Mapping poverty using mobile phone and satellite data
By J. Steele, P. Sundsøy, C. Pezzulo, V. Alegana, Tomas J. Bird, Joshua Blumenstock, Johannes Bjelland, Kenth Engø-Monsen, Y. de Montjoye, Asif M. Iqbal and 5 more. Journal of the Royal Society Interface, 2017
313 citations recorded by the source corpus.
Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study demonstrates how public and private data sources that are commonly available for LMICs can be used to provide novel insight into the spatial distribution of poverty. We evaluate the relative value of modelling three traditional poverty measures using aggregate data from mobile operators and widely available geospatial data. Taken together, models combining these data sources provide the best predictive power (highest r2 = 0.78) and lowest error, but generally models employing mobile data only yield comparable results, offering the potential to measure poverty more frequently and at finer granularity. Stratifying models into urban and rural areas highlights the advantage of using mobile data in urban areas and different data in different contexts. The findings indicate the possibility to estimate and continually monitor poverty rates at high spatial resolution in countries with limited capacity to support traditional methods of data collection.
Subfields: Global and Planetary Change, Modeling and Simulation, Transportation
View Paper: https://www.semanticscholar.org/paper/2ca1ef554511c4f44642445b14b983113e88933d
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In Global and Planetary Change
Citations in the corpus
- Mobile phone location data for disasters: A review from natural hazards and epidemics (2021, unknown): “Combining these datasets with mobile phone location data and human mobility analytics (e.g., application in poverty estimation (Steele et al., 2017) could enable a more holistic understanding of the social, physical, and economic dimensions of the disaster response and recovery dynamics.”
- Using satellite imagery to understand and promote sustainable development (2019, unknown): “These data fusion approaches have become increasingly common, with researchers demonstrating how combining imagery with data from cell phones (27), Wikipedia (48), social media (49), or Open StreetMap (51) can improve predictions.”
- Local Private Hypothesis Testing: Chi-Square Tests (2017, background): “Hypothesis testing was initially developed for scientific and survey data, but today it is also an essential tool to test models over collections of social network, mobile, and crowdsourced data (American Statistical Association, 2014; Hunter et al., 2008; Steele et al., 2017).”
- Climate vulnerability mapping: A systematic review and future prospects (2019, background): “…frequent small-increment top-ups on cell phone plans signal real-time reductions in household income that can be linked to increased vulnerability (Steele et al., 2017), and lighting as observed from satellite night time lights is similarly linked to wellbeing (Ghosh, Anderson, Elvidge, & Sutton,…”