Data Visualization in Environmental Public Health

Data Visualization in Environmental Public Health: From Date to Insight, National Environmental Health Association. October 2026.

Data Visualization in Environmental Public Health: From Data to Insight

OCTOBER, 2026

Data Visualization in Environmental Public Health: from data to insight

CONTENTS

Turning a Visualization Into a Message....................................................................................20 Identify the Takeaway ....................................................................................................................... 21 Communicate Context and Limitations.......................................................................................21 Make the Takeaway Visible ............................................................................................................. 22 Avoid Common Pitfalls..................................................................................................................23 Tools and Platforms for Visualization.......................................................................................25 Think Beyond a Single Tool.............................................................................................................25 Common Visualization Platforms..................................................................................................25 Microsoft Excel........................................................................................................................... 25 Microsoft Power BI ................................................................................................................... 25 Tableau.........................................................................................................................................25 ArcGIS Online and ArcGIS Dashboards...........................................................................26 Google Looker Studio..............................................................................................................26 Graphic Design Tools...............................................................................................................26 Python and R/RStudio............................................................................................................26 Choosing the Right Platform...........................................................................................................27 Practical Exercises and Resources......................................................................................................28 Mini Exercise: Visualizing Inspection Data.................................................................................28 Learning and Practice Resources..................................................................................................29 GIS and Mapping......................................................................................................................29 Dashboards and Analytics....................................................................................................29 Communication and Design..................................................................................................29 Environmental Public Health................................................................................................29 General Learning....................................................................................................................... 29 Conclusion........................................................................................................................................ 30 Appendix A: Pitfall Examples......................................................................................................31 Using the Wrong Visualization Type............................................................................................31 Overloading the Visualization.........................................................................................................32 No Clear Takeaway ............................................................................................................................. 33 Using Misleading Axes, Scale, or Categories............................................................................34 Using Color Ineffectively...................................................................................................................35 Using Inappropriate Visual Design Elements............................................................................36 Using Unclear or Incomplete Titles, Labels, or Legends.......................................................37 Presenting Findings Without Context or Benchmarks..........................................................38 Using Incomplete, Inaccurate, or Inconsistent Data...............................................................39

Acknowledgments............................................................................................................................ ii Foreword............................................................................................................................................. iii Introduction. .............................................................................................................................................. 1 Why Visualization Matters.................................................................................................................1 Visualization and Data Modernization............................................................................................1 About This Guide...................................................................................................................................1 Data Visualization Workflow ............................................................................................................. 1 Using Visualization Responsibly.......................................................................................................2 Core Principles of Data Visualization...........................................................................................2 Clarity and Simplicity............................................................................................................................2 Accuracy and Integrity.........................................................................................................................2 Privacy and Disclosure Risk.....................................................................................................3 Audience-Centered Design................................................................................................................3 Accessibility............................................................................................................................................. 3 Building a Foundation for Visualization..........................................................................................4 Before You Visualize: Start With the Question.........................................................................4 Start With the Question......................................................................................................................4 Connect the Visualization to the Data............................................................................................4 Let Visualization Reveal Data Quality Issues...............................................................................5 Treat Visualization as an Iterative Process....................................................................................5 Common Visualization Types and When to Use Them...........................................................6 Choosing the Right Visualization.....................................................................................................6 Common Types of Visualization.......................................................................................................7 Bar Charts...................................................................................................................................... 7 Line Charts..................................................................................................................................... 8 Histograms.....................................................................................................................................9 Identifying Geographic Patterns....................................................................................................10 Point Maps.................................................................................................................................. 11 Choropleth Maps....................................................................................................................... 12 Graduated Symbol Maps.......................................................................................................13 Heat Maps................................................................................................................................... 14 Density Maps. ............................................................................................................................ 15 Time-Enabled Maps................................................................................................................. 16 Monitoring Operations and Performance...................................................................................17 Infographics: Communicating the Big Picture...........................................................................18

