Putting spatial crime patterns in their social contexts through a contextualized colocation analysis

Basit Öğe Kaydı
dc.authoridHAKYEMEZ, Tugrul Cabir/0000-0003-0646-8950
dc.contributor.authorHakyemez, Tugrul Cabir
dc.contributor.authorBabaoglu, Ceni
dc.contributor.authorBasar, Ayse
dc.date.accessioned2024-07-18T20:40:39Z
dc.date.available2024-07-18T20:40:39Z
dc.date.issued2023
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractThis study proposes a novel contextualized colocation analysis to examine spatial crime patterns within their social contexts. The sample includes all reported MCI crime incidents (i.e., assault, break and enter, robbery, auto theft, and theft over incidents) in the city of Toronto between 2014 and 2019 (n = 178,892). Following a stepwise clustering feature selection, we begin our analysis by regionalizing the city based on the relevant social context indicators through a ward-like hierarchical spatial clustering algorithm. Then, we use a modified colocation miner algorithm with a novel Validity Score (VS) to select significant citywide and regional crime colocation patterns. The results indicate that eating establishments, commercial parking lots, and retail food stores are the most frequent urban facilities in citywide and regional crime colocation patterns. We also note several peculiar crime colocation patterns across disadvantaged neighborhoods. Additionally, the proposed analysis selects the patterns that explain an average of 11% more crime events through the use of VS. Our study offers an alternative method for colocation analysis by effectively identifying crime-specific citywide and regional crime colocation patterns. It also prioritizes the identified colocation patterns by ranking them based on their significance.en_US
dc.identifier.doi10.1007/s10708-023-10931-5
dc.identifier.endpage5741en_US
dc.identifier.issn0343-2521
dc.identifier.issn1572-9893
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85171300001en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage5721en_US
dc.identifier.urihttps://doi.org/10.1007/s10708-023-10931-5
dc.identifier.urihttps://hdl.handle.net/11411/7168
dc.identifier.volume88en_US
dc.identifier.wosWOS:001094590300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofGeojournalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCrime Colocation Analysisen_US
dc.subjectHierarchical Spatial Clusteringen_US
dc.subjectSpatial Colocation Miningen_US
dc.subjectSocial Contexten_US
dc.subjectRoutine Activitiesen_US
dc.subjectData Setsen_US
dc.subjectCriminologyen_US
dc.subjectValidityen_US
dc.subjectLawen_US
dc.titlePutting spatial crime patterns in their social contexts through a contextualized colocation analysisen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

Web of Science Indexed Publications
Scopus Indexed Publications