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dc.contributor.authorCampedelli, GM
dc.contributor.authorFavarin, S
dc.contributor.authorAziani, A
dc.contributor.authorPiquero, AR
dc.date.accessioned2020-11-11T03:31:26Z
dc.date.available2020-11-11T03:31:26Z
dc.date.issued2020
dc.identifier.issn2193-7680en_US
dc.identifier.doi10.1186/s40163-020-00131-8en_US
dc.identifier.urihttp://hdl.handle.net/10072/399209
dc.description.abstractRecent studies exploiting city-level time series have shown that, around the world, several crimes declined after COVID-19 containment policies have been put in place. Using data at the community-level in Chicago, this work aims to advance our understanding on how public interventions affected criminal activities at a finer spatial scale. The analysis relies on a two-step methodology. First, it estimates the community-wise causal impact of social distancing and shelter-in-place policies adopted in Chicago via Structural Bayesian Time-Series across four crime categories (i.e., burglary, assault, narcotics-related offenses, and robbery). Once the models detected the direction, magnitude and significance of the trend changes, Firth’s Logistic Regression is used to investigate the factors associated to the statistically significant crime reduction found in the first step of the analyses. Statistical results first show that changes in crime trends differ across communities and crime types. This suggests that beyond the results of aggregate models lies a complex picture characterized by diverging patterns. Second, regression models provide mixed findings regarding the correlates associated with significant crime reduction: several relations have opposite directions across crimes with population being the only factor that is stably and positively associated with significant crime reduction.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.ispartofpagefrom21en_US
dc.relation.ispartofissue1en_US
dc.relation.ispartofjournalCrime Scienceen_US
dc.relation.ispartofvolume9en_US
dc.subject.fieldofresearchCauses and Prevention of Crimeen_US
dc.subject.fieldofresearchcode160201en_US
dc.titleDisentangling community-level changes in crime trends during the COVID-19 pandemic in Chicagoen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationCampedelli, GM; Favarin, S; Aziani, A; Piquero, AR, Disentangling community-level changes in crime trends during the COVID-19 pandemic in Chicago, Crime Science, 2020, 9 (1), pp. 21en_US
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/en_US
dc.date.updated2020-11-11T01:13:55Z
dc.description.versionVersion of Record (VoR)en_US
gro.rights.copyright© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativeco mmons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.en_US
gro.hasfulltextFull Text
gro.griffith.authorPiquero, Alex R.


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