Year: 2025
Type: article
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${\sf FlexiCrime}$, a novel event-centric framework for predicting crime hotspots with flexible time intervals.${\sf FlexiCrime}$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. Together, the crime context and evolving features allow us to predict whether an urban area is a crime hotspot given a future time interval. To evaluate${\sf FlexiCrime}$'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.
Authors: Jiahui Jin, Yi Hong, Guandong Xu, Jinghui Zhang, Jun Tang, Hancheng Wang
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
Awards: G36496830, G4773635152, G7670255191
OpenAlex ID: W7084978430