Visualisation - techniques, interaction dynamics, big data

Post-‐academic
course
Big
Data

Post-‐academic
course
Big
Data

Joris Klerkx
Research Expert, PhD.
joris.klerkx@cs.kuleuven.be
@jkofmsk
Erik Duval
Professor
erik.duval@cs.kuleuven.be
@erikduval
Visualisatie - deel 2
Big Data - module 3
IVPV - Instituut voor PermanenteVorming
28-05-2015

To research, design, create and evaluate useful tools
that augment the human intellect
By
‘augmen+ng
human
intellect’
we
mean
increasing
the
capability
of
a
man
to
approach

a
complex
problem
situa+on,
to
gain
comprehension
to
suit
his
particular
needs,
and
to

derive
solu+ons
to
problems
(Douglas
Engelbart,
1962).
Augment group - HCI research lab
Dept. Computerwetenschappen
KU Leuven
https://augmenthuman.wordpress.com
Music
Technology Enhanced
Learning
e-health
Research 2.0
Health
Media
(Consumption)
Technology Enhanced Learning
Science 2.0

https://augmenthuman.wordpress.com/

http://eng.kuleuven.be/datavislab/

Humans have advanced perceptual abilities
Humans have little short term memory
Externalize data by using interactive, visual encodings
Our brains makes us extremely good at recognizing visual patterns
Our brains remember relatively little of what we perceive

Before visualisation…
Data
- structure
time, hierarchy, network, 1D, 2D, nD, …
- questions
where, when, how often, …
- audience
domain & visualisation expertise, …

Audience vs purpose
Explore Explain
Lay person
Domain experts

Visual mapping
Encode data characteristics into visual form
Each mark (point, line, area,…) represents a data element
Think about relationships between elements (position)
“Simplicity is the ultimate sophistication.”
Leonardo daVinci

J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions On Graphics, 5(2):110–141, 1986.

Many ways to visualize
simple dataset

Some ways are more
effective than others,
depending on your
question at hand

http://gravyanecdote.com/visual-analytics/which-chart-should-you-use-to-show-this-data/

Information Visualisation is the use
of interactive visual
representations to amplify
cognition [Card. et. al]
Definition

Information visualization
signiﬁcantly improves insight
generations and user productivity
Accelerates time to insight

Today
• Tour through the visualisation zoo
• Interactive Dynamics
• Is there too much data to visualize?
• Tools?

A tour through the
visualization zoo
Heer,
J.,
Bostock,
M.,
&
Ogievetsjy,
V.
(2010,
May).
A
Tour
through
the
VisualizaMon
Zoo
-‐
A
survey
of
powerful
visualisaMon

techniques,
from
the
obvious
to
the
obscure.
ACM
Graphics
,
8
(5),
hTps://queue.acm.org/detail.cfm?id=1805128

Time-series data
Sets of values changing over time

Relative changes in time-series data
An index chart is an interactive line chart that shows percentage changes
for a collection of time-series data based on a selected index point.

http://homes.cs.washington.edu/~jheer//ﬁles/zoo/ex/time/index-chart.html

Aggregated time-series data
A stream graph visually summates time-series values
http://hci.stanford.edu/jheer/ﬁles/zoo/ex/time/stack.html

Time-series data
Small multiples: showing each series in its own chart.
http://homes.cs.washington.edu/~jheer//ﬁles/zoo/ex/time/multiples.html

https://augmenthuman.wordpress.com/portfolio/visualizing-gaming-trends-on-steam/

http://bl.ocks.org/oyyd/859fafc8122977a3afd6
Day-Hours Heatmap

http://www.trulia.com/vis/tru247/

The horizon graph is a technique for increasing the data density
of a time-series view while preserving resolution.
Sizing the Horizon: The Effects of Chart Size and Layering on the Graphical Perception of Time Series Visualizations
Jeffrey Heer, Nicholas Kong, Maneesh Agrawala
ACM Human Factors in Computing Systems (CHI), 2009. pp. 1303 - 1312. Best Paper Award
PDF (442K)

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

http://www.perceptualedge.com/blog/?p=390
A year’s worth of stock prices for 50 separate stocks

http://neuralengr.com/asifr/journals/
Publication counts over time

Statistical Distributions
Reveal how a set of numbers is distributed and thus help an
analyst better understand the statistical properties of the data

