Relações entre Crimes e o Espaço Urbano: Um Estudo de Caso Baseado em Pontos de Interesses Extraídos da Web
Abstract
High crime rates are one of the problems that negatively affect the quality of life in urban centers. In Brazil, in particular, an average rate of 20 deaths per month for every 100,000 inhabitants is estimated as a result of situations of violence. The high crime rates in Brazilian cities could be better analyzed and understood from alternative data sources that explore characteristics of the urban space. In this article, we investigate the relationship between crime rates and these characteristics reflected in points of interest (POIs) that people have registered on a web service for the city of São Paulo. We show the potential of this type of data to predict crime rates by city regions. In this sense, we built regression models with satisfactory performance for this prediction and explored these models to discover the most important categories of POIs to explain the most frequent crimes by city regions. Additionally, we analyzed the performance gain with the increase of POIs registered in the city over the years.
References
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Yuan, J., Zheng, Y., and Xie, X. (2012). Discovering regions of different functions in In Proceedings of the 18th ACM SIGKDD a city using human mobility and pois. international conference on Knowledge discovery and data mining, pages 186–194.
Becker, K. L. and Kassouf, A. L. (2017). Uma análise do efeito dos gastos públicos em educação sobre a criminalidade no brasil. Economia e Sociedade, 26(1):215–242.
Belesiotis, A., Papadakis, G., and Skoutas, D. (2018). Analyzing and predicting spatial crime distribution using crowdsourced and open data. ACM Transactions on Spatial Algorithms and Systems (TSAS), 3(4):1–31.
Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.
Castro, U. R., Rodrigues, M. W., and Brandao, W. C. (2020). Predicting crime by exploiting supervised learning on heterogeneous data. In ICEIS (1), pages 524–531.
Drucker, H., Burges, C. J., Kaufman, L., Smola, A., Vapnik, V., et al. (1997). Support vector regression machines. Advances in neural information processing systems, 9:155–161.
Groß, J. (2012). Linear regression, volume 175. Springer Science & Business Media.
Huang, C., Zhang, J., Zheng, Y., and Chawla, N. V. (2018). Deepcrime: Attentive hierarchical recurrent networks for crime prediction. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 1423– 1432.
Iranmanesh, A. and Alpar Atun, R. (2020). Reading the urban socio-spatial network through space syntax and geo-tagged twitter data. Journal of Urban Design, 25(6):738– 757.
Masi, C. M., Hawkley, L. C., Piotrowski, Z. H., and Pickett, K. E. (2007). Neighborhood economic disadvantage, violent crime, group density, and pregnancy outcomes in a diverse, urban population. Social science & medicine, 65(12):2440–2457.
Mueller, W., Silva, T. H., Almeida, J. M., and Loureiro, A. A. (2017). Gender matters! analyzing global cultural gender preferences for venues using social sensing. EPJ Data Science, 6(1):5.
Nery, M. B., Souza, A. A. L. d., and Adorno, S. (2019). Os padrões urbano-demográficos da capital paulista. Estudos Avançados, 33(97):5–36.
NEV-USP (2021). Monitor da violência. Disponível em: Acesso https://nev.prp.usp.br/projetos/projetos-especiais/monitor-da-violencia/. em 07 de jun. 2021.
Noronha, C. V., Machado, E. P., Tapparelli, G., Cordeiro, T. R. F., Laranjeira, D. H. P., and Santos, C. A. T. (1999). Violência, etnia e cor: um estudo dos diferenciais na região metropolitana de salvador, bahia, brasil. Revista Panamericana de Salud Pública, 5:268–277.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011). Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830.
Ramm, F., Topf, J., and Chilton, S. (2011). OpenStreetMap: using and enhancing the free map of the world. UIT Cambridge Cambridge.
Silva, T. H., de Melo, P. O. V., Almeida, J. M., and Loureiro, A. A. (2017). Uma fotografia do instagram: Caracterização e aplicação. Revista Brasileira de Redes de Computadores e Sistemas Distribuídos.
Silva, T. H., Viana, A. C., Benevenuto, F., Villas, L., Salles, J., Loureiro, A., and Quercia, D. (2019). Urban computing leveraging location-based social network data: a survey. ACM Computing Surveys (CSUR), 52(1):1–39.
SSP-SP (2021). Dados estatísticos do estado de são paulo. Disponível em: http://www.ssp.sp.gov.br/estatistica/pesquisa.aspx. Acesso em 10 de mai. 2021.
São Paulo (2015). Disponível em: Diário oficial do estado de são paulo. https://www.imprensaoficial.com.br. Acesso em 07 de jul. 2021.
Tonry, M. (1997). Ethnicity, crime, and immigration. Crime and justice, 21:1–29.
Tucker, R., O’Brien, D. T., Ciomek, A., Castro, E., Wang, Q., and Phillips, N. E. (2021). Who ‘tweets’ where and when, and how does it help understand crime rates at places? measuring the presence of tourists and commuters in ambient populations. Journal of Quantitative Criminology, 37(2):333–359.
Wang, H., Jenkins, P., Wei, H., Wu, F., and Li, Z. (2019). Learning task-specific city region partition. In The World Wide Web Conference, pages 3300–3306.
Wang, H., Yao, H., Kifer, D., Graif, C., and Li, Z. (2017). Non-stationary model for crime rate inference using modern urban data. IEEE transactions on big data, 5(2):180–194.
Wang, Z., Ma, D., Sun, D., and Zhang, J. (2021). Identification and analysis of urban functional area in hangzhou based on osm and poi data. Plos one, 16(5):e0251988.
Weisburd, D., Groff, E. R., and Yang, S.-M. (2012). The criminology of place: Street segments and our understanding of the crime problem. Oxford University Press.
Yuan, J., Zheng, Y., and Xie, X. (2012). Discovering regions of different functions in In Proceedings of the 18th ACM SIGKDD a city using human mobility and pois. international conference on Knowledge discovery and data mining, pages 186–194.
Published
2021-08-16
How to Cite
SOUSA, Denilson da Silva; FEITOSA, Marcos Paulo Fontes; GONÇALVES, Glauber Dias.
Relações entre Crimes e o Espaço Urbano: Um Estudo de Caso Baseado em Pontos de Interesses Extraídos da Web. In: URBAN COMPUTING WORKSHOP (COURB), 5. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 196-208.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2021.17114.
