Modeling Uncertainty in Crime Underreporting: An Indicator Kriging Approach
Resumo
This study confronts crime underreporting in São Paulo, a critical challenge to public policy in emerging nations. We introduce a suitable geostatistical methodology to estimate crime probability from official police reports. The framework employs Indicator Kriging, a non-parametric method that excels with incomplete and missing data. It transforms crime counts into binary indicators based on hotspots, enabling robust probability estimation even in areas with sparse or no records. This approach effectively overcomes critical data gaps, generating detailed and accurate spatial probability maps of known criminal patterns. The resulting model offers a powerful analytical tool for targeted public security, revealing latent crime dynamics.Referências
Akpinar, N., De-Arteaga, M., and Chouldechova, A. (2021). The effect of differential victim crime reporting on predictive policing systems. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, pages 838–849, Virtual Event, Canada. ACM.
Braga, A. A., Turchan, B. S., Papachristos, A. V., and Hureau, D. M. (2019). Hot spots policing and crime reduction: an update of an ongoing systematic review and meta-analysis. Journal of Experimental Criminology, 15(3):289–311.
Brantingham, P. L. and Brantingham, P. J. (1993). Nodes, paths and edges: Considerations on the complexity of crime and the physical environment. Journal of Environmental Psychology, 13(1):3–28.
Buil-Gil, D., Medina, J., and Shlomo, N. (2021). Measuring the dark figure of crime in geographic areas: Small area estimation from the crime survey for england and wales. The British Journal of Criminology, 61(2):364–388.
Carvalho, B. O. d., Oliveira, A. R. d., Zanetic, A., Paes Manso, B., Neme, C., et al. (2021). Atlas da dinâmica criminal em São Paulo (SP): roubos a transeuntes e de veículos. Universidade de São Paulo – Faculdade de Filosofia, Letras e Ciências Humanas, São Paulo.
Cohen, L. E. and Felson, M. (2010). Social change and crime rate trends: A routine activity approach. In Andresen, M. A., Brantingham, P. J., and Kinney, J. B., editors, Classics in Environmental Criminology, pages 203–232. Routledge, New York, NY, 1st edition.
Gorr, W., Olligschlaeger, A., and Thompson, Y. (2003). Short-term forecasting of crime. International Journal of Forecasting, 19(4):579–594.
Guimarães, R. J. P. S., Freitas, C. C., Dutra, L. V., Felgueiras, C. A., Drummond, S. C., Tibiriça, S. H. C., Oliveira, G., and Carvalho, O. S. (2012). Use of indicator kriging to investigate schistosomiasis in minas gerais state, brazil. Journal of Tropical Medicine, 2012:1–10.
Jaitman, L. and Anauati, V. (2020). The dark figure of crime in latin america and the caribbean. Journal of Economics, Race, and Policy, 3(1):76–95.
Jones, D. R., Schonlau, M., and Welch, W. J. (1998). Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 13(4):455–492.
Journel, A. G. (1983). Nonparametric estimation of spatial distributions. Journal of the International Association for Mathematical Geology, 15:445–468.
Kounadi, O., Ristea, A., Araujo Jr., A., and Leitner, M. (2020). A systematic review on spatial crime forecasting. Crime Science, 9(1):7.
Lisowska-Kierepka, A. (2022). How to analyse spatial distribution of crime? crime risk indicator in an attempt to design an original method of spatial crime analysis. Cities, 120:1–5.
Matheron, G. (1963). Principles of geostatistics. Economic geology, 58(8):1246–1266.
Močkus, J. (1975). On bayesian methods for seeking the extremum. In Marchuk, G. I., editor, Optimization Techniques IFIP Technical Conference Novosibirsk, July 1–7, 1974, pages 400–404, Berlin, Heidelberg. Springer Berlin Heidelberg.
Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2):17–23.
Osgood, D. W., Wilson, J. K., O’malley, P. M., Bachman, J. G., and Johnston, L. D. (1996). Routine activities and individual deviant behavior. American sociological review, pages 635–655.
Pannatier, Y. (1996). Model — Interactive Variogram Modeling, pages 46–60. Springer New York, New York, NY.
Piccini, C., Marchetti, A., Farina, R., and Francaviglia, R. (2012). Application of indicator kriging to evaluate the probability of exceeding nitrate contamination thresholds. International Journal of Environmental Research, 6(4):853–862.
Prathap, B. R. (2022). Chapter 7 geospatial crime analysis and forecasting with machine learning techniques. In Artificial Intelligence and Machine Learning for EDGE Computing, pages 87–102. Academic Press.
Sherman, L. W., Gartin, P. R., and Buerger, M. E. (1989). Hot spots of predatory crime: Routine activities and the criminology of place. Criminology, 27(1):27–56.
Usman, U., Suleiman, S., Yar’adua, S., and Ismail, A. A. (2021). Spatial analysis on the crimes rate using regression kriging model: A case study of katsina state. European Journal of Advances in Engineering and Technology, 8(10):64–69.
