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Spatio-temporal variations in the urban rhythm: the travelling waves of crime

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posted on 2025-08-01, 06:47 authored by M Oliveira, E Ribeiro, C Bastos-Filho, R Menezes
In the last decades, the notion that cities are in a state of equilibrium with a centralised organisation has given place to the viewpoint of cities in disequilibrium and organised from bottom to up. In this perspective, cities are evolving systems that exhibit emergent phenomena built from local decisions. While urban evolution promotes the emergence of positive social phenomena such as the formation of innovation hubs and the increase in cultural diversity, it also yields negative phenomena such as increases in criminal activity. Yet, we are still far from understanding the driving mechanisms of these phenomena. In particular, approaches to analyse urban phenomena are limited in scope by neglecting both temporal non-stationarity and spatial heterogeneity. In the case of criminal activity, we know for more than one century that crime peaks during specific times of the year, but the literature still fails to characterise the mobility of crime. Here we develop an approach to describe the spatial, temporal, and periodic variations in urban quantities. With crime data from 12 cities, we characterise how the periodicity of crime varies spatially across the city over time. We confirm one-year criminal cycles and show that this periodicity occurs unevenly across the city. These ‘waves of crime’ keep travelling across the city: while cities have a stable number of regions with a circannual period, the regions exhibit non-stationary series. Our findings support the concept of cities in a constant change, influencing urban phenomena—in agreement with the notion of cities not in equilibrium.

Funding

1032/13-5

Army Research Office

Leibniz Association

Science Without Borders program (CAPES, Brazil)

W911NF-17-1-0127-P00001

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© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Notes

This is the final version. Available from EDP Sciences via the DOI in this record.

Journal

EPJ Data Science

Publisher

EDP Sciences

Version

  • Version of Record

Language

en

FCD date

2019-07-01T12:48:31Z

FOA date

2019-07-01T12:53:14Z

Citation

Vol. 7: 29

Department

  • Computer Science

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