{"id":"https://openalex.org/W2586749094","doi":"https://doi.org/10.1080/13658816.2017.1290252","title":"Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data","display_name":"Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data","publication_year":2017,"publication_date":"2017-02-08","ids":{"openalex":"https://openalex.org/W2586749094","doi":"https://doi.org/10.1080/13658816.2017.1290252","mag":"2586749094"},"language":"en","primary_location":{"id":"doi:10.1080/13658816.2017.1290252","is_oa":false,"landing_page_url":"https://doi.org/10.1080/13658816.2017.1290252","pdf_url":null,"source":{"id":"https://openalex.org/S4210181446","display_name":"International Journal of Geographical Information Systems","issn_l":"0269-3798","issn":["0269-3798","1362-3087"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Geographical Information Science","raw_type":"journal-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/A5100389423","display_name":"Yao Yao","orcid":"https://orcid.org/0000-0002-2225-0903"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Yao","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong province, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong province, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120746624","display_name":"Xiaoping Liu","orcid":"https://orcid.org/0009-0007-1270-6595"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoping Liu","raw_affiliation_strings":["School of Geography and Planning, Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, Guangdong province, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, Guangdong province, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100445622","display_name":"Xia Li","orcid":"https://orcid.org/0000-0003-3050-8529"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xia Li","raw_affiliation_strings":["School of Geography and Planning, Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, Guangdong province, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, Guangdong province, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100623090","display_name":"Jinbao Zhang","orcid":"https://orcid.org/0000-0001-8510-149X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinbao Zhang","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong province, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong province, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072036171","display_name":"Zhaotang Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaotang Liang","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong province, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong province, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103023065","display_name":"Ke Mai","orcid":"https://orcid.org/0000-0002-3532-9872"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Mai","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong province, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong province, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101641066","display_name":"Yatao Zhang","orcid":"https://orcid.org/0000-0001-5701-2836"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yatao Zhang","raw_affiliation_strings":["School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong province, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong province, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5120746624"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":39.2623,"has_fulltext":false,"cited_by_count":220,"citation_normalized_percentile":{"value":0.99846619,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"25"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"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/T10298","display_name":"Urban Transport and Accessibility","score":0.9807999730110168,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/volunteered-geographic-information","display_name":"Volunteered geographic information","score":0.8261374831199646},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.7028003931045532},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.6805697679519653},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.612280011177063},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.5965671539306641},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.4705486595630646},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4080747365951538},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3545164465904236},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3364737033843994},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3335907459259033}],"concepts":[{"id":"https://openalex.org/C57380593","wikidata":"https://www.wikidata.org/wiki/Q933625","display_name":"Volunteered geographic information","level":2,"score":0.8261374831199646},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.7028003931045532},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.6805697679519653},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.612280011177063},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.5965671539306641},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.4705486595630646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4080747365951538},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3545164465904236},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3364737033843994},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3335907459259033},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/13658816.2017.1290252","is_oa":false,"landing_page_url":"https://doi.org/10.1080/13658816.2017.1290252","pdf_url":null,"source":{"id":"https://openalex.org/S4210181446","display_name":"International Journal of Geographical Information Systems","issn_l":"0269-3798","issn":["0269-3798","1362-3087"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Geographical