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

Jul 1, 2017·
Gabriel Spadon
,
Lucas C. Scabora
,
Marcus v. S. Araujo
,
Paulo H. Oliveir
,
Bruno B. Machado
,
Elaine P. M. Sousa
,
Caetano Traina
,
Jose F. Rodrigues
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.
Type
Publication
Information Technology - New Generations