Data Visualization
Learning Objectives
- Select appropriate chart types for different data and questions
- Apply principles of effective visual design
- Create time series plots that reveal trends and patterns
- Build spatial visualizations of air quality data
- Design visualizations for different audiences
Why Visualization Matters
"The greatest value of a picture is when it forces us to notice what we never expected to see." - John Tukey
Chart Selection Guide
| Data Type | Question | Recommended Chart |
|---|---|---|
| Time series | How does concentration change over time? | Line plot, area chart |
| Distribution | What is the shape of the data? | Histogram, box plot, violin plot |
| Comparison | How do groups differ? | Bar chart, grouped box plot |
| Relationship | Are two variables related? | Scatter plot, bubble chart |
| Composition | What are the parts of a whole? | Stacked bar, pie chart (if few categories) |
| Spatial | How does air quality vary by location? | Choropleth map, heat map |
| Temporal pattern | Are there diurnal/seasonal cycles? | Polar plot, calendar heat map |
Principles of Effective Visualization
Do
- Use clear, descriptive titles
- Label axes with units
- Start y-axis at zero (usually)
- Use color meaningfully
- Include data source
- Keep it simple
- Consider colorblind accessibility
Avoid
- 3D effects (distort perception)
- Excessive decoration ("chartjunk")
- Truncated axes without indication
- Too many categories/colors
- Dual y-axes (can mislead)
- Pie charts for >5 categories
- Rainbow color scales
Air Quality-Specific Visualizations
Polar Plots
Display concentration as a function of wind direction and speed. Useful for identifying pollution source directions.
Pollution Roses
Show frequency of concentrations from different wind directions. Indicates which directions bring polluted air.
Calendar Heat Maps
Display daily values in calendar format, colored by concentration. Reveals day-of-week and seasonal patterns.
Diurnal Profiles
Average concentration by hour of day. Shows traffic rush hours, photochemical production (ozone), and other temporal patterns.
Color for Air Quality
AQI Color Scale
0-50
51-100
101-150
151-200
201-300
301+
Sequential scales: For continuous data (light to dark for low to high)
Diverging scales: For data with meaningful midpoint (e.g., above/below standard)
Audience-Appropriate Design
Public/Media
- Simple, familiar chart types
- Clear takeaway message
- Minimal technical jargon
- Bold colors and labels
Policy Makers
- Comparison to standards
- Trend information
- Geographic context
- Uncertainty indication
Scientists
- Technical detail acceptable
- Statistical annotations
- Multiple panels OK
- Full methodology noted
Activity: Visualization Portfolio
Using one year of hourly PM2.5 data from a monitoring station, create:
- Time series plot: Show the full year with 7-day moving average and NAAQS annual standard line
- Distribution visualization: Histogram or box plot comparing weekend vs. weekday concentrations
- Calendar heat map: Daily averages colored by AQI category
- Diurnal profile: Average concentration by hour, with weekday/weekend comparison
- Summary graphic: Design a one-page infographic suitable for community presentation
Evaluation criteria: Appropriate chart type, clear labels, effective use of color, accurate representation, visual appeal.
Key Takeaway
Effective data visualization transforms air quality data into understanding. The best visualizations match chart type to data structure, follow design principles that enhance rather than obscure meaning, and are tailored to their audience. In an era of abundant data, the ability to create clear, compelling, and honest visualizations is an essential skill for communicating environmental information.