# Encoding Values and Designing Figures

### The effective display of quantitative information involves two fundamental challenges

1. Best medium (table/graph type).

2. Designing graph to convey message.

### Encoding Values in Graphs

Data graphics ~ verbal language

The rules of graphical communication are rarely arbitrary

Usually based on an understanding of visual perception:

1. How we see,

2. Visually encode information for easy and accurate decoding by audience.

### Most Graphs Are ...

• 2D

• 2 axes (horizontal, x; vertical, y)

• Points

• Lines

• Bars

• Boxes

### Points

• Pinpoint specific location on graph,

• Encode values associated with scale on each axis.

• Shapes (dots, squares), filled/open.

• YES: dot plot, strip plot, scatter plot.

• NO: time series.

### Lines

• Lines connect points.

• Encode values by location.

• Ends of line segments mark values.

• Overall shape of data values.

• Slope is meaningful.

• YES: time series.

### Points & Lines

• Overall shape & individual values.

• Multiple time series.

• Lines should only connect points that are related/connected.

### Bars

• Encode values 2 ways:

• location of end point,
• length/height
• Easy to compare lengths to determine relative magnitude.

• Discrete values, not connected.

### Bars

• Bars can be used for connected values (e.g., time series) when:

• you want to compare individual values at specific times, or
• histogram

## Time series

Values change through time (e.g., \$ per mo.)

## Ranking

Values ordered by size (e.g., sales, population)

## Part-to-Whole

Values represent parts/proportions of a whole (e.g., relative cover, regional sales)

## Deviation

Values represent the difference between two sets of values (e.g., income vs outgoing)

## Distribution

Counts of values per interval/bin (e.g., number of trees per size class)

## Correlation

Comparison of two paired sets of values (e.g., height vs weight)

## Geospatial

Values displayed on a map (e.g., population per city, species richness per site)

## Nominal Comparison

Values are compared for unordered categories (e.g., regions, fruit type)