Spatio-temporal prediction of crimes using network analytic approach

TL;DR

This paper employs network analytic techniques to analyze and fuse social data for spatio-temporal crime prediction in Chicago, demonstrating improved accuracy with multi-layered data integration.

Contribution

It introduces a novel network analytic approach for detailed crime prediction across different regions and crime types in Chicago.

Findings

Adding more social data layers improves prediction accuracy.

Models predict crime counts for specific regions and crime categories.

Multi-layer data fusion enhances urban crime forecasting.

Abstract

It is quite evident that majority of the population lives in urban area today than in any time of the human history. This trend seems to increase in coming years. A study [5] says that nearly 80.7% of total population in USA stays in urban area. By 2030 nearly 60% of the population in the world will live in or move to cities. With the increase in urban population, it is important to keep an eye on criminal activities. By doing so, governments can enforce intelligent policing systems and hence many government agencies and local authorities have made the crime data publicly available. In this paper, we analyze Chicago city crime data fused with other social information sources using network analytic techniques to predict criminal activity for the next year. We observe that as we add more layers of data which represent different aspects of the society, the quality of prediction is improved. Our prediction models not just predict total number of crimes for the whole Chicago city, rather they predict number of crimes for all types of crimes and for different regions in City of Chicago.