Abstract:
Data visualization is a tool used to represent the vast amount of information created each day. Although current literature about data visualization illustrates many ways to visualize data, only a few methods are used on a recurring basis. Common visualizations like bar charts, pie charts, and scatter plots are seen across the internet in news articles, scholarly works, and scientific papers. These methods, although prevalent and easy to produce, are not always created with the viewer in mind. They can be too complex, over-simplified, or misrepresent the data. This research aims to measure whether a human-centered approach to designing data visualizations could portray the information more effectively, and allow the user to understand the argument the data presents. More specifically, this study will measure whether a human-centered approach to visualizing COVID-19 case data will more effectively change behavior intentions with regards to social-distancing. Results of this research reflect that the human-centered approach did not produce higher rates of comprehension, visual appeal, or willingness to change behavior, and suggests that a more distinct and interactive method is necessary for meaningful differences in understanding of the information.