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.
@article{arxiv.1803.04474,
title = {Predicting Crime Using Spatial Features},
author = {Fateha Khanam Bappee and Amilcar Soares Junior and Stan Matwin},
journal= {arXiv preprint arXiv:1803.04474},
year = {2020}
}
Comments
Paper accepted to 31st Canadian Conference in Artificial Intelligence, 2018