Windows, B. (1982). The police and neighborhood safety. Atlantic monthly, 249(3):29–38.
Yu, H., Liu, L., Yang, B., and Lan, M. (2020). Crime prediction with historical crime and movement data of potential offenders using a spatio-temporal cokriging method. ISPRS international journal of geo-information, 9(12):732.
Braga, A. A., Turchan, B. S., Papachristos, A. V., and Hureau, D. M. (2019). Hot spots policing and crime reduction: an update of an ongoing systematic review and meta-analysis. Journal of Experimental Criminology, 15(3):289–311.
Brantingham, P. L. and Brantingham, P. J. (1993). Nodes, paths and edges: Considerations on the complexity of crime and the physical environment. Journal of Environmental Psychology, 13(1):3–28.
Buil-Gil, D., Medina, J., and Shlomo, N. (2021). Measuring the dark figure of crime in geographic areas: Small area estimation from the crime survey for england and wales. The British Journal of Criminology, 61(2):364–388.
Carvalho, B. O. d., Oliveira, A. R. d., Zanetic, A., Paes Manso, B., Neme, C., et al. (2021). Atlas da dinâmica criminal em São Paulo (SP): roubos a transeuntes e de veículos. Universidade de São Paulo – Faculdade de Filosofia, Letras e Ciências Humanas, São Paulo.
Cohen, L. E. and Felson, M. (2010). Social change and crime rate trends: A routine activity approach. In Andresen, M. A., Brantingham, P. J., and Kinney, J. B., editors, Classics in Environmental Criminology, pages 203–232. Routledge, New York, NY, 1st edition.
Gorr, W., Olligschlaeger, A., and Thompson, Y. (2003). Short-term forecasting of crime. International Journal of Forecasting, 19(4):579–594.
Guimarães, R. J. P. S., Freitas, C. C., Dutra, L. V., Felgueiras, C. A., Drummond, S. C., Tibiriça, S. H. C., Oliveira, G., and Carvalho, O. S. (2012). Use of indicator kriging to investigate schistosomiasis in minas gerais state, brazil. Journal of Tropical Medicine, 2012:1–10.
Jaitman, L. and Anauati, V. (2020). The dark figure of crime in latin america and the caribbean. Journal of Economics, Race, and Policy, 3(1):76–95.
Jones, D. R., Schonlau, M., and Welch, W. J. (1998). Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 13(4):455–492.
Journel, A. G. (1983). Nonparametric estimation of spatial distributions. Journal of the International Association for Mathematical Geology, 15:445–468.
Kounadi, O., Ristea, A., Araujo Jr., A., and Leitner, M. (2020). A systematic review on spatial crime forecasting. Crime Science, 9(1):7.
Lisowska-Kierepka, A. (2022). How to analyse spatial distribution of crime? crime risk indicator in an attempt to design an original method of spatial crime analysis. Cities, 120:1–5.
Matheron, G. (1963). Principles of geostatistics. Economic geology, 58(8):1246–1266.
Močkus, J. (1975). On bayesian methods for seeking the extremum. In Marchuk, G. I., editor, Optimization Techniques IFIP Technical Conference Novosibirsk, July 1–7, 1974, pages 400–404, Berlin, Heidelberg. Springer Berlin Heidelberg.
Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2):17–23.
Osgood, D. W., Wilson, J. K., O’malley, P. M., Bachman, J. G., and Johnston, L. D. (1996). Routine activities and individual deviant behavior. American sociological review, pages 635–655.
Pannatier, Y. (1996). Model — Interactive Variogram Modeling, pages 46–60. Springer New York, New York, NY.
Piccini, C., Marchetti, A., Farina, R., and Francaviglia, R. (2012). Application of indicator kriging to evaluate the probability of exceeding nitrate contamination thresholds. International Journal of Environmental Research, 6(4):853–862.
Prathap, B. R. (2022). Chapter 7 geospatial crime analysis and forecasting with machine learning techniques. In Artificial Intelligence and Machine Learning for EDGE Computing, pages 87–102. Academic Press.
Sherman, L. W., Gartin, P. R., and Buerger, M. E. (1989). Hot spots of predatory crime: Routine activities and the criminology of place. Criminology, 27(1):27–56.
Usman, U., Suleiman, S., Yar’adua, S., and Ismail, A. A. (2021). Spatial analysis on the crimes rate using regression kriging model: A case study of katsina state. European Journal of Advances in Engineering and Technology, 8(10):64–69.
Windows, B. (1982). The police and neighborhood safety. Atlantic monthly, 249(3):29–38.
Yu, H., Liu, L., Yang, B., and Lan, M. (2020). Crime prediction with historical crime and movement data of potential offenders using a spatio-temporal cokriging method. ISPRS international journal of geo-information, 9(12):732.
Publicado
29/09/2025
Como Citar
JESSÉ, Thiago Corrêa de Oliveira; RANIERI, Caeatano Mazzoni.
Modeling Uncertainty in Crime Underreporting: An Indicator Kriging Approach. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 22. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 772-782.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2025.14189.
