Summary of An Event-centric Framework For Predicting Crime Hotspots with Flexible Time Intervals, by Jiahui Jin et al.
An Event-centric Framework for Predicting Crime Hotspots with Flexible Time Intervals
by Jiahui Jin, Yi Hong, Guandong Xu, Jinghui Zhang, Jun Tang, Hancheng Wang
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, FlexiCrime, tackles the complex task of predicting crime hotspots in a city by introducing a novel event-centric approach with flexible time intervals. This method incorporates a continuous-time attention network to capture correlations between crime events and learns crime context features that represent general crime patterns across time points and locations. Additionally, a type-aware spatiotemporal point process is introduced to learn crime-evolving features, measuring the risk of specific crime types at a given time and location. The results demonstrate FlexiCrime’s superiority over baseline techniques in predicting crime hotspots for flexible time intervals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predicting crime hotspots in cities is crucial and challenging. Existing methods often struggle with fixed-time granularities and sequence prediction models. To address this, researchers introduced FlexiCrime, a new way to predict crime hotspots with flexible time intervals. This method uses attention networks to understand patterns between crimes and learns features that help identify where and when crimes are likely to happen. |
Keywords
* Artificial intelligence * Attention * Spatiotemporal
