Geographic Data Science - Lecture III

(Geo-)Visualization

Dani Arribas-Bel

Today

Visualization
- What and why
- History
- Examples
Geovisualization
- What
- “A map for everyone”
- Dangers of geovisualization

Visualization

“Data graphics visually display measured quantities by means of the combined use of points, lines, a coordinate system, numbers, symbols, words, shading, and color.”

The Visual Display of Quantitative Information. Edward R. Tufte.

[Source]

A bit of history

Maps –> Data Maps (XVIIth.C.) –> Time series (1786) –> Scatter plots

Surprisingly recent: 1750-1800 approx. (much later than many other advances in math and stats!)
William Playfair’s “linear arithmetic”: encode/replace numbers in tables into visual representations.
Other relevant names throughout history: Lambert, Minard, Marey.

Visualization

The Visual Display of Quantitative Information. Edward R. Tufte.

By encoding information visually, they allow to present large amounts of numbers in a meaninful way.
If well made, visualizations provide leads into the processes underlying the graphic.

Historical examples

[Source] XVIIIth. Cent. - Pytometrie by J. H Lambert	[Source] Playfair’s bar chart in The Commercial and Political Atlas (1786)
[Source] Lambert - Evaporation rate against temperature, 1769	Minard - Napoleon army map (XIXth. Cent.)

Geovisualization

Tufte (1983)

“The most extensive data maps […] place millions of bits of information on a single page before our eyes. No other method for the display of statistical information is so powerful”

MacEachren (1994)

“Geographic visualization can be defined as the use of concrete visual representations –whether on paper or through computer displays or other media–to make spatial contexts and problems visible, so as to engage the most powerful human information processing abilities, those associated with vision.”

GeoVisualization

End goal is not to replace the human in the loop, but to augment her/him.
Augmentation here comes through engaging the pattern recognition capabilities that our brain inherently has.
Combines:
- Traditional maps
- Statistical maps
- Statistical devices of other kind (charts, scatter plots, etc.)
Different roles in the analysis process…

A map for everyone

Maps can fulfill several needs

Depending on which one we want to stress, the best map will look very different

MacEachren & Kraak (1997) identify three main dimensions:

Knowledge of what is being plotted
Target audience
Degree of interactivity

MacEachren & Kraak (1997) map cube

[Source]

Un/known: fast and slow maps

Fast maps [Source]

Slow maps [Source]

Audience: easy and hard maps

Easy map

[Source] Map of same-sex marriage in the US, 2015

Hard map [Source]

Interaction: one or many maps in one

Static map

Interactive map

Dangers of GeoVisualization

How to lie with maps

The human brain is so good a picking up patterns…

… that it finds them even where they don’t exist!

Patternicity (Shermer, 2008) The tendency to find meaningful patterns in meaningless noise

Apophenia (Konrad, 1958) The experience of seeing patterns or connections in random or meaningless data

Twitter clusters

How to be truthful with maps

“With great power comes great responsibility” Statistics to the rescue!!!

Complement and enhance visuals
Help disentangling true from spurious patterns (a.k.a. identifying the “Jesus on the toast”)
Reciprocity: GeoVis can also enhance statistics and make them more useful

Statistics for Twitter clusters

Recapitulation

Visualization of statistical data is a fairly recent phenomenon.
Its power comes from engaging and augmenting the human in the loop, rather than replacing her/him.
Its power can be misused, but there are methods to limit this risk.

Geographic Data Science’18 by Dani Arribas-Bel is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.