ACKNOWLEDGMENTS This guide was developed through the collaboration, expertise, and dedication of numerous contributors committed to advancing the field of environmental public health through data-informed practice. Lead Authors This guide was initially drafted by subject matter experts whose experience and expertise in environmental public health, informatics, and public health data systems informed its foundational content: Serene Ong, GIS Business Systems Analyst III, Innovation and Technology Department, San Bernardino County Andrew Mackey, GIS Business Systems Analyst III, Department of Public Health, San Bernardino County Coauthors The following individuals helped shape the guide through development of the initial outline, content development, revisions and technical review, and the development of supporting examples and resources: Becky Labbo, MA, Senior Evaluation Specialist, National Environmental Health Association Christopher Walker, MSEH, REHS, Associate Director, Program & Partnership Development, National Environmental Health Association Kate Weinberger, PhD, Program Analyst, National Environmental Health Association Reviewers and Advisors NEHA gratefully acknowledges the reviewers whose thoughtful feedback and critical insights enhanced the accuracy, clarity, and relevance of this guide. Their input ensured that the guide reflects the diverse needs and realities of the environmental public health workforce: Data and Informatics Program Committee, National Environmental Health Association Support and Funding This data visualization resource is one of several projects supported by the Centers for Disease Control and Prevention (CDC) of the U.S. Department of Health and Human Services (HHS) as part of a financial assistance award totaling $300,000, with 100% funded by CDC/HHS. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement by, CDC/HHS, or the U.S. government. Suggested Citation: Ong, S., Mackey, A., Labbo, B., Walker, C., & Weinberger, K. (2026). Data visualization in environmental public health: From data to insight . National Environmental Health Association. https://pubs.neha.org/data-visualization-guide

FOREWORD In 2023, the National Environmental Health Association (NEHA) conducted a data and informatics needs assessment that identified data visualization and communication as priority areas for environmental public health (EPH) workforce development. These findings align with the Centers for Disease Control and Prevention (CDC) Data Modernization Initiative, which supports stronger public health data systems and the workforce capacity to use them. In response, NEHA developed Modernizing Data Systems in Environmental Public Health: A Blueprint for Action, which addresses data systems, management, and interoperability. This data visualization guide complements that resource by showing how EPH professionals can turn data into clear, meaningful visual information—the subject of the chapters that follow. This guide is intended as a starting point that professionals can build on as their skills and needs evolve. Portions of this document were developed with assistance from ChatGPT (GPT-4, OpenAI, accessed August 2026) for editing, illustrative examples, and content development and refinement. All AI-assisted material was reviewed, verified, and revised by the authors for accuracy, clarity, and alignment with NEHA’s standards and mission. NEHA thanks the authors, contributors, reviewers, and partners who supported this resource. We hope it helps EPH professionals communicate data more effectively and provides a foundation for continued learning and skill development.

iii

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.

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.

Data Visualization in Environmental Public Health: From Data to Insight

Privacy and Disclosure Risk Visualizations should protect confidential and potentially identifiable information. In small jurisdictions or narrowly defined categories, even aggregated data can reveal individual establishments, households, people, or events. Maps can create additional risk when they show precise locations. Before sharing a visualization, review small values, rare characteristics, and detailed locations for privacy risks. Protective approaches include combining categories, using broader geographic areas or time periods, suppressing small values, removing precise locations, or limiting access. Follow applicable policies, laws, and data-use agreements. Audience-Centered Design The same dataset might need to be visualized differently depending on who will use the information. An EPH professional, a program manager, an agency director, an elected official, and a community member can all be interested in the same topic, but they often have different questions and decision-making responsibilities. Designing for the audience requires understanding what information is most relevant to them and what actions they take as a result. Technical audiences need greater detail and the ability to explore data interactively. Executive audiences often need concise summaries of performance, trends, and emerging issues. Public-facing visualizations typically benefit from simpler layouts and plain-language explanations. Successful visualization begins with understanding audience needs, not the software's capabilities. Accessibility As EPH professionals increasingly use dashboards, maps, charts, reports, and other digital tools to communicate data, accessibility becomes an essential workforce skill rather than simply a compliance consideration. This trend emphasizes building accessibility into data visualization and communication from the start so the widest possible audience can understand and use the information. Accessibility includes technical considerations such as readable fonts, sufficient color contrast, and colorblind-friendly palettes. It also includes communication considerations such as plain language, clear labeling, logical organization, and multilingual support where appropriate. Visualization has limited public health value if its intended audience cannot easily access, understand, or use the information it provides.

