Changes in Crime Rates during the COVID-19 Pandemic

Meyer, Martha J.; Hassafy, A; Lewis, Glyn; Shrestha, Paras; Am, Haviland; Ds, Nagin

doi:10.1080/2330443x.2022.2071369

Cited by 31 publications

(15 citation statements)

References 8 publications

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“…Most existing research has detected the short-term effect of COVID-19 on crime by focusing on the initial weeks or months after the pandemic related restrictions were introduced, though some studies included pre-COVID data from prior years ( Evans et al, 2021 ; Meyer et al, 2021 ; Payne et al, 2020 ). However, the long-term effects of COVID-19 and the consequences of subsequent relaxation of the social restrictions are still unclear.…”

Section: Literature Reviewmentioning

confidence: 99%

The Effect of COVID-19 on Domestic Violence and Assaults

Demir

Park

2021

Criminal Justice Review

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The purpose of this research was to examine the effect of COVID-19 on four outcomes including calls for service for domestic violence, calls for service for assaults, arrests for domestic violence, and arrests for assaults in Burlington, Vermont. The data for each outcome collected over the time periods January 2012 through May 2021 were obtained from the Burlington Police Department website and then a monthly time-series data set were created. The analyses including an independent samples t-test, a Poisson regression test, and a monthly interrupted time-series analyses (ITSA) were employed to test the effects of COVID-19 on the previously mentioned outcomes. The results of the ITSA showed that in the first month following the onset of the COVID-19 pandemic, domestic violence calls statistically significantly increased, but no statistically significant change was observed in domestic violence arrests, while assault calls and assault arrests statistically significantly decreased. In addition, during COVID-19, there was a statistically significant decreasing trend in domestic violence calls and domestic violence arrests, while there was no statistically significant change in the trends of assault calls and assault arrests. The results suggest that COVID-19 had an immediate as well as a persistent effect on the numbers of domestic violence and assaults. The results and limitations of this study were also discussed.

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Section: Literature Reviewmentioning

confidence: 99%

The Effect of COVID-19 on Domestic Violence and Assaults

Demir

Park

2021

Criminal Justice Review

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“…Similarly, the historical crime data statistics in 2020 were also different than prior years, mostly due to social distancing measures and reduced mobility. Prior work has shown that in 2020, while homicide rates were higher throughout the US when compared to pre-pandemic statistics, robbery and larceny-which potentially requires closer contact-were significantly lower [30]. Despite these anomalies in the crime data, the results presented in this paper are valid and comparable across regions given the changes in crime volumes were similar across the US-including the cities under study in this paper [5].…”

Section: Discussion and Limitationsmentioning

confidence: 54%

Enhancing short-term crime prediction with human mobility flows and deep learning architectures

Abrar

Awasthi

et al. 2022

EPJ Data Sci.

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Place-based short-term crime prediction models leverage the spatio-temporal patterns of historical crimes to predict aggregate volumes of crime incidents at specific locations over time. Under the umbrella of the crime opportunity theory, that suggests that human mobility can play a role in crime generation, increasing attention has been paid to the predictive power of human mobility in place-based short-term crime models. Researchers have used call detail records (CDR), data from location-based services such as Foursquare or from social media to characterize human mobility; and have shown that mobility metrics, together with historical crime data, can improve short-term crime prediction accuracy. In this paper, we propose to use a publicly available fine-grained human mobility dataset from a location intelligence company to explore the effects of human mobility features on short-term crime prediction. For that purpose, we conduct a comprehensive evaluation across multiple cities with diverse demographic characteristics, different types of crimes and various deep learning models; and we show that adding human mobility flow features to historical crimes can improve the F1 scores for a variety of neural crime prediction models across cities and types of crimes, with improvements ranging from 2% to 7%. Our analysis also shows that some neural architectures can slightly improve the crime prediction performance when compared to non-neural regression models by at most 2%.

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“…This timing is consistent with a broad rise in U.S. crime and unrest in the wake of COVID-19 lockdowns, a spike in unemployment, the murder of George Floyd, and associated protests. [52][53][54][55] Having identified the number and precise location of changes in gun violence behavior for each state, we use the distance (7) and apply hierarchical clustering to the resulting distance matrix. Figure 2 displays the similarity of each state's change point profile, and reveals three primary clusters.…”

Section: Results Of Spectral Density Estimation and Change Point Anal...mentioning

confidence: 99%

Dual-domain analysis of gun violence incidents in the United States

James¹,

Menzies²

2022

Preprint

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This paper applies new and recently introduced approaches to study trends in gun violence in the United States. We use techniques in both the time and frequency domain to provide a more complete understanding of gun violence dynamics. We analyze gun violence incidents on a state-by-state basis as recorded by the Gun Violence Archive. We have numerous specific phenomena of focus, including periodicity of incidents, locations in time where behavioral changes occur, and shifts in gun violence patterns since April 2020. First, we implement a recently introduced method of spectral density estimation for nonstationary time series to investigate periodicity on a state-by-state basis, including revealing where periodic behaviors change with time. We can also classify different patterns of behavioral change among the states. We then aim to understand the most significant shifts in gun violence since numerous key events in 2020, including the COVID-19 pandemic, lockdowns, and periods of civil unrest. Our dual-domain analysis provides a more thorough understanding and challenges numerous widely held conceptions regarding the prevalence of gun violence incidents.Especially in the leadup to the 2022 election, gun violence remains a high-profile and challenging issue in the United States (U.S.). The U.S. leads the world both in gun ownership and prevalence (the only country with more guns than people 1 ) and leads the developed world in gun incidents, injuries, homicides and suicides. 2,3 The challenge of gun violence is a highly charged political issue both on the federal and state levels. 4 Gun regulation varies significantly between states, 5 with a substantial political discourse around the utility or performance of increased regulation on a state level to decrease incidence of violence. 6 Recent events such as the COVID-19 pandemic, resulting lockdowns, and an increased interest in social justice and police reform should call for increased research into trends in gun incidents, particularly on a state-by-state basis. This paper meets this need through a thorough and mathematically original investigation into numerous distinct features of the U.S. gun violence epidemic, with a focus on state-by-state differences, collective trends, and changes since 2020.

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Changes in Crime Rates during the COVID-19 Pandemic

Cited by 31 publications

References 8 publications

The Effect of COVID-19 on Domestic Violence and Assaults

The Effect of COVID-19 on Domestic Violence and Assaults

Enhancing short-term crime prediction with human mobility flows and deep learning architectures

Dual-domain analysis of gun violence incidents in the United States

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