{"id":"https://openalex.org/W4297372927","doi":"https://doi.org/10.1080/13658816.2022.2100892","title":"Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid","display_name":"Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid","publication_year":2022,"publication_date":"2022-09-27","ids":{"openalex":"https://openalex.org/W4297372927","doi":"https://doi.org/10.1080/13658816.2022.2100892"},"language":"en","primary_location":{"id":"doi:10.1080/13658816.2022.2100892","is_oa":false,"landing_page_url":"https://doi.org/10.1080/13658816.2022.2100892","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/A5111124557","display_name":"Zhen Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Dai","raw_affiliation_strings":["School of Earth Sciences, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Earth Sciences, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085580167","display_name":"Sensen Wu","orcid":"https://orcid.org/0000-0001-9322-0149"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sensen Wu","raw_affiliation_strings":["School of Earth Sciences, Zhejiang University, Hangzhou, China","Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9322-0149","affiliations":[{"raw_affiliation_string":"School of Earth Sciences, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100423196","display_name":"Yuanyuan Wang","orcid":"https://orcid.org/0000-0003-4190-6418"},"institutions":[{"id":"https://openalex.org/I31847773","display_name":"Zhejiang Ocean University","ror":"https://ror.org/03mys6533","country_code":"CN","type":"education","lineage":["https://openalex.org/I31847773"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Wang","raw_affiliation_strings":["Ocean Academy, Zhejiang University, Zhoushan, China","Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ocean Academy, Zhejiang University, Zhoushan, China","institution_ids":["https://openalex.org/I31847773","https://openalex.org/I76130692"]},{"raw_affiliation_string":"Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053149854","display_name":"Hongye Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongye Zhou","raw_affiliation_strings":["School of Earth Sciences, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Earth Sciences, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100401308","display_name":"Feng Zhang","orcid":"https://orcid.org/0000-0003-1475-8480"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Zhang","raw_affiliation_strings":["School of Earth Sciences, Zhejiang University, Hangzhou, China","Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-1475-8480","affiliations":[{"raw_affiliation_string":"School of Earth Sciences, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100705731","display_name":"Bo Huang","orcid":"https://orcid.org/0000-0002-5063-3522"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Bo Huang","raw_affiliation_strings":["Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, NT, Hong Kong","Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, NT, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, NT, Hong Kong","institution_ids":["https://openalex.org/I177725633"]},{"raw_affiliation_string":"Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, NT, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069094261","display_name":"Zhenhong Du","orcid":"https://orcid.org/0000-0001-9449-0415"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhenhong Du","raw_affiliation_strings":["School of Earth Sciences, Zhejiang University, Hangzhou, China","Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9449-0415","affiliations":[{"raw_affiliation_string":"School of Earth Sciences, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5069094261"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":10.0523,"has_fulltext":false,"cited_by_count":62,"citation_normalized_percentile":{"value":0.98009072,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"36","issue":"11","first_page":"2248","last_page":"2269"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9991999864578247,"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":0.9991999864578247,"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/T10226","display_name":"Land Use and Ecosystem Services","score":0.9983000159263611,"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/T10841","display_name":"Economic and Environmental Valuation","score":0.9972000122070312,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/geographically-weighted-regression","display_name":"Geographically Weighted Regression","score":0.5987079739570618},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5539042949676514},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.533659040927887},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5265136957168579},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5018126964569092},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47805291414260864},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40480175614356995},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35029828548431396},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.3332355320453644},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28704437613487244},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2769410312175751},{"id":"https://openalex.org/keywords/geodesy","display_name":"Geodesy","score":0.08167028427124023}],"concepts":[{"id":"https://openalex.org/C2910321205","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geographically