Understanding the role of mobility in the recorded levels of violent crimes during COVID-19 pandemic: a case study of Tamil Nadu, India

IF 3.1 Q1 CRIMINOLOGY & PENOLOGY
Kandaswamy Paramasivan, Saish Jaiswal, Rahul Subburaj, Nandan Sudarsanam
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Abstract

This research investigates the potential link between mobility and violent crimes in Tamil Nadu, India, using an empirical study centred on the COVID-19 pandemic waves (2020–2022). The goal is to understand how these events influenced crime, employing a counterfactual approach. The study employs the XGBoost algorithm to forecast counterfactual events across different timeframes with varying levels of mobility. The mobility data sources include historical bus and passenger records spanning a decade, along with Google Community Mobility Reports added during the pandemic phases. The foundation for crime analysis is built upon the univariate time series of violent crimes reported as First Information Reports from 2010 to 2022. Results indicate a significant correlation between mobility and violent crimes when mobility drops below a specific threshold. However, no such correlation is observed when mobility is above this threshold during the non-pandemic periods. The COVID-19 pandemic had a major impact on people’s and vehicular mobility, especially during the complete lockdown periods of the first two waves, and also affected crime rates. The decrease in recorded incidents could also be attributed to fewer criminal opportunities. Additionally, this could be due to unfavourable situational factors, such as victims’ limited access to appropriate health and law enforcement agencies to report crimes. Furthermore, frontline services were busy with pandemic-related commitments, which could have contributed to a lack of crime registration even when crimes were committed.

Abstract Image

了解流动性在 COVID-19 大流行期间记录的暴力犯罪水平中的作用:印度泰米尔纳德邦案例研究
本研究以 COVID-19 大流行浪潮(2020-2022 年)为中心开展实证研究,调查印度泰米尔纳德邦流动性与暴力犯罪之间的潜在联系。目的是采用反事实方法了解这些事件如何影响犯罪。研究采用 XGBoost 算法预测不同时间段内不同流动性水平的反事实事件。流动性数据源包括跨越十年的历史公交车和乘客记录,以及在大流行阶段添加的谷歌社区流动性报告。犯罪分析的基础是 2010 年至 2022 年作为首次信息报告的暴力犯罪单变量时间序列。结果表明,当流动性下降到特定阈值以下时,流动性与暴力犯罪之间存在明显的相关性。然而,在非大流行期间,当流动性高于该阈值时,则没有观察到这种相关性。COVID-19 大流行对人员和车辆的流动性产生了重大影响,尤其是在前两波完全封锁期间,同时也影响了犯罪率。记录在案的事件减少也可能是因为犯罪机会减少。此外,这也可能是由于不利的情境因素造成的,例如受害者向适当的卫生和执法机构报案的机会有限。此外,前线服务部门忙于处理与大流行病有关的事务,这可能导致即使发生了犯罪,也没有进行犯罪登记。
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来源期刊
Crime Science
Crime Science Social Sciences-Cultural Studies
CiteScore
11.90
自引率
8.20%
发文量
12
审稿时长
13 weeks
期刊介绍: Crime Science is an international, interdisciplinary, peer-reviewed journal with an applied focus. The journal''s main focus is on research articles and systematic reviews that reflect the growing cooperation among a variety of fields, including environmental criminology, economics, engineering, geography, public health, psychology, statistics and urban planning, on improving the detection, prevention and understanding of crime and disorder. Crime Science will publish theoretical articles that are relevant to the field, for example, approaches that integrate theories from different disciplines. The goal of the journal is to broaden the scientific base for the understanding, analysis and control of crime and disorder. It is aimed at researchers, practitioners and policy-makers with an interest in crime reduction. It will also publish short contributions on timely topics including crime patterns, technological advances for detection and prevention, and analytical techniques, and on the crime reduction applications of research from a wide range of fields. Crime Science publishes research articles, systematic reviews, short contributions and theoretical articles. While Crime Science uses the APA reference style, the journal welcomes submissions using alternative reference styles on a case-by-case basis.
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