part one! biological basis of information design introduction to - PowerPoint PPT Presentation


 part one! biological basis of information design introduction to what visualisations can do for us information graphics purpose - what is your (specific) goal? & data visualisation data - what kind of data do you have? 4.5 visual dimensions - representing data visually max van kleek (@emax) University of Oxford ƒor Open Data Institute Short Course communication - deception and bad infographics

what are the goals of visualisation? how do you choose a visual representation for data? how do you evaluate a visualisation? key objectives

theory

praxis ben shneiderman. University of Maryland ben fry , MIT Media Lab/fathom.info

what is the goal of of information design? 1. to help people to 
 understand & think about data. 2. to communciate facts

framebuffer(s) display typical computer architecture

framebuffer(s) display parietal lobe + frontal cortex occipital lobe v3 v2 v1 v5 v4 eye / iris / fovea spatial orientation retina focus of attention visual cortex (sensing) eye control, (pattern detection) perceptual fusion

serial / highly parallel deliberative processing “attention- focused” occipital lobe parietal lobe + frontal cortex visual v3 processing v2 v1 v5 access to routines v4 long term optimised for memory purpose spatial orientation visual cortex focus of attention (pattern detection) eye control, perceptual fusion

dorsal stream V5 “ where / how ” pathway V3 V1 V2 occipital lobe V4 ventral stream “ what ” pathway object and person recognition

London Cholera Outbreak John Snow, 1854 31 Aug 1854 - 127 deaths in 3 da 10 Sept - 500 deaths End of outbreak - 616 deaths “There was one significant anomaly - none of the monks in the adjacent monastery contracted cholera. Investigation showed that this was not an anomaly, but further evidence, for they drank only beer, which they brewed themselves.” The Story of London's Most Terrifying Epidemic – and How it Changed Science, Cities and the Modern World .

steady state plasma glucose (response) Type II Type II Type I Sir Harold Himsworth 19 May 1905 – 1 November 1993 glucose area under curve insulin area under curve

so how do we choose appropriate visual representations for our data?

1. purpose understand educate engage entertain persuade communicate

2. data types { x 1, x 2, x 3, x 4, ... } x i is... {1, 200, 5, 6, ... } integral {1.0, 2.0, 1.2, 4, ... } fixed point {‘ a ’, ‘ b ’, ‘12 c ’, ‘ d ’ ...} alpha(-numeric) {20%, 30%, 1%, 5% ...} fractional { ...} categorical , , , , { f( ) , q( ) g( ) , ...} , , , relational understanding objective - help the user to understand 
 relationships among the elements of the set

2. data types { x 1, x 2, x 3, x 4, ... } x i is... {1, 200, 5, 6, ... } integral {1.0, 2.0, 1.2, 4, ... } fixed point {‘ a ’, ‘ b ’, ‘12 c ’, ‘ d ’ ...} alpha(-numeric) {20%, 30%, 1%, 5% ...} fractional { ...} categorical , , , , { f( ) , q( ) g( ) ...} , , , , relational understanding objective - help the user to understand 
 relationships among the elements of the set

10" 10" 9" 9" 8" 8" 7" 7" sorted 6" 6" 5" 5" 4" 4" 3" 3" 2" 2" 4 1" 1" 0" 4 0" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 9 10" 4.5" histogram 9" 4" 7 8" 3.5" 7" 4 3" 6" 2.5" 4 5" 2" 4" 1.5" 9 3" 1" 2" 7 0.5" 1" 0" 7 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 6 10" 9" 8" median (middle) 7" box & whisker 6" 5" 4" 3" 2" 1" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" extrema ordering significant order insignificant (whiskers) Quartiles

so you have a dataset... it’s probably multivariate { x 1 , x 2 , x 3 , x 4 , ... } x = if these are observations of the [same] of object(s) over time [ ] a 1 a 2 a 3 “time series” b 1 b 2 b 3 if these are observations of different x = ... things at a single point in time , , “population” t 1 t 2 t 3 if these are observations of different things at a different points in time “observations” understanding objective(s) : 1. relations among dimensions of each sample (multivariate) 
 2. relations among samples/observations (multidimensional)

each dimension’s variability 10" 9" 8" 7" understanding elements 6" 10" 5" 9" 4" 8" 3" 7" 2" 4 3 1" 6" 0" 5" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 4 4 4" lines 3" relationship between dimensions 2" 9 5 7" 1" 0" 6" 7 5 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 5" clustered bar 4 0 4" 3" 4 3 elements & their totals 2" 16" 9 6 1" 14" 0" 12" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 7 5 10" scatter ??? 8" 7 5 16" 6" 14" 6 4 4" 12" 2" 10" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 8" stacked bar 6" 4" 2" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" stacked area

10" 9" 8" 7" 6" 5" 4" 3" 2" 1" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 16" 14" 12" 10" 8" 6" 4" 2" 0" 1" 2" 3" 4" 5" 6" 7" 8" 9" 10"

3. Visual Dimensions

data dimension visual dimension type types relative location position centrality integral shape saturation fixed point colour opacity width size alpha(-numeric) height orientation ordinals colour 
 stroke pattern, thickness categorical opacity texture relational movement juxtaposition ...

position only have up to 3 spatial dimensions to work with

orientation range-limited symmetry properties of the geometry pop-out

orientation popouts using multiple dimensions 2D color/ 1D colour 1D orientation orientation

Using colour for continuous values

Using colour for continuous values http://www.colormunki.com/game/huetest_kiosk problem 1: No natural ordering

Using colour for continuous values protanopia deuteranopia Protanopia affects 8% of males, 0.5% females tritanopia of Northern European ancestry problem 2: colour sensitivity

Using colour for continuous values problem 3: yellow is special

Using colour for continuous values problem 4: Details: overemphasised or obscured hue ‘borders’ overemphasise small changes, hue ‘middles’ blend potentially important details

Using colour for continuous values problem 5: pop out can drown out

multivariate relational data: hierarchical tree hyperbolic tree

multivariate relational data: hierarchical treemap

multivariate relational data: non-hierarchical chord diagram lattice venn diagram parallel sets

time series (animation) aaron koblin - flight patterns

time series (static) - small multiples

charles joseph minard napoleon’s march to moscow (1869) 1) size of the army 2) advancing / retreating at each location multivariate 3) divisions 4) path taken by each how many dimensions? 5) temperature 6) dates of waypoints

TGV E.J. Marey La méthode graphique (1885)

In conclusion Designing effective infographics is about effectively conveying or facilitating an understanding of relationships in data offloading “heavy lifting” to our trained neural circuitry While still an art, many design principles grounded in usability can provide guidance: natural mappings, simplicity, & avoiding distortion

communicating through infographics: visual + statistical sleight of hand to mislead the audience

1. Barchart baseline fail

1. Barchart baseline fail

2. Perspective and measurement fail

2. “Huge differences” fail 100 85 70 55 40 25 10 1960 1970 1980 1990 using area (2 dimensions) to

using area to represent one dimensi

Quiz: How does this fail?

Chernoff Faces