{"id":"https://openalex.org/W4385679602","doi":"https://doi.org/10.1145/3588001.3609359","title":"Poverty rate prediction using multi-modal survey and earth observation data","display_name":"Poverty rate prediction using multi-modal survey and earth observation data","publication_year":2023,"publication_date":"2023-08-08","ids":{"openalex":"https://openalex.org/W4385679602","doi":"https://doi.org/10.1145/3588001.3609359"},"language":"en","primary_location":{"id":"doi:10.1145/3588001.3609359","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3588001.3609359","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066039084","display_name":"Simone Fobi","orcid":"https://orcid.org/0000-0001-6893-3796"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Simone Fobi","raw_affiliation_strings":["AI for Good Lab, Microsoft, United States of America"],"raw_orcid":"https://orcid.org/0000-0001-6893-3796","affiliations":[{"raw_affiliation_string":"AI for Good Lab, Microsoft, United States of America","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114949522","display_name":"M. Cardona","orcid":"https://orcid.org/0009-0007-6518-644X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manuel Cardona","raw_affiliation_strings":["Innovations for Poverty Action, Mexico"],"raw_orcid":"https://orcid.org/0009-0007-6518-644X","affiliations":[{"raw_affiliation_string":"Innovations for Poverty Action, Mexico","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003626512","display_name":"Elliott J. Collins","orcid":"https://orcid.org/0000-0001-6977-0853"},"institutions":[{"id":"https://openalex.org/I1313272365","display_name":"Innovations for Poverty Action","ror":"https://ror.org/0235ad950","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1313272365"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Elliott Collins","raw_affiliation_strings":["Innovations for Poverty Action, USA"],"raw_orcid":"https://orcid.org/0000-0001-6977-0853","affiliations":[{"raw_affiliation_string":"Innovations for Poverty Action, USA","institution_ids":["https://openalex.org/I1313272365"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066063583","display_name":"Caleb Robinson","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Caleb Robinson","raw_affiliation_strings":["AI for Good Lab, Microsoft, USA"],"raw_orcid":"https://orcid.org/0000-0003-1975-4454","affiliations":[{"raw_affiliation_string":"AI for Good Lab, Microsoft, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101621929","display_name":"Anthony Ortiz","orcid":"https://orcid.org/0009-0001-5722-5273"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anthony Ortiz","raw_affiliation_strings":["AI for Good Lab, Microsoft, USA"],"raw_orcid":"https://orcid.org/0009-0001-5722-5273","affiliations":[{"raw_affiliation_string":"AI for Good Lab, Microsoft, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041292897","display_name":"Tina Sederholm","orcid":"https://orcid.org/0009-0009-7715-2700"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tina Sederholm","raw_affiliation_strings":["AI for Good Lab, Microsoft, USA"],"raw_orcid":"https://orcid.org/0009-0009-7715-2700","affiliations":[{"raw_affiliation_string":"AI for Good Lab, Microsoft, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022013110","display_name":"Rahul Dodhia","orcid":"https://orcid.org/0000-0003-3812-3906"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rahul Dodhia","raw_affiliation_strings":["AI for Good Lab, Microsoft, USA"],"raw_orcid":"https://orcid.org/0000-0003-3812-3906","affiliations":[{"raw_affiliation_string":"AI for Good Lab, Microsoft, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025368433","display_name":"Juan Lavista Ferres","orcid":"https://orcid.org/0000-0002-9654-3178"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Juan Lavista Ferres","raw_affiliation_strings":["AI for Good Lab, Microsoft, USA"],"raw_orcid":"https://orcid.org/0000-0002-9654-3178","affiliations":[{"raw_affiliation_string":"AI for Good Lab, Microsoft, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7459,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.69350596,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"23","last_page":"29"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11963","display_name":"Impact of Light on Environment and Health","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11963","display_name":"Impact of Light on Environment and Health","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10226","display_name":"Land Use and Ecosystem Services","score":0.9843999743461609,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10410","display_name":"COVID-19 epidemiological studies","score":0.9336000084877014,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.7318674325942993},{"id":"https://openalex.org/keywords/poverty","display_name":"Poverty","score":0.6190366148948669},{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"Satellite imagery","score":0.6119251847267151},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6102763414382935},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.49057191610336304},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44827577471733093},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.4432174861431122},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.43904730677604675},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.42934057116508484},{"id":"https://openalex.org/keywords/survey-data-collection","display_name":"Survey