{"id":"https://openalex.org/W4397023203","doi":"https://doi.org/10.1186/s40537-024-00935-4","title":"Multi-density crime predictor: an approach to forecast criminal activities in multi-density crime hotspots","display_name":"Multi-density crime predictor: an approach to forecast criminal activities in multi-density crime hotspots","publication_year":2024,"publication_date":"2024-05-17","ids":{"openalex":"https://openalex.org/W4397023203","doi":"https://doi.org/10.1186/s40537-024-00935-4"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-024-00935-4","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-024-00935-4","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-024-00935-4","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-024-00935-4","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055163091","display_name":"Eugenio Cesario","orcid":"https://orcid.org/0000-0002-4987-0459"},"institutions":[{"id":"https://openalex.org/I45204951","display_name":"University of Calabria","ror":"https://ror.org/02rc97e94","country_code":"IT","type":"education","lineage":["https://openalex.org/I45204951"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Eugenio Cesario","raw_affiliation_strings":["University of Calabria, Via P. Bucci 18B, 87036, Rende, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Calabria, Via P. Bucci 18B, 87036, Rende, Italy","institution_ids":["https://openalex.org/I45204951"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071625885","display_name":"Paolo Lindia","orcid":"https://orcid.org/0000-0002-7550-1331"},"institutions":[{"id":"https://openalex.org/I45204951","display_name":"University of Calabria","ror":"https://ror.org/02rc97e94","country_code":"IT","type":"education","lineage":["https://openalex.org/I45204951"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Paolo Lindia","raw_affiliation_strings":["University of Calabria, Via P. Bucci 18B, 87036, Rende, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Calabria, Via P. Bucci 18B, 87036, Rende, Italy","institution_ids":["https://openalex.org/I45204951"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071906892","display_name":"Andrea Vinci","orcid":"https://orcid.org/0000-0002-1011-1885"},"institutions":[{"id":"https://openalex.org/I3005160176","display_name":"Institute for High Performance Computing and Networking","ror":"https://ror.org/04r5fge26","country_code":"IT","type":"facility","lineage":["https://openalex.org/I3005160176","https://openalex.org/I4210155236"]},{"id":"https://openalex.org/I4210155236","display_name":"National Research Council","ror":"https://ror.org/04zaypm56","country_code":"IT","type":"nonprofit","lineage":["https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Andrea Vinci","raw_affiliation_strings":["Institute for High Performance Computing and Networking - National Research Council of Italy (ICAR-CNR), Via P. Bucci, 8/9c, 87036, Rende, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for High Performance Computing and Networking - National Research Council of Italy (ICAR-CNR), Via P. Bucci, 8/9c, 87036, Rende, Italy","institution_ids":["https://openalex.org/I3005160176","https://openalex.org/I4210155236"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5055163091"],"corresponding_institution_ids":["https://openalex.org/I45204951"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":21.4716,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.99413749,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"11","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10574","display_name":"Crime Patterns and Interventions","score":0.992900013923645,"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"}},"topics":[{"id":"https://openalex.org/T10574","display_name":"Crime Patterns and Interventions","score":0.992900013923645,"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"}},{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.98580002784729,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9682999849319458,"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/crime-analysis","display_name":"Crime analysis","score":0.7579504251480103},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.660123348236084},{"id":"https://openalex.org/keywords/metropolitan-area","display_name":"Metropolitan area","score":0.639556884765625},{"id":"https://openalex.org/keywords/hotspot","display_name":"Hotspot (geology)","score":0.5756998062133789},{"id":"https://openalex.org/keywords/crime-prevention","display_name":"Crime prevention","score":0.5478581190109253},{"id":"https://openalex.org/keywords/predictive-analytics","display_name":"Predictive analytics","score":0.5423297882080078},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5070853233337402},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.4831063747406006},{"id":"https://openalex.org/keywords/police-department","display_name":"Police department","score":0.46173346042633057},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4365033507347107},{"id":"https://openalex.org/keywords/metropolitan-police","display_name":"Metropolitan police","score":0.4238322377204895},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4195460081100464},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4132433533668518},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2667120695114136},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.21017882227897644},{"id":"https://openalex.org/keywords/criminology","display_name":"Criminology","score":0.18742719292640686}],"concepts":[{"id":"https://openalex.org/C2776876444","wikidata":"https://www.wikidata.org/wiki/Q2845200","display_name":"Crime analysis","level":2,"score":0.7579504251480103},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.660123348236084},{"id":"https://openalex.org/C158739034","wikidata":"https://www.wikidata.org/wiki/Q1907114","display_name":"Metropolitan area","level":2,"score":0.639556884765625},{"id":"https://openalex.org/C146481406","wikidata":"https://www.wikidata.org/wiki/Q105131","display_name":"Hotspot (geology)","level":2,"score":0.5756998062133789},{"id":"https://openalex.org/C2776348852","wikidata":"https://www.wikidata.org/wiki/Q855848","display_name":"Crime prevention","level":2,"score":0.5478581190109253},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.5423297882080078},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5070853233337402},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4831063747406006},{"id":"https://openalex.org/C2992711187","wikidata":"https://www.wikidata.org/wiki/Q35535","display_name":"Police department","level":2,"score":0.46173346042633057},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4365033507347107},{"id":"https://openalex.org/C2780142257","wikidata":"https://www.wikidata.org/wiki/Q1139213","display_name":"Metropolitan police","level":2,"score":0.4238322377204895},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4195460081100464},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4132433533668518},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2667120695114136},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.21017882227897644},{"id":"https://openalex.org/C73484699","wikidata":"https://www.wikidata.org/wiki/Q161733","display_name":"Criminology","level":1,"score":0.18742719292640686},{"id":"https://openalex.org/C8058405","wikidata":"https://www.wikidata.org/wiki/Q46255","display_name":"Geophysics","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-024-00935-4","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-024-00935-4","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-024-00935-4","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:5793d533d7ee4d5ba6fa47dd81858da8","is_oa":true,"landing_page_url":"https://doaj.org/article/5793d533d7ee4d5ba6fa47dd81858da8","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 11, Iss 1, Pp 1-39 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-024-00935-4","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-024-00935-4","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-024-00935-4","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G29840918","display_name":null,"funder_award_id":"PE0000013-FAIR","funder_id":"https://openalex.org/F4320331528","funder_display_name":"Ministero dell'Universit\u00e0 e della Ricerca"}],"funders":[{"id":"https://openalex.org/F4320331528","display_name":"Ministero dell'Universit\u00e0 e della