Data Visualization Basics

On the Lab you can build your own data visualizations using Tableau. Here we provide tips and guidance for creating effective data visualizations with deeply disaggregated data to power your equity campaigns.

Steps to creating a dashboard:

  1. Decide what story you are trying to tell with the data and what next steps you hope readers will take to advance the campaign.
  2. Choose your mix of data displays based on the key equity issues of your campaign.
  3. Create great data displays using best practices.
  4. Review your data displays and narrative to make sure they tell a cohesive story.

Decide what story are you trying to tell with the data

Dashboards created for equity initiatives and campaigns should tell a clear story about equity in your community (or communities), highlighting what the issues are, why they exist, and how your readers can take action to make meaningful progress on racial equity.

When displaying data, you should be able to articulate:

  • What does the data show?
  • Why does it matter?
  • What are the solutions? 

These three basic guiding questions can also help you in designing your dashboard. Each element of your data visual should support your articulation of those three questions and focusing on the goal of your data visual can also help you eliminate unneeded components that may be taking up valuable space or distracting your audience.

Choose your mix of data displays for the dashboard

There are a wide variety of data visual types you can choose from. Below are some of the data visual types we often use in the Atlas for displaying disaggregated data for equity campaigns, based on the comparisons we want to highlight in the data. 

Choosing between chart types for data visuals

Comparison Between a Few Groups

For example: Compare differences by race and ethnicity
Consider using: Bar Chart

Bar chart showing percent of high school graduates by race/ethnicity

Bar charts are useful for showing categorical data, such as data by race or by gender, where you are showing comparison between groups. A horizontal bar chart makes it easy for viewers to compare between bars to recognize which bars are longer and which are shorter. Vertical gridlines provide a quick reference to estimating length and compare differences, and labels provide exact percentages or numbers which are particularly useful when comparing between groups with smaller differences.

Comparing Between Many Groups

For example: Compare cities/states, or use disaggregated racial data
Consider using: Column Chart

Column chart showing ancestry

Column chart showing city rankings

Column charts are vertical bar charts, and are also useful for comparison between groups, particularly when there are more than seven categories. They are also useful for illustrating ranking across many groups, such as helping users to identify how their city or state compares with other cities and states.
Note that the labels for the categories can be displayed at an angle to maximize space.

Comparisons Between Groups and Subgroups

For example: Compare differences between groups by race and gender
Consider using: Grouped Bar Chart

Grouped bar chart showing median earnings by race/ethnicty and gender

Grouped Bar Charts allow you to include additional subcategories in your comparison, for example when you are showing differences between groups by race and gender. Grouping the bars together shows rates by race/ethnicity, while using two different colors for gender allows viewers to also compare each gender across racial groups.

Show Gaps and Change Over Time

For example: Show different rates over time for each racial group
Consider using: Trend Lines

Trend line chart showing rates for white and people of color over time

Trend lines are a useful way to illustrate change over time. Using markers can help users identify key points on your chart, and different colors for each line makes it visually clear for comparison between different groups represented.

Show Clusters and Gaps Between Groups Over Time

For example: Show distribution between races for different years
Consider using: Dot Plot Graph

Dot plot graph showing percent of population by race for each year

Dot Plots are useful for showing distribution comparison between categories, and also show patterns of that distribution. They are efficient for when comparing between groups. One way to highlight differences between multiple groups and to bring attention to particular groups of focus is to change the shape and size of the dots.

Display and Compare Geographically-Related Data

For example: Compare differences between and across states or cities
Consider using: Maps

Shaded map of California counties showing rent debt per household comparison

Maps are used to illustrate differences between geographies and allows users to quickly compare across multiple geographies. Maps can help users contextualize data in a familiar frame of reference and take in multiple data points at once.

Create great data displays

Below are key elements of data displays that work.

Headers provide short narrative explanations for the data visual. Types of headers include: 
  • Title for the full display
  • Narrative header that describes the key narrative frame for your visual
  • Technical title that describes in literal terms what the data displays
Chart Elements
This is the area on the chart that displays the data in the chart type chosen. Within the chart, common elements include:
  • Axis titles
  • Axis
  • Ticks 
  • Gridlines
  • Legends
  • Markers
  • Data labels
  • Tooltips: Tableau tooltips are an additional feature that appear when a viewer clicks on or hovers the mouse over a data point in the visual. Tooltips can provide additional information and increase interactivity for your visualization.

Data Sources 
It is important to share the original sources of the data displayed in the chart/graph/map. If you conducted analysis of the source data you can add “[Your name or organization] analysis of data from [source of data]…”

If you conducted analysis of data presented in the graphic, you should provide a description of the methods that you used to analyze the data. 

Elements of an effective data display

What Are the Solutions?

When displayed together, data visuals in a policy context can help create a data narrative that deepens a user’s understanding of an issue, key data points that highlight the issue, and provides information on equity solutions for policymakers. Dashboards visually display key metrics and data points to monitor and analyze different data sets in key areas. The purpose of a dashboard is to provide data for users to explore and learn about the situation being tracked in the data. The Lab Gallery page includes several examples of data visualizations with equity narratives, using a variety of Tableau formats.

Our Rent Debt in America dashboard allows users to view and explore a set of indicators of rent debt, with a focus on the mounting rent debt created by the Covid-19 pandemic and economic recession. We developed the dashboard to equip housing justice advocates with critical information to understand the magnitude of eviction risk and inform local policymaking. 

The Black Prosperity in America and How is the Black Population Doing in the Bay Area dashboards provide a focused look at the Black population in America and in the Bay Area, highlighting key indicators which can be viewed through different navigation buttons to individual pages within the dashboard. In the dashboards, users can explore the data for each indicator and learn about the drivers of inequality and equity solutions.