Histograms shows the prevalence of values grouped into bins
Histogram sliders

A stem-and-leaf plot bins numbers according to the first significant digit, and
then stacks the values within each bin by the second significant digit.
http://homes.cs.washington.edu/~jheer//files/zoo/ex/stats/stem-and-leaf.html

http://thesocietypages.org/graphicsociology/2012/11/18/stem-and-leaf-diagrams/

Box-and-whisker plot, which can convey statistical
features such as the mean, median, quartile boundaries,
or extreme outliers.
http://admin-apps.webofknowledge.com/JCR/help/h_boxplot.html

Statistical distribution of data
The Q-Q plot compares two probability distributions by graphing
their quantiles against each other.
http://hci.stanford.edu/jheer/ﬁles/zoo/ex/stats/qqplot.html

Representing
relationships/
correlations among
multiple variables.
A scatter plot matrix
(SPLOM) uses small
multiples of scatter
plots showing a set of
pairwise relations
among variables
http://homes.cs.washington.edu/~jheer//ﬁles/zoo/ex/stats/splom.html
graphing every pair
of variables in two
dimensions

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

Maps
Mostly based upon a cartographic projection: a mathematical
function that maps the three-dimensional geometry of the
Earth to a two-dimensional image
Other maps knowingly distort or abstract geographic features
to tell a richer story or highlight speciﬁc data.
http://geoawesomeness.com/topics/web-maps/
http://unfoldingmaps.org
http://ffffound.com/home/tillnm/found/

• Google Maps - Well rounded, established mapping solution, especially for non-developers
to get a basic map on the web, along with all the powers that Google is (in)famous for.
• OpenLayers - For situations when other mapping frameworks can’t solve your spatial
analysis problems.
• Leaﬂet - Currently, easily the best mapping framework for general mapping purposes,
especially if you don’t need the additional services that MapBox or CartoDB provide.
• MapBox - Fast growing and market changing mapping solution for when you want more
control over map styling or have a need for services that others are not providing, such as
detailed satellite images, geocoding or directions.
• Unfolding - to create interactive maps and geovisualizations in Processing and Java
http://www.toptal.com/web/the-roadmap-to-roadmaps-a-survey-of-the-best-online-mapping-tools
Typical Mapping Tools

http://en.wikipedia.org/wiki/John_Snow_(physician)#/media/File:Snow-cholera-map-1.jpg
John Snow, Cholera, 1854

A ﬂow map depicts the movement of a quantity in space and (implicitly)
in time.

Charles Joseph Minard, 1781-1870
Napoleon’s Russian Campaign

https://lirias.kuleuven.be/bitstream/123456789/461689/1/paper234_camera-ready.pdf

http://homes.cs.washington.edu/~jheer//ﬁles/zoo/ex/maps/choropleth.html
Choropleth Maps use color encoding to show aggregated data by
geographical areas

Choropleth maps using 2 categories
http://www.colorado.edu/geography/gcraft/notes/cartocom/section6.html

Choropleth maps using different ranging methods
Equal Range

User Deﬁned Range

Quantile Range

http://www.nieuwsblad.be/cnt/dmf20150603_01712465

Graduated Symbol Maps places symbols/glyphs over an
underlying map
http://homes.cs.washington.edu/~jheer//ﬁles/zoo/ex/maps/symbol.html
Graduated Symbol Maps places symbols over an underlying map

A cartogram distorts the shape of geographic regions so that the
area directly encodes a data variable
http://homes.cs.washington.edu/~jheer//ﬁles/zoo/ex/maps/cartogram.html

http://www.viewsoftheworld.net/?p=2541

http://cartogram.cs.arizona.edu/index.html

There is no perfect map
How could you actually compare sizes of different continents and countries?
https://www.youtube.com/watch?v=KUF_Ckv8HbE

Hierachies
Most data can be organised into natural hierarchies
Special visualization techniques exist to leverage hierarchical
structure, allowing rapid multiscale inferences: micro-observations of
individual elements and macro-observations of large groups

A node-link diagram with Reingold-Tilford algorithm
http://hci.stanford.edu/jheer/ﬁles/zoo/ex/hierarchies/tree.html

The dendrogram (or cluster) algorithm places leaf nodes of the tree at the
same level
Polar coordinates instead of cartesian coordinates
http://homes.cs.washington.edu/~jheer//ﬁles/zoo/ex/hierarchies/cluster-radial.html

