Feature engineering for crime hotspot detection

Abstract

San-Francisco in the US and Natal in Brazil are two coastal cities which are known rather for its tech scene and natural beauty than for its criminal activities. We analyze characteristics of the urban environment in these two cities, deploying a machine learning model to detect categories and hotspots of criminal activities. We propose an extensive set of spatio-temporal & urban features which can significantly improve the accuracy of machine learning models for these tasks, one of which achieved Top 1% performance on a Crime Classification Competition by kaggle.com. Extensive evaluation on several years of crime records from both cities show how some features - such as the street network - carry important information about criminal activities.