# Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data

arXiv: https://arxiv.org/abs/1409.2983 · HTML: https://mlsanity.com/paper/1409.2983/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2014.

## Built on by (most cited first, 9 of 9)

- [Trading Complexity for Sparsity in Random Forest Explanations](https://mlsanity.com/paper/2108.05276/) (2021)
- [Spatiotemporal Data Mining: A Survey on Challenges and Open Problems](https://mlsanity.com/paper/2103.17128/) (2021)
- [Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data](https://mlsanity.com/paper/2102.00848/) (2021)
- [Spatial-Temporal Meta-path Guided Explainable Crime Prediction](https://mlsanity.com/paper/2205.01901/) (2022)
- [Spatiotemporal and Semantic Zero-inflated Urban Anomaly Prediction](https://mlsanity.com/paper/2304.01569/) (2023)
- [Understanding peacefulness through the world news](https://mlsanity.com/paper/2106.00306/) (2021)
- [A Survey on Societal Event Forecasting with Deep Learning](https://mlsanity.com/paper/2112.06345/) (2021)
- [Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains](https://mlsanity.com/paper/2206.12141/) (2022)
- [Crime Prediction using Machine Learning with a Novel Crime Dataset](https://mlsanity.com/paper/2211.01551/) (2022)

## Abstract (arXiv)

In this paper, we present a novel approach to predict crime in a geographic space from multiple data sources, in particular mobile phone and demographic data. The main contribution of the proposed approach lies in using aggregated and anonymized human behavioral data derived from mobile network activity to tackle the crime prediction problem. While previous research efforts have used either background historical knowledge or offenders' profiling, our findings support the hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime. In our experimental results with real crime data from London we obtain an accuracy of almost 70% when predicting whether a specific area in the city will be a crime hotspot or not. Moreover, we provide a discussion of the implications of our findings for data-driven crime analysis.