All in One View

Content from Introduction to Data Visualization

Last updated on 2026-03-13 | Edit this page

Estimated time: 75 minutes

Overview

Questions

  • What exactly is data visualization and how does it differ from simple charts?
  • Why do humans understand visualized data much faster than raw numbers or tables?
  • What are the real advantages and hidden pitfalls of using visuals in data analysis?
  • How does visualization help (or complicate) working with big data?
  • Which tools are most suitable for beginners, intermediate users, and advanced programmers?
  • What makes a visualization “good” versus “misleading”?

Objectives

  • Define data visualization and explain its core purpose
  • List at least five advantages and three disadvantages of data visualization
  • Describe how visualization addresses challenges posed by big data
  • Identify and differentiate between exploratory and explanatory visualizations with real-world examples
  • Compare popular open-source and commercial tools for creating visualizations
  • Recognize key principles of effective visualization design
  • Understand ethical considerations and accessibility best practices
Key Points
  • Data visualization turns numbers into stories that the human brain can understand quickly.
  • Good visualizations reveal patterns, trends, and outliers that are invisible in spreadsheets.
  • Poor design can mislead audiences more powerfully than raw data ever could.
  • Big data demands interactive, scalable, and often multi-dimensional visualizations.
  • Choose the right tool for your audience and skill level — start simple and iterate.
  • Always prioritize clarity, honesty, and accessibility over visual flair.

What Is Data Visualization?

Data visualization is the graphical representation of information and data.
Instead of showing rows and columns of numbers, it uses charts, graphs, maps, diagrams, and interactive dashboards to make patterns, trends, relationships, and outliers immediately understandable.

Think of it as data storytelling with graphics. A well-designed visualization does in seconds what a 10-page spreadsheet cannot: it lets the human brain — which processes images 60,000 times faster than text — grasp complex information at a glance.

In the Carpentries context, data visualization is not just “making pretty pictures.” It is a core skill that bridges data wrangling (what you learned in previous episodes) and data-driven decision making.

Why Is Data Visualization Important?

  1. Humans are visual creatures
    Our brains devote more than 50% of their processing power to vision. A bar chart showing sales growth is far more memorable than a table of 50 monthly figures.

  2. Reveals what numbers hide

    • Trends over time
    • Clusters and correlations
    • Outliers and anomalies
    • Geographic patterns
    • Distributions and variability

  3. Enables faster decision-making
    Executives, scientists, journalists, and policymakers routinely use visualizations to justify budgets, publish papers, or influence public opinion.

  4. Democratizes data
    A clear chart can be understood by domain experts and non-technical stakeholders alike.

  5. Supports both exploration and explanation

    • Exploratory visualizations help you discover insights while analyzing data.
    • Explanatory visualizations help others understand your discoveries.

Advantages of Data Visualization

  • Speed: Spot trends in seconds instead of hours.
  • Clarity: Reduce cognitive load — one image can replace thousands of numbers.
  • Pattern recognition: Humans excel at seeing lines, clusters, and shapes.
  • Engagement: Colorful, interactive visuals capture attention and improve retention.
  • Storytelling power: Turn dry statistics into compelling narratives.
  • Error detection: Visuals often reveal data quality problems (missing values, typos, impossible ranges) that automated checks miss.
  • Accessibility for diverse audiences: Good visuals can communicate across language barriers and technical skill levels.

Disadvantages and Risks

  • Misleading results: A truncated y-axis, 3D effects, or cherry-picked colors can completely distort the truth (classic example: the “exploding” pie chart).
  • Chartjunk (Edward Tufte’s term): Decorative elements that add noise without adding information.
  • Time investment: Creating a professional, publication-ready visualization can take longer than the analysis itself.
  • Skill gap: Requires both analytical thinking and design sensibility.
  • Over-simplification: Complex multi-variable relationships can be flattened into something misleading.
  • Accessibility barriers: Poor color contrast or lack of alt text excludes people with color blindness or screen readers.

