Gun Violence

Exploratory Visualization via R Interactive Graphics:
Exploring Gun Violence Incidents in USA

Gun violence is a major public health threat in the United States and a leading cause of premature death in the country. This public health crisis is only getting worse in recent years as the rates of mass shooting have risen sharply, according to the data from the Gun Violence Archive (GVA). The U.S. experienced a record high 578 massing shootings in 2020, up from 337 mass shootings in 2018 and 417 in 2019.  In fact, the year of 2020, despite the pandemic, had the most mass shootings since the GVA began tracking the number in 2014. Worse still, the U.S. is already on a pace to have an even higher rate of mass shootings in 2021, as it has already been wracked by 194 mass shootings in just 18 weeks, averaging out to about 10 each week.
This project aims to use statistical tools to explore important characteristics of the gun violence incidences reported in the United States; for example, location and time of incidences, age and gender of victims. It intends to further identify potential risk factors associated with these incidences by addressing research questions such as: “Do more guns result in more violence incidences?” and “Do more gun laws lead to less incidents?”  Students are expected to utilize various R graphical and analytical tools (such as loon, leaflet, and shiny) to explore, visualize, and reveal patterns in the gun violence data. Emphasis will be placed on the use of interactive visualizations to gain deeper insights from the gun violence data. 
Takeaways: This project provides students the opportunity to seek data-driven answers on the patterns and causes of gun violence in the United States, to improve their coding and programming skills using the R programming language, and to build up essential collaborative, critical thinking, and problem-solving skills.
Prerequisites: A good understanding of introductory statistics is required for participants. Having good programming skills and some knowledge of intermediate statistics will be helpful, but not required. 

Dr. Lucy Kerns

Dr. Lucy Kerns received her PhD in Statistics from Bowling Green State University in 2006. She is currently working as an associate professor in the Department of Mathematics & Statistics at Youngstown State University, where she also serves as the Statistics Coordinator and co-director of the Mathematical and Statistical Consulting Center.  Her research interests have been focused mainly on simultaneous inferential techniques, environment risk assessment and environmental toxicology, and robust statistical methods. At YSU, she has taught statistics at all levels from introductory to graduate level, and has supervised 10 graduated students and guided 5 undergraduate students with their projects.


Lucy Kerns