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Disentangling the Impact of Covid-19: An Interrupted Time Series Analysis of Crime in New York City
The Covid-19 stay-at-home restrictions put in place in New York City were followed by an abrupt shift in movement away from public spaces and into the home. This study used interrupted time series analysis to estimate the impact of these changes by crime type and location (public space vs. residenti...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8776368/ https://www.ncbi.nlm.nih.gov/pubmed/35079215 http://dx.doi.org/10.1007/s12103-021-09666-1 |
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author | Koppel, Stephen Capellan, Joel A. Sharp, Jon |
author_facet | Koppel, Stephen Capellan, Joel A. Sharp, Jon |
author_sort | Koppel, Stephen |
collection | PubMed |
description | The Covid-19 stay-at-home restrictions put in place in New York City were followed by an abrupt shift in movement away from public spaces and into the home. This study used interrupted time series analysis to estimate the impact of these changes by crime type and location (public space vs. residential setting), while adjusting for underlying trends, seasonality, temperature, population, and possible confounding from the subsequent protests against police brutality in response to the police-involved the killing of George Floyd. Consistent with routine activity theory, we found that the SAH restrictions were associated with decreases in residential burglary, felony assault, grand larceny, rape, and robbery; increases in non-residential burglary and residential grand larceny motor vehicle; and no change in murder and shooting incidents. We also found that the protests were associated with increases in several crime types: felony assault, grand larceny, robbery, and shooting incidents. Future research on Covid-19’s impact on crime will need to account for these potentially confounding events. |
format | Online Article Text |
id | pubmed-8776368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-87763682022-01-21 Disentangling the Impact of Covid-19: An Interrupted Time Series Analysis of Crime in New York City Koppel, Stephen Capellan, Joel A. Sharp, Jon Am J Crim Justice Article The Covid-19 stay-at-home restrictions put in place in New York City were followed by an abrupt shift in movement away from public spaces and into the home. This study used interrupted time series analysis to estimate the impact of these changes by crime type and location (public space vs. residential setting), while adjusting for underlying trends, seasonality, temperature, population, and possible confounding from the subsequent protests against police brutality in response to the police-involved the killing of George Floyd. Consistent with routine activity theory, we found that the SAH restrictions were associated with decreases in residential burglary, felony assault, grand larceny, rape, and robbery; increases in non-residential burglary and residential grand larceny motor vehicle; and no change in murder and shooting incidents. We also found that the protests were associated with increases in several crime types: felony assault, grand larceny, robbery, and shooting incidents. Future research on Covid-19’s impact on crime will need to account for these potentially confounding events. Springer US 2022-01-21 2023 /pmc/articles/PMC8776368/ /pubmed/35079215 http://dx.doi.org/10.1007/s12103-021-09666-1 Text en © Southern Criminal Justice Association 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Koppel, Stephen Capellan, Joel A. Sharp, Jon Disentangling the Impact of Covid-19: An Interrupted Time Series Analysis of Crime in New York City |
title | Disentangling the Impact of Covid-19: An Interrupted Time Series Analysis of Crime in New York City |
title_full | Disentangling the Impact of Covid-19: An Interrupted Time Series Analysis of Crime in New York City |
title_fullStr | Disentangling the Impact of Covid-19: An Interrupted Time Series Analysis of Crime in New York City |
title_full_unstemmed | Disentangling the Impact of Covid-19: An Interrupted Time Series Analysis of Crime in New York City |
title_short | Disentangling the Impact of Covid-19: An Interrupted Time Series Analysis of Crime in New York City |
title_sort | disentangling the impact of covid-19: an interrupted time series analysis of crime in new york city |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8776368/ https://www.ncbi.nlm.nih.gov/pubmed/35079215 http://dx.doi.org/10.1007/s12103-021-09666-1 |
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