Beyond the Baseline

Abstract

Within the remit of 'Data for Development' there have been a number of promising recent works that investigate the use of mobile phone Call Detail Records (CDRs) to estimate the spatial distribution of poverty or socio-economic status. The methods being developed have the potential to offer immense value to organisations and agencies who currently struggle to identify the poorest parts of a country, due to the lack of reliable and up to date survey data in certain parts of the world. However, the results of this research have thus far only been presented in isolation rather than in comparison to any alternative approach or benchmark. Consequently, the true practical value of these methods remains unknown.Here, we seek to allay this shortcoming, by proposing two baseline poverty estimators grounded on concrete usage scenarios: one that exploits correlation with population density only, to be used when no poverty data exists at all; and one that also exploits spatial autocorrelation, to be used when poverty data has been collected for a few regions within a country. We then compare the predictive performance of these baseline models with models that also include features derived from CDRs, so to establish their real added value. We present extensive analysis of the performance of all these models on data acquired for two developing countries -Senegal and Ivory Coast. Our results reveal that CDR-based models do provide more accurate estimates in most cases; however, the improvement is modest and more significant when estimating (extreme) poverty intensity rates rather than mean wealth.

Understanding the Effects of the Neighbourhood Built Environment on Public Health with Open Data

“…There is a glaring opportunity to leverage non-conventional data sources to help characterise neighbourhoods, and an appropriate methodology that will allow this to be done continuously at scale. Alternative data sources, such as those from mobile phone call detail records, social networks such as Twitter, Foursquare, Google Street View and other web data, have been increasingly used in a wide range of applications from estimating poverty levels [30], measuring social diversity [10], to auditing the neighbourhood environment [29].…”