{"id":"https://openalex.org/W4386465669","doi":"https://doi.org/10.1080/13658816.2023.2250838","title":"On the local modeling of count data: multiscale geographically weighted Poisson regression","display_name":"On the local modeling of count data: multiscale geographically weighted Poisson regression","publication_year":2023,"publication_date":"2023-09-05","ids":{"openalex":"https://openalex.org/W4386465669","doi":"https://doi.org/10.1080/13658816.2023.2250838"},"language":"en","primary_location":{"id":"doi:10.1080/13658816.2023.2250838","is_oa":false,"landing_page_url":"https://doi.org/10.1080/13658816.2023.2250838","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/A5027859763","display_name":"Mehak Sachdeva","orcid":"https://orcid.org/0000-0003-4375-5422"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mehak Sachdeva","raw_affiliation_strings":["Center for Urban Science and Progress, Tandon School of Engineering, New York University, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Center for Urban Science and Progress, Tandon School of Engineering, New York University, New York, NY, USA","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014732659","display_name":"A. Stewart Fotheringham","orcid":"https://orcid.org/0000-0002-0407-1901"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A. Stewart Fotheringham","raw_affiliation_strings":["School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA"],"affiliations":[{"raw_affiliation_string":"School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100610862","display_name":"Ziqi Li","orcid":"https://orcid.org/0000-0002-6345-4347"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziqi Li","raw_affiliation_strings":["Department of Geography, Florida State University, Tallahassee, FL, USA"],"affiliations":[{"raw_affiliation_string":"Department of Geography, Florida State University, Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101828901","display_name":"Hanchen Yu","orcid":"https://orcid.org/0000-0002-3246-8586"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Hanchen Yu","raw_affiliation_strings":["School of Urban Governance and Design, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Urban Governance and Design, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China","institution_ids":["https://openalex.org/I200769079","https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5027859763"],"corresponding_institution_ids":["https://openalex.org/I57206974"],"apc_list":null,"apc_paid":null,"fwci":11.3763,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.97952392,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"37","issue":"10","first_page":"2238","last_page":"2261"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10298","display_name":"Urban Transport and Accessibility","score":0.995199978351593,"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/count-data","display_name":"Count data","score":0.6969274282455444},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.5480020046234131},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5135390758514404},{"id":"https://openalex.org/keywords/poisson-regression","display_name":"Poisson regression","score":0.5089163780212402},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49991631507873535},{"id":"https://openalex.org/keywords/geographically-weighted-regression","display_name":"Geographically Weighted Regression","score":0.4697965383529663},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4664299786090851},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4632132947444916},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.44965648651123047},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.44328057765960693},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29136332869529724},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.27708733081817627},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.20366856455802917},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.11975371837615967}],"concepts":[{"id":"https://openalex.org/C33643355","wikidata":"https://www.wikidata.org/wiki/Q5176731","display_name":"Count data","level":3,"score":0.6969274282455444},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.5480020046234131},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5135390758514404},{"id":"https://openalex.org/C73269764","wikidata":"https://www.wikidata.org/wiki/Q954529","display_name":"Poisson regression","level":3,"score":0.5089163780212402},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49991631507873535},{"id":"https://openalex.org/C2910321205","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geographically Weighted