Data Visualization in Environmental Public Health

Data Visualization in Environmental Public Health: From Data to Insight

The relationship works in both directions. Modernized data systems make timely, useful visualization possible, while visualization can reveal gaps in data quality, consistency, or availability that may require further improvement. In this way, visualization helps agencies put modernized data into practice while also identifying opportunities to strengthen the systems and processes that support it. About This Guide This guide provides practical guidance for creating visualizations in EPH. It introduces core principles such as clarity, accuracy, audience-centered design, and accessibility. It describes common visualization types, including charts, maps, dashboards, heat maps, and infographics, and explains when each might be useful. It also highlights common tools and platforms, provides examples, and offers guidance for communicating insights in ways that support action. The goal is not for every agency to immediately build advanced dashboards or complex interactive maps. Many strong visualization practices begin with small, manageable improvements: turning a table into a clear chart, adding a trend line to a monthly report, mapping complaint locations, simplifying a dashboard, or creating a one-page infographic for a community audience. Over time, these improvements can help agencies build a stronger culture of data use. Data Visualization Workflow The visualization process is not a single step, but an ongoing, iterative workflow. The diagram below outlines the process:

INTRODUCTION Why Visualization Matters

EPH professionals rely on data every day to protect communities, guide programs, and support informed decisions. Inspection results, complaint trends, permit records, sampling data, service requests, laboratory results, demographic indicators, and geographic patterns all contain valuable insight. These data, however, are not valuable simply because they are collected or stored in a system. Data become useful when people understand, interpret, communicate, and apply them to real decisions. Data visualization plays a crucial role in that process. Graphs, maps, dashboards, infographics, and other visual tools help transform raw data into information that can guide action. A table with hundreds or thousands of rows may contain important trends, but those trends can be difficult to identify without the right visual summary. A dashboard showing inspection backlogs by program, district, or facility type can help managers identify where staff resources are needed. A map of complaints, violations, or environmental conditions can reveal geographic patterns that might not be obvious in a spreadsheet. A simple infographic can help community members understand public health risks, prevention steps, or program outcomes in plain language. In this way, visualization is not just a technical skill. It is a communication tool, a decision-support tool, and an important part of modern environmental health practice. Visualization and Data Modernization This guide builds on the broader principles of data modernization and informatics. Modernization is often associated with new systems, automated workflows, integrated databases, and improved data standards. Those components are important, but modernization is not only about technology. At its core, data modernization helps agencies use data to improve operations, strengthen programs, support timely decisions, and communicate clearly with internal and external audiences. Visualization is one of the most practical ways to turn modernization into everyday value. Modern systems, improved data standards, and better integration create a stronger foundation for using data, while visualization helps translate that foundation into information people can understand and apply. Data visualization also depends on reliable, complete, and well-managed data, making data quality a foundational element of both modernization and communication.

This cycle provides a foundation for effective data visualization and will be revisited throughout the chapters that follow. The data visualization workflow should also consider the FAIR principles of data, which encourage data to be findable , accessible , interoperable , and reusable . Although FAIR applies broadly to data management, these principles can help ensure that the data supporting visualizations are well organized, consistently defined, and suitable for use across systems and over time. The NEHA data modernization guide discusses the FAIR principles in greater detail.