# Mining large-scale human mobility data for long-term crime prediction

arXiv: https://arxiv.org/abs/1806.01400 · HTML: https://mlsanity.com/paper/1806.01400/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2018.

## Built on by (most cited first, 7 of 7)

- [Spatial-Temporal Meta-path Guided Explainable Crime Prediction](https://mlsanity.com/paper/2205.01901/) (2022)
- [Improving the Fairness of Deep-Learning, Short-term Crime Prediction with Under-reporting-aware Models](https://mlsanity.com/paper/2406.04382/) (2024)
- [Web Mining to Inform Locations of Charging Stations for Electric Vehicles](https://mlsanity.com/paper/2203.07081/) (2022)
- [Network-Based Transfer Learning Helps Improve Short-Term Crime Prediction Accuracy](https://mlsanity.com/paper/2406.06645/) (2024)
- [Causal Discovery and Inference towards Urban Elements and Associated Factors](https://mlsanity.com/paper/2503.06395/) (2025)
- [Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model](https://mlsanity.com/paper/2508.02734/) (2025)
- [Deep Learning for Crime Forecasting: The Role of Mobility at Fine-grained Spatiotemporal Scales](https://mlsanity.com/paper/2509.20913/) (2025)

## Abstract (arXiv)

Traditional crime prediction models based on census data are limited, as they fail to capture the complexity and dynamics of human activity. With the rise of ubiquitous computing, there is the opportunity to improve such models with data that make for better proxies of human presence in cities. In this paper, we leverage large human mobility data to craft an extensive set of features for crime prediction, as informed by theories in criminology and urban studies. We employ averaging and boosting ensemble techniques from machine learning, to investigate their power in predicting yearly counts for different types of crimes occurring in New York City at census tract level. Our study shows that spatial and spatio-temporal features derived from Foursquare venues and checkins, subway rides, and taxi rides, improve the baseline models relying on census and POI data. The proposed models achieve absolute R^2 metrics of up to 65% (on a geographical out-of-sample test set) and up to 89% (on a temporal out-of-sample test set). This proves that, next to the residential population of an area, the ambient population there is strongly predictive of the area's crime levels. We deep-dive into the main crime categories, and find that the predictive gain of the human dynamics features varies across crime types: such features bring the biggest boost in case of grand larcenies, whereas assaults are already well predicted by the census features. Furthermore, we identify and discuss top predictive features for the main crime categories. These results offer valuable insights for those responsible for urban policy or law enforcement.