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arXiv · 1612.06115

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

Gabriel Spadon·Lucas C. Scabora·Marcus V. S. Araujo·Paulo H. Oliveira·Bruno B. Machado·Elaine P. M. Sousa·Caetano Traina-Jr·Jose F. Rodrigues-Jr
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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.

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Gabriel Spadon, Lucas C. Scabora, Marcus V. S. Araujo, Paulo H. Oliveira, Bruno B. Machado, Elaine P. M. Sousa, Caetano Traina-Jr, Jose F. Rodrigues-Jr. 2016-12-24. Complex Network Tools to Understand the Behavior of Criminality in Urban Areas. https://doi.org/10.1007/978-3-319-54978-1_63

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