Alternativas para la medición de la delincuencia urbana y la identificación de zonas criminógenas. Nuevos indicadores basados en la presencia de población

Alejandro  Moreno Jiménez; Aurea Esther  Grijalva Eternod

doi:10.3989/estgeogr.2022127.127

Authors

Alejandro Moreno Jiménez Universidad de Guadalajara https://orcid.org/0000-0002-3988-1031
Aurea Esther Grijalva Eternod Universidad de Guadalajara https://orcid.org/0000-0001-8399-4247

DOI:

https://doi.org/10.3989/estgeogr.2022127.127

Keywords:

citizen security, environmental criminology, crime generators, crime attractors, social media, economic units, ambient population

Abstract

The objective of this paper is to propose alternative ways of measuring crime rates in urban settings, comparing the use of the presence of population with the traditional measurement, that is, the residential population. The analysis is carried out for three types of crimes: domestic violence, personal robbery and business robbery. To achieve this, a methodology of spatial analysis of the data of the Guadalajara Metropolitan Area (ZMG), Mexico is conducted. The results show that, for some types of crime, the presence of population can more accurately identify the population at risk, which can constitute a very useful public policy tool for crime prevention, since in addition to achieving a more precise estimation of the phenomenon, the areas that generate and attract crime can be identified.

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References

Asociación Mexicana del Internet (2016). Estudio de Hábitos de los Usuarios de Internet en México. Ciudad de México: AMIPCI.

Andresen, M. A. (2006). Crime measures and the spatial analysis of criminal activity. British Journal of Criminology, 46(2), 258-285. https://doi.org/10.1093/bjc/azi054

Andresen, M. A. (2011). The ambient population and crime analysis. The Professional Geographer, 63(2), 193-212. https://doi.org/10.1080/00330124.2010.547151

Andresen, M. A. & Jenion, G. W. (2008). Crime prevention and the science of where people are. Criminal Justice Policy Review, 19(2), 164-180. https://doi.org/10.1177/0887403407311591

Andresen, M. A. & Linning, S. J. (2012). The (in)appropriateness of aggregating across crime types. Applied Geography, 35(1-2), 275-282. https://doi.org/10.1016/j.apgeog.2012.07.007

Blank, G. (2017). The Digital Divide Among Twitter Users and Its Implications for Social Research. Social Science Computer Review, 35(6), 679-697. https://doi.org/10.1177/0894439316671698

Brantingham, P. & Brantingham, P. (Eds.). (1981). Environmental Criminology. Sage Publications.

Brantingham, P. & Brantingham, P. (1995). Criminality of place. European Journal on Criminal Policy and Research, 3(3), 5-26. https://doi.org/10.1007/BF02242925

Brantingham, P. & Brantingham, P. (2008). Crime pattern theory. En R. Wortley & L. Mazerolle (Eds.) Environmental Criminology and Crime Analysis (pp. 78-93). Willan Publishing.

Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación & Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación. (2020). Medición de Movilidad usando Facebook, Google y Twitter. Consejo Nacional de Ciencia y Tecnología.

Chan, J., & Bennett M. L. (2015). Is Big Data challenging criminology? Theoretical Criminology, 20(1), 21-39. https://doi.org/10.1177/1362480615586614

Cheng, T. & Chen, T. (2021). Urban Crime and Security. En W. Shi, M. F. Goodchild, M. Batty, K. Mei-Po & Z. Anshu Zhang (Eds.), Urban Informatics (pp. 213-228). Springer. https://doi.org/10.1007/978-981-15-8983-6_14

Fiscalía General del Estado de Jalisco (2021). Número de delitos registrados en carpetas de averiguación de la Fiscalía de Jalisco por año de 2016 a agosto 2021. https://datos.jalisco.gob.mx/dataset/numero-de-delitos-registrados-en-carpetas-de-averiguacion-de-la-fiscalia-de-jalisco-de-2016

Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 588-608. https://doi.org/10.2307/2094589

Getis, A., & Ord, J. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x

Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260-271. https://doi.org/10.1080/15230406.2014.890072 PMid:27019645 PMCid:PMC4786829

Instituto Nacional de Estadística y Geografía. (2010). Censo de Población y Vivienda. Aguascalientes: Instituto Nacional de Estadística y Geografía.

Instituto Nacional de Estadística y Geografía (2017). Directorio Estadístico Anual de Unidades Económicas (DENUE). Aguascalientes: Instituto Nacional de Estadística y Geografía.

