{"id":"https://openalex.org/W2580294918","doi":"https://doi.org/10.3390/e19020053","title":"A Mixed Geographically and Temporally Weighted Regression: Exploring Spatial-Temporal Variations from Global and Local Perspectives","display_name":"A Mixed Geographically and Temporally Weighted Regression: Exploring Spatial-Temporal Variations from Global and Local Perspectives","publication_year":2017,"publication_date":"2017-01-26","ids":{"openalex":"https://openalex.org/W2580294918","doi":"https://doi.org/10.3390/e19020053","mag":"2580294918"},"language":"en","primary_location":{"id":"doi:10.3390/e19020053","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e19020053","pdf_url":"https://www.mdpi.com/1099-4300/19/2/53/pdf?version=1485423605","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/19/2/53/pdf?version=1485423605","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109448683","display_name":"Jiping Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I176808543","display_name":"Liaoning Technical University","ror":"https://ror.org/01n2bd587","country_code":"CN","type":"education","lineage":["https://openalex.org/I176808543"]},{"id":"https://openalex.org/I4210114963","display_name":"Chinese Academy of Surveying and Mapping","ror":"https://ror.org/02j693n47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114963"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiping Liu","raw_affiliation_strings":["Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China","School of Geomatics, Liaoning Technical University, Fuxin 123000, China"],"affiliations":[{"raw_affiliation_string":"Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China","institution_ids":["https://openalex.org/I4210114963"]},{"raw_affiliation_string":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China","institution_ids":["https://openalex.org/I176808543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032781215","display_name":"Yangyang Zhao","orcid":"https://orcid.org/0000-0002-0965-6482"},"institutions":[{"id":"https://openalex.org/I4210114963","display_name":"Chinese Academy of Surveying and Mapping","ror":"https://ror.org/02j693n47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114963"]},{"id":"https://openalex.org/I176808543","display_name":"Liaoning Technical University","ror":"https://ror.org/01n2bd587","country_code":"CN","type":"education","lineage":["https://openalex.org/I176808543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangyang Zhao","raw_affiliation_strings":["Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China","School of Geomatics, Liaoning Technical University, Fuxin 123000, China"],"affiliations":[{"raw_affiliation_string":"Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China","institution_ids":["https://openalex.org/I4210114963"]},{"raw_affiliation_string":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China","institution_ids":["https://openalex.org/I176808543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018939290","display_name":"Yi Yang","orcid":"https://orcid.org/0000-0001-7275-3982"},"institutions":[{"id":"https://openalex.org/I2801481043","display_name":"Jiangsu Ocean University","ror":"https://ror.org/031zps173","country_code":"CN","type":"education","lineage":["https://openalex.org/I2801481043"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yi Yang","raw_affiliation_strings":["School of Geomatics and Marine Information, Huaihai Institute of Technology, No. 57 Cangwu Road, Lianyungang 222005, China"],"affiliations":[{"raw_affiliation_string":"School of Geomatics and Marine Information, Huaihai Institute of Technology, No. 57 Cangwu Road, Lianyungang 222005, China","institution_ids":["https://openalex.org/I2801481043"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100716244","display_name":"Shenghua Xu","orcid":"https://orcid.org/0000-0001-9275-3250"},"institutions":[{"id":"https://openalex.org/I4210114963","display_name":"Chinese Academy of Surveying and Mapping","ror":"https://ror.org/02j693n47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114963"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shenghua Xu","raw_affiliation_strings":["Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China"],"affiliations":[{"raw_affiliation_string":"Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China","institution_ids":["https://openalex.org/I4210114963"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102913126","display_name":"Fuhao Zhang","orcid":"https://orcid.org/0009-0007-5702-7553"},"institutions":[{"id":"https://openalex.org/I4210114963","display_name":"Chinese Academy of Surveying and Mapping","ror":"https://ror.org/02j693n47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114963"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuhao Zhang","raw_affiliation_strings":["Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China"],"affiliations":[{"raw_affiliation_string":"Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China","institution_ids":["https://openalex.org/I4210114963"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067835338","display_name":"Xiaolu