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Introduction to the Special Issue: Urban Mobility and Crime Patterns

This Special Issue is a collection of seven papers that seek to better our understanding of how urban mobility relates to crime patterns, and how day to day movement of people in urban spaces (urban mobility) is related to spatio-temporal patterns of crime. It focusses on urban mobility, or the dyna...

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Detalles Bibliográficos
Autores principales: Newton, Andrew, Felson, Marcus, Bannister, Jon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594641/
https://www.ncbi.nlm.nih.gov/pubmed/34803232
http://dx.doi.org/10.1007/s10610-021-09501-7
Descripción
Sumario:This Special Issue is a collection of seven papers that seek to better our understanding of how urban mobility relates to crime patterns, and how day to day movement of people in urban spaces (urban mobility) is related to spatio-temporal patterns of crime. It focusses on urban mobility, or the dynamic movement of people in relation to crime risk. Moreover, it questions how to best measure this risk using an appropriate crime denominator. Building on the work of Sarah Boggs, this special issue contends that we need more than an appropriate denominator related to the type of crime we are measuring, for example violence based on the number of potential victims present (the exposed or ambient population), or the number of burglaries per households in an area, or the number of shoplifting offences per number of shops present. It argues that this denominator needs to be both ‘crime type’ appropriate, and to be spatially and temporally appropriate. When considering urban mobility as flows of people, the challenge is that the denominator can not be considered as a fixed or static concept, and that we need to consider the ‘dynamic denominator’ challenge. Indeed, crime hot spots which do not account for dynamic denominators may be misleading for resource prioritisation. This special issue explores a range of potential solutions to this including mobile/cell phone data, transportation data, land use data, and other possible measures to address this.