VISUALISATION, a Primer:

Basics, Techniques &

Some GuidelinesCAGATAY TURKAY,

giCentre, City University London

DigitalCatapult, 02 June 2015

Who?

• Lecturer in Applied Data Science, City Univ. London

• @ giCentre

• PhD @ VisGroup at Univ. of Bergen, Norway

Research on …

Methods for

Interactive VisualData Analysis

Today …

VISUALISATION

Basics – visual channels, theories

Techniques

Guidelines & Rules of Thumb

Examples

Visualisation ? “Computer-based visualization systems

provide visual representations of datasets

designed to help people carry out tasks more

effectively.” [Tamara Munzner, 2014]

“The use of computer-generated, interactive, visual representations of data to amplify cognition”[Card, Mackinlay, & Shneiderman 1999]

tasks are

COMPLEX, data are

HETEROGENOUS,

IMPERFECT

This is a straight line that “connects the dots”, i.e., best fits the points.

Small exercise

Think of straight lines for these four sets

VISUALISATION helps

Visualise & Communicate along the data analysis process!

Some classics - Deaths from cholera, John Snow 1854

https://www.udel.edu/johnmack/frec682/cholera/cholera2.html

More classics - Napoleon's Russian campaign of 1812, by Joseph Minard 1869

Alternatives by Kraak

http://www.nytimes.com/interactive/2012/05/17/business/dealbook/how-the-facebook-offering-compares.html

http://www.theguardian.com/politics/ng-interactive/2015/apr/20/election-2015-

constituency-map

Becoming interactive and complex

http://www.oecdbetterlifeindex.org/

VISUALISATION BASICS

Basic visual elements

From Visualization Analysis & Design by T. Munzner, 2014

VISUAL MARKS

i.e., primitives

VISUAL CHANNELS

i.e., how they look

Basic visual elements in action

VISUAL MARK

lines (thick ones)VISUAL CHANNEL

length (of bars)

http://rrubyperlundich.blogspot.co.uk/2012_09_01_archive.html

Basic visual elements in action

http://www.nytimes.com/interactive/2012/05/17/business/dealbook/how-the-facebook-offering-compares.html

VISUAL MARK

points

VISUAL CHANNELS

position x and y

colour

Area (redundant)

Visual channels don’t work equally well!

Visualization Analysis & Design by T. Munzner, 2014

MOST EFFECTIVE

LEAST EFFECTIVE

How many times larger is (b) from (a) ?

(a) (b) (a) (b)

Grounded on perception theory

“On the psychophysical law”, Stevens, 1957 From Visualization Analysis & Design by T. Munzner, 2014

Color Basics

what/where for categorical

Hue

how-much for ordered

Luminance or Saturation

Transparency ?

useful for creating visual layers

but cannot combine with luminance or saturation

Luminance

Saturation

Hue

From Visualization Analysis & Design by T. Munzner, 2014

http://wiki.openstreetmap.org/

Use carefully crafted colour maps!

a QUICK TOUR through

VISUALISATION

TECHNIQUES

+

Text

Material by Tamara Munzner, http://www.cs.ubc.ca/~tmm/talks.html#minicourse14

(Some)Visualization Options

For Tabular Data

Scatterplot Matrix - nD relations

http://homes.cs.washington.edu/~jheer//files/zoo/ex/stats/splom.html

Parallel Coordinates

http://homes.cs.washington.edu/~jheer//files/zoo/ex/stats/parallel.html

(Some)Visualization Options

For Temporal Data

Xkcd, Movie Narrative Charts

The usual Suspect: Line (Function) Plot

Small multiples of time series

http://homes.cs.washington.edu/~jheer//files/zoo/ex/time/multiples.html

• Less vertical space, achieves higher density

• Good for comparison, aligned by time

Horizon graphs

Reijner, 2008

Calendar-like heatmap

http://scottcheng.github.io/d3js-101

Spiral Graph -- for cyclic data

(Some)Visualization Options

For Geographical Data

Hierarchical Cartograms

Slingsby 2011Output Area Categories

Necklace Maps

Internet users in Africa [Speckmann et al. 2011] Gender Pay Gap [Speckmann et al. 2011]

(Some)Visualization Options

For Network Data

Radial layouts

http://bl.ocks.org/mbostock/4063550

Matrix representation

Characters in

Les Misérables

http://bost.ocks.org/mike/miserables/

Tree maps

http://bl.ocks.org/mbostock/4063582

WHAT ELSE?

Glyphs

http://theinformationcapital.com/project/islington-has-issues/

Sketchiness to indicate imprecise results

INTERACTION !!(where exciting things happen)

Basic -- Linking & Brushing

Re-partition

Slingsby et al. IEEE TVCG, 2009https://vimeo.com/channels/gicentre/9870257

Reorderable Glyphs for Exploring Crowd-sourced

Subjective Survey Classification

http://photoassessment.org/results/

Re-order

http://caleydo.github.io/projects/lineup/

SOME

RECOMMENDATIONS

Use visual variables effectively

Less accurate due to angle/area !

Slide adapted from Tamara Munzner

Minimize distractions (a.k.a. data:ink ratio)

Nature Methods Points of view: Axes, ticks and grids, Krzywinski, Martin, 2013

Consider the semantics of data

Slide adapted from Tamara Munzner

Always careful with animation !!

Robertson, 2008

Alternative:

Visualize change

explicitly

Use abstract representations

… and overlay information carefully

Never forget the tasks !!!

Thank you !

Cagatay.Turkay.1@city.ac.uk

@cagatay_turkay

http://staff.city.ac.uk/cagatay.turkay.1/