Evidence map›Paper›PMID 40477498›Full record

ArticleJournal of safety science and resilience = An quan ke xue yu ren xing (Ying wen)2022

Investigating the impact of the COVID-19 pandemic on crime incidents number in different cities.

Miaomiao Hou, Zhaolong Zeng, Xiaofeng Hu, Jinming Hu

Abstract read
In one paragraph

Article in Journal of safety science and resilience = An quan ke xue yu ren xing (Ying wen), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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.

Miaomiao HouSchool of Information Technology and Cyber Security, People's Public Security University of China, Beijing 102628, China.
Zhaolong ZengSchool of Information Technology and Cyber Security, People's Public Security University of China, Beijing 102628, China.
Xiaofeng HuSchool of Information Technology and Cyber Security, People's Public Security University of China, Beijing 102628, China.
Jinming HuSchool of Information Technology and Cyber Security, People's Public Security University of China, Beijing 102628, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic is strongly affecting many aspects of human life and society around the world. To investigate whether this pandemic also influences crime, the differences in crime incidents numbers before and during the pandemic in four large cities (namely Washington DC, Chicago, New York City and Los Angeles) are investigated. Moreover, the Granger causal relationships between crime incident numbers and new cases of COVID-19 are also examined. Based on that, new cases of COVID-19 with significant Granger causal correlations are used to improve the crime prediction performance. The results show that crime is generally impacted by the COVID-19 pandemic, but it varies in different cities and with different crime types. Most types of crimes have seen fewer incidents numbers during the pandemic than before. Several Granger causal correlations are found between the COVID-19 cases and crime incidents in these cities. More specifically, crime incidents numbers of theft in Washington DC, Chicago and New York City, fraud in Washington DC and Los Angeles, assault in Chicago and New York City, and robbery in Los Angeles and New York City, are significantly Granger caused by the new case of COVID-19. These results may be partially explained by the Routine Activity theory and Opportunity theory that people may prefer to stay at home to avoid being infected with COVID-19 during the pandemic, giving fewer chances for crimes. In addition, involving new cases of COVID-19 as a variable can slightly improve the performance of crime prediction in terms of some specific types of crime. This study is expected to obtain deeper insights into the relationships between the pandemic and crime in different cities, and to provide new attempts for crime prediction during the pandemic.

Indexed as

COVID-19 pandemicCrime incidents numbersCrime predictionGranger causalityLong short-term memory network

Identifiers

PMID40477498
PMCPMC8849845

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.