Indented tree layout
http://homes.cs.washington.edu/~jheer//ﬁles/zoo/ex/hierarchies/indent.html

The adjacency diagram is a space-ﬁlling variant of the node-link
diagram; rather than drawing a link between parent and child in the
hierarchy, nodes are drawn as solid areas (either arcs or bars), and their
placement relative to adjacent nodes reveals their position in the
hierarchy
http://homes.cs.washington.edu/~jheer//ﬁles/zoo/ex/hierarchies/icicle.html

The sunburst layout, shown in ﬁgure 4E, is equivalent to the
icicle layout, but in polar coordinates.
http://homes.cs.washington.edu/~jheer//ﬁles/zoo/ex/hierarchies/sunburst.html

Enclosure diagrams use containment rather than adjacency to
represent the hierarchy
Squarified Treemaps - space filling
http://homes.cs.washington.edu/~jheer//files/zoo/ex/hierarchies/treemap.html

Circle-packing layout
Non - space ﬁlling
http://hci.stanford.edu/jheer/ﬁles/zoo/ex/hierarchies/pack.html

Networks
Non-hierarchical relationships between data points

https://queue.acm.org/detail.cfm?id=1805128
A force-directed layout where nodes are charged particles that repel each
other, and links are dampened springs that pull related nodes together.
http://hci.stanford.edu/jheer/ﬁles/zoo/ex/networks/force.html

http://homes.cs.washington.edu/~jheer//ﬁles/zoo/ex/networks/arc.html
An arc diagram uses a one-dimensional layout of nodes, with
circular arcs to represent links.

http://www.youtube.com/watch?v=wQpTM7ASc-w
T. Nagel, M. Maitan, E. Duval,A.Vande Moere, J. Klerkx, K. Kloeckl, and C. Ratti.Touching transport - a case study on visualizing metropolitan public
transit on interactive tabletops. In AVI2014: 12th ACM International Working Conference on AdvancedVisual Interfaces, pages 281–288, 2014.

http://homes.cs.washington.edu/~jheer//ﬁles/zoo/ex/networks/matrix.html
Adjacency
matrix: each
value in row i
and column j in
the matrix
corresponds to
the link from
node i to node j.

Chord diagrams show directed relationships among a
group of entities. Relationship can be quantitative or
binary
http://bl.ocks.org/mbostock/4062006
Ye L, Amberg J, Chapman D et al. 2013 Fish gut
microbiota analysis differentiates physiology and
behavior of invasive Asian carp and indigenous American
fish The ISME journal

A Circo
http://circos.ca/guide/tables/img/guide-table-large.png

Choices of representation (e.g., matrix- diagram) and
interactive parameterization (e.g., default sort order) can be
critical to unearthing data quality issues that can otherwise
undermine accurate analysis.

Facebook friends
Heer&Kandel,2012.Interactiveanalysisofbigdata.ManagesXRDS:Crossroads,TheACM
MagazineforStudents-BigData,19(1),50-54.http://dl.acm.org/citation.cfm?
id=2331042.2331058

Many(!!) more techniques are out there
e.g. check the visual index at  
https://github.com/mbostock/d3/wiki/Gallery

http://www.visual-literacy.org/periodic_table/periodic_table.html
https://eagereyes.org/blog/2009/
visualization-is-not-periodic-html

When“static”visualizations are
not enough
Too many datapoints or variables
True understanding and insight generation require
interaction
Data is exploratory in nature

Interaction
Should contribute to successful analytic dialogues!
Support exploration at the rate of human thought!

Interactive Dynamics enables analysts
to explore large data sets (Task types)
Heer,
J.,
&
Shneiderman,
B.
(2012,
February).
InteracMve
Dynamics
for
Visual
Analysis.
Magazine
Queue

-‐
Microprocessors
,
10
(2),
p.
30.
hTp://queue.acm.org/detail.cfm?id=2146416

• Data & View Speciﬁcation controls
• View Manipulation
• Process & Provenance

Custom programming of a specific visualization component
Specific tools that use a chart typology (eg excel)
Data-flow graphs deconstruct the visualization process into fine-grained set of
operators (data import, transformation, layout, coloring, etc
Visualization construction using formal grammars (eg ggplot2, R, Prototvis, etc)
Data & View Specification controls serve to visually &
interactively encode data

