{"id":"https://openalex.org/W2914724279","doi":"https://doi.org/10.1109/bigdata.2018.8621904","title":"Inferring Housing Demand based on Express Delivery Data","display_name":"Inferring Housing Demand based on Express Delivery Data","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2914724279","doi":"https://doi.org/10.1109/bigdata.2018.8621904","mag":"2914724279"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8621904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8621904","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","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/A5008990445","display_name":"Qingyang Li","orcid":"https://orcid.org/0000-0003-2602-9250"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qingyang Li","raw_affiliation_strings":["Northwestern Polytechnical University, Xi\u2019an, China","Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi\u2019an, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100701166","display_name":"Zhiwen Yu","orcid":"https://orcid.org/0000-0002-9905-3238"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiwen Yu","raw_affiliation_strings":["Northwestern Polytechnical University, Xi\u2019an, China","Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi\u2019an, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078005520","display_name":"Bin Guo","orcid":"https://orcid.org/0000-0001-6097-2467"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Guo","raw_affiliation_strings":["Northwestern Polytechnical University, Xi\u2019an, China","Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University, Xi\u2019an, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047229109","display_name":"Xinjiang Lu","orcid":"https://orcid.org/0000-0002-3602-0391"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinjiang Lu","raw_affiliation_strings":["Baidu Inc, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Baidu Inc, Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5008990445"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":0.8522,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.83875957,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"155","issue":null,"first_page":"1445","last_page":"1454"},"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":0.9995999932289124,"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":0.9995999932289124,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9835000038146973,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9769999980926514,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.686987042427063},{"id":"https://openalex.org/keywords/real-estate","display_name":"Real estate","score":0.6632766723632812},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6007733345031738},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.544224739074707},{"id":"https://openalex.org/keywords/attractiveness","display_name":"Attractiveness","score":0.4573154151439667},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44822224974632263},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.39531564712524414},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.374767929315567},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.2538776397705078},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21678778529167175},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.17544245719909668},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1747971475124359},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.1025753915309906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.686987042427063},{"id":"https://openalex.org/C82279013","wikidata":"https://www.wikidata.org/wiki/Q684740","display_name":"Real estate","level":2,"score":0.6632766723632812},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6007733345031738},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.544224739074707},{"id":"https://openalex.org/C31173074","wikidata":"https://www.wikidata.org/wiki/Q2632514","display_name":"Attractiveness","level":2,"score":0.4573154151439667},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44822224974632263},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.39531564712524414},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.374767929315567},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.2538776397705078},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21678778529167175},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.17544245719909668},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1747971475124359},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.1025753915309906},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata.2018.8621904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8621904","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"mag:3088662217","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201902254941383969","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8199999928474426,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W170719175","https://openalex.org/W1673310716","https://openalex.org/W1971402834","https://openalex.org/W1987228002","https://openalex.org/W1992536119","https://openalex.org/W2004683304","https://openalex.org/W2044809283","https://openalex.org/W2080007346","https://openalex.org/W2090096981","https://openalex.org/W2095998117","https://openalex.org/W2138972650","https://openalex.org/W2153207204","https://openalex.org/W2153458569","https://openalex.org/W2153635508","https://openalex.org/W2158634829","https://openalex.org/W2165178985","https://openalex.org/W2223926708","https://openalex.org/W2278051381","https://openalex.org/W2338697662","https://openalex.org/W2471528185","https://openalex.org/W2508432084","https://openalex.org/W2509780072","https://openalex.org/W2513521614","https://openalex.org/W2515500989","https://openalex.org/W2590814815","https://openalex.org/W2605300519","https://openalex.org/W2605969872","https://openalex.org/W2732888442","https://openalex.org/W2743808405","https://openalex.org/W2754930015","https://openalex.org/W2768009948","https://openalex.org/W2783599958","https://openalex.org/W2784292547","https://openalex.org/W3022514790","https://openalex.org/W4307768504","https://openalex.org/W6606915157","https://openalex.org/W6637131181","https://openalex.org/W6680915480","https://openalex.org/W6733642711"],"related_works":["https://openalex.org/W4200508654","https://openalex.org/W4375829797","https://openalex.org/W2577455382","https://openalex.org/W2398900508","https://openalex.org/W4245080958","https://openalex.org/W1966920876","https://openalex.org/W2362921324","https://openalex.org/W2359525282","https://openalex.org/W2947836062","https://openalex.org/W3208363075"],"abstract_inverted_index":{"Estimation":[0],"of":[1,12,30,34,70,134,143,149,161,170],"housing":[2,35,51,63,165,188,208],"requirement":[3,36],"is":[4,37,204],"beneficial":[5],"for":[6,119,210],"many":[7],"applications":[8],"such":[9],"as":[10,159],"guidance":[11],"house":[13],"trading":[14],"and":[15,109,173,181],"real":[16],"estate":[17],"market":[18],"regulation.":[19],"Although":[20],"there":[21],"have":[22],"been":[23],"studies":[24],"focusing":[25],"on":[26,73,197],"the":[27,67,83,114,150,162,168,177],"demand":[28,52,64,166,189,209],"analysis":[29],"urban":[31,213],"resources,":[32],"estimation":[33],"still":[38],"under":[39],"explored.":[40],"To":[41],"this":[42,45,78],"end,":[43],"in":[44,212],"paper":[46],"we":[47,80,94,122,138,186],"propose":[48,95],"a":[49,96,191],"systematic":[50],"inference":[53],"method,":[54],"named":[55],"Housing":[56],"Demand":[57],"Inference":[58],"Model":[59,128],"(HDIM),":[60],"to":[61,99,130,206],"estimate":[62,187],"by":[65,103,146],"exploiting":[66],"residential":[68,101,140,178],"mobility":[69,102,141,179],"communities":[71,211],"based":[72],"express":[74,84,105],"delivery":[75,85,106],"data.":[76,175],"In":[77],"work,":[79],"first":[81],"aggregate":[82],"records":[86],"at":[87],"community":[88,110,145,157,171,182],"scale":[89],"with":[90,167,190],"clustering":[91],"methods.":[92],"Then,":[93],"useful":[97],"method":[98],"infer":[100,139,207],"extracting":[104],"related":[107,111],"features":[108,115],"features.":[112,135,153],"Since":[113],"extracted":[116],"are":[117],"sparse":[118,152],"some":[120],"residents,":[121],"utilize":[123],"Regularized":[124],"Singular":[125],"Value":[126],"Decomposition":[127],"(RSVD)":[129],"construct":[131],"missing":[132],"values":[133],"After":[136],"that,":[137],"probability":[142,180],"each":[144],"taking":[147],"advantage":[148],"less":[151],"We":[154],"also":[155],"consider":[156],"attractiveness":[158,183],"one":[160],"factors":[163],"influencing":[164],"help":[169],"profiles":[172],"geographical":[174],"With":[176],"being":[184],"obtained,":[185],"regression":[192],"model.":[193],"Finally,":[194],"experimental":[195],"results":[196],"real-world":[198],"data":[199],"show":[200],"that":[201],"our":[202],"model":[203],"effective":[205],"areas.":[214]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}