Data Visualization


Data visualization is the graphical representation of information. It involves charts, graphs, maps, and other visual tools to present data to make them easier to understand. The purpose of data visualization is to communicate information clearly and effectively to a particular audience and to make it easier to identify patterns, trends, and insights that might be missed in exploring raw data alone. Data visualization enables people to quickly grasp complex data, understand meaning and implications, make informed decisions, and communicate findings to others.


A good data visualization can make data accessible to a wide range of audiences. Visual representations of data can be better understood by people of different ages, cultures, and educational backgrounds, aiding in data sharing and stakeholder engagement.


Data visualizations can have system-level impacts by improving decision-making, increasing efficiency, improving communication, identifying and prioritizing opportunities for community investment, and increasing transparency. By using data visualizations effectively, organizations can gain insights, make better decisions, and achieve goals more effectively.


Early examples of data visualizations include Florence Nightingale's 1858 "Rose Diagram,” which was used to illustrate the mortality rates of soldiers in the Crimean War, as well as John Snow's iconic 1854 Cholera Map. John Snow created a map of cholera deaths in London, showing that the disease was concentrated around a single water pump. This visualization helped convince local officials to shut down the pump and ultimately led to vast improvements in public health and disease prevention.


More recent examples include prolific COVID-19 data dashboards—in response to the COVID-19 pandemic, many organizations and governments created dashboards and other data visualizations to track the spread of the virus and its impacts on health and society. These visualizations have helped to inform public health policy and to provide the public with up-to-date information on the pandemic.


Equity considerations are an important aspect of data visualization. Data visualizations can perpetuate bias and reinforce existing power structures if they are not designed and presented with equity in mind. Here are some considerations to keep in mind:


  1. Representation: Ensure that the data being visualized is representative of all groups, including marginalized communities. This means being intentional about including diverse perspectives and experiences in data collection and analysis.
  2. Accessibility: Data visualizations should be accessible to all users, including those with disabilities. This can be achieved by providing alt text for images, using high-contrast colors, and making sure that the visualization can be navigated using a keyboard.
  3. Transparency: Data visualizations should be transparent about their sources and methods. This includes disclosing any biases or limitations in the data and acknowledging any subjective decisions made in the creation of the visualization.

Data visualization can be a powerful tool in addressing health and social issues by helping to accurately and effectively identify trends, track progress, and inform policy decisions. Data visualizations can reveal insights and discoveries that might be missed in other forms of information presentation and can help users explore and discover relationships and patterns that were not immediately apparent.


An effective data visualization is one that presents complex information in a way that is easy to understand, engaging, and relevant to the audience. The visualization should:

  • Be relevant to the audience and clearly communicate the insights that are most important to them.
  • Be accurate and presented in a way that is truthful and transparent. 

  • Provide context for the data, helping the audience understand the larger story behind the numbers. 

  • Be engaging and visually appealing, with clear and well-designed charts, graphs, and other visual elements.

Resources & Tools


Bold letters
Maps and Data
Story - Original
Brought to you by Community Commons
Screen capture of Health-Related Surveys for Epidemiologists webpage
Health-Related Surveys for Epidemiologists
Resource - Data Bank/repository
Screen capture of Sage Data - Data Basics
Sage Data - Data Basics
Resource - Website/webpage
Brought to you by SAGE Publications
Screen capture of Data Visualization
Data Visualization
Resource - Website/webpage
Screen grab of Exploring the Public Health Workforce Through Data Visualization
Exploring the Public Health Workforce Through Data Visualization
Resource - Map
Brought to you by de Beaumont Foundation Inc.
First page of What is a Survey booklet
What is a Survey
Resource - Book
Cover page of Toolkit for Conducting Focus Groups document
Toolkit for Conducting Focus Groups
Resource - Guide/handbook
Screenshot of COVID-19 Maps, Graphs, & Figures
COVID-19 Maps, Graphs, & Figures
Resource - Data Bank/repository
Brought to you by National Coalition of STD Directors
Screen capture of Conducting Successful Virtual Focus Groups webpage
Conducting Successful Virtual Focus Groups
Resource
Brought to you by Child Trends
First page of A Guide to Conducting Online Focus Groups guide
A Guide to Conducting Online Focus Groups
Resource - Guide/handbook
Brought to you by Vital Strategies
Screen capture of Data Visualization Basics
Data Visualization Basics
Resource - Website/webpage
Brought to you by PolicyLink
Screen capture of Conducting Surveys webpage
Conducting Surveys
Resource - Website/webpage
Cover page of Data Playbook
Data Playbook
Resource - Guide/handbook
First page of Focus Group Tip Sheet document
Focus Group Tip Sheet
Resource - Fact Sheet
Brought to you by U.S. Department of Health and Human Services
First page of Guidelines for Conducting a Focus Group document
Guidelines for Conducting a Focus Group
Resource - Guide/handbook
Cover page of Introduction to Conducting Focus Groups document
Introduction to Conducting Focus Groups
Resource - Guide/handbook
Cover page of Understanding and Using American Community Survey Data: What All Data Users Need to Know
Understanding and Using American Community Survey Data: What All Data Users Need to Know
Resource - Guide/handbook
Brought to you by Census Bureau
Cover page of Do No Harm Guide: Applying Equity Awareness in Data Visualization
Do No Harm Guide: Applying Equity Awareness in Data Visualization
Resource - Guide/handbook
Brought to you by Urban Institute
Screen capture of Survey Explorer webpage
Survey Explorer
Resource - Data Bank/repository
Staff Pick!
State postage stamps on a blue background. White text reads
Our Favorite State Data Tools
Story - Original
Brought to you by Community Commons
Rectangles contain titles that read
Refreshing Data Basics
Story
Brought to you by IP3
Published on 05/30/2023
Rectangles contain titles that read
Using Benchmarks to Explore Population Health Data
Story
Brought to you by IP3
Published on 08/23/2022
A screen grab of State Health Compare tool
State Health Compare
Tool - Data/mapping Tool
Staff Pick!
Tableau Public home page
Screen capture of Healthy Kids Colorado Survey Dashboard webpage
Healthy Kids Colorado Survey Dashboard
Tool - Data/mapping Tool
Staff Pick!
Screen grab of The Climate Explorer
The Climate Explorer
Tool - Data/mapping Tool
Screen capture of Mapping Race in America
Mapping Race in America
Tool - Data/mapping Tool
Screen capture of Pennsylvania Cancer Statistics Dashboard webage
Pennsylvania Cancer Statistics Dashboard
Tool - Data/mapping Tool
Screen capture of the Opportunity Atlas mapping tool
The Opportunity Atlas
Tool - Data/mapping Tool

 Related Topics


Card image
Data Equity

Card image
Geospatial Data

Card image
Data Granularity

Card image
Data Visualization

Card image
Data Accessibility

Card image
Demographic Data