Callout: The “lying with charts” phenomenon
Always ask: “Does this visual tell the whole story, or just the story I want to tell?”

Big Data and the Need for Visualization

The explosion of big data (volume, velocity, variety, veracity) has made visualization not just helpful — but essential.

  • Volume: A 1-million-row dataset is impossible to read. A heatmap or density plot shows the entire distribution instantly.
  • Velocity: Real-time dashboards (stock markets, COVID-19 trackers, IoT sensor networks) update every second.
  • Variety: Combining structured tables, text, images, and geospatial data requires multi-layered visuals (e.g., a map with overlaid time-series).
  • Dimensionality: With 50+ variables, we use techniques like PCA, t-SNE, or parallel coordinates plots to reduce complexity.

Modern big-data visualizations are often: - Interactive (zoom, filter, hover tooltips) - Scalable (handle millions of points without crashing) - Collaborative (shared dashboards in Tableau Server, Power BI, or Plotly Dash)

Real-World Examples

Simple but powerful

  • Line chart: Global temperature rise 1880–2025 (clear upward trend)
  • Bar chart: Top 10 countries by renewable energy adoption
  • Scatter plot: Relationship between study hours and exam scores (with regression line)

More advanced

  • Heatmap: Correlation matrix of 20 variables in a genomics dataset
  • Treemap: Breakdown of a company’s revenue by department and region
  • Network graph: Social media connections or protein interaction maps
  • Choropleth map: U.S. election results or COVID case rates by county
  • Animated bubble chart (Hans Rosling style): 200 years of health and wealth data showing global progress

Good vs. Bad examples (you will practice these in exercises)

  • Good: Minimalist line chart with clear labels and honest scale.
  • Bad: 3D exploding pie chart with 12 slices, rainbow colors, and no legend.
Skill Level Tool/Library Best For Open Source? Carpentries Recommendation
Beginner Excel / Google Sheets Quick bar/line/pie charts No Great starting point
Beginner–Intermediate Tableau Public / Power BI Interactive dashboards Partial Excellent for non-coders
Intermediate Python + Matplotlib/Seaborn Publication-quality static plots Yes Highly recommended
Intermediate–Advanced Python + Plotly Interactive web-ready charts Yes Perfect for sharing
Advanced R + ggplot2 Statistical graphics Yes Data Carpentry favorite
Advanced JavaScript + D3.js Fully custom web visualizations Yes For web developers
All levels Observable / Vega-Lite Notebook-style interactive viz Yes Modern & flexible

In this workshop series we will focus primarily on Python (Matplotlib, Seaborn, Plotly) and briefly touch on R’s ggplot2 because they integrate seamlessly with the data-cleaning skills you already learned.

Principles of Effective Visualization (The “Rules”)

Inspired by Edward Tufte, William Cleveland, and Alberto Cairo:

  1. Maximize data-ink ratio — remove everything that is not data.
  2. Use small multiples instead of overloading one chart.
  3. Choose the right chart type for the message (never use pie charts for >5 categories!).
  4. Label everything clearly — titles, axes, legends, units.
  5. Be honest — never truncate axes without disclosure.
  6. Consider color carefully — use colorblind-friendly palettes (ColorBrewer, viridis).
  7. Make it accessible — alt text, high contrast, patterns in addition to color.
  8. Tell a story — guide the viewer’s eye with titles, annotations, and sequence.

Ethical Considerations

  • Visualizations can influence policy, investment, and public opinion.
  • Avoid cherry-picking time windows or subsets.
  • Disclose data sources and limitations.
  • Respect privacy (especially with geospatial or personal data).

