Visualisation for Digital Humanities

Thomas Wielfaert

KU Leuven - Quantitative Lexicology and Variational Linguistics

Digital Humanities Spring Event

29 April 2015

Why?

Increasingly bigger data sets: 3 V's

source: gtcorp.com

The fourth research paradigm (Jim Gray)

Rather than finding data to test a hypothesis, find a hypothesis that can be tested on the existing data.

  1. Empirical: describing natural phenomena
  2. Theoretical: laws i.e. Newton, Kepler, ...
  3. Computational: modelling, simulation
  4. Data exploration: data processing a.k.a. Big Data

Black box algorithms

Visual Analytics can reveal properties of algorithms that were not detected before.

source: visual-analytics.eu

What is a good visualisation?

What is not?

Caveats of visual perception

Hermann grid

Checker shadow illusion

Properties of good visualisations

  • Visual perception theory: research field proper
    • Even 'simple' standard visualisations such as boxplots and histograms can easily mislead. (see Lem et al. 2013)

Semiology of graphics (Bertin)

Reminder: types of variables

  1. Numerical (quantitative): 23.4; 0.2; 5e-1
  2. Categorical (nominal): man/woman/X
  3. Ordinal (ordered): low/medium/high

 

Ranking of perceptual tasks

Mackinlay (1986)

colorbrewer2.org

Never ever pick colors yourself!

Different kinds of data

  • Geo(spatial) data: maps
  • Multivariate/high-dimensional data: line, point, region-based
  • Graphs/networks

source: D3.js gallery

Get inspired

Don't try to reinvent the wheel...

 

Good starting point: D3.js Gallery

https://github.com/mbostock/d3/wiki/Gallery

So what is a good visualisation?

Interplay between several factors:

  • Type and quality of the data
  • Human cognition (visual perception theory)
  • Usability (interactions fast enough?)

Evaluation:

  • Interaction between programmer/designer and user/domain expert
  • Discussions during visualisation work shops

Is this for me?

Empirical DH fields

  • Computational linguistics
    • Corpus linguistics
    • Also literature
  • Social sciences
  • Philosophy
  • Musicology
  • ....

How?

Use commercial software

i.e. Tableau (also free version available)

Use commercial software

Pro:

  • You can get started immediately; no need to program yourself.

 

Cons:

  • Step into a closed ecosystem
  • For technical support you will have to pay
  • Depend on a company rather than a community for new features

Reuse what is freely available

  • Tools written by others​
    • Specialised, only one type of visualisation
    • Reusability not always optimal (i.e. convert your own data)
    • Adding extra features difficult or impossible
  • R libraries (i.e. ggplot, lattice)
    • +: Statistical analysis output can be directly fed to a visualisation library
    • -: Not the most suitable tool for textual data
    • -: Limited interactivity

Google's Magic Table (load unsafe scripts to see this)

​Program it yourself

Programming languages designed for the job:

  1. Processing: based on Java, runs as a stand-alone program on your computer.
  2. D3.js (Data Driven Documents): Javascript, works in any modern browser; easy to publish online; the future.

 

Bottom line: extremely flexible and versatile, but comes with a steep learning curve.

Middle ground

R data frame

D3.js

Google Charts

...

R libraries:

  • rCharts
  • networkD3
  • googleVis
  • ...

Some DH visualisations

DoubleTreeJS (Chris Culy)

Slash/A (Todorova and Chinkina)

Very basic introduction to D3.js

Today 14:45-15:45, MSI 02.18

 

Hands on: bring your own laptop or befriend someone with a laptop with Chrome or Firefox installed (pretty please).

 

Topics:

  • The very basics of D3
  • Import a dataset (!)
  • Make a basic visualisation of the data set

 

Comments? Questions?

 

thomas.wielfaert@kuleuven.be

References

Bertin, J., (1967). Sémiologie graphique, Mouton/Gauthier-Villars, Paris.

Collins, J.P., (2009). The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research, Redmond, Washington.

Lem, S., Onghena, P., Verschaffel, L., Van Dooren, W. (2013). On the misinterpretation of histograms and box plots. Educational Psychology: An International Journal of Experimental Educational Psychology, 33 (2), 155-174.

Mackinlay, J.D., (1986). Automating the Design of Graphical Presentations of Relational Information, ACM Transactions on Graphics, 5, 110-141.

 

 

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