Zhang","orcid":"https://orcid.org/0000-0003-2667-9325"},"institutions":[{"id":"https://openalex.org/I4210114963","display_name":"Chinese Academy of Surveying and Mapping","ror":"https://ror.org/02j693n47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114963"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolu Zhang","raw_affiliation_strings":["Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China"],"affiliations":[{"raw_affiliation_string":"Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China","institution_ids":["https://openalex.org/I4210114963"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101912959","display_name":"Lihong Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114963","display_name":"Chinese Academy of Surveying and Mapping","ror":"https://ror.org/02j693n47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114963"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lihong Shi","raw_affiliation_strings":["Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China"],"affiliations":[{"raw_affiliation_string":"Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China","institution_ids":["https://openalex.org/I4210114963"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018103296","display_name":"Agen Qiu","orcid":"https://orcid.org/0009-0007-2195-9252"},"institutions":[{"id":"https://openalex.org/I4210114963","display_name":"Chinese Academy of Surveying and Mapping","ror":"https://ror.org/02j693n47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114963"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Agen Qiu","raw_affiliation_strings":["Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China"],"affiliations":[{"raw_affiliation_string":"Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing 100830, China","institution_ids":["https://openalex.org/I4210114963"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5018939290"],"corresponding_institution_ids":["https://openalex.org/I2801481043"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":12.5251,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.9800366,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"19","issue":"2","first_page":"53","last_page":"53"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11911","display_name":"Spatial and Panel Data Analysis","score":0.9994999766349792,"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":0.9994999766349792,"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/T11014","display_name":"Regional Economics and Spatial Analysis","score":0.9889000058174133,"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/T10841","display_name":"Economic and Environmental Valuation","score":0.9883999824523926,"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/akaike-information-criterion","display_name":"Akaike information criterion","score":0.922634482383728},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5784981846809387},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5379420518875122},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5118445754051208},{"id":"https://openalex.org/keywords/least-squares-function-approximation","display_name":"Least-squares function approximation","score":0.4581730365753174},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4577158987522125},{"id":"https://openalex.org/keywords/ordinary-least-squares","display_name":"Ordinary least squares","score":0.449419766664505},{"id":"https://openalex.org/keywords/coefficient-of-determination","display_name":"Coefficient of determination","score":0.4492008090019226},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4449104964733124},{"id":"https://openalex.org/keywords/local-regression","display_name":"Local regression","score":0.43890780210494995},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4319768249988556},{"id":"https://openalex.org/keywords/polynomial-regression","display_name":"Polynomial regression","score":0.22538632154464722}],"concepts":[{"id":"https://openalex.org/C126674687","wikidata":"https://www.wikidata.org/wiki/Q1662573","display_name":"Akaike information criterion","level":2,"score":0.922634482383728},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5784981846809387},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5379420518875122},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5118445754051208},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.4581730365753174},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4577158987522125},{"id":"https://openalex.org/C99656134","wikidata":"https://www.wikidata.org/wiki/Q2912993","display_name":"Ordinary least squares","level":2,"score":0.449419766664505},{"id":"https://openalex.org/C128990827","wikidata":"https://www.wikidata.org/wiki/Q192830","display_name":"Coefficient of determination","level":2,"score":0.4492008090019226},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4449104964733124},{"id":"https://openalex.org/C60316415","wikidata":"https://www.wikidata.org/wiki/Q6664520","display_name":"Local