Challenges You Will Face (and How We Solve Them)

  • Too many variables → dimensionality reduction + faceting
  • Performance with millions of points → sampling, aggregation, or WebGL-based tools
  • Reproducibility → always save code alongside the image
  • Version control → store plots in Git (we’ll show you how)

Key Takeaways

Data visualization is not decoration — it is analysis made visible.
Mastering it will transform how you explore data, how you communicate results, and how much impact your work has.

In the next episodes we will:

  • Build your first publication-ready plots in Python.
  • Learn how to critique and improve existing visualizations.
  • Combine visualization with statistical analysis.

Exercise (30 min)
1. Open the google collab file in Start Here Module here. Make sure to make your own copy to save progress!

  1. Alternatively, refer the Bad and Good Plotting techniques in this module. Understand what the basic requirements of producing a good plot looks like.

Module Overview

Lesson Overview
Beginner Basics of Good & Bad Plotting
Intermediate Basics of More Complex Visuals
Complex Basics of Some Other Visuals

Content from Cartography Checklists

Last updated on 2026-03-27 | Edit this page

Estimated time: 75 minutes

Overview

Questions

  • Who is the primary audience for your map?
  • What message or story are you trying to communicate?
  • Which data attributes are most important to show?
  • How will your audience interpret or react to your map?
  • What medium will your map be presented in (web, print, presentation)?
  • Will your map be used to inform decisions?
  • What does your audience already know, and what do they need explained?
  • Do you need more data to support your map?
  • Do you fully understand the topic you are mapping?

Objectives

  • Identify the purpose and audience of a map
  • Choose appropriate data and variables to visualize
  • Design maps that communicate clearly and accurately
  • Evaluate whether additional data or research is needed
  • Apply a checklist-based approach to cartographic design

Why Thoughtful Map Design Matters

Maps are powerful tools for communication. A well-designed map can reveal patterns, support decisions, and tell compelling stories. A poorly designed map can mislead, confuse, or hide important insights

Before making a map, it’s essential to ask the right questions. Good cartography follows core design principles:

  • legibility (easy to read)
  • visual contrast (important elements stand out)
  • figure-ground (main features pop from the background)
  • hierarchy (what’s most important first)
  • balance (pleasing layout without clutter)

1. Know Your Audience

Your audience determines everything about your map.

Ask yourself:

  • Are they experts, policymakers, or the general public?
  • What is their familiarity with maps and your topic?
  • What level of detail is appropriate?

Example:

  • General audience → simple labels, clear legend, minimal jargon
  • Scientific audience → more detail, precise scales, technical terminology
Callout

Key Idea

A map for scientists and a map for the public should not look the same.

2. Define Your Message

Every map should answer a clear question.

Ask yourself:

  • What is the single most important takeaway?
  • Are you showing patterns, comparisons, or changes over time?

Avoid:

  • Trying to show too many variables at once
  • Making the user “figure it out” without guidance

Good Example:

“This map shows areas at highest risk of flooding.”

Flood-Risk USA Map
Flood-Risk USA Map

3. Choose the Right Data Attributes

Not all data belongs on your map. Select variables that support your message and are spatially meaningful.

Ask yourself:

  • Which variable is most important?
  • Are there supporting variables (e.g., population, elevation)?
  • Is your data spatially appropriate (points, lines, polygons)?

Tips:

  • Use color for magnitude (e.g., rainfall)
  • Use size for comparison (e.g., population)
  • Use symbols for categories (e.g., land use)
Discussion

Quick Check

You have temperature, precipitation, and elevation data.
Which one would you prioritize if your goal is to show drought risk?

4. Consider Audience Perception

Maps are not neutral — design choices influence interpretation.

Ask yourself:

  • Could colors be misleading (e.g., red = danger)?
  • Are you introducing bias unintentionally?
  • Is the map easy to interpret at a glance?

Example:

  • Darker/Very Light colors may imply higher value and importance
  • Certain color schemes may exclude colorblind users (see next module)
Bad USA Map
Bad USA Map

Best Practice: Establish clear contrast between foreground (data) and background (basemap). Use figure-ground techniques, such as subtle vignettes or lighter basemaps, so your data stands out.

