Mining COVID-19 and NJ Crimes

Ching Yu Huang, Oluwatunmise Alabi, Egan Okumu, Ibarra Joussef

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationMISNC 2023 - 10th Multidisciplinary International Social Networks Conference
PublisherAssociation for Computing Machinery
Pages67-73
Number of pages7
ISBN (Electronic)9798400708176
DOIs
StatePublished - 4 Sep 2023
Event10th Multidisciplinary International Social Networks Conference, MISNC 2023 - Phuket, Thailand
Duration: 4 Sep 20236 Sep 2023

Publication series

NameACM International Conference Proceeding Series

Conference

Conference10th Multidisciplinary International Social Networks Conference, MISNC 2023
Country/TerritoryThailand
CityPhuket
Period4/09/236/09/23

Keywords

  • COVID-19
  • Crime
  • Data Mining
  • Data Visualization
  • MatPlotLib
  • Outlier Detection
  • Pandemic
  • Pearson Correlation Coefficient
  • Python
  • SciPy
  • SQL

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