Instituto Nacional de Estadística y Geografía. (2020a). Encuesta Nacional de Victimización y Percepción sobre Seguridad Pública (ENVIPE) 2020. Aguascalientes: Instituto Nacional de Estadística y Geografía.

Instituto Nacional de Estadística y Geografía. (2020b). Encuesta Nacional sobre Disponibilidad y Uso de Tecnologías de la Información en los Hogares (ENDUTIH) 2020. Aguascalientes: Instituto Nacional de Estadística y Geografía.

Kinney, J. B., Brantingham, P. L., Wuschke, K., Kirk, M. G., & Brantingham, P. J. (2008). Crime attractors, generators and detractors: Land use and urban crime opportunities. Built Environment, 34(1), 62-74. https://doi.org/10.2148/benv.34.1.62

Lubbe, S., Filzmoser, P., & Templ, M. (2021). Comparison of zero replacement strategies for compositional data with large numbers of zeros. Chemometrics and Intelligent Laboratory Systems, 210, 104248. https://doi.org/10.1016/j.chemolab.2021.104248

Malleson, N., & Andresen, M. A. (2014). The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns. Cartography and Geographic Information Science, 42(2), 112-121. https://doi.org/10.1080/15230406.2014.905756

Malleson, N., & Andresen, M. A. (2015). Spatio-temporal crime hotspots and the ambient population. Crime Science, 4(1), 1-8. https://doi.org/10.1186/s40163-015-0023-8

Malleson, N., & Andresen, M. A. (2016). Exploring the impact of ambient population measures on London crime hotspots. Journal of Criminal Justice, 46, 52-63. https://doi.org/10.1016/j.jcrimjus.2016.03.002

Maltz, M. D. (1999). Bridging gaps in police crime data. DIANE Publishing.

Osorio, J. (2020). Análisis de los patrones espacio-temporales de eventos a partir de datos de Twitter: el caso de la World Pride 2017 en Madrid. Estudios Geográficos, 81(288), e032. https://doi.org/10.3989/estgeogr.202047.027

Pramanik, M. I., Lau, R. Y. K., Yue, W. T., Ye, Y., & Li, C. (2017). Big data analytics for security and criminal investigations. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(4), e1208. https://doi.org/10.1002/widm.1208

Ristea, A., Langford, C., & Leitner, M. (2017, August). Relationships between crime and Twitter activity around stadiums. 2017 25th International Conference on Geoinformatics (pp. 1-5). IEEE. https://doi.org/10.1109/GEOINFORMATICS.2017.8090933

Secretariado Ejecutivo del Sistema Nacional de Seguridad Pública. (2017). Datos abiertos de incidencia delictiva. Ciudad de México: Secretariado Ejecutivo del Sistema Nacional de Seguridad Pública.

Snaphaan, T., & Hardyns, W. (2019). Environmental criminology in the big data era. European Journal of Criminology, 18(5), 147737081987775. https://doi.org/10.1177/1477370819877753

Traunmueller, M., Quattrone, G., & Capra, L. (2014, November). Mining mobile phone data to investigate urban crime theories at scale. En L. Aiello & D. McFarland (Eds.) Social Informatics (pp. 396-411). Springer. https://doi.org/10.1007/978-3-319-13734-6_29

Trinidad, A., San Juan, C., & Vozmediano, L. (2019). Escenarios de delincuencia juvenil en el ámbito urbano: una perspectiva situacional. Revista Criminalidad, 61(2), 9-24.

Wang, X., Gerber, M. S., & Brown, D. E. (2012). Automatic crime prediction using events extracted from twitter posts. En S. Yang; A. Greenberg & M. Ensley (Eds.) Social Computing, Behavioral-Cultural Modeling, and Prediction (pp. 231-238). Springer. https://doi.org/10.1007/978-3-642-29047-3_28

Wang, H., Kifer, D., Graif, C., & Li, Z. (2016, August). Crime rate inference with big data. En Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 635-644). https://doi.org/10.1145/2939672.2939736

Williams, M. L., Burnap, P., & Sloan, L. (2017). Crime sensing with big data: The affordances and limitations of using open-source communications to estimate crime patterns. The British Journal of Criminology, 57(2), 320-340. https://doi.org/10.1093/bjc/azw031

Wortley, R., & Mazerolle, L. (2008). Environmental criminology and crime analysis: situating the theory, analytic approach and application. En R. Wortley & L. Mazerolle (Eds.) Environmental criminology and crime analysis (pp. 1-18). Willan Publishing.