Incorporating park events into crime hotspot prediction on street networks: A spatiotemporal graph learning approach

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dc.authoridHAKYEMEZ, Tugrul Cabir/0000-0003-0646-8950
dc.contributor.authorHakyemez, Tugrul Cabir
dc.contributor.authorBadur, Bertan
dc.date.accessioned2024-07-18T20:42:30Z
dc.date.available2024-07-18T20:42:30Z
dc.date.issued2023
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractPark events elevate crime risk in and around parks for brief periods by granting offenders close contact with abundant suitable targets in outdoor spaces. This study proposes to capture the formulated transient crime risk with a network-based feature, Park Event Density (PED), that monitors the dynamic event density across parks. We incorporate the PED into various crime hotspot prediction models to test its effectiveness. The sample includes all the robbery(n = 1555) and theft(n = 22596) incidents between 2016 and 2018 in the Center Side of Chicago. We generate daily and intraday crime hotspot predictions using two spatiotemporal graph learning algorithms (i.e., Graph Wavenet and Spatiotemporal Graph Convolution Neural Networks) and a traditional counterpart (i.e., LSTM). The results reveal that the PED-incorporated models have up to 25% higher accuracy, particularly in the intraday theft predictions. Another significant result indicates that the predictive accuracies of spatiotemporal graph learning algorithms are up to three times higher than their traditional counterpart. The proposed method provides additional information to security decision-makers with crime hotspot prediction models sensitive to the changing crime risk landscape across a region during park events. It also helps organize safer outdoor public events by enacting timely security interventions through more accurate crime hotspot predictions.en_US
dc.description.sponsorshipBogazici University Scientific Research Fund [15385]en_US
dc.description.sponsorshipThe authors have no competing interests to declare that are relevant to the content of this article. Tugrul Cabir HAKYEMEZ, Bertan BADUR. This research was funded by Bogazici University Scientific Research Fund, grant number 15385. We also would like to express our sincere gratitude to Dr. James Ward for his invaluable support in proofreading this manuscript.en_US
dc.identifier.doi10.1016/j.asoc.2023.110886
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85173059525en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2023.110886
dc.identifier.urihttps://hdl.handle.net/11411/7294
dc.identifier.volume148en_US
dc.identifier.wosWOS:001149609000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCrime Hotspot Predictionen_US
dc.subjectPark Eventen_US
dc.subjectSpatiotemporal Graph Learningen_US
dc.subjectDeep Learningen_US
dc.subjectTimeen_US
dc.subjectPerceptionsen_US
dc.subjectTwitteren_US
dc.subjectAreasen_US
dc.titleIncorporating park events into crime hotspot prediction on street networks: A spatiotemporal graph learning approach
dc.typeArticle

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