More Web Proxy on the site http://driver.im/

research-article

Poverty on the cheap: estimating poverty maps using aggregated mobile communication networks

Authors:

Christopher Smith-Clarke,

Licia CapraAuthors Info & Claims

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 511 - 520

https://doi.org/10.1145/2556288.2557358

Published: 26 April 2014 Publication History

Abstract

Governments and other organisations often rely on data collected by household surveys and censuses to identify areas in most need of regeneration and development projects. However, due to the high cost associated with the data collection process, many developing countries conduct such surveys very infrequently and include only a rather small sample of the population, thus failing to accurately capture the current socio-economic status of the country's population. In this paper, we address this problem by means of a methodology that relies on an alternative source of data from which to derive up to date poverty indicators, at a very fine level of spatio-temporal granularity. Taking two developing countries as examples, we show how to analyse the aggregated call detail records of mobile phone subscribers and extract features that are strongly correlated with poverty indexes currently derived from census data.

References

[1]

Aker, J. C., and Mbiti, I. M. Mobile Phones and Economic Development in Africa. Journal of Economic Perspectives 24, 3 (2010), 207--232.

[2]

Balcan, D., Colizza, V., Gonçalves, B., Hu, H., Ramasco, J. J., and Vespignani, A. Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences of the USA of America 106, 51 (Dec. 2009), 21484--9.

[3]

Barrat, A., BarthŘlemy, M., Pastor-Satorras, R., and Vespignani, A. The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the USA of America 101, 11 (Mar. 2004), 3747--52.

[4]

Blondel, V. D., Esch, M., Chan, C., Clerot, F., Deville, P., Huens, E., Morlot, F., Smoreda, Z., and Ziemlicki, C. Data for Development: the D4D Challenge on Mobile Phone Data. 10.

[5]

Blumenstock, J., and Eagle, N. Mobile divides: gender, socioeconomic status, and mobile phone use in rwanda. In Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development, ACM (2010), 6.

Digital Library

[6]

Burke, M., Marlow, C., and Lento, T. Social network activity and social well-being. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '10, ACM (New York, NY, USA, 2010), 1909--1912.

Digital Library

[7]

Doll, C. H., Muller, J.-P., and Elvidge, C. D. Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions. AMBIO: a Journal of the Human Environment 29, 3 (2000), 157--162.

[8]

Eagle, N., Macy, M., and Claxton, R. Network diversity and economic development. Science (New York, N.Y.) 328, 5981 (May 2010), 1029--31.

[9]

Ebener, S., Murray, C., Tandon, A., and Elvidge, C. C. From wealth to health: modelling the distribution of income per capita at the sub-national level using night-time light imagery. International Journal of Health Geographics 4, 1 (2005), 5.

[10]

Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., and Davis, E. R. Mapping city lights with nighttime data from the dmsp operational linescan system. Photogrammetric Engineering and Remote Sensing 63, 6 (1997), 727--734.

[11]

Frias-martinez, V., Soto, V., Virseda, J., and Frias-martinez, E. Computing Cost-Effective Census Maps From Cell Phone Traces. In Pervasive Urban Applications (PURBA) (Newcastle, 2012).

[12]

Frias-Martinez, V., and Virseda, J. On the relationship between socio-economic factors and cell phone usage. In Fifth International Conference on Information and Communication Technologies and Development (ICTD '12), ACM Press (New York, New York, USA, Mar. 2012).

Digital Library

[13]

Frias-Martinez, V., Virseda-Jerez, J., and Frias-Martinez, E. On the relation between socio-economic status and physical mobility. Information Technology for Development 18, 2 (Apr. 2012), 91--106.

[14]

Gutierrez, T., Krings, G., and Blondel, V. D. Indicators of wealth, economic diversity and segregation in cøte divoire using mobile phone datasets. In Netmob 2013 Book of Abstracts (2013).

[15]

International Monetary Fund. Côte divoire: Poverty reduction strategy paper. Tech. rep., 2009.

[16]

Jung, W., and Wang, F. Gravity model in the Korean highway. Europhysics Letters 81 (2008).

[17]

Kaluza, P., Kölzsch, A., Gastner, M. T., and Blasius, B. The complex network of global cargo ship movements. Journal of the Royal Society, Interface / the Royal Society 7, 48 (July 2010), 1093--103.

[18]

Kramer, A. D. I. An Unobtrusive Behavioral Model of Gross National Happiness. In Proceedings of the 28th ACM CHI, ACM Press (New York, New York, USA, Apr. 2010), 287--290.

