Enhancing Deep Learning-based Crime Hotspot Predictions With Theory-based Environmental Risk Scores

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dc.contributor.authorHakyemez, Tugrul Cabir
dc.contributor.authorBadur, Bertan
dc.date.accessioned2026-04-04T18:55:24Z
dc.date.available2026-04-04T18:55:24Z
dc.date.issued2026
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractThis study introduces a novel network-based crime risk score, the Street Segment Risk Score (SSRS), designed to enhance crime hotspot predictions on street networks. The SSRS evaluates the risk of individual street segments by incorporating the dynamic spatial influence of nearby urban features on local crime patterns. Our dataset comprises all reported incidents of robbery (n = 2,016) and theft (n = 31,493) from 2015 to 2018 in Chicago's Central Side (CS). We developed both daily and intraday crime hotspot prediction models that integrate the SSRS and compared their performance-with and without the SSRS-using two graph-based deep learning algorithms, Graph WaveNet (GWNet) and the Spatiotemporal Graph Convolutional Neural Network (STGCN); a traditional deep learning model, Long Short-Term Memory (LSTM); and two baseline methods, Multilayer Perceptron (MLP) and Spatiotemporal Network Kernel Density Estimation (STNetKDE). Results indicate that incorporating the SSRS improves daily robbery hotspot prediction accuracy by up to 5.3% and intraday theft prediction accuracy by as much as 33%. The proposed SSRS demonstrates strong potential to support more precise, street-level security interventions by enhancing daily and intraday crime hotspot predictions.
dc.description.sponsorshipIstanbul Bilgi University
dc.description.sponsorshipOpen access funding provided by the Scientific and Technological Research Council of Turkiye (TUB & Idot;TAK).
dc.identifier.doi10.1007/s12061-025-09789-6
dc.identifier.doi10.1007/s12061-025-09789-6
dc.identifier.issn1874-463X
dc.identifier.issn1874-4621
dc.identifier.issue1
dc.identifier.scopus2-s2.0-105030158498
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s12061-025-09789-6
dc.identifier.urihttps://hdl.handle.net/11411/10406
dc.identifier.volume19
dc.identifier.wosWOS:001689784100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofApplied Spatial Analysis and Policy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectCrime Hotspot Prediction
dc.subjectDeep Learning
dc.subjectEnvironmental Crime Risk
dc.subjectSpatiotemporal Graph Learning
dc.titleEnhancing Deep Learning-based Crime Hotspot Predictions With Theory-based Environmental Risk Scores
dc.typeArticle

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