An Event-centric Framework for Predicting Crime Hotspots with Flexible Time Intervals

• URL: http://arxiv.org/abs/2411.01134v1
• Date: Sat, 02 Nov 2024 04:38:10 GMT
• Title: An Event-centric Framework for Predicting Crime Hotspots with Flexible Time Intervals
• Authors: Jiahui Jin, Yi Hong, Guandong Xu, Jinghui Zhang, Jun Tang, Hancheng Wang,
• Abstract summary: FlexiCrime is a novel event framework for predicting crime hotspots with flexible time intervals. It incorporates a continuous-time attention network to capture correlations between crime events, which learns crime context features. It measures the risk of specific crime types at a given time and location by considering frequency of past crime events.
• Score: 15.749155522310941
• License:
• Abstract: Predicting crime hotspots in a city is a complex and critical task with significant societal implications. Numerous spatiotemporal correlations and irregularities pose substantial challenges to this endeavor. Existing methods commonly employ fixed-time granularities and sequence prediction models. However, determining appropriate time granularities is difficult, leading to inaccurate predictions for specific time windows. For example, users might ask: What are the crime hotspots during 12:00-20:00? To address this issue, we introduce FlexiCrime, a novel event-centric framework for predicting crime hotspots with flexible time intervals. FlexiCrime incorporates a continuous-time attention network to capture correlations between crime events, which learns crime context features, representing general crime patterns across time points and locations. Furthermore, we introduce a type-aware spatiotemporal point process that learns crime-evolving features, measuring the risk of specific crime types at a given time and location by considering the frequency of past crime events. The crime context and evolving features together allow us to predict whether an urban area is a crime hotspot given a future time interval. To evaluate FlexiCrime's effectiveness, we conducted experiments using real-world datasets from two cities, covering twelve crime types. The results show that our model outperforms baseline techniques in predicting crime hotspots over flexible time intervals.

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