For state and local government agencies, accessibility is also an important compliance consideration. The Americans with Disabilities Act (ADA) and Web Content Accessibility Guidelines (WCAG) provide standards that can help agencies make digital information more accessible. EPH professionals should follow applicable accessibility requirements and their organization's policies when developing and publishing visualizations. Accessible Data Visualization: What EPH Professionals Should Know Tell the story in text. Summarize the key findings so users do not have to rely solely on the visual. Provide the information another way. For complex charts, maps, and graphics, consider an accessible table, spreadsheet, text description, or other alternative format. Do not rely on color alone. Use labels, patterns, shapes, symbols, or line styles to communicate meaning, and ensure sufficient color contrast. Make interactive content accessible. Dashboards, filters, controls, and other interactive elements should be usable without a mouse. Avoid placing important information only in hover features. Design for different devices and levels of magnification. Consider how the visualization works across screen sizes, orientations, and zoom levels. Provide multiple ways to access important information. Combining visualizations with text summaries, descriptions, data tables, or downloadable data can help more people understand and use the information. Accessibility References Bentle, K. (n.d.). How do EdTech products make data visualizations accessible for the April 2026 ADA deadline? Openfield. https://openfieldx.com/data-visualization-accessibility-tips- for-edtech/ Minnesota IT Services. (n.d.-a). Data visualization accessibility. State of Minnesota. https:// mn.gov/mnit/about-mnit/accessibility/data-visualization/ Minnesota IT Services. (n.d.-b). Data visualization and WCAG 2.1. State of Minnesota. https:// mn.gov/mnit/about-mnit/accessibility/training/wcag2.1/data-viz.jsp University of Wisconsin–Madison. (2026, July 7). Accessible complex images. Digital Accessibility @ UW–Madison. https://digitalaccessibility.wisc.edu/make-it-accessible/ complex-images/ World Wide Web Consortium. (2025, May 6). Web content accessibility guidelines (WCAG) 2.1. https://www.w3.org/TR/2025/REC-WCAG21-20250506/

Data Visualization in Environmental Public Health: From Data to Insight

Building a Foundation for Visualization These principles provide a foundation for all forms of visualization, from simple charts and reports to interactive dashboards and Geographic Information Systems (GIS) applications. Regardless of the tool or platform used, visualizations should communicate information clearly and quickly, support accurate interpretation, and remain useful to the intended audience for informed decisions. With these principles in mind, the next step is to understand the question, audience, and underlying data before selecting a visualization approach. BEFORE YOU VISUALIZE: START WITH THE QUESTION Start With the Question Strong visualization begins before a chart, map, or dashboard is created. It starts with the question: What does the audience need to understand, decide, or do? Once that need is clear, consider the available data and the visualization approach together. The data determine what you can accurately show, while the intended visualization can reveal what data you need, what is missing, or what requires additional preparation. The audience could initially describe a desired product rather than the underlying need. For example, a program manager may request a dashboard when the real need is to identify overdue inspections, compare workloads across districts, or monitor progress toward a performance target. Through discussion, the visualization developer can clarify the intended audience, key questions, required level of detail, update frequency, and measures of success. These conversations help ensure that the final product supports a meaningful operational or public health need rather than simply displaying available data. Connect the Visualization to the Data Once the need is understood, determine how the data will be prepared and managed to address it. This step involves identifying data sources, defining calculations, standardizing categories, establishing update processes, and determining how data will move from collection systems into reports, maps, or dashboards. Preparation includes extraction, transformation, and loading (ETL), which is the process of retrieving data from their original sources, preparing and standardizing them, and moving them into a system for analysis or visualization.

Additional data cleaning and modeling might also be needed, as discussed in more detail in the data modernization guide. The questions the visualization is intended to answer should guide these activities. Consider the data structure and visualization requirements together from the beginning. For example, a dashboard designed to show inspection timeliness could require consistent inspection dates, status fields, program categories, facility identifiers, and clear rules for determining whether an inspection is overdue. As the dashboard takes shape, gaps or inconsistencies in the underlying data can become apparent. Addressing these needs early reduces costly rework and produces information that better supports decision-making.

This planning should also determine whether the visualization will use counts, percentages, rates, or other standardized measures. Meaningful comparisons often require an appropriate denominator. For example, comparing the number of private wells with elevated arsenic levels across counties could be misleading if counties tested different numbers of wells. Reporting the percentage of tested wells that exceeded the health-based standard provides a more meaningful comparison because it accounts for both the number of wells exceeding the standard and the total number tested. Identifying the denominator early helps ensure that the necessary data are collected.

