EXAM Flashcards
What is Data Visualisation
mapping data (variables) to aesthetics (visual elements like size, shape, colour) to simplify complexity, aid understanding and enable comparison
Data Visualisation goal
Make data comprehensible and visually engaging
types of Data
quantitate
qualitative
quantitate types
continuous (time, temp)
discrete (number kids)
qualitative
categorical
country, gender, category
Colour Scales
Continuous: Light → Dark (e.g., temperature)
Qualitative: Distinct colors (e.g., countries)
Diverging: Midpoint reference (e.g., red-white-blue for political leaning)
Design Principles
Think about audience and purpose: Is it informative or persuasive?
Maintain consistency and proportionality
Edward Tufte’s rules:
Data-ink ratio: Maximize data, reduce unnecessary graphics
Avoid chartjunk: No decorative clutter
Critical Theory – Data Feminism
Visualisations are rhetorical, not neutral
Emotions and positionality are valid
Strong Objectivity: Rejects neutrality; embraces context and experience
Mapping & Spatial Humanities
why use maps?
To show spatial patterns; useful when “where?” matters.
Types of Maps:
Raster: Pixel-based (e.g., satellite images)
Vector: Points, lines, polygons (e.g., city boundaries)
Geographic Data:
Points: Coordinates (e.g., landmarks)
Lines: Routes (e.g., roads)
Polygons: Areas (e.g., countries)
Data Maps
Quantitative; visualizes numbers
Deep Maps:
Qualitative + narrative; includes multimedia/context
Networks
A system of nodes (things) and edges (connections)
Examples:
People (nodes), friendships (edges)
Authors (nodes), books (edges)
edge weight
Strength or frequency of connection
edge direction
incoming vs outgoing
degree
number of connections per node
betweenness centrality
importance based on being between others
Accessibility in Data Visualisation
Common Issues:
Incompatible with screen readers
Poor color contrast
Not keyboard navigable
Color-Blindness:
Avoid red-green. Use blue-orange. Label data directly.
Ethics in Data Visualisation
Fair Representation:
Avoid misleading visuals
Bias:
All design choices carry rhetorical weight
Ethical Practices:
Be transparent
Avoid chartjunk
Show full context
Be culturally aware
Sensitivity in Data Visualisation
Cultural Considerations:
Colors have different meanings (e.g., red = danger vs luck)
Icons and symbols may not translate cross-culturally
Critiquing a Visualisation
Checklist:
Story & Chart Type: Main message and appropriate visual
Labels: Clear and informative?
Aesthetics: Are visual attributes used meaningfully?
Ethics & Accessibility: Inclusive and fair?
Color Scheme: Correct for data type (categorical/ordinal/sequential)?
