Predicting Crime Using Spatial Features
This work addresses crime prediction for law enforcement agencies, but it is incremental as it builds on existing spatial analysis and machine learning methods.
The researchers tackled crime prediction by engineering spatial features from crime hotspots and open street map data, achieving significant performance improvement in predicting different crime categories using Royal Canadian Mounted Police data from Halifax, NS.
Our study aims to build a machine learning model for crime prediction using geospatial features for different categories of crime. The reverse geocoding technique is applied to retrieve open street map (OSM) spatial data. This study also proposes finding hotpoints extracted from crime hotspots area found by Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). A spatial distance feature is then computed based on the position of different hotpoints for various types of crime and this value is used as a feature for classifiers. We test the engineered features in crime data from Royal Canadian Mounted Police of Halifax, NS. We observed a significant performance improvement in crime prediction using the new generated spatial features.