Complex Network Tools to Understand the Behavior of Criminality in Urban Areas

TL;DR

This paper introduces a comprehensive methodology using complex networks to identify and analyze high-criminality areas in urban environments, demonstrated through a case study in San Francisco.

Contribution

It presents a novel, complete framework for crime analysis with assessment measures, filling gaps in existing complex network applications for urban crime analysis.

Findings

Effective identification of high-criminality areas in San Francisco

Provides a set of assessment measures for criminal community analysis

Demonstrates the methodology's applicability to real-world data

Abstract

Complex networks are nowadays employed in several applications. Modeling urban street networks is one of them, and in particular to analyze criminal aspects of a city. Several research groups have focused on such application, but until now, there is a lack of a well-defined methodology for employing complex networks in a whole crime analysis process, i.e. from data preparation to a deep analysis of criminal communities. Furthermore, the "toolset" available for those works is not complete enough, also lacking techniques to maintain up-to-date, complete crime datasets and proper assessment measures. In this sense, we propose a threefold methodology for employing complex networks in the detection of highly criminal areas within a city. Our methodology comprises three tasks: (i) Mapping of Urban Crimes; (ii) Criminal Community Identification; and (iii) Crime Analysis. Moreover, it provides a proper set of assessment measures for analyzing intrinsic criminality of communities, especially when considering different crime types. We show our methodology by applying it to a real crime dataset from the city of San Francisco - CA, USA. The results confirm its effectiveness to identify and analyze high criminality areas within a city. Hence, our contributions provide a basis for further developments on complex networks applied to crime analysis.