5. Choose the Right Medium

Where your map is displayed affects design decisions.

Common mediums:

  • Web maps → interactive, zoomable
  • Print maps → static, high resolution
  • Presentations → simple, bold visuals

Ask yourself:

  • Will users zoom in?
  • Will the map be printed in black and white?
  • How large will it appear?

Tip for Web: Simplify basemaps and use halos on labels for readability over varied backgrounds. For print, test a physical proof.

6. Will Your Map Inform Decisions?

Some maps are purely exploratory, while others guide real-world actions.

Decision-making maps should:

  • Be highly accurate
  • Include uncertainty (if possible)
  • Avoid misleading simplifications

Example:

  • Flood risk maps used by city planners
  • Public health maps used during outbreaks
Callout

Important

If your map influences decisions, accuracy and clarity are critical.

7. Understand Your Audience’s Knowledge

Ask yourself:

  • Do they understand your variables?
  • Do you need to explain units or scales?
  • Should you include annotations or context?

Tips:

  • Add legends and labels
  • Use plain language when possible
  • Provide context (e.g., time period, data source)
  • Include essential map elements: scale bar (when distance matters), north arrow (if orientation isn’t standard), and source citation

Pro Tip: Aim for “maximum information at minimum effort” — the viewer should grasp the main idea quickly without struggling.

8. Do You Need More Data?

Incomplete data can lead to misleading maps.

Ask yourself:

  • Are there missing variables that affect interpretation?
  • Is your data up to date?
  • Is the spatial resolution sufficient?

Example:

Mapping income without population density may mislead conclusions.

This is a good map but needs supporting information to better understand this distribution of income across the country. Green here indicates higher income.
This is a good map but needs supporting information to better understand this distribution of income across the country. Green here indicates higher income.
This population density map is in positive correlation with the map. As in, a higher population density would ideally mean a greater household income in that county. Lighter colors indicate greater population density.
This population density map is in positive correlation with the map. As in, a higher population density would ideally mean a greater household income in that county. Lighter colors indicate greater population density.

9. Do You Understand Your Data?

Before mapping, you should fully understand your dataset.

Ask yourself:

  • What does each variable represent?
  • Are there biases or limitations?
  • Have you explored the data (e.g., summary statistics)?

If not:

  • Perform exploratory data analysis (EDA)
  • Read metadata and documentation
  • Consult someone if needed
  • Research about the data more online!
  • Advanced: Conduct sensitivity tests as in, do the results change under different assumptions?

Cartography Checklist (Summary)

Before finalizing your map, review this checklist:

Final Thought

A good map is not just visually appealing — it is honest, clear, and purposeful. It respects the data, serves the audience, and communicates effectively without distortion.

Discussion

  • Think of a map you’ve seen recently.
    What did it do well? What could be improved?

  • How might the same data be presented differently for another audience?

  • Share an example of a map that misled you (or succeeded brilliantly) and why.

Content from Fundamentals of Map Design

Last updated on 2026-03-27 | Edit this page

Estimated time: 105 minutes

Overview

Questions

  • What is a map and what makes it effective?
  • How do visual hierarchy and design influence interpretation?
  • How should colors and symbols be used in maps?
  • What are map scales and projections, and why do they matter?
  • What are common thematic map types and when should you use them?
  • Should your map be static or interactive?
  • How should data be classified for choropleth maps?

Objectives

  • Understand the core components of a map
  • Apply visual hierarchy principles to improve clarity
  • Choose appropriate colors, scales, and projections
  • Identify and use different thematic map types
  • Decide between static and interactive maps
  • Select appropriate classification methods for data

What is a Map?

A map is a visual representation of spatial data designed to communicate information about locations, patterns, and relationships.