Regression","level":2,"score":0.4697965383529663},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4664299786090851},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4632132947444916},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.44965648651123047},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.44328057765960693},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29136332869529724},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.27708733081817627},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.20366856455802917},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.11975371837615967},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/13658816.2023.2250838","is_oa":false,"landing_page_url":"https://doi.org/10.1080/13658816.2023.2250838","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"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-131799","is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-131799","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W148230327","https://openalex.org/W1497419282","https://openalex.org/W1599043334","https://openalex.org/W1967330083","https://openalex.org/W1975529556","https://openalex.org/W1984913191","https://openalex.org/W2031567285","https://openalex.org/W2032807833","https://openalex.org/W2049040920","https://openalex.org/W2076983043","https://openalex.org/W2079478691","https://openalex.org/W2084527325","https://openalex.org/W2114314286","https://openalex.org/W2148323323","https://openalex.org/W2162430620","https://openalex.org/W2166163519","https://openalex.org/W2316206413","https://openalex.org/W2747207142","https://openalex.org/W2888005101","https://openalex.org/W2901615696","https://openalex.org/W2912855477","https://openalex.org/W2977350438","https://openalex.org/W3005470619","https://openalex.org/W3005960504","https://openalex.org/W3014404004","https://openalex.org/W3023736901","https://openalex.org/W3035792471","https://openalex.org/W3040850180","https://openalex.org/W3075038947","https://openalex.org/W3081236293","https://openalex.org/W3120822987","https://openalex.org/W3139158003","https://openalex.org/W3143479390","https://openalex.org/W3181503659","https://openalex.org/W3203434175","https://openalex.org/W4205820543","https://openalex.org/W4210739809","https://openalex.org/W4281745985","https://openalex.org/W4288625073","https://openalex.org/W4378469608","https://openalex.org/W4385400191","https://openalex.org/W7005849454","https://openalex.org/W7082566961"],"related_works":["https://openalex.org/W1578743158","https://openalex.org/W3088295586","https://openalex.org/W2060931646","https://openalex.org/W1849881158","https://openalex.org/W1590941618","https://openalex.org/W3210390693","https://openalex.org/W2503331259","https://openalex.org/W3134844154","https://openalex.org/W3096804274","https://openalex.org/W2270206682"],"abstract_inverted_index":{"A":[0],"recent":[1],"addition":[2],"to":[3,25,74,102],"the":[4,13,16,36,48,54,62,68,96,99,141,148,152,155,178],"suite":[5],"of":[6,15,56,60,98,115,154],"techniques":[7],"for":[8,64,82],"local":[9],"statistical":[10],"modeling":[11,83],"is":[12,72,123],"implementation":[14],"multiscale":[17,23],"geographically":[18,26],"weighted":[19,27],"regression":[20,28,120],"(MGWR),":[21],"a":[22,31,57,90,111,126],"extension":[24],"(GWR).":[29],"Using":[30],"back-fitting":[32],"algorithm,":[33],"MGWR":[34,70,100,118],"relaxes":[35],"restrictive":[37],"assumption":[38],"in":[39,158,184],"GWR":[40],"that":[41,85,163],"all":[42],"processes":[43,162],"being":[44],"modeled":[45],"operate":[46],"at":[47,140],"same":[49],"spatial":[50,161,173,182],"scale":[51],"and":[52,130,189],"allows":[53],"estimation":[55],"unique":[58],"indicator":[59],"scale,":[61],"bandwidth,":[63],"each":[65],"process.":[66],"However,":[67],"current":[69],"framework":[71,101],"limited":[73],"use":[75],"with":[76,125,132],"continuous":[77],"data":[78,84,109,128,150,166],"making":[79],"it":[80],"unsuitable":[81],"do":[86],"not":[87],"typically":[88],"exhibit":[89],"Gaussian":[91],"distribution.":[92],"This":[93],"study":[94],"expands":[95],"application":[97],"scenarios":[103],"involving":[104],"discrete":[105],"response":[106],"outcomes":[107],"(count":[108],"following":[110],"Poisson's":[112],"distribution).":[113],"Use":[114],"this":[116],"new":[117],"Poisson":[119],"(MGWPR)":[121],"model":[122,157],"demonstrated":[124],"simulated":[127,149],"set":[129],"then":[131],"COVID-19":[133,190],"case":[134],"counts":[135],"within":[136],"New":[137],"York":[138],"City":[139],"zip":[142],"code":[143],"level.":[144],"The":[145],"results":[146,179],"from":[147],"underscore":[151],"superiority":[153],"MGWPR":[156],"effectively":[159],"capturing":[160],"influence":[164],"count":[165],"patterns,":[167],"particularly":[168],"those":[169],"operating":[170],"across":[171],"diverse":[172],"scales.":[174],"For":[175],"empirical":[176],"data,":[177],"reveal":[180],"significant":[181],"variations":[183,193],"relationships":[185],"between":[186],"socio-ecological":[187],"factors":[188],"cases":[191],"-":[192],"often":[194],"missed":[195],"by":[196],"traditional":[197],"'global'":[198],"models.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":4}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}