Data Visualization in Environmental Public Health: From Data to Insight

Let Visualization Reveal Data Quality Issues Visualization can also expose data quality problems that might be difficult to detect in the original database. Misspelled categories, inconsistent date formats, duplicate records, missing values, outdated fields, and unexpected outliers become apparent when the data are summarized or displayed visually. Rather than treating these findings as a failure, agencies should view it as a valuable part of the process. It creates an opportunity to improve the quality and usefulness of the underlying data before decisions are made from them. For example, a chart that groups violations by type reveals that the same violation has been entered under several slightly different names. A map shows records in the wrong city or outside the program’s service area because addresses were incomplete or entered inconsistently. A trend line reveals an unexpected increase or decrease due to changes in data-entry practices rather than a true change in environmental health conditions. These types of issues often originate during data collection and become much more apparent once the data are summarized or displayed visually. Because data collection and visualization are closely connected, improvements to data entry can improve the quality of future visualizations. When possible, data- entry systems should reduce unnecessary variation through tools such as required fields, standardized categories, drop-down menus, validation rules, and consistent date and numeric formats. Free-text fields remain useful for notes and context, but they are often difficult to summarize reliably and should not replace structured fields when consistent reporting is required. Address data require particular attention because small inconsistencies can affect geocoding and spatial analysis. Missing street numbers, incomplete ZIP Codes, misspelled street names, inconsistent abbreviations, or information entered in the wrong fields can prevent records from being mapped accurately. Separating address components into standardized fields and validating addresses during data entry can substantially improve the reliability of GIS maps and location- based analysis. Terms to Know: Geocoding is the process of converting location information, such as a street address, ZIP code, or place name, into geographic coordinates (latitude and longitude) that can be displayed on a map. Spatial analysis refers to the analysis of mapped data to answer questions about where, how, and why patterns occur across geographic space.

Treat Visualization as an Iterative Process Visualization is best understood as part of an iterative cycle rather than a final, isolated step. This process reflects the workflow introduced earlier in this guide. When new questions emerge or data problems surface, returning to an earlier stage is expected and often necessary, not a sign that something went wrong. Each cycle can reveal new questions, changing audience needs, or opportunities to improve data quality. Returning to earlier steps is therefore a normal part of effective visualization.

Data Visualization in Environmental Public Health: From Data to Insight

COMMON VISUALIZATION TYPES AND WHEN TO USE THEM Choosing the Right Visualization

The previous section emphasized the importance of understanding audience needs, decision-making requirements, and the questions that data are intended to answer. Visualization selection should follow those discussions. Once the analysis purpose is clear, the next step is to choose a visualization that communicates the information effectively. Different visualization types reveal different patterns, relationships, and insights, and the choice of visualization directly influences what the audience notices, understands, and remembers. Selecting the right one helps ensure that information is communicated clearly and supports the decision at hand. The best visualization is not necessarily the most sophisticated. It is the one that communicates the information needed to answer a question, support a decision, or prompt an action. The visualization types that follow are among the most common and useful approaches used in EPH. As a quick reference, the table below maps common questions to the visualization types best suited to answer them. The sections that follow explore each type in more detail. Table 1: What Visualization to Choose

IF YOU WANT TO:

CONSIDER USING:

The decision table to the left is a starting point; as analytical needs grow more complex, combining these visualization types is often more practical.

Compare groups or categories

Bar chart

Show change over time

Line chart

Understand distributions or variability

Histogram

Show where events occur

Point map

Compare rates geographically

Choropleth map

Compare counts geographically

Graduated symbol map

Identify patterns across categories or time Heat map Visualize concentrations or clusters Density map Visualize change across space and time

Time-enabled map

Monitor operations and performance

Dashboard

Communicate to public audience

Infographic

Present detailed records or exact values

Table

Data Visualization in Environmental Public Health: From Data to Insight

Figure 1 Bar Chart Example: Grade Distribution of Inspections

Common Visualization Types Comparing Groups and Categories

Many EPH questions involve comparison. Programs might need to understand how inspection outcomes differ across districts, whether complaint volume varies by facility type, or how workloads are distributed among staff. When the goal is comparison, bar charts are often the most appropriate choice. Bar charts make differences in values between categories easy to identify and interpret, even when comparing multiple groups. Common examples include: • Number of inspections conducted by district • Complaints by facility type • Violations by category • Permits issued by program area Stacked bar charts can be useful when showing the composition of a larger category. For example, a stacked bar chart can display critical, major, and minor violations within each inspection district.

Pie charts and donut charts show how categories contribute to a whole, particularly when there are five or fewer categories. As the number of categories increases, differences become more difficult to compare and interpret. In many situations, a bar chart provides a clearer and more accurate comparison. Examples The following examples illustrate different types of data visualizations but do not represent every way each type can be designed or presented. Data attribution The maps and selected charts in this guide use King County Food Establishment Inspection Data from 2022 to 2025. Data provided with the permission of King County. This attribution does not apply to the histogram, heat map, or infographic examples.

