# Spatial-Temporal-Textual Point Processes for Crime Linkage Detection

arXiv: https://arxiv.org/abs/1902.00440 · HTML: https://mlsanity.com/paper/1902.00440/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

## Built on by (most cited first, 12 of 12)

- [Spatio-temporal point processes with deep non-stationary kernels](https://mlsanity.com/paper/2211.11179/) (2022)
- [Conditional Generative Modeling for High-dimensional Marked Temporal Point Processes](https://mlsanity.com/paper/2305.12569/) (2023)
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- [New User Event Prediction Through the Lens of Causal Inference](https://mlsanity.com/paper/2407.05625/) (2024)
- [Likelihood-Free Estimation for Spatiotemporal Hawkes processes with missing data and application to predictive policing](https://mlsanity.com/paper/2502.07111/) (2025)
- [Deep spatio-temporal point processes: Advances and new directions](https://mlsanity.com/paper/2504.06364/) (2025)
- [Score-Based Change-Point Detection and Region Localization for Spatio-Temporal Point Processes](https://mlsanity.com/paper/2602.04798/) (2026)
- [Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approaches](https://mlsanity.com/paper/2411.00864/) (2024)
- [Graph-Based Prediction Models for Data Debiasing](https://mlsanity.com/paper/2504.09348/) (2025)
- [Uncovering Social Network Activity Using Joint User and Topic Interaction](https://mlsanity.com/paper/2506.12842/) (2025)
- [Long-range Modeling and Processing of Multimodal Event Sequences](https://mlsanity.com/paper/2602.01125/) (2026)

## Abstract (arXiv)

Crimes emerge out of complex interactions of human behaviors and situations. Linkages between crime incidents are highly complex. Detecting crime linkage given a set of incidents is a highly challenging task since we only have limited information, including text descriptions, incident times, and locations. In practice, there are very few labels. We propose a new statistical modeling framework for it spatio-temporal-textual data and demonstrate its usage on crime linkage detection. We capture linkages of crime incidents via multivariate marked spatio-temporal Hawkes processes and treat embedding vectors of the free-text as it marks of the incident, inspired by the notion of it modus operandi (M.O.) in crime analysis. Numerical results using real data demonstrate the good performance of our method as well as reveals interesting patterns in the crime data: the joint modeling of space, time, and text information enhances crime linkage detection compared with the state-of-the-art, and the learned spatial dependence from data can be useful for police operations.