GGR278 Lecture 03

Completed Notes Status

Central objective: Understand the psychological and cartographic principles of map design, focusing on visual hierarchy, colour theory, and data classification.
Key concepts:
- Visual Hierarchy (Cartography): Techniques to guide the reader's eye to the most important information first. Includes manipulating size (larger = more important), establishing a clear figure-ground relationship (darkening background to highlight study areas), and using contrast.
- Map typography and colour: Use harmonious designs (e.g., Serif for titles, Sans-serif for body). Colour schemes should match the data type (e.g., diverging colours for variations from a median, categorical colours for Qualitative Data). The HSV Colour Model is heavily utilized.
- Data Classification Methods (GIS): Techniques to bin quantitative data for mapping. Includes equal intervals (good for uniform data, bad for skewed), quantiles (equal observations per class), natural breaks (visually distinct groupings), and manual breaks (best for time-series consistency).
- Quantitative Map Types: Common mapping formats include dot maps, choropleth maps (ideal for population density), and graduated symbol maps.
Connections:
- Ties back to GGR278 Lab 1 regarding the importance of selecting the correct coordinate system (e.g., equal area for size comparison, direction-preserving for navigation) to avoid misleading representations.

#tk: put on cheat sheet. (Referring to HSV)
- Answer: HSV stands for Hue, Saturation, and Value. Hue represents the base colour/pigment, Saturation represents the intensity or purity of the colour, and Value represents the lightness or darkness.

Remember/Understand:
- What does HSV stand for in cartographic colour models?
- What is the figure-ground relationship in map design?
- Which map type is best suited for visualizing population density?
Apply/Analyze:
- If you are mapping temperature anomalies showing variations above and below a historical average, what type of colour scheme should you use and why?
- Why might using "equal intervals" for data classification be problematic if your dataset contains extreme outliers?
Evaluate/Create:
- Compare and contrast the use of quantiles versus natural breaks for mapping income distribution in a highly unequal city. Which method would provide a more accurate visual representation, and what are the trade-offs?

Data Classification Methods (GIS):
- Why it's challenging: Choosing between quantiles, natural breaks, and equal intervals can drastically change the visual narrative of the same dataset. It requires understanding the underlying statistical distribution (e.g., skewness).
- Study strategy: Create a small mock dataset with outliers and manually bin it using all three methods to visually observe how the resulting map classes differ.
Visual Hierarchy (Cartography):
- Why it's challenging: Balancing figure-ground, contrast, and harmonious typography simultaneously without cluttering the map.
- Study strategy: Review poor map examples (e.g., "cartographic design is my passion" memes) and systematically identify which visual hierarchy principles they violate.

Immediate review actions:
- Review all [COMPLETED] insertions and verify with course materials
- Address all #tk items (add HSV to cheat sheet)
- Re-read Lecture Summary and confirm key links
Practice and application:
- Answer Remember/Understand questions
- Answer Apply/Analyze questions
- Attempt Evaluate/Create questions
Deep dive study:
- Focus on Challenging Concepts (above)
- Extend atomic notes only where the new lecture adds content
Verification and integration:
- Cross-reference notes with textbook/slides
- Identify remaining gaps
- Link this lecture to prior/future lecture notes (lecture notes only; no MOCs)