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    Enhancing Deep Learning-based Crime Hotspot Predictions With Theory-based Environmental Risk Scores
    (Springer, 2026) Hakyemez, Tugrul Cabir; Badur, Bertan
    This 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.
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    Incorporating park events into crime hotspot prediction on street networks: A spatiotemporal graph learning approach
    (Elsevier, 2023) Hakyemez, Tugrul Cabir; Badur, Bertan
    Park 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.