Digital Library

[19]

Krings, G., Calabrese, F., Ratti, C., and Blondel, V. D. Urban gravity: a model for inter-city telecommunication flows. Journal of Statistical Mechanics: Theory and Experiment 2009, 07 (May 2009), L07003.

[20]

Masucci, A. P., Serras, J., Johansson, A., and Batty, M. Gravity vs radiation model: on the importance of scale and heterogeneity in commuting flows.

[21]

Noor, A. M., Alegana, V. a., Gething, P. W., Tatem, A. J., and Snow, R. W. Using remotely sensed night-time light as a proxy for poverty in Africa. Population health metrics 6 (Jan. 2008), 5.

[22]

Parate, A., and Miklau, G. A framework for safely publishing communication traces. In Proceedings of the 18th ACM Conference on Information and Knowledge Management (2009), 1469--1472.

Digital Library

[23]

Quercia, D., Ellis, J., Capra, L., and Crowcroft, J. Tracking Gross Community Happiness from Tweets. In Proceedings of ACM CSCW 2012 (2012).

Digital Library

[24]

Quercia, D., Seaghdha, D. O., and Crowcroft, J. Talk of the City: Our Tweets, Our Community Happiness. In Proc. of AAAI ICWSM (2012).

[25]

Sachs, J. D., and Warner, A. M. Source of Slow Growth in African Economies. Journal of African Economics 6, 3 (1997), 335--376.

[26]

Simini, F., González, M. C., Maritan, A., and Barabási, A.-L. A universal model for mobility and migration patterns. Nature 484, 7392 (Apr. 2012), 96--100.

[27]

Smith, C., Quercia, D., and Capra, L. Finger on the pulse. In Proceedings of the 2013 conference on Computer supported cooperative work CSCW '13, ACM Press (Feb. 2013), 683.

Digital Library

[28]

Soto, V., and Frías-Martínez, E. Automated land use identification using cell-phone records. In Proceedings of the 3rd ACM international workshop on MobiArch, ACM (2011), 17--22.

Digital Library

[29]

Soto, V., Frias-Martinez, V., Virseda, J., and Frias-Martinez, E. Prediction of socioeconomic levels using cell phone records. User Modeling, Adaption and Personalization (2011), 377--388.

Digital Library

[30]

Sutton, P., Roberts, D., Elvidge, C., and Baugh, K. Census from heaven: an estimate of the global human population using night-time satellite imagery. International Journal of Remote Sensing 22, 16 (2001), 3061--3076.

[31]

Viboud, C., Bjørnstad, O. N., Smith, D. L., Simonsen, L., Miller, M. A., and Grenfell, B. T. Synchrony, waves, and spatial hierarchies in the spread of influenza. Science (New York, N.Y.) 312, 5772 (Apr. 2006), 447--51.

[32]

Yan, X.-y., Zhao, C., Fan, Y., Di, Z., and Wang, W.-x. Universal Predictability of Mobility Patterns in Cities. 1--19.

[33]

Zipf, G. The P 1 P 2/D hypothesis: On the intercity movement of persons. American Sociological Review 11, 6 (1946), 677--686.

Cited By

Palma ABitoulas ADepire AReis FRiera PSopranidis I(2024)A web intelligence information system to support the production of EuroGroups Register (EGR) statisticsStatistical Journal of the IAOS10.3233/SJI-240032(1-14)Online publication date: 8-Jul-2024
https://doi.org/10.3233/SJI-240032
Milusheva SBarriga-Cabanillas OMakhlouk OZhang R(2024)Rapid poverty estimation using ready-to-use mobile phone data: An application to Côte d’IvoireProceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3674829.3675061(60-73)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3674829.3675061
Wobcke WMariyah S(2023)Machine learning and data augmentation in the proxy means test for poverty targetingStatistical Journal of the IAOS10.3233/SJI-23003339:4(961-977)Online publication date: 15-Dec-2023
https://doi.org/10.3233/SJI-230033
Show More Cited By

Index Terms

Poverty on the cheap: estimating poverty maps using aggregated mobile communication networks
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Social and professional topics
  1. Professional topics
    1. Computing and business

Recommendations

Increasing participation in the information society by people with disabilities and their families in lower-income countries using mainstream technologies

Assistive technology (AT) has been actively researched, developed and implemented throughout higher-income countries, but is relatively absent from lower-income countries. In lower-income countries, there is very little AT for reading, writing, ...
Socio-Economic Differences and Deployment of the LDC Micro-Finance Bottom-up Approach in DCs

