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Effects of COVID-19 in Mexico City: Street Robbery and Vehicle Theft Spatio-Temporal Patterns

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Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

As a result of the changes in social behavior due to lockdown measures aimed to avoiding COVID-19 infection, changes in crime patterns have been observed in several cities around the world. This study has two objectives: (1) Analyze the spatio-temporal patterns of the incidence of street robbery and vehicle theft in Mexico City, before and after the social distancing measures begun. Throughout this period, it has been shown a decrease in high-impact robberies in Mexico City. However, changes in spatial patterns have not been studied yet. (2) Propose an algorithm for the visualization of spatio-temporal relationships of crimes to identify near repeat patterns. These two objectives are considered relevant to identify areas of repeat victimization, especially before an imminent return to routine activities in the city, such as the return to school, the reopening of restaurants, movie theaters, shopping malls and other businesses; and thus be able to contribute to identify and prevent these crimes. One of the main results is that despite crime volumes decreased, some specific crime locations remained after the lockdown.

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  • DOI: 10.1007/978-3-030-98096-2_14
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Fig. 1

Source Elaborated by the authors based on information from the investigation files https://datos.cdmx.gob.mx/

Fig. 2

Source Elaborated by the authors

Fig. 3

Source Elaborated by the authors

Fig. 4

Source Elaborated by the authors

Notes

  1. 1.

    https://covid19.apple.com/mobility.

  2. 2.

    https://www.google.com/covid19/mobility/.

  3. 3.

    https://datos.cdmx.gob.mx/.

  4. 4.

    According to the results of the National Survey of Victimization and Perception on Public Safety (ENVIPE-2020) the overall black figure for Mexico City is 94%, this means that it is the proportion of crimes committed in which there was no complaint or an investigation folder was not initiated during 2019.

  5. 5.

    Fisher’s algorithm allows to calculate exactly an optimal partition from a continuous numerical variable into a given number of classes. Unlike other classification methods such as standard deviations, quartiles or Jenks’ natural breaks, this method allows to obtain a better categorization for this case.

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Acknowledgements

We would like to thank Camilo Caudillo Cos, Rodrigo Tapia McClung, Elvia Martínez Viveros and José Ignacio Chapela Castañares (CentroGeo), whose previous work for Mexico City made it possible to follow up and prepare this article. Finally we like to thank José Luis Silván Cárdenas and Carlos Javier Vilalta Perdomo.

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Correspondence to Ana J. Alegre-Mondragón .

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Alegre-Mondragón, A.J., Silva-Arias, C. (2022). Effects of COVID-19 in Mexico City: Street Robbery and Vehicle Theft Spatio-Temporal Patterns. In: Tapia-McClung, R., Sánchez-Siordia, O., González-Zuccolotto, K., Carlos-Martínez, H. (eds) Advances in Geospatial Data Science. iGISc 2021. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-030-98096-2_14

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