{"id":"https://openalex.org/W4220732194","doi":"https://doi.org/10.1145/3501807","title":"<scp>CrimeTensor</scp> : Fine-Scale Crime Prediction via Tensor Learning with Spatiotemporal Consistency","display_name":"<scp>CrimeTensor</scp> : Fine-Scale Crime Prediction via Tensor Learning with Spatiotemporal Consistency","publication_year":2022,"publication_date":"2022-03-25","ids":{"openalex":"https://openalex.org/W4220732194","doi":"https://doi.org/10.1145/3501807"},"language":"en","primary_location":{"id":"doi:10.1145/3501807","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3501807","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","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/A5081342921","display_name":"Weichao Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weichao Liang","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-8035-5255","affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063565261","display_name":"Zhiang Wu","orcid":"https://orcid.org/0000-0002-0591-1861"},"institutions":[{"id":"https://openalex.org/I206777745","display_name":"Nanjing Audit University","ror":"https://ror.org/04zj2bd87","country_code":"CN","type":"education","lineage":["https://openalex.org/I206777745"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiang Wu","raw_affiliation_strings":["Nanjing Audit University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-0591-1861","affiliations":[{"raw_affiliation_string":"Nanjing Audit University, Nanjing, China","institution_ids":["https://openalex.org/I206777745"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100356702","display_name":"Zhe Li","orcid":"https://orcid.org/0000-0003-1680-1083"},"institutions":[{"id":"https://openalex.org/I4210154338","display_name":"Hubei Engineering University","ror":"https://ror.org/05amnwk22","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210154338"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Li","raw_affiliation_strings":["Hubei Engineering University, Xiaogan, China"],"raw_orcid":"https://orcid.org/0000-0003-1680-1083","affiliations":[{"raw_affiliation_string":"Hubei Engineering University, Xiaogan, China","institution_ids":["https://openalex.org/I4210154338"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050609316","display_name":"Yong Ge","orcid":"https://orcid.org/0000-0002-9630-795X"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yong Ge","raw_affiliation_strings":["University of Arizona, Tucson, Arizona, USA"],"raw_orcid":"https://orcid.org/0000-0002-9630-795X","affiliations":[{"raw_affiliation_string":"University of Arizona, Tucson, Arizona, USA","institution_ids":["https://openalex.org/I138006243"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3407,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.77041312,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"13","issue":"2","first_page":"1","last_page":"24"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.996399998664856,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.996399998664856,"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"}},{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10574","display_name":"Crime Patterns and Interventions","score":0.9724000096321106,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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/computer-science","display_name":"Computer science","score":0.7990269660949707},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.6972241401672363},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6678533554077148},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6525415778160095},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5604126453399658},{"id":"https://openalex.org/keywords/notice","display_name":"Notice","score":0.5534239411354065},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.509933590888977},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.48802676796913147},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.4861904978752136},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4586179256439209},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.4583437442779541},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43071502447128296},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41225236654281616},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11532044410705566},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08374232053756714}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7990269660949707},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.6972241401672363},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6678533554077148},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6525415778160095},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5604126453399658},{"id":"https://openalex.org/C2779913896","wikidata":"https://www.wikidata.org/wiki/Q7063001","display_name":"Notice","level":2,"score":0.5534239411354065},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.509933590888977},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.48802676796913147},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.4861904978752136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4586179256439209},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.4583437442779541},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43071502447128296},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41225236654281616},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11532044410705566},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08374232053756714},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3501807","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3501807","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.800000011920929}],"awards":[{"id":"https://openalex.org/G2537193519","display_name":null,"funder_award_id":"72072091, 