Data Visualization in Environmental Public Health: From Data to Insight
Using Visualization Responsibly Visualizations do more than display data. They help EPH agencies identify priorities, monitor conditions, communicate findings, and support informed decisions and actions. Dashboards, maps, and other reporting tools can help programs recognize emerging trends, understand community needs, and make information more accessible to staff, leadership, partners, and the communities they serve. At the same time, visualization must be done carefully. Choices about what information to include, emphasize, or compare, and how to present it can influence how people interpret the data. The goal is not to make data look impressive but to make data understandable, accurate, and useful.
Clarity and Simplicity The primary purpose of visualization is to make information easier to understand. Every visual should have a clear purpose and communicate a clear message. Viewers should be able to quickly identify what information is being presented and why it matters. If the audience must expend significant effort to interpret a chart or map before understanding its meaning, the visualization is not achieving its objective. Simplicity does not mean oversimplifying the data. Rather, it means reducing unnecessary complexity. Useful visualizations emphasize the most important information and minimize distractions such as excessive colors, unnecessary labels, decorative effects, or overcrowded layouts. A useful question during design is: What is the most important insight this visualization should communicate? The answer should be immediately visible to the audience. Accuracy and Integrity Visualization design choices influence how people interpret information. For that reason, accuracy must extend beyond the underlying data to include how those data are presented. Scale, classification methods, color schemes, aggregation levels, and geographic boundaries all shape the story a visualization tells. Additionally, even technically correct visualizations can mislead if they omit important context. For example, displaying the number of complaints from a community might produce a very different interpretation than displaying complaint rates adjusted by population. Similarly, a map of reported cases might highlight where events occurred, while a map of case rates could better represent risk to different communities. Well-designed visualizations provide the context necessary for accurate interpretation. They help viewers understand what the data show, while also recognizing which conclusions the data support. Visualizations should also acknowledge uncertainty when it exists, such as incomplete reporting, small sample sizes, missing data, or data-quality limitations. Communicating these limitations helps viewers interpret findings appropriately and supports transparency and trust in the information being presented.
CORE PRINCIPLES OF DATA VISUALIZATION
Data visualization turns EPH data into information people can understand and use. Charts, maps, dashboards, and other visual formats can reveal patterns, highlight priorities, and support decisions about programs, resources, risks, and performance. A visualization's effectiveness depends not only on the quality of the underlying data but also on how clearly and responsibly they are presented. Poor design can obscure important findings, create confusion, or contribute to incorrect conclusions. While visualization tools and technologies continue to evolve, the principles that guide strong visualization remain consistent. Overall, four data visualization principles are particularly important in EPH: 1) clarity and simplicity, 2) accuracy and integrity, 3) audience-centered design, and 4) accessibility.
Clarity & Simplicity Focus on message first. Remove clutter, unnecessary color, extra labels, and decorative effects.
Accuracy & Integrity Show the data in a way that supports correct interpretation. Include context, scale, denominators, and limitations.
Audience-Centered Design A technical audience, executive audience, and public audience may need very different presentations of the same data.
Accessibility Use readable type, strong contrast, clear labels, plain language, and color-safe choices.
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