Visual Analytics of Demographics and COVID-19 Outcomes in New Jersey

Ching-yu (Austin) Huang

College: Hennings College of Science Mathematics and Technology

Department: Computer Science

Abstract:

This project analyzes COVID-19 outcomes across New Jersey counties using data collected from March 2020 to March 2023. The primary objective is to examine how demographic and socioeconomic factors—such as educational attainment, income levels, and health insurance coverage—are associated with differences in COVID-19 case and mortality ratios at the county level.County-level COVID-19 case and death data were integrated with demographic indicators from publicly available sources. Exploratory data analysis and interactive data visualization techniques were employed to identify temporal trends, geographic patterns, and disparities across counties. Visual analytics tools enable dynamic comparisons across demographic variables, supporting a more intuitive and comprehensive examination of complex relationships within the data.The analysis reveals substantial variation in COVID-19 outcomes among New Jersey counties, with observable associations between pandemic impacts and underlying demographic characteristics. These findings suggest that socioeconomic and structural factors played an important role in shaping pandemic outcomes, contributing to uneven burdens across communities.The significance of this work lies in its use of interactive data visualization as both an analytical and communication tool for public health research. By combining demographic data with visual analytics, the project offers a novel, accessible framework for exploring health disparities and supporting data-driven decision-making. A live demonstration of the interactive visualization platform will be presented to illustrate its practical applications.