@inproceedings{7e06d55c9ef64585a4c812c2e7a97453,
title = "Mining COVID-19 and NJ Crimes",
abstract = "The COVID-19 pandemic which took the world by storm in 2020 had a profound impact on societies across the world. New Jersey was not immune to the crisis. Between the confusion-induced panic of the unknown surrounding the pandemic, the mass layoffs, the lockdowns, and the growing economic pains, New Jersey experienced a spike in crime. This research study aimed to explore the relationship between the COVID-19 pandemic and crime rates in New Jersey counties during the year 2020, while also comparing the crime rates of 2020 to the previous year. Using publicly available data on COVID-19 case cases [1] and crime rates from official government sources [2] [3], we deployed crucial data mining techniques such as outlier detection and the Pearson Correlation Coefficient to investigate any potential links between these two variables. It was concluded that there is a significant correlation between the effects of COVID and certain crime rates in New Jersey counties. Furthermore, it was determined there was a significant change in crime rates from 2019 to 2020 which can be attributed to the Covid-19 pandemic.",
keywords = "COVID-19, Crime, Data Mining, Data Visualization, MatPlotLib, Outlier Detection, Pandemic, Pearson Correlation Coefficient, Python, SciPy, SQL",
author = "Huang, {Ching Yu} and Oluwatunmise Alabi and Egan Okumu and Ibarra Joussef",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 10th Multidisciplinary International Social Networks Conference, MISNC 2023 ; Conference date: 04-09-2023 Through 06-09-2023",
year = "2023",
month = sep,
day = "4",
doi = "10.1145/3624875.3624887",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "67--73",
booktitle = "MISNC 2023 - 10th Multidisciplinary International Social Networks Conference",
}