A good map:-

  • Has a clear purpose
  • Accurately represents data
  • Is easy to interpret
  • Minimizes misleading elements
  • Has all the key map elements
To have every map element is necessary to convey the correct information to the audience. You do not want to mislead the audience/observer.
To have every map element is necessary to convey the correct information to the audience. You do not want to mislead the audience/observer.
Callout

Key Idea

A map is not just a picture — it is a communication tool.

Visual Hierarchy

Visual hierarchy controls what the viewer sees first, second, and last.

How to create hierarchy:

  • Size → larger elements draw attention
  • Color → brighter or contrasting colors stand out
  • Position → central elements are noticed first
  • Contrast → strong differences highlight importance

Example:

  • Main data layer → bold colors
  • Background (basemap) → muted tones
  • Labels → readable but not overpowering
Discussion

Challenge

Look at a map and ask: What do you notice first? Is that what the mapmaker intended?

Variables in Mapping

Cartographic variables (visual variables) represent data visually.

Common variables:

  • Color (hue, lightness)
  • Size
  • Shape
  • Orientation
  • Texture

Use cases:

  • Quantitative data → size, lightness
  • Categorical data → distinct colors, shapes

Colors on Maps

Color choice is critical for readability and accuracy.

Types of color schemes:

  • Sequential → low to high values (e.g., light → dark)
  • Diverging → values around a midpoint (e.g., blue–white–red)
  • Categorical → distinct groups
Examples of different color palettes with each having its own unique usage. If not used correctly, the representation of a dataset can be inaccurate.
Examples of different color palettes with each having its own unique usage. If not used correctly, the representation of a dataset can be inaccurate.

Best practices:

  • Avoid overly bright or clashing colors
  • Use colorblind-friendly palettes
  • Ensure contrast between classes
Callout

Tip

Use lighter colors for lower values and darker colors for higher values in most cases.

Scale

Map scale defines the relationship between distance on the map and distance in reality.

Types:

  • Large-scale maps → small area, high detail (e.g., city map)
  • Small-scale maps → large area, less detail (e.g., world map)

Why it matters:

  • Determines level of detail
  • Affects interpretation of patterns

Projections

A projection transforms the Earth (a sphere) onto a flat surface.

Key issue:

All projections introduce distortion in: - Area - Shape - Distance - Direction

Examples:

  • Equal-area → preserves area
  • Conformal → preserves shape
  • Equidistant → preserves distance
Examples of different projections and their names with each having its pros and cons. Each have their own usage to best represent a specific data.
Examples of different projections and their names with each having its pros and cons. Each have their own usage to best represent a specific data.

Check here to play around how Mercator Projection effects size of countries. You can move each countries across latitudes to compare its true size with another country.

Tip: Try selecting Russia and drag it all the way down to where Africa is. You will be amazed by the result!

Callout

Important

There is no “perfect” projection — only projections suited for specific purposes.

Labeling and Legends

Labels and legends help users understand your map.

Labels:

  • Clear and readable
  • Avoid overlap
  • Use hierarchy (important places larger)

Legends:

  • Explain symbols and colors
  • Keep simple and intuitive
  • Include units where necessary

Thematic Map Types

Choropleth Maps

Used to show values aggregated by regions (e.g., counties, states).

A map different states in the USA as shades of green.
A map different states in the USA as shades of green.

Best for: - Rates, ratios, normalized data (e.g., per capita)

Avoid: - Raw counts (can mislead due to area size)

Proportional Symbol Maps

Symbols sized according to data values.

A map showing varying sizes of circles as a function of population in the USA. Bigger the circle means more population.
A map showing varying sizes of circles as a function of population in the USA. Bigger the circle means more population.

Best for: - Comparing magnitudes across locations

Dot Density Maps

Dots represent occurrences or quantities.

A map showing density of a variable as a function of number of dots in an area in the USA. More dots in an area means a greater influence of that variable.
A map showing density of a variable as a function of number of dots in an area in the USA. More dots in an area means a greater influence of that variable.