Data Visualization in Environmental Public Health: From Data to Insight

Figure 2 Line Chart Example: Monthly Inspection Counts

Understanding Change Over Time EPH programs routinely monitor conditions and activities that change over time, whether that is across days, months, or years. Examples include complaint volume, inspection completion rates, environmental monitoring results, and disease trends. Line charts are particularly useful for showing change over time because they let viewers quickly identify increases, decreases, seasonal patterns, and long-term trends. Examples include:

Trend visualizations provide important context. A single value might have limited meaning on its own, but a series of values over time can reveal whether conditions are improving, worsening, or remaining stable. When presenting time-based information, consistent intervals (e.g., days, months, years) and clear labeling are essential to accurate interpretation.

• Monthly mosquito surveillance counts • Annual inspection completion rates • Daily air quality measurements • Quarterly permit activity

Data Visualization in Environmental Public Health: From Data to Insight

Figure 3 Histogram Example: Duration of Food Facility Inspections

Understanding Distributions and Variability Sometimes the most important question is not how much or how many, but how values are distributed. A histogram shows how continuous numerical data are distributed by grouping values into ranges, or bins. Rather than comparing categories, it shows how the data are spread, where values cluster, and whether the distribution is symmetrical, skewed, or contains unusual values. Histograms can reveal patterns that averages alone might not show. For example, environmental sampling results might show that most observations fall within an expected range while a small number of unusually high values warrant additional investigation.

Distribution-based visualizations are particularly useful for environmental monitoring data, laboratory results, response times, or other continuous measurements. Although these visualizations are less common in operational reporting than bar charts, they can provide valuable insight when understanding variability is important. Histograms are not well suited for categorical data or comparing values across groups; bar charts are more appropriate for those purposes.

Data Visualization in Environmental Public Health: From Data to Insight

Identifying Geographic Patterns

Many environmental health issues have a geographic component. Understanding where conditions occur is often just as important as understanding how often they occur.

GIS allows EPH professionals to visualize, analyze, and communicate spatial relationships. Maps can reveal patterns, concentrations, service gaps, and geographic disparities that tables or charts might not show. EPH commonly uses several map types. Different map types answer different geographic questions: where events occurred, how a rate varies by area, or how a pattern changes over time.

Data Visualization in Environmental Public Health: From Data to Insight

Figure 4 Point Map Example: Inspection Locations

Point Maps Point maps display the locations of

individual features or events. Point maps work well when the exact location of each event or observation is important. Keep the number of points manageable and keep individual locations visible. If too many points overlap, consider using a graduated symbol or density map instead. Examples include: • Food establishment locations • Mosquito trap sites • Water sampling locations • Complaint locations

Map image is the intellectual property of Esri and is used herein under license. Copyright © 2026 Esri and its licensors. All rights reserved. Data: King County Food Establishment Inspections, 2022–2025 Sources: Esri, TomTom, Garmin, FAO, NOAA, USGS, © OpenStreetMap

Data Visualization in Environmental Public Health: From Data to Insight

Figure 5 Choropleth Map Example: Inspection Violation Rates by ZIP Code

Choropleth Maps Choropleth maps use shading or color to represent values within geographic areas such as census tracts, ZIP Codes, cities, or service districts. The color variations make it easy to identify geographic patterns and compare locations at a glance. Examples include: • Childhood lead exposure rates

• Foodborne illness rates • Inspection activity rates • Environmental justice indicators

Choropleth maps work best for displaying rates, percentages, or other normalized values. Mapping raw counts can be misleading because larger geographic areas or more populous communities naturally tend to have higher totals. For example, a map showing the number of complaints could primarily reflect where more people live. In contrast, a map showing complaint rates could provide a more meaningful comparison of conditions across communities.

Data Visualization in Environmental Public Health: From Data to Insight

Figure 6 Graduated Symbol Map Example: Inspection Scores by Food Establishment

Graduated Symbol Maps If the goal is to compare rates, percentages, or other normalized measures across geographic areas, choropleth maps are often the most appropriate choice. When the goal is to show the magnitude of counts or totals while preserving geographic context, graduated symbol maps could be the better option. The choice should reflect the question being asked, the data's characteristics, the geographic units, and the intended audience. Graduated symbol maps vary symbol size based on the magnitude of a value. Because the symbols are placed at their geographic locations, this type of map preserves spatial context while making it easy to compare magnitude differences. Examples include: • Number of complaints by community • Number of inspections by district

• Number of facilities by city These maps allow viewers to compare values while maintaining geographic context.