regression","level":4,"score":0.43890780210494995},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4319768249988556},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.22538632154464722},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/e19020053","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e19020053","pdf_url":"https://www.mdpi.com/1099-4300/19/2/53/pdf?version=1485423605","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f5821ea13fc149c090708cb4b78dbbbe","is_oa":true,"landing_page_url":"https://doaj.org/article/f5821ea13fc149c090708cb4b78dbbbe","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 19, Iss 2, p 53 (2017)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/19/2/53/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/e19020053","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e19020053","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e19020053","pdf_url":"https://www.mdpi.com/1099-4300/19/2/53/pdf?version=1485423605","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7224065189","display_name":null,"funder_award_id":"41004003","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":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2580294918.pdf","grobid_xml":"https://content.openalex.org/works/W2580294918.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1499566225","https://openalex.org/W1598151839","https://openalex.org/W1773028378","https://openalex.org/W1829241487","https://openalex.org/W1971794754","https://openalex.org/W1979683229","https://openalex.org/W1980398988","https://openalex.org/W1991694179","https://openalex.org/W1993913957","https://openalex.org/W2041868040","https://openalex.org/W2041890084","https://openalex.org/W2046607127","https://openalex.org/W2047120335","https://openalex.org/W2054806977","https://openalex.org/W2057218833","https://openalex.org/W2058815839","https://openalex.org/W2063143131","https://openalex.org/W2068377985","https://openalex.org/W2077372391","https://openalex.org/W2084032127","https://openalex.org/W2099686024","https://openalex.org/W2109785413","https://openalex.org/W2119194442","https://openalex.org/W2156378058","https://openalex.org/W2184087153","https://openalex.org/W2284873887","https://openalex.org/W2306844409","https://openalex.org/W2322456781","https://openalex.org/W2514803558","https://openalex.org/W2515561179","https://openalex.org/W2531293538","https://openalex.org/W2545192738","https://openalex.org/W3121996782","https://openalex.org/W3123322087","https://openalex.org/W3123727356","https://openalex.org/W6630155578","https://openalex.org/W6686247909"],"related_works":["https://openalex.org/W4320029652","https://openalex.org/W4280590961","https://openalex.org/W2907193694","https://openalex.org/W2530466803","https://openalex.org/W1984191672","https://openalex.org/W2011894828","https://openalex.org/W2167126078","https://openalex.org/W1997130844","https://openalex.org/W4250719734","https://openalex.org/W1965287366"],"abstract_inverted_index":{"To":[0],"capture":[1],"both":[2,25,197],"global":[3,20,198],"stationarity":[4,199],"and":[5,12,21,27,34,58,65,117,131,135,144,160,171,176,183,200],"spatiotemporal":[6,201],"non-stationarity,":[7,202],"a":[8,45,83],"novel":[9],"mixed":[10,125],"geographically":[11,126],"temporally":[13],"weighted":[14,103,127,146],"regression":[15,104,128,147],"(MGTWR)":[16],"model":[17,85,94,110,130,149,165],"accounting":[18],"for":[19,150],"local":[22],"effects":[23],"in":[24,42],"space":[26],"time":[28],"is":[29,50,80],"presented.":[30],"Since":[31],"the":[32,54,67,70,92,108,124,142,151,158,163,167,177,194,206,210],"constant":[33],"spatial-temporal":[35],"varying":[36],"coefficients":[37],"could":[38],"not":[39],"be":[40],"estimated":[41],"one":[43],"step,":[44],"two-stage":[46],"least":[47],"squares":[48],"estimation":[49],"introduced":[51],"to":[52,63,121,139,157],"calibrate":[53],"model.":[55,73],"Both":[56],"simulations":[57],"real-world":[59],"datasets":[60],"are":[61],"used":[62],"test":[64],"verify":[66],"performance":[68],"of":[69,123,141,180,186,196,209],"proposed":[71,211],"MGTWR":[72,93,109,164],"Additionally,":[74],"an":[75],"Akaike":[76],"Information":[77],"Criterion":[78],"(AIC)":[79],"adopted":[81],"as":[82,203,205],"key":[84],"fitting":[86],"diagnostic.":[87],"The":[88],"experiments":[89,192],"demonstrate":[90],"that":[91],"yields":[95],"more":[96],"accurate":[97],"results":[98],"than":[99],"do":[100],"traditional":[101],"spatially":[102],"models.":[105],"For":[106],"instance,":[107],"decreased":[111],"AIC":[112,169],"value":[113,170],"by":[114,132],"2.7066,":[115],"36.368":[116],"112.812":[118],"with":[119,137],"respect":[120,138],"those":[122,140],"(MGWR)":[129],"45.5628,":[133],"\u221238.774":[134],"35.656":[136],"geographical":[143],"temporal":[145],"(GTWR)":[148],"three":[152],"simulation":[153],"datasets.":[154],"Moreover,":[155],"compared":[156],"MGWR":[159],"GTWR":[161],"models,":[162],"obtained":[166],"lowest":[168],"mean":[172],"square":[173],"error":[174],"(MSE)":[175],"highest":[178],"coefficient":[179,185],"determination":[181,187],"(R2)":[182],"adjusted":[184],"(R2adj).":[188],"In":[189],"addition,":[190],"our":[191],"proved":[193],"existence":[195],"well":[204],"practical":[207],"ability":[208],"method.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}