Ricerca","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4397023203.pdf"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1486034269","https://openalex.org/W1491984529","https://openalex.org/W1944425239","https://openalex.org/W1987799824","https://openalex.org/W1987971958","https://openalex.org/W2046454733","https://openalex.org/W2051224630","https://openalex.org/W2060346657","https://openalex.org/W2060979493","https://openalex.org/W2064675550","https://openalex.org/W2113586398","https://openalex.org/W2120438042","https://openalex.org/W2132560265","https://openalex.org/W2136848157","https://openalex.org/W2162357792","https://openalex.org/W2460931943","https://openalex.org/W2514525802","https://openalex.org/W2811507150","https://openalex.org/W2884002274","https://openalex.org/W2908786777","https://openalex.org/W2996192178","https://openalex.org/W3031759008","https://openalex.org/W3084405808","https://openalex.org/W3107119534","https://openalex.org/W3162886737","https://openalex.org/W3208517261","https://openalex.org/W3214432783","https://openalex.org/W4220732194","https://openalex.org/W4224911291","https://openalex.org/W4233588482","https://openalex.org/W4238235003","https://openalex.org/W4292417064","https://openalex.org/W4295854162","https://openalex.org/W4312238713","https://openalex.org/W4319753182","https://openalex.org/W6679955751"],"related_works":["https://openalex.org/W3199089672","https://openalex.org/W4230105120","https://openalex.org/W2511702358","https://openalex.org/W2602120138","https://openalex.org/W4246594116","https://openalex.org/W219256912","https://openalex.org/W181166483","https://openalex.org/W1523000771","https://openalex.org/W2110535999","https://openalex.org/W2898333600"],"abstract_inverted_index":{"Abstract":[0],"The":[1,63,156,203],"increasing":[2,18],"pervasiveness":[3],"of":[4,20,47,65,75,98,122,166,169,178,192,206,218,230,274],"ICT":[5],"technologies":[6],"and":[7,16,44,50,80,120,135,147,175,244,252,268],"sensor":[8],"infrastructures":[9],"is":[10,71,159],"enabling":[11],"police":[12,53],"departments":[13,54],"to":[14,28,33,104,138,148,188,197,280],"gather":[15],"store":[17],"volumes":[19],"spatio-temporal":[21,161],"crime":[22,36,48,61,66,83,99,113,133,142,151,162,171,180,225,254],"data.":[23],"This":[24,115,233],"offers":[25],"the":[26,118,190,207,237,275,281],"opportunity":[27],"apply":[29],"data":[30,70],"analytics":[31],"methodologies":[32],"extract":[34],"useful":[35],"predictive":[37,78,186],"models,":[38,246,270],"which":[39],"can":[40,51],"effectively":[41],"detect":[42,140],"spatial":[43,96,251],"temporal":[45,253],"patterns":[46],"events,":[49,100],"support":[52],"in":[55,144,153,199,250],"implementing":[56,81],"more":[57,106,221],"effective":[58,77,107],"strategies":[59],"for":[60,111],"prevention.":[62],"detection":[64],"hotspots":[67,134],"from":[68],"geo-referenced":[69],"a":[72,160,167,176,185,215,228,263],"crucial":[73],"aspect":[74],"discovering":[76,112],"models":[79,137],"efficient":[82],"prevention":[84],"decisions.":[85],"In":[86],"particular,":[87],"since":[88],"metropolitan":[89],"cities":[90],"are":[91,195],"heavily":[92],"characterized":[93],"by":[94,213],"variable":[95],"densities":[97,174],"multi-density":[101,132,170,242],"clustering":[102,243],"seems":[103],"be":[105],"than":[108,222],"classic":[109],"techniques":[110],"hotspots.":[114],"paper":[116],"presents":[117,262],"design":[119],"implementation":[121],"MD-CrimePredictor":[123],"(Multi-":[124],"Density":[125],"Crime":[126],"Predictor),":[127],"an":[128],"approach":[129,209],"based":[130,240],"on":[131,241],"regressive":[136,245],"automatically":[139],"high-risk":[141],"areas":[143],"urban":[145],"environments,":[146],"reliably":[149],"forecast":[150,189],"trends":[152],"each":[154,182],"area.":[155],"algorithm":[157],"result":[158],"forecasting":[163,255],"model,":[164],"composed":[165],"set":[168,177],"hotspots,":[172],"their":[173],"associated":[179],"predictors,":[181],"one":[183],"representing":[184],"model":[187],"number":[191],"crimes":[193],"that":[194,236],"estimated":[196],"happen":[198],"its":[200],"specific":[201],"hotspot.":[202],"experimental":[204],"evaluation":[205,234],"proposed":[208,238],"has":[210],"been":[211],"performed":[212],"analyzing":[214],"large":[216],"area":[217],"Chicago,":[219],"involving":[220],"two":[223],"million":[224],"events":[226],"(over":[227],"period":[229],"19":[231],"years).":[232],"shows":[235],"approach,":[239],"achieves":[247],"good":[248],"accuracy":[249,273],"over":[256],"rolling":[257],"prediction":[258],"horizons.":[259],"It":[260],"also":[261],"comparative":[264],"analysis":[265],"between":[266],"SARIMA":[267],"LSTM":[269],"showing":[271],"higher":[272],"first":[276],"method":[277],"with":[278],"respect":[279],"second":[282],"one.":[283]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}