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

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