  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.
