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

**Authors:** Cristina Kadar, Irena Pletikosa  
**arXiv:** [1806.01400](https://arxiv.org/abs/1806.01400)  
**Date:** 2018-06-04  
**Summary page:** https://www.summarizepaper.com/en/paper/1806.01400v1/

## Key points

- Traditional crime prediction models have limited use of census data
- Ubiquitous computing provides an opportunity to improve these models by incorporating data that represent human presence in cities
- The paper titled "Mining large-scale human mobility data for long-term crime prediction" by Cristina Kadar and Irena Pletikosa uses large human mobility data to develop features for crime prediction
- Features are informed by theories in criminology and urban studies
- Spatial and spatio-temporal features derived from Foursquare venues, check-ins, subway rides, and taxi rides significantly improve baseline models that rely solely on census and point-of-interest (POI) data
- Proposed models achieve impressive absolute R^2 metrics with high accuracy on geographical and temporal out-of-sample test sets
- Ambient population strongly predicts crime levels in addition to residential population
- Predictive gain of human dynamics features varies across different types of crimes, with the greatest boost seen in predicting grand larcenies
- Top predictive features for each main crime category are identified and discussed
- Geo-tagged human dynamics data can be leveraged to measure aspects of criminological theories at a large scale
- Using large-scale human mobility data alongside traditional census data can enhance understanding of crime patterns and develop more effective strategies for public safety.

## Summary

In traditional crime prediction models, the use of census data has been limited as it fails to capture the complexity and dynamics of human activity. However, with the advent of ubiquitous computing, there is an opportunity to improve these models by incorporating data that better represent human presence in cities. In their paper titled "Mining large-scale human mobility data for long-term crime prediction," Cristina Kadar and Irena Pletikosa leverage large human mobility data to develop a comprehensive set of features for crime prediction. These features are informed by theories in criminology and urban studies. The authors employ averaging and boosting ensemble techniques from machine learning to investigate the predictive power of these features for different types of crimes occurring at the census tract level in New York City. They find that spatial and spatio-temporal features derived from Foursquare venues and check-ins, subway rides, and taxi rides significantly improve baseline models that rely solely on census and point-of-interest (POI) data. The proposed models achieve impressive absolute R^2 metrics, with up to 65% accuracy on a geographical out-of-sample test set and up to 89% accuracy on a temporal out-of-sample test set. This demonstrates that, in addition to the residential population of an area, the ambient population strongly predicts crime levels in that area. The authors delve deeper into specific crime categories and observe that the predictive gain of human dynamics features varies across different types of crimes. For instance, these features provide the greatest boost in predicting grand larcenies while assaults are already well predicted by census features alone. Furthermore, Kadar and Pletikosa identify and discuss the top predictive features for each main crime category. These findings offer valuable insights for those responsible for urban policy or law enforcement. By leveraging geo-tagged human dynamics data such as public transportation or taxi usage patterns researchers can now empirically measure aspects of criminological theories that were previously difficult to study at a large scale. Overall this research highlights the potential of using large-scale human mobility data for long-term crime prediction models. By incorporating these dynamic data sources such as Foursquare venues and check-ins subway rides and taxi rides alongside traditional census data policymakers and law enforcement agencies can enhance their understanding of crime patterns and develop more effective strategies to ensure public safety and security.