A Data-Driven Approach for Spatio-Temporal Crime Predictions in Smart Cities

The steadily increasing urbanization is causing significant economic and social transformations in urban areas and it will be posing several challenges in city management issues. In particular, given that the larger cities the higher crime rates, crime spiking is becoming one of the most important social problems in large urban areas. To handle with the increase in crimes, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends, finalized to an efficient deployment of police officers over the territory and more effective crime prevention. This paper presents an approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and reliably forecast crime trends in each region. The final result of the algorithm is a spatio-temporal crime forecasting model, composed of a set of crime dense regions and a set of associated crime predictors, each one representing a predictive model for forecasting the number of crimes that will happen in its specific region. The experimental evaluation, performed on real-world data collected in a big area of Chicago, shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.

I. Introduction

The 21st Century is frequently referenced as the “Century of the City”, reflecting the unprecedented global migration into urban areas that is happening nowadays [1], [2]. Such a steadily increasing urbanization is bringing huge social, economic and environmental transformations and, at the same time, presenting challenges in city management issues, like resource planning (water, electricity), traffic, air and water quality, public policy and public safety services [3]. Moreover, given that the larger cities the higher crime rates, crime spiking is becoming one of the most important social problems in large urban areas, because it affects public safety, children development, and adult socio-economic status [4], [5].