The WorldPop project, in collaboration with researchers at the University of Oxford’s Malaria Atlas Project, and the Bill and Melinda Gates Foundation’s Financial Services for the Poor Team, have developed and applied novel approaches to poverty mapping built on geolocated household survey data.

In a Bayesian geostatistical modelling framework, spatiotemporal relationships between household survey data and an extensive set of satellite-based covariates are leveraged to yield estimates of poverty, with associated robust confidence intervals at 1x1km resolution (Fig. 1). Predicted proportions of the population below the $1.25 and $2 a day poverty lines per 1x1km grid cell and associated uncertainty metrics are available for free download at the WorldPop site.

Fig 1 Bayesian Geostatistical Modelling Framework

Fig 1: (Top left) Geolocated household survey clusters from the 2011 Living Standards Measurement Survey (LSMS) showing the proportion of people below the $1.25 a day poverty line; (Top right) Example geospatial covariate layer showing an index of ‘accessibility’ measured through estimated travel time to the nearest large settlement; (Bottom left) Output poverty map showing predicted proportion of people below the $1.25 a day poverty line per 1x1km grid cell (Bottom right) Measurement of per-grid cell mapping uncertainty. 

Ref: The WorldPop Project

Integrating Mobile Network Data

Within the WorldPop project, with funding from the Bill & Melinda Gates Foundation, and in collaboration with the researchers at the University of Washington and with multiple mobile network operators globally, Flowminder is working on overcoming the drawbacks of the satellite-based approach through the use of anonymised mobile phone network data. Anonymised data on mobile calling patterns, phone user mobility and credit top ups amounts and frequencies have been shown in a range of studies to correlate strongly with income and poverty metrics. Moreover, such data are produced in near real-time and are spatially detailed in urban areas (see figure below), providing valuable and complementary additions to the mapping approach outlined above.

High resolution poverty map for Nigeria (available to download here) showing proportion of the population living on <$1.25 a day, and (Right) close-up showing lack of spatial detail in the poverty map for Lagos, but high number of mobile phone towers, enabling mobile network data to capture important within-city variations.

Initial results show that the addition of mobile data can significantly improve mapping accuracies over the satellite and survey approaches, as well as providing a mechanism for rapid mapping updates. Flowminder, WorldPop and partners are continuing to build and scale-up this mapping across low- and middle-income countries, with output maps available on soon.

High Resolution Poverty Map For Nigeria