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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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Porta., S., Strano, E., Iacoviello, V., Messora, R., Latora, V., Cardillo, A., Wang, F., & Scellato, S. (2009). Street centrality and densities of retail and services in Bologna, Italy. Environment and Planning B: Planning and Design, 36(3), 450–465.
Deryol, R., Wilcox, P., Logan, M., & Wooldredge, J. (2016). Crime places in context: An illustration of the multilevel nature of hot spot development. Journal of Quantitative Criminology, 32(2), 305–325. CrossRef
Spicer, V., Song, J., Brantingham, P., Park, A., & Andresen, M. A. (2016). Street profile analysis: A new method for mapping crime on major roadways. Applied Geography, 69, 65–74.
Shiode, S., & Shiode, N. (2013). Network-based space-time search-window technique for hotspot detection of street-level crime incidents. International Journal of Geographical Information Science, 27(5), 866–882. CrossRef
Serrano, H., Oliveira, M., & Menezes, R. (2015). The spatial structure of crime in urban environments (Lecture notes in computer science (including its subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), Vol. 8852, pp. 102–111).
Rey, S. J., Mack, E. A., & Koschinsky, J. (2012). Exploratory space-time analysis of burglary patterns. Journal of Quantitative Criminology, 28(3), 509–531.
Haklay, M., & Weber, P. (2008). OpenStreet map: User-generated street maps. IEEE Pervasive Computing, 7(4), 12–18. CrossRef
Konstantopoulos, T. (2012). Introduction to projective geometry. Mineola: Dover Publications.
Estrada, E., Fox, M., Higham, D. J., & Oppo, G. L. (2010). Network science: Complexity in nature and technology. Cambridge: Cambridge university press.
Rosenberg, A., & Hirschberg, J. (2007). V-measure: A conditional entropy-based external cluster evaluation measure. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational. Natural Language Learning, Prague (Vol. 1, pp. 410–420).
- Complex-Network Tools to Understand the Behavior of Criminality in Urban Areas
Lucas C. Scabora
Marcus V. S. Araujo
Paulo H. Oliveir
Bruno B. Machado
Elaine P. M. Sousa
Jose F. Rodrigues
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