data collection","score":0.4254530072212219},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37612175941467285},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.35551416873931885},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3372640609741211},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.33650627732276917},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3357403874397278},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.32377755641937256},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15720972418785095},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.08259603381156921}],"concepts":[{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.7318674325942993},{"id":"https://openalex.org/C189326681","wikidata":"https://www.wikidata.org/wiki/Q10294","display_name":"Poverty","level":2,"score":0.6190366148948669},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.6119251847267151},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6102763414382935},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.49057191610336304},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44827577471733093},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.4432174861431122},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.43904730677604675},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.42934057116508484},{"id":"https://openalex.org/C198477413","wikidata":"https://www.wikidata.org/wiki/Q7647069","display_name":"Survey data collection","level":2,"score":0.4254530072212219},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37612175941467285},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.35551416873931885},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3372640609741211},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33650627732276917},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3357403874397278},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.32377755641937256},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15720972418785095},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.08259603381156921},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3588001.3609359","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3588001.3609359","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 6th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/1","score":0.6000000238418579,"display_name":"No poverty"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2149033360","https://openalex.org/W2513506629","https://openalex.org/W2566701672","https://openalex.org/W2583949569","https://openalex.org/W2766068522","https://openalex.org/W3043541567","https://openalex.org/W3133962347","https://openalex.org/W3165855014","https://openalex.org/W4200003491","https://openalex.org/W4306179905","https://openalex.org/W6894143524"],"related_works":["https://openalex.org/W2028495302","https://openalex.org/W2002261065","https://openalex.org/W1513656766","https://openalex.org/W2780186965","https://openalex.org/W2145230572","https://openalex.org/W2166312020","https://openalex.org/W2150750161","https://openalex.org/W4384389756","https://openalex.org/W3136989387","https://openalex.org/W3082335021"],"abstract_inverted_index":{"This":[0,192],"work":[1],"presents":[2],"an":[3,114],"approach":[4,29,115,141,153,193],"for":[5,116],"combining":[6],"household":[7,68],"demographic":[8],"and":[9,147,150,217],"living":[10],"standards":[11],"survey":[12,56,121,138,146,162,175],"questions":[13,57,122,163,176],"with":[14,54],"features":[15,32,51,81,107,129,149,214],"derived":[16],"from":[17,34,90,131,205],"satellite":[18,47,105,132],"imagery":[19,106],"to":[20,40,64,92,103,126,154,164,207],"predict":[21],"the":[22,71,77,83,127,144,152,156,171,183,187,196],"poverty":[23,72,87,184,202],"rate":[24,88,185,203],"of":[25,79,120,160,173,181,190],"a":[26,35,59,67,95,118,137,167,178],"region.":[27],"Our":[28],"utilizes":[30],"visual":[31,50,80,128,213],"obtained":[33],"single-step":[36],"featurization":[37],"method":[38],"applied":[39],"freely":[41],"available":[42],"10m/px":[43],"Sentinel-2":[44],"surface":[45],"reflectance":[46],"imagery.":[48,133],"These":[49],"are":[52,124],"combined":[53],"ten":[55],"in":[58,86,108,166,177,195,201],"proxy":[60,109],"means":[61,110],"test":[62,99],"(PMT)":[63],"estimate":[65],"whether":[66],"is":[69],"below":[70],"line.":[73],"We":[74,169,209],"show":[75,210],"that":[76,123,211],"inclusion":[78],"reduces":[82],"mean":[84],"error":[85],"estimates":[89],"4.09%":[91,206],"3.88%":[93],"over":[94],"nationally":[96],"representative":[97],"out-of-sample":[98],"set.":[100],"In":[101],"addition":[102],"including":[104],"tests,":[111],"we":[112,135],"propose":[113],"selecting":[117],"subset":[119],"complementary":[125],"extracted":[130,212],"Specifically,":[134],"design":[136],"variable":[139],"selection":[140],"guided":[142],"by":[143],"full":[145],"image":[148],"use":[151],"determine":[155],"most":[157],"relevant":[158],"set":[159,189],"small":[161,174,188],"include":[165],"PMT.":[168],"validate":[170],"choice":[172],"downstream":[179],"task":[180],"predicting":[182],"using":[186],"questions.":[191],"results":[194],"best":[197],"performance":[198],"\u2013":[199],"errors":[200],"decrease":[204],"3.71%.":[208],"encode":[215],"geographic":[216],"urbanization":[218],"differences":[219],"between":[220],"regions.":[221]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
