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特刊介绍:城市交通与犯罪模式

Introduction to the Special Issue: Urban Mobility and Crime Patterns.

作者信息

Newton Andrew, Felson Marcus, Bannister Jon

机构信息

Department of Criminology and Criminal Justice (CCJ), School of Social Sciences, Nottingham Trent University, Room 3011, Chaucer Building, 50 Shakespeare Street, Nottingham, NG1 4FQ UK.

Texas State University, San Marcos, TX USA.

出版信息

Eur J Crim Pol Res. 2021;27(3):307-311. doi: 10.1007/s10610-021-09501-7. Epub 2021 Nov 16.

Abstract

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.

摘要

本期特刊收录了七篇论文,旨在加深我们对城市流动性与犯罪模式之间关系的理解,以及城市空间中人们的日常移动(城市流动性)与犯罪时空模式之间的关系。它关注城市流动性,即人们与犯罪风险相关的动态移动。此外,它还探讨了如何使用合适的犯罪分母来最佳地衡量这种风险。基于莎拉·博格斯的研究成果,本期特刊认为,除了与我们所衡量的犯罪类型相关的合适分母外,我们还需要更多的东西。例如,基于潜在受害者数量(暴露或周围人群)的暴力犯罪、某地区每户家庭的入室盗窃数量,或每家商店的 shoplifting 犯罪数量。它认为这个分母需要在犯罪类型上合适,并且在空间和时间上合适。当将城市流动性视为人员流动时,挑战在于分母不能被视为一个固定或静态的概念,我们需要考虑“动态分母”的挑战。事实上,不考虑动态分母的犯罪热点可能会在资源优先级排序方面产生误导。本期特刊探讨了一系列潜在的解决方案,包括移动/手机数据、交通数据、土地利用数据以及其他可能解决此问题的措施。

相似文献

1
Introduction to the Special Issue: Urban Mobility and Crime Patterns.特刊介绍:城市交通与犯罪模式
Eur J Crim Pol Res. 2021;27(3):307-311. doi: 10.1007/s10610-021-09501-7. Epub 2021 Nov 16.
3
Urban crime prediction based on spatio-temporal Bayesian model.基于时空贝叶斯模型的城市犯罪预测。
PLoS One. 2018 Oct 31;13(10):e0206215. doi: 10.1371/journal.pone.0206215. eCollection 2018.

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