72002067 and 71801123","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1544274373","https://openalex.org/W1582826279","https://openalex.org/W1987971958","https://openalex.org/W1999136078","https://openalex.org/W2024165284","https://openalex.org/W2035770620","https://openalex.org/W2038420726","https://openalex.org/W2045377163","https://openalex.org/W2123906784","https://openalex.org/W2128338591","https://openalex.org/W2147876157","https://openalex.org/W2165705190","https://openalex.org/W2604738573","https://openalex.org/W2607541615","https://openalex.org/W2767949765","https://openalex.org/W2774459974","https://openalex.org/W2775402959","https://openalex.org/W2779342116","https://openalex.org/W2780029216","https://openalex.org/W2796062295","https://openalex.org/W2802119699","https://openalex.org/W2864767203","https://openalex.org/W2884502219","https://openalex.org/W2885650385","https://openalex.org/W2895806569","https://openalex.org/W2901339881","https://openalex.org/W2907002026","https://openalex.org/W2908786777","https://openalex.org/W2911535719","https://openalex.org/W2914236597","https://openalex.org/W2918050465","https://openalex.org/W2920329036","https://openalex.org/W2942843559","https://openalex.org/W2944187290","https://openalex.org/W2945490408","https://openalex.org/W2963909158","https://openalex.org/W2980455371","https://openalex.org/W2995220619","https://openalex.org/W3012735076","https://openalex.org/W3088611441","https://openalex.org/W3102730661","https://openalex.org/W3141797743","https://openalex.org/W3157597645","https://openalex.org/W4232533539","https://openalex.org/W4292363360"],"related_works":["https://openalex.org/W4320074517","https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4224009465","https://openalex.org/W4255117927","https://openalex.org/W4286629047","https://openalex.org/W4306321456","https://openalex.org/W4285260836","https://openalex.org/W3046775127","https://openalex.org/W4224323965"],"abstract_inverted_index":{"Crime":[0],"poses":[1],"a":[2,34,50,100,134,159,168,196],"major":[3],"threat":[4],"to":[5,43,65,77,105,113,143,176,183,201,206],"human":[6],"life":[7],"and":[8,82,136,151,171,224,227],"property,":[9],"which":[10,141,163,194],"has":[11,48],"been":[12,49],"recognized":[13],"as":[14,133,205],"one":[15],"of":[16,27,33,40,53,72,86,109,147,198,212,237],"the":[17,25,73,87,107,130,148,165,178,185,209,213,233],"most":[18,71],"crucial":[19],"problems":[20],"in":[21,30,92,155,235],"our":[22,228],"society.":[23],"Predicting":[24],"number":[26,108],"crime":[28,57,93,110,131,156],"incidents":[29,111],"each":[31,117,238],"region":[32,119],"city":[35],"before":[36],"they":[37],"happen":[38],"is":[39,181],"great":[41,51],"importance":[42],"fight":[44],"against":[45],"crime.":[46],"There":[47],"deal":[52],"research":[54],"focused":[55],"on":[56,221],"prediction,":[58],"ranging":[59],"from":[60],"introducing":[61],"diversified":[62],"data":[63,132],"sources":[64],"exploring":[66],"various":[67],"prediction":[68,80,203],"models.":[69],"However,":[70],"existing":[74],"approaches":[75],"fail":[76],"offer":[78],"fine-scale":[79],"results":[81],"take":[83,144],"little":[84],"notice":[85],"intricate":[88],"spatial-temporal-categorical":[89],"correlations":[90,153],"contained":[91,154],"incidents.":[94,157],"In":[95,126],"this":[96],"article,":[97],"we":[98,128,189],"propose":[99],"tailor-made":[101],"framework":[102,193,229],"called":[103],"CrimeTensor":[104],"predict":[106],"belonging":[112],"different":[114],"categories":[115],"within":[116],"target":[118],"via":[120],"tensor":[121,135],"learning":[122],"with":[123],"spatiotemporal":[124],"consistency.":[125],"particular,":[127],"model":[129],"present":[137],"an":[138,191],"objective":[139,166,186],"function":[140],"tries":[142],"full":[145],"advantage":[146],"spatial,":[149],"temporal,":[150],"categorical":[152],"Moreover,":[158],"well-designed":[160],"optimization":[161,214],"algorithm":[162],"transforms":[164],"into":[167],"compact":[169],"form":[170],"then":[172],"applies":[173],"CP":[174],"decomposition":[175],"find":[177],"optimal":[179],"solution":[180],"elaborated":[182],"solve":[184],"function.":[187],"Furthermore,":[188],"develop":[190],"enhanced":[192],"takes":[195],"set":[197],"pre-selected":[199],"regions":[200],"conduct":[202],"so":[204],"further":[207],"improve":[208],"computational":[210],"efficiency":[211],"algorithm.":[215],"Finally,":[216],"extensive":[217],"experiments":[218],"are":[219],"performed":[220],"both":[222],"proprietary":[223],"public":[225],"datasets":[226],"significantly":[230],"outperforms":[231],"all":[232],"baselines":[234],"terms":[236],"evaluation":[239],"metric.":[240]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
