >1.0
RWI: 0.5
RWI: <-1.0
RWI: 0
Population
Height
higher
lower
Relative
Wealth
Color
RWI: >1.0
RWI: 0.5
RWI: <-1.0
RWI: 0
Population
Height
higher
lower
Confidence
Index
Color
RWI: >1.0
RWI: 0.5
RWI: <-1.0
RWI: 0
higher
lower
Relative
Wealth
Color
Confidence
lesser
greater
Opacity
POP+RWI simultaneously displays the Population (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.
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.
RWI: >1.0
RWI: 0.5
RWI: <-1.0
RWI: 0
Population
Height
higher
lower
Relative
Wealth
Color
RWI: >1.0
RWI: 0.5
RWI: <-1.0
RWI: 0
higher
lower
Relative
Wealth
Color
Confidence
lesser
greater
Opacity
The mean confidence index indicates the average confidence of the wealth estimates, averaged over the entire region shown in the current view.
For each village, we also obtain “big data” from a variety of nontraditional sources
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.
Nigeria
(N=899)
Geospatial Big Data
Ground Truth Data
For each village, we also obtain “big data” from a variety of nontraditional sources
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.
Nigeria
(N=899)
Geospatial Big Data
Ground Truth Data
Machine learning algorithms are trained to predict village wealth from the novel sources of “big” data.
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.
Geospatial Big Data
Ground Truth Data
Supervised Machine Learning
Trained Model
The trained model produces high-resolution predictions, which are then validated
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.
Additional Resources
BETA TESTING
Population
MAX
0
Height
higher
lower
Relative Wealth
MAX
MIN
Color
Confidence
Index
1
0
Color
Mean Confidence:
