{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T14:53:47Z","timestamp":1793458427261,"version":"4.5.0"},"reference-count":58,"publisher":"National Academy of Sciences","issue":"46","license":[{"start":{"date-parts":[[2017,10,31]],"date-time":"2017-10-31T00:00:00Z","timestamp":1509408000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000865","name":"Bill and Melinda Gates Foundation","doi-asserted-by":"publisher","award":["OPP1114791"],"award-info":[{"award-number":["OPP1114791"]}],"id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003134","name":"FNRS | Fonds pour la Formation \u00e0 la Recherche dans l\u2019Industrie et dans l\u2019Agriculture","doi-asserted-by":"publisher","award":["5211815F"],"award-info":[{"award-number":["5211815F"]}],"id":[{"id":"10.13039\/501100003134","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.pnas.org"],"crossmark-restriction":true},"short-container-title":["Proc. Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2017,11,14]]},"abstract":"<jats:title>Significance<\/jats:title>\n                  <jats:p>Spatially finest poverty maps are essential for improved diagnosis and policy planning, especially keeping in view the Sustainable Development Goals. \u201cBig Data\u201d sources like call data records and satellite imagery have shown promise in providing intercensal statistics. This study outlines a computational framework to efficiently combine disparate data sources, like environmental data, and mobile data, to provide more accurate predictions of poverty and its individual dimensions for finest spatial microregions in Senegal. These are validated using the concurrent census data.<\/jats:p>","DOI":"10.1073\/pnas.1700319114","type":"journal-article","created":{"date-parts":[[2017,10,31]],"date-time":"2017-10-31T11:59:02Z","timestamp":1509451142000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":155,"title":["Combining disparate data sources for improved poverty prediction and mapping"],"prefix":"10.1073","volume":"114","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9410-7084","authenticated-orcid":false,"given":"Neeti","family":"Pokhriyal","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, State University of New York, Buffalo, NY 14221;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9069-4143","authenticated-orcid":false,"given":"Damien Christophe","family":"Jacques","sequence":"additional","affiliation":[{"name":"Earth and Life Institute\u2013Environment, Universit\u00e9 Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"341","published-online":{"date-parts":[[2017,10,31]]},"reference":[{"key":"e_1_3_4_1_2","doi-asserted-by":"crossref","DOI":"10.1596\/978-1-4648-0723-7","volume-title":"Poverty in a Rising Africa","author":"Beegle K","year":"2016","unstructured":"K Beegle, L Christiaensen, A Dabalen, I Gaddis Poverty in a Rising Africa (World Bank Publications, Washington, DC, 2016)."},{"key":"e_1_3_4_2_2","doi-asserted-by":"crossref","first-page":"S9","DOI":"10.1111\/roiw.12013","article-title":"Africa\u2019s statistical tragedy","volume":"59","author":"Devarajan S","year":"2013","unstructured":"S Devarajan, Africa\u2019s statistical tragedy. Rev Income Wealth 59, S9\u2013S15 (2013).","journal-title":"Rev Income Wealth"},{"key":"e_1_3_4_3_2","doi-asserted-by":"crossref","first-page":"S16","DOI":"10.1111\/roiw.12006","article-title":"Comparability of GDP estimates in Sub-Saharan Africa: The effect of revisions in sources and methods since structural adjustment","volume":"59","author":"Jerven M","year":"2013","unstructured":"M Jerven, Comparability of GDP estimates in Sub-Saharan Africa: The effect of revisions in sources and methods since structural adjustment. Rev Income Wealth 59, S16\u2013S36 (2013).","journal-title":"Rev Income Wealth"},{"key":"e_1_3_4_4_2","doi-asserted-by":"crossref","unstructured":"S Alkire Mobilising the Household Data Required to Progress toward the Sdgs (University of Oxford Oxford). Available at unsdsn.org\/wp-content\/uploads\/2014\/09\/Mobilising-the-household-data-required-to-progress-toward-the-SDGs-WEB.pdf. Accessed October 24 2017. (2014).","DOI":"10.35648\/20.500.12413\/11781\/ii038"},{"key":"e_1_3_4_5_2","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1111\/1468-0262.00399","article-title":"Micro-level estimation of poverty and inequality","volume":"71","author":"Elbers C","year":"2003","unstructured":"C Elbers, JO Lanjouw, P Lanjouw, Micro-level estimation of poverty and inequality. Econometrica 71, 355\u2013364 (2003).","journal-title":"Econometrica"},{"key":"e_1_3_4_6_2","volume-title":"Small Area Estimation","author":"Rao JN","year":"2015","unstructured":"JN Rao, I Molina Small Area Estimation (John Wiley & Sons, Hoboken, NJ, 2015)."},{"key":"e_1_3_4_7_2","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1126\/science.aac4420","article-title":"Predicting poverty and wealth from mobile phone metadata","volume":"350","author":"Blumenstock J","year":"2015","unstructured":"J Blumenstock, G Cadamuro, R On, Predicting poverty and wealth from mobile phone metadata. Science 350, 1073\u20131076 (2015).","journal-title":"Science"},{"key":"e_1_3_4_8_2","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1126\/science.aaf7894","article-title":"Combining satellite imagery and machine learning to predict poverty","volume":"353","author":"Jean N","year":"2016","unstructured":"N Jean, , Combining satellite imagery and machine learning to predict poverty. Science 353, 790\u2013794 (2016).","journal-title":"Science"},{"key":"e_1_3_4_9_2","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.gloenvcha.2007.05.005","article-title":"Rural household demographics, livelihoods and the environment","volume":"18","author":"De Sherbinin A","year":"2008","unstructured":"A De Sherbinin, , Rural household demographics, livelihoods and the environment. Glob Environ Change 18, 38\u201353 (2008).","journal-title":"Glob Environ Change"},{"key":"e_1_3_4_10_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.worlddev.2014.10.015","article-title":"Conceptualizing spatial diversity in Latin American rural development: Structures, institutions, and coalitions","volume":"73","author":"Berdegu\u00e9 JA","year":"2015","unstructured":"JA Berdegu\u00e9, A Bebbington, J Escobal, Conceptualizing spatial diversity in Latin American rural development: Structures, institutions, and coalitions. World Dev 73, 1\u201310 (2015).","journal-title":"World Dev"},{"key":"e_1_3_4_11_2","doi-asserted-by":"crossref","first-page":"16769","DOI":"10.1073\/pnas.0611107104","article-title":"Spatial determinants of poverty in rural Kenya","volume":"104","author":"Okwi PO","year":"2007","unstructured":"PO Okwi, , Spatial determinants of poverty in rural Kenya. Proc Natl Acad Sci USA 104, 16769\u201316774 (2007).","journal-title":"Proc Natl Acad Sci USA"},{"key":"e_1_3_4_12_2","doi-asserted-by":"crossref","first-page":"15274","DOI":"10.1073\/pnas.0900282106","article-title":"Inferring friendship network structure by using mobile phone data","volume":"106","author":"Eagle N","year":"2009","unstructured":"N Eagle, AS Pentland, D Lazer, Inferring friendship network structure by using mobile phone data. Proc Natl Acad Sci USA 106, 15274\u201315278 (2009).","journal-title":"Proc Natl Acad Sci USA"},{"key":"e_1_3_4_13_2","volume-title":"World Development Report 2016: Digital Dividends","author":"World Bank","year":"2016","unstructured":"; World Bank World Development Report 2016: Digital Dividends (World Bank, Washington, DC, 2016)."},{"key":"e_1_3_4_14_2","unstructured":"BA Oumar Poverty map in Senegal: A disparity in sharing wealth. Available at www.lesoleil.sn\/2016-03-22-23-21-32\/item\/65075-carte-de-pauvrete-au-senegal-une-disparite-dans-le-partage-des-richesses.html. Accessed October 25 2017. (2017)."},{"key":"e_1_3_4_15_2","volume-title":"Gaussian Processes for Machine Learning","author":"Rasmussen CE","year":"2006","unstructured":"CE Rasmussen, CKI Williams Gaussian Processes for Machine Learning (MIT Press, Cambridge, MA, 2006)."},{"key":"e_1_3_4_16_2","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/BF00889887","article-title":"The origins of kriging","volume":"22","author":"Cressie N","year":"1990","unstructured":"N Cressie, The origins of kriging. Math Geol 22, 239\u2013252 (1990).","journal-title":"Math Geol"},{"key":"e_1_3_4_17_2","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou H","year":"2005","unstructured":"H Zou, T Hastie, Regularization and variable selection via the elastic net. J R Stat Soc Ser B 67, 301\u2013320 (2005).","journal-title":"J R Stat Soc Ser B"},{"key":"e_1_3_4_18_2","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1111\/j.1600-0706.2012.00299.x","article-title":"Testing the predictive performance of distribution models","volume":"122","author":"Bahn V","year":"2013","unstructured":"V Bahn, BJ McGill, Testing the predictive performance of distribution models. Oikos 122, 321\u2013331 (2013).","journal-title":"Oikos"},{"key":"e_1_3_4_19_2","unstructured":"P Sunds\u00f8y Can mobile usage predict illiteracy in a developing country? arXiv:1607.01337. (2016)."},{"key":"e_1_3_4_20_2","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.jbusres.2016.08.005","article-title":"Constructing spatiotemporal poverty indices from Big Data","volume":"70","author":"Njuguna C","year":"2017","unstructured":"C Njuguna, P McSharry, Constructing spatiotemporal poverty indices from Big Data. J Business Res 70, 318\u2013327 (2017).","journal-title":"J Business Res"},{"key":"e_1_3_4_21_2","doi-asserted-by":"crossref","first-page":"8118","DOI":"10.1080\/01431161.2013.833358","article-title":"Detection of rural electrification in Africa using DMSP-OLS night lights imagery","volume":"34","author":"Min B","year":"2013","unstructured":"B Min, KM Gaba, OF Sarr, A Agalassou, Detection of rural electrification in Africa using DMSP-OLS night lights imagery. Int J Remote Sensing 34, 8118\u20138141 (2013).","journal-title":"Int J Remote Sensing"},{"key":"e_1_3_4_22_2","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1126\/science.1186605","article-title":"Network diversity and economic development","volume":"328","author":"Eagle N","year":"2010","unstructured":"N Eagle, M Macy, R Claxton, Network diversity and economic development. Science 328, 1029\u20131031 (2010).","journal-title":"Science"},{"key":"e_1_3_4_23_2","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/978-3-642-22362-4_35","volume-title":"Proceedings of the 19th International Conference on User Modeling, Adaption and Personalization","author":"Soto V","year":"2011","unstructured":"V Soto, V Fr\u00edas-Mart\u00ednez, J Virseda, E Fr\u00edas-Mart\u00ednez, Prediction of socioeconomic levels using cell phone records. Proceedings of the 19th International Conference on User Modeling, Adaption and Personalization (Springer, Gerona, Spain), pp. 377\u2013388 (2011)."},{"key":"e_1_3_4_24_2","unstructured":"; ITU World Telecommunication Key ICT indicators for developed and developing countries and the world. Available at www.itu.int\/en\/ITU-D\/Statistics\/Pages\/default.aspx. Accessed October 21 2017. (2016)."},{"key":"e_1_3_4_25_2","unstructured":"; Autorite de R\u00e9gulation des Telecommunications et des Postes Observatoire de la telephonie mobile: Tableau de bord au 31 decembre 2013 ((Autorit\u00e9 de R\u00e9gulation des T\u00e9l\u00e9communications et des Postes Dakar Senegal) Technical Report. (2013)."},{"key":"e_1_3_4_26_2","unstructured":"; Oxford Poverty and Human Development Initiative Country briefing: Senegal. Available at www.ophi.org.uk\/wp-_content\/uploads\/Senegal-_2013.pdf?79d835. Accessed October 21 2017. (2013)."},{"key":"e_1_3_4_27_2","unstructured":"; World Bank Overview. Available at www.worldbank.org\/en\/country\/senegal\/overview. Accessed October 21 2017. (2014)."},{"key":"e_1_3_4_28_2","volume-title":"Third Conference on the Analysis of Mobile Phone Datasets, NetMob 2013","author":"Fr\u00edas-Mart\u00ednez V","year":"2013","unstructured":"V Fr\u00edas-Mart\u00ednez, V Soto, J Virseda, E Fr\u00edas-Mart\u00ednez, Can cell phone traces measure social development? Third Conference on the Analysis of Mobile Phone Datasets, NetMob 2013 (MIT Media Labs, Boston, 2013)."},{"key":"e_1_3_4_29_2","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1145\/2663204.2663254","volume-title":"Proceedings of the 16th International Conference on Multimodal Interaction","author":"Bogomolov A","year":"2014","unstructured":"A Bogomolov, , Once upon a crime: Towards crime prediction from demographics and mobile data. Proceedings of the 16th International Conference on Multimodal Interaction (Association for Computing Machinery, New York), pp. 427\u2013434 (2014)."},{"key":"e_1_3_4_30_2","doi-asserted-by":"crossref","unstructured":"YA de Montjoye J Quoidbach F Robic AS Pentland Predicting personality using novel mobile phone-based metrics. (Springer New York) pp. 48\u201355 (2013).","DOI":"10.1007\/978-3-642-37210-0_6"},{"key":"e_1_3_4_31_2","first-page":"1","article-title":"bandicoot: A python toolbox for mobile phone metadata","volume":"17","author":"de Montjoye YA","year":"2016","unstructured":"YA de Montjoye, L Rocher, AS Pentland, bandicoot: A python toolbox for mobile phone metadata. J Machine Learn Res 17, 1\u20135 (2016).","journal-title":"J Machine Learn Res"},{"key":"e_1_3_4_32_2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1177\/0022343316630359","article-title":"Using night light emissions for the prediction of local wealth","volume":"54","author":"Weidmann NB","year":"2016","unstructured":"NB Weidmann, S Schutte, Using night light emissions for the prediction of local wealth. J Peace Res 54, 125\u2013140 (2016).","journal-title":"J Peace Res"},{"key":"e_1_3_4_33_2","article-title":"Accuracy assessment of a 300 m global land cover map: The globcover experience","author":"Defourny P","year":"2009","unstructured":"P Defourny, , Accuracy assessment of a 300 m global land cover map: The globcover experience. Proceedings of the 33rd International Symposium on Remote Sensing of Environment (International Center for Remote Sensing of Environment, Tucson, AZ, 2009).","journal-title":"Proceedings of the 33rd International Symposium on Remote Sensing of Environment"},{"key":"e_1_3_4_34_2","doi-asserted-by":"crossref","first-page":"7959","DOI":"10.3390\/rs70607959","article-title":"Mapping priorities to focus cropland mapping activities: Fitness assessment of existing global, regional and national cropland maps","volume":"7","author":"Waldner F","year":"2015","unstructured":"F Waldner, S Fritz, A Di Gregorio, P Defourny, Mapping priorities to focus cropland mapping activities: Fitness assessment of existing global, regional and national cropland maps. Remote Sensing 7, 7959\u20137986 (2015).","journal-title":"Remote Sensing"},{"key":"e_1_3_4_35_2","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.agee.2005.02.024","article-title":"Effects of cultivation practices on spatial variation of soil fertility and millet yields in the Sahel of Mali","volume":"109","author":"Samak\u00e9 O","year":"2005","unstructured":"O Samak\u00e9, E Smaling, M Kropff, T Stomph, A Kodio, Effects of cultivation practices on spatial variation of soil fertility and millet yields in the Sahel of Mali. Agric Ecosyst Environ 109, 335\u2013345 (2005).","journal-title":"Agric Ecosyst Environ"},{"key":"e_1_3_4_36_2","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1023\/A:1004233303066","article-title":"Water, nutrients and slope position in on-farm pearl millet cultivation in the Sahel","volume":"195","author":"Rockstr\u00f6m J","year":"1997","unstructured":"J Rockstr\u00f6m, A De Rouw, Water, nutrients and slope position in on-farm pearl millet cultivation in the Sahel. Plant and Soil 195, 311\u2013327 (1997).","journal-title":"Plant and Soil"},{"key":"e_1_3_4_37_2","first-page":"3011","article-title":"Gaussian processes for machine learning (GPML) toolbox","volume":"11","author":"Rasmussen CE","year":"2010","unstructured":"CE Rasmussen, H Nickisch, Gaussian processes for machine learning (GPML) toolbox. J Mach Learn Res 11, 3011\u20133015 (2010).","journal-title":"J Mach Learn Res"},{"key":"e_1_3_4_38_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani R","year":"1996","unstructured":"R Tibshirani, Regression shrinkage and selection via the lasso. J R Stat Soc Ser B 58, 267\u2013288 (1996).","journal-title":"J R Stat Soc Ser B"},{"key":"e_1_3_4_39_2","first-page":"129","article-title":"Ridge regression","volume":"8","author":"Hoerl A","year":"1988","unstructured":"A Hoerl, R Kennard, Ridge regression. Encyclopedia Stat Sci 8, 129\u2013136 (1988).","journal-title":"Encyclopedia Stat Sci"},{"key":"e_1_3_4_40_2","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.jpubeco.2010.11.006","article-title":"Counting and multidimensional poverty measurement","volume":"95","author":"Alkire S","year":"2011","unstructured":"S Alkire, J Foster, Counting and multidimensional poverty measurement. J Public Econ 95, 476\u2013487 (2011).","journal-title":"J Public Econ"},{"key":"e_1_3_4_41_2","doi-asserted-by":"crossref","first-page":"15888","DOI":"10.1073\/pnas.1408439111","article-title":"Dynamic population mapping using mobile phone data","volume":"111","author":"Deville P","year":"2014","unstructured":"P Deville, , Dynamic population mapping using mobile phone data. Proc Natl Acad Sci USA 111, 15888\u201315893 (2014).","journal-title":"Proc Natl Acad Sci USA"},{"key":"e_1_3_4_42_2","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.worlddev.2014.01.026","article-title":"Measuring acute poverty in the developing world: Robustness and scope of the multidimensional poverty index","volume":"59","author":"Alkire S","year":"2014","unstructured":"S Alkire, ME Santos, Measuring acute poverty in the developing world: Robustness and scope of the multidimensional poverty index. World Dev 59, 251\u2013274 (2014).","journal-title":"World Dev"},{"key":"e_1_3_4_43_2","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/B978-1-4832-3213-3.50012-9","volume-title":"Mathematical Theory of Probability and Statistics","author":"von Mises R","year":"1964","unstructured":"R von Mises, Chapter IX\u2014Analysis of statistical data. Mathematical Theory of Probability and Statistics, ed R von Mises (Academic, Cambridge, MA), pp. 431\u2013493 (1964)."},{"key":"e_1_3_4_44_2","volume-title":"On More Robust Estimation of Skewness and Kurtosis: Simulation and Application to the S&P500 Index","author":"Kim TH","year":"2003","unstructured":"TH Kim, H White On More Robust Estimation of Skewness and Kurtosis: Simulation and Application to the S&P500 Index (Department of Economics, University of California, San Diego, La Jolla, CA, 2003)."},{"key":"e_1_3_4_45_2","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1002\/joc.1276","article-title":"Very high resolution interpolated climate surfaces for global land areas","volume":"25","author":"Hijmans RJ","year":"2005","unstructured":"RJ Hijmans, SE Cameron, JL Parra, PG Jones, A Jarvis, Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25, 1965\u20131978 (2005).","journal-title":"Int J Climatol"},{"key":"e_1_3_4_46_2","unstructured":"; Direction de l\u2019Analyse de la Pr\u00e9vision et des Statistiques Agricoles Rapport de Pr\u00e9sentation des R\u00e9sultats D\u00e9finitifs de la Campagne Agricole 2012-2013 (Minist\u00e8 re de l\u2019Agriculture et de l\u2019\u00c9quipement Rural Dakar Senegal) Technical Report. (2013)."},{"key":"e_1_3_4_47_2","volume-title":"Fourth Conference on the Analysis of Mobile Phone Datasets, NetMob 2015","author":"Jacques D","year":"2015","unstructured":"D Jacques, , Genesis of millet prices in Senegal: The role of production, markets and their failures. Fourth Conference on the Analysis of Mobile Phone Datasets, NetMob 2015 (MIT Media Labs, Boston, 2015)."},{"key":"e_1_3_4_48_2","unstructured":"U Leonardi Senegal Land Cover Mapping (Food and Agriculture Organization Rome) Technical Report. (2008)."},{"key":"e_1_3_4_49_2","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.worlddev.2004.10.003","article-title":"Where is the poverty\u2013environment nexus? Evidence from Cambodia, Lao PDR, and Vietnam","volume":"33","author":"Dasgupta S","year":"2005","unstructured":"S Dasgupta, U Deichmann, C Meisner, D Wheeler, Where is the poverty\u2013environment nexus? Evidence from Cambodia, Lao PDR, and Vietnam. World Development 33, 617\u2013638 (2005).","journal-title":"World Development"},{"key":"e_1_3_4_50_2","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/978-94-007-0671-2_16","volume-title":"Spatial Analysis and Modeling in Geographical Transformation Process","author":"Vista BM","year":"2011","unstructured":"BM Vista, Y Murayama, Spatial determinants of poverty using GIS-based mapping. Spatial Analysis and Modeling in Geographical Transformation Process (Springer, New York), pp. 275\u2013296 (2011)."},{"key":"e_1_3_4_51_2","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.foodpol.2005.09.006","article-title":"Spatial clustering of rural poverty and food insecurity in Sri Lanka","volume":"30","author":"Amarasinghe U","year":"2005","unstructured":"U Amarasinghe, M Samad, M Anputhas, Spatial clustering of rural poverty and food insecurity in Sri Lanka. Food Pol 30, 493\u2013509 (2005).","journal-title":"Food Pol"},{"key":"e_1_3_4_52_2","unstructured":"N Minot Poverty and inequality in Vietnam: Spatial patterns and geographic determinants (International Food Policy Research Institute World Bank Washington DC). Available at siteresources.worldbank.org\/INTPGI\/Resources\/342674-1092157888460\/Minot.PovertyInequalityVietnam.pdf. Accessed October 24 2017. (2006)."},{"key":"e_1_3_4_53_2","unstructured":"D Rogers T Emwanu T Robinson Poverty mapping in Uganda: An analysisusing remotely sensed and other environmental data (Pro-Poor Livestock Policy Initiative Food and Agriculture Organization Rome) Working Paper No. 36. (2006)."},{"key":"e_1_3_4_54_2","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1016\/j.foodpol.2005.09.004","article-title":"An investigation of the spatial determinants of the local prevalence of poverty in rural Malawi","volume":"30","author":"Benson T","year":"2005","unstructured":"T Benson, J Chamberlin, I Rhinehart, An investigation of the spatial determinants of the local prevalence of poverty in rural Malawi. Food Pol 30, 532\u2013550 (2005).","journal-title":"Food Pol"},{"key":"e_1_3_4_55_2","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.foodpol.2005.10.001","article-title":"Spatial patterns of rural poverty and their relationship with welfare-influencing factors in Bangladesh","volume":"30","author":"Kam SP","year":"2005","unstructured":"SP Kam, M Hossain, ML Bose, LS Villano, Spatial patterns of rural poverty and their relationship with welfare-influencing factors in Bangladesh. Food Pol 30, 551\u2013567 (2005).","journal-title":"Food Pol"},{"key":"e_1_3_4_56_2","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.worlddev.2015.10.031","article-title":"Understanding the evidence base for poverty\u2013environment relationships using remotely sensed satellite data: An example from Assam, India","volume":"78","author":"Watmough GR","year":"2016","unstructured":"GR Watmough, PM Atkinson, A Saikia, CW Hutton, Understanding the evidence base for poverty\u2013environment relationships using remotely sensed satellite data: An example from Assam, India. World Dev 78, 188\u2013203 (2016).","journal-title":"World Dev"},{"key":"e_1_3_4_57_2","volume-title":"Third Conference on the Analysis of Mobile Phone Datasets, NetMob 2013","author":"Smith C","year":"2013","unstructured":"C Smith, A Mashhadi, L Capra, Ubiquitous sensing for mapping poverty in developing countries. Third Conference on the Analysis of Mobile Phone Datasets, NetMob 2013 (MIT Media Labs, Boston, 2013)."},{"key":"e_1_3_4_58_2","volume-title":"Fourth Conference on the Analysis of Mobile Phone Datasets, NetMob 2015","author":"Pokhriyal N","year":"2015","unstructured":"N Pokhriyal, W Dong, Virtual network and poverty analysis in Senegal. Fourth Conference on the Analysis of Mobile Phone Datasets, NetMob 2015 (MIT Media Labs, Boston, 2015)."}],"container-title":["Proceedings of the National Academy of Sciences"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.pnas.org\/syndication\/doi\/10.1073\/pnas.1700319114","content-type":"unspecified","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pnas.org\/doi\/pdf\/10.1073\/pnas.1700319114","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T01:29:43Z","timestamp":1719538183000},"score":1,"resource":{"primary":{"URL":"https:\/\/pnas.org\/doi\/full\/10.1073\/pnas.1700319114"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,31]]},"references-count":58,"journal-issue":{"issue":"46","published-print":{"date-parts":[[2017,11,14]]}},"alternative-id":["10.1073\/pnas.1700319114"],"URL":"https:\/\/doi.org\/10.1073\/pnas.1700319114","relation":{},"ISSN":["0027-8424","1091-6490"],"issn-type":[{"value":"0027-8424","type":"print"},{"value":"1091-6490","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10,31]]},"assertion":[{"value":"2017-10-31","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}