Data Visualization in Environmental Public Health: From Data to Insight
AVOID COMMON PITFALLS Even well-designed visualizations can lose their impact when common design or analytical mistakes interfere with the message. Some pitfalls come from how you build a visualization; others come from how you interpret the data. The following table highlights common pitfalls encountered in EPH visualization, explains why they matter, and provides examples of how they can affect interpretation. See Appendix A for additional examples of common visualization pitfalls and recommended improvements.
Table 2: Common Data Visualization Pitfalls
COMMON PITFALL WHY IT MATTERS
ENVIRONMENTAL PUBLIC HEALTH EXAMPLE
VISUAL DESIGN PITFALLS Using the wrong visualization type
Different visualization types answer different questions. Choosing the wrong one can make patterns difficult to recognize or obscure important findings.
A pie chart displaying monthly inspection numbers is misleading. Pie charts show parts of a whole, while line charts better show how a measure changes over time. In this case, use a line chart or bar chart to show monthly values. A program performance chart combines inspections, complaints, violations, compliance rates, response times, foodborne illness reports, training sessions, and rainfall in one visualization, making trends and relationships difficult to interpret. A dashboard displays multiple food safety metrics, but it is unclear which trend is important, whether the results are good or bad, or what the viewer should learn from the data. Sharing a complex GIS map with multiple technical layers during a community meeting instead of a simplified public-facing map. A bar chart showing the monthly number of inspections conducted begins at 500 instead of zero, making small differences appear much larger than they are. A heat map uses similar shades of green and yellow that are difficult to distinguish, making priority areas unclear for many users. A restaurant inspection chart uses 3D bars, gradients, shadows, multiple bright colors, and decorative food icons. These design elements add no meaning and make it harder to compare inspection results accurately. A bar chart compares the percentage of food establishments meeting inspection standards across five jurisdictions, but the jurisdictions are labeled only “A,” “B,” “C,” “D,” and “E,” the y-axis has no label, and the chart does not define what “meeting standards” means. Viewers cannot determine exactly what is being measured or how to interpret the results.
Overloading the visualization
Too many charts, filters, colors, or indicators can compete for attention and make the primary message difficult to identify.
Failing to communicate a clear takeaway
A visualization without a clear purpose or message can leave viewers unsure about what they should learn from the information.
Designing without the audience in mind
A visualization that does not reflect the audience's needs, knowledge, or intended use can be difficult to understand or apply. Choices about axes, scales, and categories can exaggerate differences, minimize meaningful patterns, or otherwise distort interpretation. Excessive colors, insufficient contrast, or reliance on color alone can reduce clarity and accessibility. Three-dimensional effects, shadows, excessive patterns or textures, and unnecessary gridlines, borders, or outlines can distract from the data and make comparisons more difficult.
Using misleading axes, scale, or categories
Using color ineffectively
Using inappropriate visual design elements Using unclear or incomplete titles, labels, or legends
Viewers should be able to determine what the visualization shows, what time period it represents, and how to interpret its values, symbols, or colors.
23
Powered by FlippingBook