# Mapping

Mapping is a function to take data space to a visual space.

# Data space

Datasets are formed of dimensions, and each dimension has a type.

The type informs effective mapping choices.

• Categorical: cats, dogs, birds
• Ordinal: small, medium, large
• Continuous: 1, 2, 3

# Visual space

AKA "Marks"

There are many:

• position
• color
• shape
• orientation
• size
• value/lightness
• motion
• texture

# Mapping strategy

• What visual encoding do we use?

• How do we compute the visual representation from the data value?

Expressiveness: Express all the data, but nothing else

Effectiveness: The importance of the attribute should match the salience of the channel

# Tips

Log scales can help with uneven distributions of data, but can make your chart harder to read.

Mapping data to size can be misleading. EX: twice as big diameter makes a circle 4 times as large in terms of area.

QTons: use shape complexity to represent a gradient of change. Texture density helps show complexity increase.

Use motion very sparingly: direction, velocity, acceleration.

Texture: Density, variation, pattern

Color: Hue, chroma, luminance

Percentages should add up to 100%

Don't truncate the Y axis in general, if you think you need to perhaps your chart should graph the rate of change or something else

Data in 3d can lead to occlusion, it's better to use mapping tools to show extra dimensions

Avoid desert fog: zooming in should show where you are in the big picture. Overview + detail and focus+context

# Detail

1. Overview first
2. Zoom and filter
3. Details on demand

# Interaction

Selection

• highlight
• low light
• filter

Explore

• panning

Abstract/elaborate

• zoom
• semantic zoom: break apart aggregates

filtering

• direct manipulation
• dynamic queries

encode

• change mapping
• layering visualization

coordination

• brushing
• linking