Custom programming
“hacking”
Processing demo

google spreadsheets
Tools that use a chart typology (eg excel)

Excel for mac 2015
Tools that use a chart typology (eg excel)

http://www-969.ibm.com/software/analytics/manyeyes/

Cfr. http://gravyanecdote.com/visual-analytics/which-chart-should-you-use-to-show-
this-data/
Data-ﬂow graphs deconstruct the visualization process into ﬁne-grained set of
operators (data import, transformation, layout, coloring, etc

Let me know how this works for you ;)

• Categorical/ordinal data
• radio buttons, checkboxes, scrollable lists,
hierachies, search boxes (with autocomplete)
• Ordinal, quantitative, and temporal data
• a standard slider (for a single threshold value) or a
range slider (for specifying multiple endpoints).
Filtering allows rapid and reversible
exploration of data subsets

Heer,
J.,
&
Shneiderman,
B.
(2012,
February).
InteracMve
Dynamics
for
Visual
Analysis.
Magazine
Queue
-‐
Microprocessors
,
10
(2),
p.

30.

http://doug.cs.kuleuven.be/wittgenstein2/

http://localhost:63342/DataAnalysis/report/dre-userpaths_ﬁlters.html

http://www.trulia.com/vis/tru247/
Query controls can be further augmented with
visualizations of their own

Query controls can be further augmented with
visualizations of their own

Sorting enables popping up of
trends, clusters,…
• Choices in a toolbar
• Clicks on the header in a table
• Can be complicated in the case of multiple view
displays

https://augmenthuman.wordpress.com/portfolio/emotions-in-the-classroom/

Select items to hightlight, ﬁlter or
manipulate them
• Mouse clicks, free-form lassos, area cursors
(‘brushes’), mouse hovering, etc
• depends on the device
• Various expressive power
• selections of a collection of items
• selections as queries over the data (eg drawing
rectangle -> range query)

https://perswww.kuleuven.be/~u0074988/mapc/
Selection queries
https://augmenthuman.wordpress.com/portfolio/mapc_interactive_dashboard/

Select by slope and tolerance
Heer,
J.,
&
Shneiderman,
B.
(2012,
February).
InteracMve
Dynamics
for
Visual
Analysis.
Magazine
Queue
-‐
Microprocessors
,
10
(2),
p.

30.

Mapping mouse gestures to query patterns
Heer,
J.,
&
Shneiderman,
B.
(2012,
February).
InteracMve
Dynamics
for
Visual
Analysis.
Magazine
Queue
-‐
Microprocessors
,
10
(2),
p.

30.

Navigate to examine high-
mede patterns & low-level detail
• Overview ﬁrst, zoom & ﬁlter, then details-on-demand
• Start with what you know, then grow
• Search, show context, expand on demand.
• Focus + Context
• Semantic Zooming
• Magical lenses

$
f con-
cation
n this
of the
zoom
what
isible,
e user
on the
more
rectly
3, for
ber of
dmark
Figure 4: Setting of the evaluation.
B. Vandeputte, E. Duval, and J. Klerkx. Interactive sensemaking in authorship networks. Proceedings of the ACM International
Conference on Interactive Tabletops and Surfaces, ITS11, pp. 246–247, 2011.
Overview ﬁrst, zoom and ﬁlter, details on demand

B. Vandeputte, E. Duval, and J. Klerkx. Applying design principles in authorship networks-a case study. In CHI EA’12:
Proceedings of the 2012 ACM annual conference extended abstracts on Human Factors in Computing Systems, pages 741–
744, 2012. (https://www.youtube.com/watch?v=R5CeTEejdBA)
Start with what you know, then grow
Search, show context, expand on demand

http://sanfrancisco.crimespotting.org
Overview ﬁrst, zoom and ﬁlter, details on demand
Viewport on the data: zooming & panning controls

Focus+context
Large hierarchies
https://philogb.github.io/jit/static/v20/Jit/Examples/Hypertree/example1.html
Limited screen space

Focus + Context
Semantic Zooming
Heer,
J.,
&
Shneiderman,
B.
(2012,
February).
InteracMve
Dynamics
for
Visual
Analysis.
Magazine
Queue
-‐
Microprocessors
,
10
(2),
p.

30.