Best for: - Showing distribution patterns

Non-Contiguous Cartograms

Regions resized based on data values.

A map different states in the USA clearly separated by a gap in order to avoid distortion such that recognizable shapes can be made. This looks good visually.
A map different states in the USA clearly separated by a gap in order to avoid distortion such that recognizable shapes can be made. This looks good visually.

Best for: - Emphasizing magnitude over geography

Multivariate Maps

Show multiple variables at once.

A map showing both choropleth and dot density being implement with each showcasing a different variable best suited to it.
A map showing both choropleth and dot density being implement with each showcasing a different variable best suited to it.

Best for: - Exploring relationships between variables

Static vs Interactive Maps

Static Maps:

  • Fixed image
  • Best for print and reports
  • Easier to control design

The map images that we have shown above are all examples of static maps.

Interactive (Web) Maps:

  • Allow zooming, filtering, tooltips
  • Ideal for exploration
  • Require more development effort

See here. Scroll down and you should see an interactive map of West Lafayette that we implemented in our website!

What is a Web Map?

A web map is an interactive map delivered through a browser.

Examples include: - Zoomable maps - Layer toggles - Hover/click information

Callout

Guideline

Use interactive maps when users need to explore data.
Use static maps when you want to communicate a single message clearly.

Data Classification Methods

Classification determines how numeric data is grouped into categories.

Equal Interval

  • Divides range into equal-sized bins
  • Best for evenly distributed data

Quantile

  • Each class has the same number of observations
  • Best for comparing relative rankings

Natural Breaks (Jenks)

  • Minimizes variance within classes
  • Best for clustered data

Standard Deviation

  • Shows deviation from the mean
  • Best for highlighting extremes
See how choropleth map is effected when using different classifications. Each classification as a certain use case scenario.
See how choropleth map is effected when using different classifications. Each classification as a certain use case scenario.

Choosing the Right Classification

Method Best Use Case
Equal Interval Uniform distributions
Quantile Ranking/comparison
Natural Breaks Uneven, clustered data
Standard Deviation Highlighting anomalies/outliers

Beginner Recommendation: Start with Natural Breaks (Jenks) — it usually gives the most honest visual pattern.

Discussion

Challenge

You are mapping income data with strong clustering.
Which classification method would you choose and why?

Discussion

Challenge

You have U.S. county median household income data ranging from $25k to $150k with a strong cluster around $55k–$70k.

Which classification method would you choose and why?

Final Takeaways

  • Maps are communication tools — design intentionally
  • Choose map types based on your data and message
  • Use classification methods carefully to avoid misleading results
  • Always consider your audience and purpose
Discussion
  • When might an interactive map be worse than a static map?
  • How can classification choices change the story your map tells?

Content from Getting Started with QGIS: Your First Map

Last updated on 2026-04-22 | Edit this page

Estimated time: 105 minutes

Overview

Questions

  • How do I load spatial data into QGIS?
  • How can I style and visualize data on a map?
  • How do I export a finished map?

Objectives

  • Load and explore spatial datasets
  • Create and style a simple map
  • Export a publication-ready map

Loading Your First Dataset

We will start with a simple dataset (e.g., shapefile or GeoJSON). You can find a sample dataset here

For our example, we will use airport.shp in the shapefiles folder in the link. Along with the .shp file make sure to download its supporting files to load in the .shp correctly. They are:

  • airports.cpg
  • airports.dbf
  • airports.prj
  • airports.shx

If you lead help with the interface of QGIS refer to the setup guide here.

Step 0: Add Base Map

  1. In the Browser Panel click on XYZ Tiles.
  2. You should see 2 options: Global Terrain and Open Street Map.
  3. Right click on Open Street Map and Add Layer to Project.
  4. You should now see a world map on you Map Panel.

