Evidence map›Paper›PMID 36497600›Full record

ArticleInternational journal of environmental research and public health2022

Analyzing the Impact of COVID-19 Lockdowns on Violent Crime.

Lin Liu, Jiayu Chang, Dongping Long, Heng Liu

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Lin LiuCenter of Geoinformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China.ORCID 0000-0002-7202-3418
Jiayu ChangCenter of Geoinformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China.
Dongping LongCenter of Geoinformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China.ORCID 0000-0001-8623-8586
Heng LiuCenter of Geoinformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Existing research suggests that COVID-19 lockdowns tend to contribute to a decrease in overall urban crime rates. Most studies have compared pre-lockdown and post-lockdown periods to lockdown periods in Western cities. Few have touched on the fine variations during lockdowns. Equally rare are intracity studies conducted in China. This study tested the relationship between violent crime and COVID-19 lockdown policies in ZG City in southern China. The distance from the isolation location to the nearest violent crime site, called "the nearest crime distance", is a key variable in this study. Kernel density mapping and the Wilcoxon signed-rank test are used to compare the pre-lockdown and post-lockdown periods to the lockdown period. Panel logistic regression is used to test the fine variations among different stages during the lockdown. The result found an overall decline in violent crime during the lockdown and a bounce-back post-lockdown. Violent crime moved away from the isolation location during the lockdown. This outward spread continued for the first two months after the lifting of the lockdown, suggesting a lasting effect of the lockdown policy. During the lockdown, weekly changes in COVID-19 risk ratings at the district level in ZG City also affected changes in the nearest crime distance. In particular, an increase in the risk rating increased that distance, and a drop in the risk rating decreased that distance. These findings add new results to the literature and could have policy implications for joint crime and pandemic prevention and control.

Indexed as

COVID-19Communicable Disease ControlCrimeHumansPandemicsViolenceCOVID-19COVID-19 outbreak prevention and control policycrime changepandemic risk ratingviolent crime

Identifiers

PMID36497600
PMCPMC9739108

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.