Information Science","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6800000071525574,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G6390032288","display_name":null,"funder_award_id":"41601420","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7464793345","display_name":null,"funder_award_id":"41531176","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8825392273","display_name":null,"funder_award_id":"41371376","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W623355866","https://openalex.org/W1015921946","https://openalex.org/W1537295780","https://openalex.org/W1543116876","https://openalex.org/W1543801710","https://openalex.org/W1563656983","https://openalex.org/W1974849189","https://openalex.org/W1981966150","https://openalex.org/W1985725498","https://openalex.org/W1987767032","https://openalex.org/W1992794304","https://openalex.org/W2000070202","https://openalex.org/W2005239152","https://openalex.org/W2007443624","https://openalex.org/W2015388101","https://openalex.org/W2021603915","https://openalex.org/W2024177114","https://openalex.org/W2032306335","https://openalex.org/W2033176844","https://openalex.org/W2044609898","https://openalex.org/W2052331316","https://openalex.org/W2055992762","https://openalex.org/W2057442840","https://openalex.org/W2063628808","https://openalex.org/W2064594797","https://openalex.org/W2069686198","https://openalex.org/W2085009188","https://openalex.org/W2085512681","https://openalex.org/W2095998117","https://openalex.org/W2100147696","https://openalex.org/W2112620321","https://openalex.org/W2117036437","https://openalex.org/W2138106796","https://openalex.org/W2144314902","https://openalex.org/W2144506299","https://openalex.org/W2156861904","https://openalex.org/W2158550732","https://openalex.org/W2167095787","https://openalex.org/W2186295243","https://openalex.org/W2214916291","https://openalex.org/W2276327097","https://openalex.org/W2488006770","https://openalex.org/W2520193187","https://openalex.org/W2534538876","https://openalex.org/W2540967156","https://openalex.org/W2911964244","https://openalex.org/W2939248049","https://openalex.org/W4229825038","https://openalex.org/W4230355682","https://openalex.org/W4241683126","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2733029865","https://openalex.org/W1991837421","https://openalex.org/W2955098766","https://openalex.org/W2269136550","https://openalex.org/W3041947657","https://openalex.org/W2114948960","https://openalex.org/W2464857583","https://openalex.org/W2587563374","https://openalex.org/W2615781907","https://openalex.org/W2467634386"],"abstract_inverted_index":{"Fine-scale":[0],"population":[1,41,53,60,103,123,147,164,209,231,242],"distribution":[2,42,148],"data":[3,73,79,203],"at":[4,62,105,166],"the":[5,63,106,119,145,150,167,180,190,205,215,251],"building":[6,107,168],"level":[7],"play":[8],"an":[9,156],"essential":[10,117],"role":[11],"in":[12,35],"numerous":[13,40],"fields,":[14],"for":[15,118],"example":[16],"urban":[17,102],"planning":[18],"and":[19,30,54,74,129,204],"disaster":[20],"prevention.":[21],"The":[22,228],"rapid":[23],"technological":[24],"development":[25],"of":[26,47,68,71,122,192,207,239,253],"remote":[27],"sensing":[28],"(RS)":[29],"geographical":[31],"information":[32,84],"system":[33],"(GIS)":[34],"recent":[36],"decades":[37],"has":[38],"benefited":[39],"mapping":[43,61,121,210],"studies.":[44],"However,":[45],"most":[46],"these":[48],"studies":[49],"focused":[50],"on":[51],"global":[52],"environmental":[55],"changes;":[56],"few":[57],"considered":[58],"fine-scale":[59,120,230],"local":[64],"scale,":[65],"largely":[66],"because":[67],"a":[69,97,139,173,197,235],"lack":[70],"reliable":[72],"models.":[75],"As":[76],"geospatial":[77,112],"big":[78,113],"booms,":[80],"Internet-collected":[81],"volunteered":[82],"geographic":[83],"(VGI)":[85],"can":[86,185,233,245],"now":[87],"be":[88,186],"used":[89,187],"to":[90,100,143,149,162,188,196],"solve":[91],"this":[92],"problem.":[93],"This":[94],"article":[95],"establishes":[96],"novel":[98],"framework":[99],"map":[101,163,232],"distributions":[104,165],"scale":[108],"by":[109,137,179],"integrating":[110],"multisource":[111],"data,":[114],"which":[115,184,244],"is":[116],"distributions.":[124],"First,":[125],"Baidu":[126],"points-of-interest":[127],"(POIs)":[128],"real-time":[130],"Tencent":[131],"user":[132],"densities":[133],"(RTUD)":[134],"are":[135],"analyzed":[136],"using":[138],"random":[140],"forest":[141],"algorithm":[142],"down-scale":[144],"street-level":[146],"grid":[151],"level.":[152,169],"Then,":[153],"we":[154,171],"design":[155],"effective":[157],"iterative":[158],"building-population":[159],"gravity":[160,182],"model":[161],"Meanwhile,":[170],"introduce":[172],"densely":[174],"inhabited":[175],"index":[176],"(DII),":[177],"generated":[178],"proposed":[181],"model,":[183],"estimate":[189],"degree":[191],"residential":[193],"crowding.":[194],"According":[195],"comparison":[198],"with":[199],"official":[200],"community-level":[201],"census":[202],"results":[206],"previous":[208],"methods,":[211],"our":[212],"method":[213],"exhibits":[214],"best":[216],"accuracy":[217],"(Pearson":[218],"R":[219],"=":[220,223],".8615,":[221],"RMSE":[222],"663.3250,":[224],"p":[225],"<":[226],".0001).":[227],"produced":[229],"offer":[234],"more":[236],"thorough":[237],"understanding":[238],"inner":[240],"city":[241],"distributions,":[243],"thus":[246],"help":[247],"policy":[248],"makers":[249],"optimize":[250],"allocation":[252],"resources.":[254]},"counts_by_year":[{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":24},{"year":2023,"cited_by_count":32},{"year":2022,"cited_by_count":30},{"year":2021,"cited_by_count":39},{"year":2020,"cited_by_count":39},{"year":2019,"cited_by_count":26},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