Step 1: Add a Vector Layer

  1. Go to: Layer → Add Layer → Add Vector Layer. Since we are working with point data here.
  2. Browse to your dataset
  3. Click Add or double click on the file.
  4. (If Applicable), drag the airports layer above the Open Street Map Layer since you want the points on top of the world map.

Step 2: View the Data

  • Your data will appear on the map
  • The layer will show in the Layers Panel

Step 3: Explore Attributes

  1. Right-click the layer
  2. Click Open Attribute Table
  3. You should see 76 entries for number of airports in Alaska.

This table contains the data behind your map.

Note: Make sure to save the project at regular intervals to save your progress!

Styling Your First Map

Step 1: Open Layer Properties

  • Right-click the layer → Properties
  • Go to the Symbology tab

Step 2: Choose a Style

You can change marker size, symbols, transparency etc. Make your map visually good such that it is clear to viewer!
You can change marker size, symbols, transparency etc. Make your map visually good such that it is clear to viewer!
  • Set the Magnifier at the bottom of the Map Panel to 75%.
  • To change layer name right click on the Layer → Properties → Source → Change Layer Name and apply. This is important as later this reflects the naming of the Legend when exporting the map.

Common styling options:

  • Single Symbol → same style for all features
  • Categorized → different colors for categories
  • Graduated → color ramp for numeric data

Step 3: Apply Colors (Applicable if working with a lot more data like elevation, climate data)

  • Try downloading the elevp file in csv folder
  • Choose a color ramp
  • Adjust classes (for graduated maps)
  • Click Apply

Creating a Map Layout

To export your map, use the Print Layout.

Step 1: Open Layout

  • Go to: Project → New Print Layout
  • Give it a name

Step 2: Add Map

  • Click Add Item then Add Map
  • Draw a rectangle on the page

Step 3: Add Map Elements

Add all the necessary map items as mentioned in the previous module. Your map needs to convey information as clearly as possible.
Add all the necessary map items as mentioned in the previous module. Your map needs to convey information as clearly as possible.

Include: - Title, Add Item → Add Label → Draw Rectangle on top of map - Legend - Scale Bar - North Arrow - Add Metadata (who created the map?). Do this at the bottom of the map

Exporting Your Map

Step 1: Export

  • In layout window:
    • Export as Image
    • Export as PDF
  • You can leave the default options and click ok.

Step 2: Save Your Project

  • Always save your QGIS project file (.qgz)
This is the map we came up with for representing Airports in Alaska.
This is the map we came up with for representing Airports in Alaska.

Common Beginner Mistakes

  • Forgetting to save the project
  • Using raw counts instead of normalized data
  • Overcomplicating symbology
  • Ignoring legends and labels/naming
Callout

Keep It Simple

Start with a clear, simple map before adding complexity.

Hands-On Exercise

Task:

Create a choropleth map showing a variable of your choice.

Steps:

  1. Load a dataset
  2. Open Symbology → Graduated
  3. Choose a numeric field
  4. Apply a color ramp
  5. Export the map

Final Takeaways

  • QGIS is a powerful, free tool for spatial analysis
  • Good maps start with clean data and simple design
  • Symbology and layout are key to communication
Discussion
  • What challenges did you face while creating your first map?
  • How would you improve your map for a different audience?

Content from Accessibility in Map Design

Last updated on 2026-04-12 | Edit this page

Estimated time: 90 minutes

Overview

Questions

  • Why is accessibility important in cartography?
  • How do different types of color vision deficiency affect map reading?
  • How can we design maps that are readable for colorblind users?
  • What role do hue, saturation, and value play in accessible design?
  • Which color palettes work best in QGIS for accessible maps?

Objectives

  • Understand why accessibility matters in map design
  • Recognize major types of color vision deficiency (CVD)
  • Apply color-safe cartographic principles
  • Select accessible color palettes in QGIS
  • Test maps for readability across audiences

Why Accessibility Matters in Maps

Maps are communication tools.
If part of your audience cannot interpret your colors, your map fails to communicate effectively.

