Slope charts can be an alternative to scatter plots
CS-GY 6313 - Information Visualization - Fall 2025
NYU Tandon School of Engineering
2025-09-12
Learning Objectives:
Special thanks to:
Prof. Enrico Bertini NYU Tandon School of Engineering
Prof. Jeff Heer University of Washington
This course builds upon their foundational contributions to visualization education and research.
The central question: Given data and a task, which visualization technique will be most effective?
Understanding your data:
The type determines suitable encodings
Design decisions:
Chart selection: The attribute types can guide the chart selection!
Bar Chart:
Line Chart:
Scatterplot:
Matrix Chart:
Symbol Map:
Purpose: Compare quantities across categories
Data Types: - Categorical/Ordinal + Quantitative - Example: Sales by product category
Best For: - Rankings and comparisons - Part-to-whole relationships
Purpose: Show trends and changes over time
Data Types: - Temporal + Quantitative - Example: Stock prices over months
Best For: - Trends and patterns - Multiple series comparison
Purpose: Explore relationships between variables
Data Types: - Quantitative + Quantitative - Example: Height vs. weight
Best For: - Correlation analysis - Outlier detection - Pattern recognition
Purpose: Compare across two categorical dimensions
Data Types: - Categorical + Categorical + Quantitative - Example: Sales by month and region
Best For: - Cross-tabulations - Correlation matrices - Dense data display
Purpose: Show spatial distribution of data
Data Types: - Spatial coordinates + Quantitative - Example: Population by city
Best For: - Geographic patterns - Location-based analysis - Spatial clustering
Question: Is it possible to create different representations of the same data? It is. However, some representations might not follow the best design guidelines
Slope charts can be an alternative to scatter plots
Scenario 1: Monthly sales data for different product categories
Answer: Line chart (multiple series) - shows trends over time by category
Scenario 2: Customer satisfaction ratings across departments
Answer: Bar chart - compares quantitative values across categories
Scenario 3: Relationship between advertising spend and sales revenue
Answer: Scatter plot - explores correlation between two quantitative variables
Marks:
Channels:
Expressiveness: The visual encodings must show all and only the facts in the data.
Effectiveness: Information should be readily perceived
Problem: Ordered visual channel (line) with unordered data
Problem: Arbitrary ordering implies non-existent relationship
Which value is larger?
Which value is larger?
Result: Length comparison is faster and more accurate than color comparison
Ranking of visual channels by data type
Key principle: Use the most effective channel for your most important data
More Effective:
Position encoding enables accurate comparison
Less Effective:
Area and angle are harder to compare accurately
How do you choose the right chart?
Ask these questions:
Decision Tree:
Rule of thumb: Start simple, add complexity only when needed
Scale: A function mapping data domain to visual range
Data Domain → Scale Function → Visual Range
Linear Scale: - Absolute differences - Additive changes - Most common choice
Logarithmic Scale: - Relative differences - Multiplicative changes - Wide data ranges
Truncated Axis: Exaggerates small differences
Zero Baseline: Accurate magnitude representation
Log scales instead of zero baseline: - Data spans multiple orders of magnitude - Ratios matter more than absolute values
Truncated axes for line charts: - Small changes in large values - When trend matters more than magnitude
Context determines when breaking rules is acceptable
Scenario: Visualizing country populations
Data range: 1,000 (Vatican) to 1.4 billion (China)
Questions: 1. What scale would you choose? 2. How would you handle the extreme range? 3. What alternatives might you consider?
Consider: Log scale vs. filtering vs. grouping
The Visualization Design Process:
Workflow: Question → Transform → Encode → Scale → Iterate
Expressiveness: Match visual properties to data properties
Effectiveness: Use the most effective encoding for your most important data
Transformation: Prepare data to answer your specific questions
Scales: Choose scales that honestly represent relationships
Iteration: Test your designs with real users when possible
Question 1: For comparing sales across product categories, which encoding is most effective?
Answer: B) Bar length (position along common scale)
Why? Position is the most effective visual channel for quantitative comparison.
Question 2: You have website traffic data spanning 5 years. For showing long-term growth trends, you should:
Answer: B) Use a log scale if growth is exponential
Why? Log scales reveal multiplicative relationships and growth rates.
For next class: - Read Tufte Chapter 1-2 (Graphical Excellence & Integrity) - Practice chart selection with your own datasets - Complete Exercise 3: Chart design and encoding alternatives
Lab activities: - Build all five chart types with real data - Compare encoding choices for the same dataset - Apply the chart selection framework to new scenarios
Looking ahead: - Interactive visualization techniques - Advanced visual encodings and multi-dimensional data - Design critique and evaluation methods
Think about: - What visualization challenges do you face in your work/research? - How might these principles apply to your domain? - What questions do you have about applying these techniques?
Next class: Interactive visualization techniques and advanced encodings