Magical Lenses
C. Tominski, S. Gladisch, U. Kister, R. Dachselt, and H. Schumann. A Survey on Interactive Lenses in Visualization. EuroVis State-of-the-Art Reports, Swansea, UK, Eurographics
Association, 2014.

C. Tominski, S. Gladisch, U. Kister, R. Dachselt, and H. Schumann. A Survey on Interactive Lenses in
Visualization. EuroVis State-of-the-Art Reports, Swansea, UK, Eurographics Association, 2014.

Coordinate views for linked, multi-
dimensional exploration
Enables seeing data from different perspectives
Multiple views can facilitate comparison

http://square.github.io/crossﬁlter/

https://perswww.kuleuven.be/~u0074988/mapc/
https://augmenthuman.wordpress.com/portfolio/mapc_interactive_dashboard/

Small multiples
https://queue.acm.org/detail.cfm?id=2146416
Heer,
J.,
&
Shneiderman,
B.
(2012,
February).
InteracMve
Dynamics
for
Visual
Analysis.
Magazine
Queue
-‐
Microprocessors
,
10
(2),
p.

30.

https://augmenthuman.wordpress.com/portfolio/an-interactive-visualization-of-butterﬂy-observations/
https://www.youtube.com/watch?v=NvqJi8NwLCI

Organize multiple windows & workspaces
• Tiled approaches (different widgets) allows to see
all information and selectors at once, minimizing
distracting scrolling or window operations, while
enabling analysts to concentrate on extracting and
reporting insights.
• Layout organization tools will become decisive
factors in creating effective user experience

Orchestrate attention and mentally integrate patterns among views
Heer,
J.,
&
Shneiderman,
B.
(2012,
February).
InteracMve
Dynamics
for
Visual
Analysis.
Magazine
Queue
-‐
Microprocessors
,
10
(2),
p.

30.

Orchestrating visualizations over various devices

Record analysis histories for
revisitation, review and sharing

Heer,
J.,
&
Shneiderman,
B.
(2012,
February).
InteracMve
Dynamics
for
Visual
Analysis.
Magazine
Queue
-‐
Microprocessors
,
10
(2),
p.
30.

Annotate patterns to document
ﬁndings
Record, organize, and communicate insights gained
during visual exploration

Freeform graphical annotations without explicit tie to the
underlying data
Data-aware annotations
Heer,
J.,
&
Shneiderman,
B.
(2012,
February).
InteracMve
Dynamics
for
Visual
Analysis.
Magazine
Queue
-‐
Microprocessors
,
10
(2),
p.

30.

Share views and annotations to
enable collaboration
Real-world analysis is very much a social process
that may involve multiple interpretations,
discussion, and dissemination of results.

https://www.youtube.com/watch?v=g91_EBxVh3E

http://www-969.ibm.com/software/analytics/manyeyes/ https://www.tableau.com/products
Many other tools!

Guide users through analysis tasks or
stories
• Incorporate guided analytics to lead analysts
through workﬂows for common tasks.
• Narrative visualization

Artikel - tekst
Visualisatie
Narrative
visualization

Interval 1 Interval 2 Interval 3 Interval 4

Interactive Dynamics: Summary
Heer,
J.,
&
Shneiderman,
B.
(2012,
February).
InteracMve
Dynamics
for
Visual
Analysis.
Magazine

Queue
-‐
Microprocessors
,
10
(2),
p.
30.

How much “big” data
can we visualise?
Petabytes? Exabytes? Yottabytes?!
Direct visualisation of big data ‘in the raw’ is
probably not so effective

Is there too much data to
visualize?
You only have so many pixels on a screen
Each pixel, one data point
E.g Typical hdtv screen contains 1920 * 1080 = 2.073.600 pixels
prysm.com

Multiple data sources with varied data types
“Diverse” data
I talk geoJSON
i talk custom
xml
i talk apache
logs

millions of records
“Tall” data

Cluttered displays
Heer, J. & Kandel, S. (2012), Interactive Analysis of Big Data, XRDS, 19 (1)

Cluttered displays
Binned density scatterplot
Hexagonal instead of rectangular

Perceptual scalability of a display
should be limited by the chosen
resolution of the data, not the numbers
of records (Heer & Kandel, 2012)

