Analyzing time dependent data visually is a quick and effective way to identify patterns and make comparisons. The Centers for Disease Control publishes a weekly surveillance report during the flu season that contains a number of graphical displays including bar charts and line graphs. In this form, flu information is easily understood by both the general public and health care professionals. Many states also publish weekly flu surveillance reports through their websites.

The data plotted in this case is the number of reported cases in each state and we are limited to comparing time patterns between the two states. New York has a larger population and as such would be expected to have a higher number of seasonal flu cases. In order to compare the weekly flu incidence between the two states, these raw counts would need to be transformed to rates, such as number per 100,000 population.

Graph Builder is a very flexible platform that allows a variety of graphs to be easily created. This case illustrated only a few of the available options. Best practices in data visualization such as equally scaled axes, proper labeling, and small multiples are implemented in Graph Builder. Further discussion of best practices in data visualization can be found in the seminal works of Tufte, Cleveland, and Few.

When building data displays it is important to design them to deliver the desired message to the intended audience. Static displays such as those presented in this case are appropriate for printed publications. Such displays need to conform to publication standards, and these visualizations should be geared to the publication’s target audience. In some circumstances, stakeholders are interested in interacting with the data. In that case, a dynamic dashboard to visualize the data would be more appropriate to fulfill the user’s needs.