Rashid Sungida
Department of Economics, Southern Illinois University, Carbondale, IL USA.
Asian J Criminol. 2021;16(1):5-17. doi: 10.1007/s11417-020-09341-0. Epub 2021 Mar 12.
The COVID-19 pandemic had a substantial impact on the historical criminal trend around the world. This study explores the early impact of COVID-19 lockdown on selected crimes in Dhaka, Bangladesh. Based on open data of the total number of arrests reported by Dhaka Metropolitan Police (DMP), an uninterrupted historical time series analysis is applied to evaluate the immediate impact during and after the official order due to COVID-19. Auto-regressive moving average (ARIMA) modeling technique was used to compute 6-month-ahead forecasts of the expected frequency of the total number of arrests for illegal arms dealing, vehicle theft, and narcotics trafficking in the absence of the pandemic. These forecasts were compared with the observed data from April 2020 to September 2020. The results suggest that the observed numbers of total arrests for vehicle thefts and illegal arms dealing are not significantly different from their predicted values. However, the observed frequency of the total number of arrests for illegal drug trafficking shows a steep upward trend, which is 75% more than that of the expected frequencies. Estimated results are used to recognize scopes and suggestions for future research on the relationship between crimes and the pandemic.
新冠疫情对全球历史犯罪趋势产生了重大影响。本研究探讨了新冠疫情封锁措施对孟加拉国达卡市特定犯罪的早期影响。基于达卡市警察局（DMP）报告的逮捕总数的公开数据，采用不间断历史时间序列分析来评估因新冠疫情发布官方命令期间及之后的直接影响。运用自回归移动平均（ARIMA）建模技术计算在无疫情情况下非法武器交易、车辆盗窃和毒品贩运逮捕总数预期频率的6个月超前预测值。将这些预测值与2020年4月至2020年9月的观测数据进行比较。结果表明，车辆盗窃和非法武器交易的实际逮捕总数与预测值无显著差异。然而，非法毒品贩运逮捕总数的实际频率呈急剧上升趋势，比预期频率高出75%。研究结果用于识别犯罪与疫情关系未来研究的范围及建议。
