{"id":"https://openalex.org/W2805373902","doi":"https://doi.org/10.48550/arxiv.1806.01486","title":"Forecasting Crime with Deep Learning","display_name":"Forecasting Crime with Deep Learning","publication_year":2018,"publication_date":"2018-06-05","ids":{"openalex":"https://openalex.org/W2805373902","doi":"https://doi.org/10.48550/arxiv.1806.01486","mag":"2805373902"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1806.01486","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1806.01486","pdf_url":"https://arxiv.org/pdf/1806.01486","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1806.01486","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020224294","display_name":"Alexander Stec","orcid":"https://orcid.org/0009-0005-2205-9665","observed_orcids":["https://orcid.org/0009-0005-2205-9665"]},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stec, Alexander","raw_affiliation_strings":["Northwestern University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern University","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013049879","display_name":"Diego Klabjan","orcid":"https://orcid.org/0000-0003-4213-9281","observed_orcids":["https://orcid.org/0000-0003-4213-9281"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Klabjan, Diego","raw_affiliation_strings":["Industrial Engineering and Management Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Industrial Engineering and Management Sciences","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"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":0.9854000210762024,"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":0.9854000210762024,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.006399999838322401,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.000699999975040555,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/crime-prediction","display_name":"crime prediction","score":0.8090000152587891},{"id":"https://openalex.org/keywords/deep-neural-network","display_name":"deep neural network","score":0.8679999709129333},{"id":"https://openalex.org/keywords/spatiotemporal-prediction","display_name":"spatiotemporal prediction","score":0.7870000004768372},{"id":"https://openalex.org/keywords/chicago-crime-data","display_name":"Chicago crime data","score":0.5860000252723694},{"id":"https://openalex.org/keywords/meteorological-observations","display_name":"meteorological observations","score":0.671999990940094},{"id":"https://openalex.org/keywords/census-data","display_name":"census data","score":0.9810000061988831},{"id":"https://openalex.org/keywords/external-data-integration","display_name":"external data integration","score":0.5619999766349792},{"id":"https://openalex.org/keywords/spatial-binning","display_name":"spatial binning","score":0.335999995470047}],"concepts":[{"id":"https://openalex.org/C156273044","wikidata":"https://www.wikidata.org/wiki/Q4913766","display_name":"Bin","level":2,"score":0.8366432189941406},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6314977407455444},{"id":"https://openalex.org/C52130261","wikidata":"https://www.wikidata.org/wiki/Q39825","display_name":"Census","level":3,"score":0.6077306866645813},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5884339809417725},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.436509907245636},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.43141061067581177},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.4287929832935333},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4193081855773926},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41542309522628784},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3145378530025482},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12103533744812012},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09960386157035828},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"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/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:1806.01486","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1806.01486","pdf_url":"https://arxiv.org/pdf/1806.01486","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1806.01486","doi":"https://doi.org/10.48550/arxiv.1806.01486","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1806.01486","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1806.01486","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1806.01486","pdf_url":"https://arxiv.org/pdf/1806.01486","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"score":0.5600000023841858,"display_name":"Peace, justice, and strong institutions","id":"https://metadata.un.org/sdg/16"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W86385803","https://openalex.org/W612782763","https://openalex.org/W1943218881","https://openalex.org/W2056712395","https://openalex.org/W2189105228","https://openalex.org/W2512173100","https://openalex.org/W2950359997"],"related_works":["https://openalex.org/W2128472366","https://openalex.org/W2107701374","https://openalex.org/W621243299","https://openalex.org/W5594354","https://openalex.org/W4244351752","https://openalex.org/W2601163983","https://openalex.org/W2364090708","https://openalex.org/W2145323372","https://openalex.org/W2360358450","https://openalex.org/W2744241569"],"abstract_inverted_index":{"The":[0,49,126],"objective":[1],"of":[2,9,95,131,141],"this":[3,76],"work":[4],"is":[5,37],"to":[6,15,89,107,147],"take":[7],"advantage":[8],"deep":[10],"neural":[11,81,132],"networks":[12,133],"in":[13,22,145],"order":[14],"make":[16,28],"next":[17],"day":[18],"crime":[19,34,50,97,114,149],"count":[20,115],"predictions":[21,29],"a":[23,66,71],"fine-grain":[24],"city":[25],"partition.":[26],"We":[27,74],"using":[30,78,142],"Chicago":[31,122],"and":[32,46,57,92,118,123,138],"Portland":[33],"data,":[35,45],"which":[36],"augmented":[38],"with":[39,116],"additional":[40],"datasets":[41,144],"covering":[42],"weather,":[43],"census":[44],"public":[47],"transportation.":[48],"counts":[51],"are":[52,87,105],"broken":[53],"into":[54],"10":[55],"bins":[56],"our":[58,101],"model":[59,103],"predicts":[60],"the":[61,90,96,109,129,135,139],"most":[62],"likely":[63],"bin":[64,111],"for":[65,112,121,134],"each":[67],"spatial":[68,91],"region":[69],"at":[70],"daily":[72],"level.":[73],"train":[75],"data":[77],"increasingly":[79],"complex":[80],"network":[82],"structures,":[83],"including":[84],"variations":[85],"that":[86],"suited":[88],"temporal":[93],"aspects":[94],"prediction":[98,136],"problem.":[99],"With":[100],"best":[102],"we":[104],"able":[106],"predict":[108],"correct":[110],"overall":[113],"75.6%":[117],"65.3%":[119],"accuracy":[120],"Portland,":[124],"respectively.":[125],"results":[127],"show":[128],"efficacy":[130],"problem":[137],"value":[140],"external":[143],"addition":[146],"standard":[148],"data.":[150]},"counts_by_year":[{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-10-01T08:54:57.152042","created_date":"2025-10-10T00:00:00"}