Multi-variate data with 100s to 1000s of variables
“Wide” data

Visualizations might help reveal multidimensional patterns
Use the power of the machine to ﬁnd a proxy in the data that
predicts the selected variables
Depending on their speciﬁc questions, domain experts might
select a subset of variables they are interested in

http://www.perceptualedge.com/blog/?p=2046
In this day of so-called Big Data,
organizations are scrambling to
implement new software and
hardware to increase the amount of
data that they collect and store.
In so doing they are unwittingly
making it harder to ﬁnd the needles of
useful information in the rapidly
growing mounds of hay.
If you don’t know how to
differentiate signals from noise,
adding more noise only makes
matters worse.
Monday, June 1st, 2015

When we rely on data for decision making, how do we tell
what qualiﬁes as a signal and what is merely noise?
In and of itself, data is neither. It is merely a collection of
facts.
When a fact is true, useful, and deserves a response, only
then is it a signal. When it isn’t, it’s noise. It’s that simple  
(Few, http://www.perceptualedge.com/blog/?p=2046, 2015)

Avoid the All-You-Can-Eat buffet! (Ben Fry)

Visual-information Seeking Mantra
Visual Analytics Mantra
Overview First, Zoom and Filter, Details-on-Demand
Analyze First, Show the Important, Zoom and Analyse
Further, Details-on-Demand
Ben Shneiderman
Daniel Keim

Interactive analysis tools can help
quell “big data” by augmenting our
ability to manipulate and reason
about it (Heer & Kandel, 2012)

In the face of a data deluge, what remains
relatively constant is our own cognitive ability
to make sense of the data and reach reliable,
informed decisions. Big data is of little help
when decoupled from sound judgment.
J. Heer

Large data is a wild beast and you’d better treat it
with the right tools. Visualization is a great tool to
convey what automatic data analysis algorithms
discover. And often it is a very challenging task!
What the algorithms spit is exciting new complex
data that requires creativity and knowledge as well.
E. Bertini
http://fellinlovewithdata.com/guides/how-do-you-visualize-too-much-data

▪ Interactive visualization of a million items 
J.D. Fekete and C. Plaisant.
▪ Random Sampling as a Clutter Reduction Technique to Facilitate Interactive Visualisation of Large
Datasets 
G. Ellis (part of it in collab. with yours truly).
▪ A Sampling Approach to Deal with Cluttered Information Visualizations 
E. Bertini (my phd thesis).
▪ TreeJuxtaposer: Scalable Tree Comparison using Focus+Context with Guaranteed Visibility 
T. Munzner, F. Guimbretiere, S. Tasiran, L. Zhang, and Y. Zhou.
▪ Beyond visual acuity: the perceptual scalability of information visualizations for large displays 
B. Yost, Y. Haciahmetoglu, and C. North.
▪ Extreme visualization: squeezing a billion records into a million pixels 
B. Shneiderman.
▪ Measuring Data Abstraction Quality in Multiresolution Visualization 
Q. Cui, M. O. Ward, E. A. Rundensteiner, and J. Yang.
• imMens: Real-time Visual Querying of Big Data
Zhicheng Liu, Biye Jiang, Jeffrey Heer
Some papers about big data
visualisation
http://fellinlovewithdata.com/guides/how-do-you-visualize-too-much-data

http://selection.datavisualization.ch

Don’t start from the tool
Start from your data
and the questions about
your data

https://www.youtube.com/watch?v=vc1bq0qIKoA

The need for multidisciplinary teams
http://holisticsofa.com/
The Data Visualization Pyramid

POINTERS
• http://wearecolorblind.com/articles/quick-tips/
• http://infosthetics.com
• http://www.visualcomplexity.com/vc/
• http://bestario.org/research/remap
• ... (a lot more online! )

BOOKS
• “Readings in InformationVisualization: UsingVision toThink”,
Card, S et al.
• “Signals”,“Now i see”,“Show Me the Numbers”, Few, S.
• “Beautiful Evidence”,Tufte, E.
• “InformationVisualization. Perception for design”,Ware, C.
• BeautifulVisualization: Looking at Data through the Eyes of
Experts (Theory in Practice): Julie Steele, Noah Iliinsky

?
Joris Klerkx
Research Expert, PhD.
joris.klerkx@cs.kuleuven.be
@jkofmsk
https://augmenthuman.wordpress.com

Visualisation - techniques, interaction dynamics, big data

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Visualisation - techniques, interaction dynamics, big data