Spatially Varying Unemployment and Crime Effects in the Long Run and Short Run
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Accepted Manuscript (AM)
Author(s)
Andresen, Martin A
Ha, Olivia K
Davies, Garth
Griffith University Author(s)
Year published
2020
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Show full item recordAbstract
The Cantor and Land model of unemployment and crime separates the effects of long- and short-run unemployment. In the long run, increases in unemployment are expected to increase crime, whereas the same increases are expected to decrease crime in the short run. This model has been tested for decades, generally supporting these predictions. In this article, we investigate spatial variations in these relationships using geographically weighted regression. Using crime data from Vancouver, Canada (commercial burglary, residential burglary, mischief, theft, theft from vehicle, theft of vehicle, and aggregate property), we find ...
View more >The Cantor and Land model of unemployment and crime separates the effects of long- and short-run unemployment. In the long run, increases in unemployment are expected to increase crime, whereas the same increases are expected to decrease crime in the short run. This model has been tested for decades, generally supporting these predictions. In this article, we investigate spatial variations in these relationships using geographically weighted regression. Using crime data from Vancouver, Canada (commercial burglary, residential burglary, mischief, theft, theft from vehicle, theft of vehicle, and aggregate property), we find global models do not exhibit statistically significant unemployment–crime relationships, but they do emerge in local (geographically weighted) regression. These results have important implications for theoretical development, policy formation, and policy evaluation.
View less >
View more >The Cantor and Land model of unemployment and crime separates the effects of long- and short-run unemployment. In the long run, increases in unemployment are expected to increase crime, whereas the same increases are expected to decrease crime in the short run. This model has been tested for decades, generally supporting these predictions. In this article, we investigate spatial variations in these relationships using geographically weighted regression. Using crime data from Vancouver, Canada (commercial burglary, residential burglary, mischief, theft, theft from vehicle, theft of vehicle, and aggregate property), we find global models do not exhibit statistically significant unemployment–crime relationships, but they do emerge in local (geographically weighted) regression. These results have important implications for theoretical development, policy formation, and policy evaluation.
View less >
Journal Title
The Professional Geographer
Copyright Statement
This is an Author's Accepted Manuscript of an article published in The Professional Geographer, 15 Dec 2020, copyright Taylor & Francis, available online at: https://doi.org/10.1080/00330124.2020.1838928
Note
This publication has been entered as an advanced online version in Griffith Research Online.
Subject
Physical geography and environmental geoscience
Criminology
Human geography
Sociology
Social Sciences
Geography
crime
geographically weighted regression
long run