Microfinance MF, after showing great success in poverty-relief in less-developed countries LDCs, has experiencing rapid growth and interest in developed countries DCs. However, current DC MF literature gives the impression that survival concerns are ...
Moving Beyond Digital Literacy and Towards e-Governance in Rural India
ICEGOV '17: Proceedings of the Special Collection on eGovernment Innovations in India

The ongoing Digital India Programme envisions universal digital literacy. This reflexive piece considers the context of this bold declaration to universalize digital literacy in rural India. We present civil society perspective on social challenges of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2014

4206 pages

ISBN:9781450324731

DOI:10.1145/2556288

General Chairs:
Matt Jones
Swansea University, Wales, UK
,
Philippe Palanque
Université Paul Sabatier, France
,
Program Chairs:
Albrecht Schmidt
University of Stuttgart, Germany
,
Tovi Grossman
Autodesk Research, Canada

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CHI '14

Sponsor:

SIGCHI

CHI '14: CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2014

Ontario, Toronto, Canada

Acceptance Rates

CHI '14 Paper Acceptance Rate 465 of 2,043 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

51
Total Citations
View Citations
767
Total Downloads

Downloads (Last 12 months)31
Downloads (Last 6 weeks)3

Reflects downloads up to 12 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Palma ABitoulas ADepire AReis FRiera PSopranidis I(2024)A web intelligence information system to support the production of EuroGroups Register (EGR) statisticsStatistical Journal of the IAOS10.3233/SJI-240032(1-14)Online publication date: 8-Jul-2024
https://doi.org/10.3233/SJI-240032
Milusheva SBarriga-Cabanillas OMakhlouk OZhang R(2024)Rapid poverty estimation using ready-to-use mobile phone data: An application to Côte d’IvoireProceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3674829.3675061(60-73)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3674829.3675061
Wobcke WMariyah S(2023)Machine learning and data augmentation in the proxy means test for poverty targetingStatistical Journal of the IAOS10.3233/SJI-23003339:4(961-977)Online publication date: 15-Dec-2023
https://doi.org/10.3233/SJI-230033
Nurek MMichalski RLizardo ORizoiu M(2023)Predicting Relationship Labels and Individual Personality Traits From Telecommunication History in Social Networks Using Hawkes ProcessesIEEE Access10.1109/ACCESS.2023.323897011(8492-8503)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3238970
Leite PTesliuc EWai-Poi MGrosh M(2022)How to Harness the Power of Data and Inference: Technical Discussion for Selected Targeting MethodsRevisiting Targeting in Social Assistance: A New Look at Old Dilemmas10.1596/978-1-4648-1814-1_ch6(341-465)Online publication date: 12-Sep-2022
https://doi.org/10.1596/978-1-4648-1814-1_ch6
Iwata TTanaka Y(2022)Few-shot learning for spatial regression via neural embedding-based Gaussian processesMachine Language10.1007/s10994-021-06118-z111:4(1239-1257)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10994-021-06118-z
Egedy TSágvári B(2021)Urban geographical patterns of the relationship between mobile communication, social networks and economic development – the case of HungaryHungarian Geographical Bulletin10.15201/hungeobull.70.2.370:2(129-148)Online publication date: 30-Jun-2021
https://doi.org/10.15201/hungeobull.70.2.3
B SChen SSi HZhu YBai ZLi S(2021)A Feature-based Deep Neural Framework for Poverty Prediction2021 2nd International Conference on Computing and Data Science (CDS)10.1109/CDS52072.2021.00103(568-573)Online publication date: Jan-2021
https://doi.org/10.1109/CDS52072.2021.00103
Steele JPezzulo CAlbert MBrooks Czu Erbach-Schoenberg EO’Connor SSundsøy PEngø-Monsen KNilsen KGraupe BNyachhyon RSilpakar PTatem A(2021)Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settingsHumanities and Social Sciences Communications10.1057/s41599-021-00953-08:1Online publication date: 22-Nov-2021
https://doi.org/10.1057/s41599-021-00953-0
Ohki YIkeda YIyetomi H(2021)Design of Social Isolation IndexBig Data Analysis on Global Community Formation and Isolation10.1007/978-981-15-4944-1_13(435-463)Online publication date: 13-Jun-2021
https://doi.org/10.1007/978-981-15-4944-1_13
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents