Yazar "Hakyemez, Tugrul Cabir" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    Putting spatial crime patterns in their social contexts through a contextualized colocation analysis
    (Springer, 2023) Hakyemez, Tugrul Cabir; Babaoglu, Ceni; Basar, Ayse
    This 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.