Weighted Regression","level":2,"score":0.5987079739570618},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5539042949676514},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.533659040927887},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5265136957168579},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5018126964569092},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47805291414260864},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40480175614356995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35029828548431396},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.3332355320453644},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28704437613487244},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2769410312175751},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.08167028427124023}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/13658816.2022.2100892","is_oa":false,"landing_page_url":"https://doi.org/10.1080/13658816.2022.2100892","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":[],"awards":[{"id":"https://openalex.org/G1016183770","display_name":null,"funder_award_id":"41871287","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2121646124","display_name":null,"funder_award_id":"2021YFB3900902","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G3603946509","display_name":null,"funder_award_id":"41922043","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5879540094","display_name":null,"funder_award_id":"42001323","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"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1535106372","https://openalex.org/W1677182931","https://openalex.org/W1976840258","https://openalex.org/W2047120335","https://openalex.org/W2061730269","https://openalex.org/W2284050935","https://openalex.org/W2602210755","https://openalex.org/W2747207142","https://openalex.org/W2910630817","https://openalex.org/W2912855477","https://openalex.org/W2948613401","https://openalex.org/W2951023864","https://openalex.org/W2994652626","https://openalex.org/W2995502148","https://openalex.org/W2998570674","https://openalex.org/W3000363244","https://openalex.org/W3005960504","https://openalex.org/W3008779521","https://openalex.org/W3019445951","https://openalex.org/W3026124902","https://openalex.org/W3063110676","https://openalex.org/W3080576325","https://openalex.org/W3081120957","https://openalex.org/W3090956332","https://openalex.org/W3092499571","https://openalex.org/W3107128509","https://openalex.org/W3127361658","https://openalex.org/W3133659432","https://openalex.org/W4223976737","https://openalex.org/W4238301485"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4399638395","https://openalex.org/W3015453265","https://openalex.org/W2329292098","https://openalex.org/W3195716473","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4239306820","https://openalex.org/W2370106417","https://openalex.org/W2383138325"],"abstract_inverted_index":{"Geographically":[0],"weighted":[1,17,35,96,119],"regression":[2,18,120,128],"(GWR)":[3],"is":[4,45],"a":[5,33,77,94,114],"classical":[6],"method":[7],"of":[8,24,28,43,54,65,88,140,151,194],"modeling":[9],"spatially":[10,34,95,188],"non-stationary":[11,189],"relationships.":[12],"The":[13,155],"geographically":[14,115],"neural":[15,36,98,117],"network":[16,99,118],"(GNNWR)":[19],"model":[20,122,130,157,179],"solves":[21],"the":[22,25,40,47,52,63,66,71,85,103,106,149,152,167,177,192],"problem":[23],"inaccurate":[26],"construction":[27],"spatial":[29,41,48,79,86,109,133],"weight":[30],"kernels":[31],"using":[32],"network.":[37],"However,":[38],"when":[39,191],"distribution":[42,193],"observations":[44,195],"uneven,":[46],"proximity":[49,80,87],"expression":[50],"in":[51,186],"input":[53],"GWR":[55,171],"and":[56,92,108,125,143,162,173,184],"GNNWR":[57,174],"models":[58],"does":[59],"not":[60],"fully":[61],"represent":[62],"impact":[64],"whole":[67],"research":[68],"space":[69],"on":[70],"estimating":[72,90,187],"point.":[73],"Therefore,":[74],"we":[75,112],"established":[76],"global":[78],"grid":[81],"(GSPG)":[82],"to":[83,101,131,147],"express":[84],"each":[89],"point":[91],"proposed":[93,113],"convolutional":[97,116],"(SWCNN)":[100],"extract":[102],"relationship":[104],"between":[105],"GSPG":[107],"weights.":[110],"Finally,":[111],"(GCNNWR)":[121],"combining":[123],"SWCNN":[124],"ordinary":[126],"linear":[127],"(OLR)":[129],"estimate":[132],"non-stationarity.":[134],"We":[135],"used":[136],"two":[137],"case":[138],"studies":[139],"simulated":[141],"data":[142,146],"real":[144],"environment":[145],"demonstrate":[148],"advancements":[150],"GCNNWR":[153,156,178],"model.":[154],"achieved":[158],"higher":[159],"estimation":[160],"accuracy":[161,185],"greater":[163],"predictive":[164],"power":[165],"than":[166],"OLR,":[168],"GWR,":[169],"multi-scale":[170],"(MGWR),":[172],"models.":[175],"Moreover,":[176],"maintained":[180],"its":[181],"better":[182],"stability":[183],"relationships":[190],"was":[196],"uneven.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":15},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":5}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}