By Ernesto Lopez, Robert Boxerman and Keley CundiffEErnes

Since shortly after the onset of the COVID-19 pandemic, the Council on Criminal Justice has tracked changing rates of violent and property crime in large cities across the United States. The pandemic, as well as the social justice protests during the summer of 2020 and other factors, have altered the motives, means, and opportunities to commit crimes.

Prepared for the Council on Criminal Justice’s Crime Trends Working Group, this report focuses on trends in shoplifting, a subset of retail theft which, in turn, is a subset of overall larceny-theft. The FBI defines larceny-theft as the unlawful taking of property without force, violence, or fraud.

The report looks at shoplifting patterns from before the onset of the COVID-19 pandemic through mid-year 2023. To date, attempts to measure changes in retail theft, including organized retail theft, have relied on retail industry data5 or have been limited to one state.

The city-specific data included in this report are drawn from open-data sources from 24 cities that, over the past five years, have consistently reported specific shoplifting data. Additional data come from the U.S. Justice Department’s National Incident-Based Reporting Program (NIBRS).7 The NIBRS data include a sample of 3,812 local law enforcement agencies. The analyses examine the changing frequency of reported shoplifting, trends in other property offenses, changes in the value of stolen goods, offenses that co-occur with shoplifting, and the number of people involved in each incident.

This report does not discuss in detail shoplifting data from the National Retail Federation’s Retail Security Survey.8 The 2021 survey (data ending in 2020) was the last year the survey reported figures on the number of incidents and the value of stolen goods. Because of this change, data from the survey could not be included.

Due to a lack of available data, this report does not examine factors that could be influencing the trends. Potential factors include changes in retailers’ anti-theft measures and changes in how retailers report shoplifting to law enforcement, which could be based on their perceptions of the extent to which local police or prosecutors will apprehend suspects and pursue criminal charges. Because these data rely on reported incidents, they almost certainly undercount total shoplifting. The findings presented here should be viewed with these considerations in mind.

Washington, DC: Council on Criminal Justice, 2023. 8p.

By Michael J. Frith  , Kate J. Bowers  , Shane D. Johnson 

Introduction: In response to COVID-19, governments imposed various restrictions on movement and activities. According to the routine activity perspective, these should alter where crime occurs. For burglary, greater household occupancy should increase guardianship against residential burglaries, particularly during the day considering factors such as working from home. Conversely, there should be less eyes on the street to protect against non-residential burglaries. Methods: In this paper, we test these expectations using a spatio-temporal model with crime and Google Community Mobility data. Results: As expected, burglary declined during the pandemic and restrictions. Different types of burglary were, however, affected differently but largely consistent with theoretical expectation. Residential and attempted residential burglaries both decreased significantly. This was particularly the case during the day for completed residential burglaries. Moreover, while changes were coincident with the timing and relaxation of restrictions, they were better explained by fluctuations in household occupancy. However, while there were significant decreases in non-residential and attempted non-residential burglary, these did not appear to be related to changes to activity patterns, but rather the lockdown phase. Conclusions: From a theoretical perspective, the results generally provide further support for routine activity perspective. From a practical perspective, they suggest considerations for anticipating future burglary trends 

Journal of Criminal Justice, v. 82, 2022

By Muhammad Amien Ibrahim, Samsul Arifin and Eko Setyo Purwanto

Social media moderation is a crucial component to establish healthy online communities and ensuring online safety from hate speech and offensive language. In many cases, hate speech may be targeted at specific gender which could be expressed in many different languages on social media platforms such as Indonesian Twitter. However, difficulties such as data scarcity and the imbalanced gender-based hate speech dataset in Indonesian tweets have slowed the development and implementation of automatic social media moderation. Obtaining more data to increase the number of samples may be costly in terms of resources required to gather and annotate the data. This study looks at the usage of data augmentation methods to increase the amount of textual dataset while keeping the quality of the augmented data. Three augmentation strategies are explored in this study: Random insertion, back translation, and a sequential combination of back translation and random insertion. Additionally, the study examines the preservation of the increased data labels. The performance result demonstrates that classification models trained with augmented data generated from random insertion strategy outperform the other approaches. In terms of label preservation, the three augmentation approaches have been shown to offer enough label preservation without compromising the meaning of the augmented data. The findings imply that by increasing the amount of the dataset while preserving the original label, data augmentation could be utilized to solve issues such as data scarcity and dataset imbalance.

United States, Journal Of Computer Science. 2023, 9pg