Accessible maps: - Reach wider audiences - Improve readability for everyone - Reduce misunderstanding and bias - Support inclusive science communication

Callout

Key Idea

Accessibility is not optional — it is part of good cartographic design.

Understanding Color Vision Deficiency (Colorblindness)

Color vision deficiency affects how some people distinguish colors.

Approximately: - ~8% of men - ~0.5% of women

experience some form of color vision deficiency.

Common Types of Colorblindness

1. Deuteranopia (Green-Blind)

Most common form.

People may confuse: - Green ↔︎ Red - Green ↔︎ Brown

2. Protanopia (Red-Blind)

Reduced sensitivity to red light.

People may confuse: - Red ↔︎ Green - Red appears darker

3. Tritanopia (Blue-Blind)

Rare.

People may confuse: - Blue ↔︎ Green - Yellow ↔︎ Violet

4. Achromatopsia (Monochromacy)

Very rare.

Little or no color perception.

Maps may appear nearly grayscale.

Discussion

Challenge

Why might a red-green choropleth map fail for many users?

Common Mapping Problems for Colorblind Users

Poor design choices include: - Red vs green comparisons - Similar lightness values - Too many hues with low contrast - Relying only on color to encode meaning

Example bad pairing: ❌ Red and green categories on same map

The Solution: Use Hue, Saturation, and Value Wisely

Hue

The color family (red, blue, green)

Avoid: - Red-green combinations - Blue-purple confusion in tritanopia

Saturation

Intensity or purity of color

Use: - Moderate saturation - Avoid oversaturated bright colors

Value (Lightness)

Most important for accessibility.

If colors differ in brightness, they remain distinguishable even if hue is unclear.

Callout

Best Practice

When in doubt, vary lightness more than hue.

Designing Accessible Maps

Use More Than Color

Combine color with: - Patterns - Labels - Symbols - Line styles

Example: Instead of only red vs green, use: - Blue circles - Orange squares

These palettes work well across most users.

Sequential Data:

Best choices: - Blues - Viridis - Cividis - YlGnBu

Diverging Data:

Best choices: - Blue–Orange - Purple–Green (carefully tested)

Categorical Data:

Best choices: - ColorBrewer Set2 - Dark2 - Tableau palettes

Callout

Avoid Rainbow Palettes

Rainbow color ramps create misleading emphasis and poor accessibility.

Best QGIS Palettes for Accessibility

In QGIS, use built-in ramps such as:

Excellent Choices:

  • Viridis
  • Cividis
  • Plasma
  • Inferno
  • Blues
  • ColorBrewer Safe

Avoid:

  • Rainbow
  • Red-Green diverging ramps
  • Neon saturated ramps

How to Apply Accessible Palettes in QGIS

Step 1: Open Symbology

Right-click layer → Properties → Symbology

Step 2: Choose Color Ramp

Select: - Viridis - Cividis - Blues

Step 3: Preview Contrast

Check: - Are adjacent classes clearly distinguishable? - Do values differ in brightness?

Testing Your Map for Accessibility

Always test your design.

Tools:

  • QGIS Preview Modes
  • Color Oracle
  • Coblis Color Blindness Simulator

Ask:

Can someone distinguish categories without relying only on hue?

Accessibility Beyond Colorblindness

Remember: Accessibility also includes: - Readable font sizes - Clear legends - Sufficient contrast - Screen-reader compatible web maps

Example: Good vs Bad Design

Bad:

❌ Red-green choropleth with equal brightness

Good:

✅ Blue-orange palette with strong value contrast

Accessibility Checklist for Maps

Before publishing:

Final Takeaways

Accessible maps are: - Clearer - More inclusive - More professional

Good cartography means designing for all users.

Discussion
  • Have you seen maps that were difficult to read because of color?
  • How can accessibility improve scientific communication?