Mapping poverty using mobile phone and satellite data.
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poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date.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.
Geography and Environment, University of Southampton, University Road, Building 44, Southampton, UK steele.jessica@gmail.com.|Flowminder Foundation, Roslagsgatan 17, Stockholm, Sweden.
Jessica E
0000-0001-6741-1195
JE
Steele
Telenor Group Research, Oslo, Norway.
Pål Roe
0000-0003-4596-3209
PR
Sundsøy
Geography and Environment, University of Southampton, University Road, Building 44, Southampton, UK.
Carla
C
Pezzulo
Geography and Environment, University of Southampton, University Road, Building 44, Southampton, UK.
Victor A
0000-0001-5177-9227
VA
Alegana
Geography and Environment, University of Southampton, University Road, Building 44, Southampton, UK.
Tomas J
0000-0003-1345-6480
TJ
Bird
School of Information, University of California, Berkeley, CA, USA.
Joshua
0000-0002-1813-7414
J
Blumenstock
Telenor Group Research, Oslo, Norway.
Johannes
J
Bjelland
Telenor Group Research, Oslo, Norway.
Kenth
0000-0003-1618-7597
K
Engø-Monsen
Data Science Institute, Imperial College London, London, UK.
Yves-Alexandre
0000-0002-2559-5616
YA
de Montjoye
Grameenphone Ltd, Dhaka, Bangladesh.
Asif M
AM
Iqbal
Grameenphone Ltd, Dhaka, Bangladesh.
Khandakar N
KN
Hadiuzzaman
Flowminder Foundation, Roslagsgatan 17, Stockholm, Sweden.|Public Health Sciences, Karolinska Institute, Stockholm, Sweden.|College of Information System and Management, National University of Defense Technology, Changsha, Hunan, People's Republic of China.
Xin
X
Lu
Flowminder Foundation, Roslagsgatan 17, Stockholm, Sweden.|Stockholm School of Economics, Saltmätargatan 13-17, Stockholm, Sweden.
Erik
E
Wetter
Geography and Environment, University of Southampton, University Road, Building 44, Southampton, UK.|Flowminder Foundation, Roslagsgatan 17, Stockholm, Sweden.|John E Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
Andrew J
AJ
Tatem
Flowminder Foundation, Roslagsgatan 17, Stockholm, Sweden.|Public Health Sciences, Karolinska Institute, Stockholm, Sweden.
Linus
0000-0001-9712-2669
L
Bengtsson
D040421:Cell Phone; D006801:Humans; D008962:Models, Theoretical; D011203:Poverty; D011237:Predictive Value of Tests; D017215:Satellite Communications
Bayesian geostatistical modelling; mobile phone data; poverty mapping; remote sensing