{"id":"https://openalex.org/W3097428809","doi":"https://doi.org/10.1145/3423455.3430319","title":"Exploiting points of interest for predictive policing","display_name":"Exploiting points of interest for predictive policing","publication_year":2020,"publication_date":"2020-11-03","ids":{"openalex":"https://openalex.org/W3097428809","doi":"https://doi.org/10.1145/3423455.3430319","mag":"3097428809"},"language":"en","primary_location":{"id":"doi:10.1145/3423455.3430319","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3423455.3430319","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities","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/A5076089244","display_name":"Lu\u00eds Gustavo Coutinho do R\u00eago","orcid":"https://orcid.org/0000-0003-2039-3297"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu\u00eds Gustavo Coutinho do R\u00eago","raw_affiliation_strings":["Insight Data Science Lab, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Insight Data Science Lab, Brazil","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060055439","display_name":"Ticiana Linhares Coelho da Silva","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ticiana Linhares Coelho da Silva","raw_affiliation_strings":["Insight Data Science Lab, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Insight Data Science Lab, Brazil","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056615439","display_name":"R\u00e9gis Pires Magalh\u00e3es","orcid":"https://orcid.org/0000-0001-6737-4750"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Regis Pires Magalh\u00e3es","raw_affiliation_strings":["Insight Data Science Lab, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Insight Data Science Lab, Brazil","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065118727","display_name":"Jos\u00e9 Ant\u00f4nio Fernandes de Mac\u00eado","orcid":"https://orcid.org/0000-0002-0661-2978"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jos\u00e9 Ant\u00f4nio Fernandes de Mac\u00eado","raw_affiliation_strings":["Insight Data Science Lab, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Insight Data Science Lab, Brazil","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055249933","display_name":"Wellington Clay Porcino Silva","orcid":"https://orcid.org/0000-0002-4311-4070"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wellington Clay Porcino Silva","raw_affiliation_strings":["National Department of Public Security, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Department of Public Security, Brazil","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4692,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.88574198,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"2020","issue":null,"first_page":"20","last_page":"28"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10574","display_name":"Crime Patterns and Interventions","score":0.9991000294685364,"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":0.9991000294685364,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9983999729156494,"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/T10370","display_name":"Traffic and Road Safety","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/relevance","display_name":"Relevance (law)","score":0.7635120153427124},{"id":"https://openalex.org/keywords/point-of-interest","display_name":"Point of interest","score":0.7455744743347168},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7266995906829834},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.554779052734375},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4742495119571686},{"id":"https://openalex.org/keywords/urban-computing","display_name":"Urban computing","score":0.4504488706588745},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4495759904384613},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4267783463001251},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42123839259147644}],"concepts":[{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.7635120153427124},{"id":"https://openalex.org/C150140777","wikidata":"https://www.wikidata.org/wiki/Q960648","display_name":"Point of interest","level":2,"score":0.7455744743347168},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7266995906829834},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.554779052734375},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4742495119571686},{"id":"https://openalex.org/C2778459138","wikidata":"https://www.wikidata.org/wiki/Q7900107","display_name":"Urban computing","level":2,"score":0.4504488706588745},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4495759904384613},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4267783463001251},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42123839259147644},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3423455.3430319","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3423455.3430319","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.699999988079071,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W84561140","https://openalex.org/W1531081428","https://openalex.org/W1776456436","https://openalex.org/W1922017469","https://openalex.org/W1974118495","https://openalex.org/W1974429651","https://openalex.org/W1989344766","https://openalex.org/W2009383658","https://openalex.org/W2095065398","https://openalex.org/W2102524069","https://openalex.org/W2134089414","https://openalex.org/W2135008766","https://openalex.org/W2147876157","https://openalex.org/W2161551760","https://openalex.org/W2169738360","https://openalex.org/W2295598076","https://openalex.org/W2504658994","https://openalex.org/W2514525802","https://openalex.org/W2767949765","https://openalex.org/W2779342116","https://openalex.org/W2895806569","https://openalex.org/W2986427358","https://openalex.org/W3102476541","https://openalex.org/W4236338840","https://openalex.org/W4285722178"],"related_works":["https://openalex.org/W2139872359","https://openalex.org/W2752457693","https://openalex.org/W2743808405","https://openalex.org/W2889535606","https://openalex.org/W4385270215","https://openalex.org/W2900873536","https://openalex.org/W2576554443","https://openalex.org/W2905252995","https://openalex.org/W2051234849","https://openalex.org/W3004827789"],"abstract_inverted_index":{"High":[0],"crime":[1,26,155],"rates":[2],"have":[3,20,80],"become":[4],"a":[5,100,107,181],"public":[6],"health":[7],"problem":[8],"in":[9,99,130],"many":[10],"important":[11],"cities,":[12],"according":[13],"to":[14,24,28,53,142],"World":[15],"Health":[16],"Organization.":[17],"Many":[18],"researchers":[19],"been":[21],"developing":[22],"algorithms":[23,58],"predict":[25],"occurrences":[27],"tackle":[29],"this":[30,92],"problem.":[31,132],"The":[32,160],"smart":[33],"cities'":[34],"environment":[35],"can":[36],"provide":[37,65],"us":[38],"enough":[39],"ubiquitous":[40],"data,":[41],"e.g.,":[42],"traffic":[43],"flow,":[44],"human":[45],"mobility,":[46],"and":[47,59,75,120,123,136,150,209],"Points":[48],"of":[49,106,117,178],"Interest":[50],"(POI)":[51],"information,":[52],"feed":[54],"those":[55],"predictive":[56,89,214],"policing":[57,215],"reflect":[60],"city":[61,108],"dynamics.":[62],"POIs":[63,105,118,128,207],"data":[64,129,156,208],"essential":[66],"information":[67,174,185,212],"such":[68],"as":[69],"geographical":[70,84],"location,":[71],"category,":[72],"customer":[73],"reviews,":[74],"busy":[76],"hours.":[77],"Recent":[78],"studies":[79,200],"shown":[81],"that":[82,163,199],"POI":[83],"locations":[85],"are":[86],"useful":[87],"for":[88,213],"policing.":[90],"In":[91],"paper,":[93],"we":[94],"aim":[95],"at":[96],"predicting":[97],"crimes":[98,179],"delimited":[101],"region":[102],"around":[103,180],"the":[104,115,121,124,164,172,176,191,197,201,217],"with":[109],"new":[110],"environmental":[111],"features.":[112],"We":[113,133],"investigate":[114],"relevance":[116],"location":[119],"semantic":[122],"temporal":[125],"features":[126,149],"from":[127,206,216],"our":[131],"also":[134],"propose":[135],"analyze":[137],"different":[138],"machine":[139],"learning":[140],"approaches":[141],"train":[143],"prediction":[144],"functions":[145],"based":[146],"on":[147,153],"these":[148],"conduct":[151],"experiments":[152,161],"real":[154],"over":[157],"multiple":[158],"years.":[159],"demonstrate":[162],"popular":[165,202],"time":[166,203],"feature":[167,204],"is":[168,186,196],"more":[169],"relevant":[170],"than":[171,190],"historical":[173,210],"about":[175],"number":[177],"POI,":[182],"but":[183],"both":[184],"much":[187],"less":[188],"critical":[189],"spatio-temporal":[192],"information.":[193],"This":[194],"work":[195],"first":[198],"extracted":[205],"criminal":[211],"authors'":[218],"knowledge.":[219]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}