Data Visualization in Environmental Public Health: From Data to Insight

Figure 7 Heat Map Example: West Nile Virus Activity by Surveillance Zone and Week

Heat Maps In general data visualization, a heat map is a grid or matrix in which color represents the value in each cell. For example, a heat map might show the number of inspections by program and month. This format can reveal patterns across categories and time, including consistently high or low values and areas that could need attention. Examples include: • Number of inspections by program and month • Percentage of inspections completed on time by office and quarter • Number of violations by violation type and facility category • Average response time by complaint type and jurisdiction • Training participation by staff role and month • Compliance rates by program and geographic area Clearly label the measure and color scale so readers can interpret the values correctly. Use counts carefully when categories differ substantially in size. Percentages or rates provide more meaningful comparisons when appropriate denominators are available.

Source: Synthetic data created for demonstration purposes.

Data Visualization in Environmental Public Health: From Data to Insight

Figure 8 Density Map Example: Food Inspection Violations

Density Maps In GIS, a density map uses the

locations of individual observations to estimate where events are more or less concentrated across a geographic area. Color intensity represents the estimated density of events, such as clusters of environmental health complaints. Examples include: • Environmental health complaints • Mosquito observations or activity reports • Environmental sampling events • Repeated environmental incidents Density maps are particularly useful for identifying clusters, hotspots, and areas of concentrated activity that could warrant further investigation. They show the concentration of observations, not necessarily the severity of a condition. For example, a density map of environmental sampling events would show where more samples were collected, not where contaminant levels were highest. Density maps are sometimes called geographic heat maps. They differ, however, from the heat maps used in general data visualization. A heat map compares values across categories in a grid or matrix, while a density map shows the estimated geographic concentration of observations.

Data Visualization in Environmental Public Health: From Data to Insight

Figure 9 Time-Enabled Map Example: Mobile Food Unit Inspections Over Time

Time-Enabled Maps Some environmental health issues change across both space and time. Time- enabled maps allow users to visualize how conditions evolve geographically over time. Rather than showing a single snapshot, a time-enabled map lets viewers explore data across different dates or time periods, revealing trends, movement, and emerging patterns. Examples include: • Disease outbreaks • Emergency response activities • Environmental exposure events • Complaint patterns over time These visualizations can help identify emerging trends and support more proactive decision-making. If you do not have access to interactive mapping tools, you can achieve a similar effect by displaying a series of static maps, with one map for each year or time period.

Data Visualization in Environmental Public Health: From Data to Insight

Figure 10 Dashboard Example: Environmental Health Operations Dashboard

Monitoring Operations and Performance EPH programs often need to monitor ongoing activities, workloads, and performance measures. Dashboards have become one of the most widely used tools for this purpose. A dashboard combines multiple visualizations into a single view and can include charts, maps, summary statistics, key performance indicators (KPIs), and interactive filters to provide an overall picture of what's happening.

Common dashboard applications include: • Inspection backlog monitoring • Complaint tracking and response times • Permit processing activity • Environmental monitoring programs • Emergency response operations

Dashboards are most helpful when they focus attention on the information needed to support decisions. A common mistake is attempting to display too much information in a single view. Well-designed dashboards prioritize the most important indicators and present them clearly and in an organized way.

Data Visualization in Environmental Public Health: From Data to Insight

Figure 11 Infographic Example: Critical Violation Infographic

Infographics: Communicating the Big Picture Not all visualizations are intended for analysis. When the goal is public communication rather than operational decision-making, infographics can be appropriate. Many are designed to communicate information to community members, the media, elected officials, or other nontechnical audiences. Infographics combine visual elements, concise text, key statistics, and simple graphics to communicate important messages quickly and in an engaging format.

An infographic is not the right choice when the audi - ence needs to: • Explore detailed data or conduct analysis • Compare many variables or precise values • Interact with the data through filtering or drill-down capabilities • Review technical methods or statistical results

Common uses include: • Food safety education

• Heat illness prevention campaigns • Community outreach initiatives • Program accomplishments and annual reports • Public health campaigns An infographic's goal is not detailed analysis. Instead, it aims to improve understanding and communicate key messages in a format accessible to a broad audience.

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