HCI 2018 (8/10) An Introduction to Data Visualization. Design, processes, techniques, use cases

Master on Software Engineering :: Human-Computer Interaction
Dr. Sabin-Corneliu Buraga – profs.info.uaic.ro/~busaco/
an introduction to data visualization
Human-Computer Interaction

“Above all else show the data.”
Edward R. Tufte

data characteristics: variety, volume, velocity
adapted from blog.sqlauthority.com
data
variety
data
velocity
data
volume
real-time (stream)
interval (continuous)
batch
(atomic)
sparse
(discrete)
unstructured
RDBMS
semi-
struc-
tured
XML
multi-
media
JSON
CSV
bi-
nary
KB
MBGB
TBEB
PBZB
YB

reality
Data is no longer scarce

www.worldometers.info

reality
Data is no longer scarce
we need to integrate, simplify, and capitalize on
existing information systems and
the massive amounts of data they hold
see also www.softviscollection.org/intro/a-thousand-words/

DIKW (DataInformationKnowledgeWisdom)
after S. Carpenter, 2008 & J. Liebowitz, 2012

What information visualization means?

data visualization
A class of techniques for augmenting cognition
“the use of computer-supported, interactive,
visual representations of abstract data
in order to amplify cognition”
Card, Mackinlay & Shneiderman, 1999

data visualization
A process of mapping information to visuals
“data visualization is expert storytelling” (Murray, 2013)
crafting rules that interpret data and
express its values as visual properties

informa-
tion
design
data per-
ceptuali-
zation
data
visuali-
zation
scientific
visuali-
zation

data visualization
Minimal criteria that any visualization has to fulfill
to be considered a pragmatic visualization
based on (non-visual) data
produce an image
the result must be readable and recognizable
R. Kosara, 2008 – eagereyes.org/criticism/definition-of-visualization

data visualization
Benefits – adapted from Card et al. (2009):
reducing the search for information

data visualization
enhancing the pattern detection

data visualization
encoding information in an interactive medium
www.informationisbeautiful.net/visualizations/worlds-biggest-data-breaches-hacks/

data visualization
monitoring of data/information/knowledge evolution

data visualization
Benefits:
enabling inferences
(contexts: artificial intelligence,
semantic Web)
decision making
+ predictions
(context: machine learning)
www.r2d3.us/visual-intro-to-machine-learning-part-1/

data visualization
allowing exploration of a space of parameter values
and enhancing user operations
(from data to information and knowledge)

data visualization
see Hans Rolins, “New insights on poverty”, TED 2007
www.ted.com/talks/hans_rosling_reveals_new_insights_on_poverty

data visualization
Other benefits (Chibana, 2015):
revealing trends

data visualization
placing data/information into a specific context
Universcale (Nikon): www.nikon.com/about/sp/universcale/

data visualization
providing perspectives regarding a topic/domain
V. Markovtsev, Daily commit activity on GitHub (2017): blog.sourced.tech/post/activity_hours/

data visualization
stimulating the user imagination (“what if…?”)
Anon (2018) – director: Andrew Niccol: www.imdb.com/title/tt5397194/

data visualization
Origins:
maps
used from ancient times to convey, in an abstract way,
known geographic areas + to provide orientation
later on, give insights
for creating strategies in case of hostilities

Imago Mundi – Babilon (V Century, B.C.)
www.ancient-wisdom.co.uk/cartography.htm
images provided by Wikimedia Commons

data visualization
Origins:
diagrams
see Euclid works on geometry
used in science (e.g., by Newton) to record observations,
to induct relationships,
to explicate methodology of experiments,
to classify and conceptualize phenomena

data visualization
Newton’s optics illustration – reported by Robin (1992)

data visualization
Origins:
abstract diagrams
employs non-physical information

data visualization
an early example: merchandise imports/exports
W. Playfair, The Commercial and Political Atlas (1786)

data visualization
Origins:
visual design + data graphics

design principles of information visualization (infovis)
Edward Tufte (1983, 1990, 1997)

data visualization
Origins:
statistics

exploratory (multidimensional) data analysis
Tukey (1977), Cleveland & McGill (1988)

data visualization
Origins:
scientific visualization

analytical software instruments
for scientific analysis of large datasets
McCormick & DeFanti (1987)

data visualization
Origins:
computer graphics + artificial intelligence

automatic design of visual presentations of data
Mackinlay (1986), Roth & Mattis (1990), Casner (1991)

data visualization
Origins:
human-computer interaction

new user interfaces & interactions, including animations
Robertson, Card & Mackinlay (1989), Shneiderman (1992)

data visualization
Data visualization vs. infographics
visualization is – automatically – created
that can be applied to many datasets
infographics are made – manually –
for a particular dataset, concerning a specific purpose
eagereyes.org/blog/2010/the-difference-between-infographics-and-visualization

data visualization
“The nature of the visualization
depends on which relationship is dominant.”
N. Iliinsky & J. Steele, Designing Data Visualizations, O’Reilly, 2011
Data
User
De-
sig-
ner
informative
visual art
persuasive

data visualization
Data
User
De-
sig-
ner
informative
visual art
persuasive
data/info viz. infographics
e.g., generative art
see also www.slideshare.net/busaco/generative-art-a-gentle-introduction

data visualization design

visualization modeling
Visualization
the mapping of data to visual form
that supports human interaction in a workplace
for visual sense making

Algorithm Visualizer – algo-visualizer.jasonpark.me

task
 questions
 goals
 assumptions
data
 physical data type
 conceptual data type
domain
 meta-data
 semantics
 conventions
image
 visual channel
 graphical
representations
processing
algorithms
mapping
visual encoding
data visualization overview
adapted from Jeffrey Heer, Data Visualization (2017)
courses.cs.washington.edu/courses/cse442/17au/

Stuart Card, “Information Visualization”, Human-Computer
Interaction Handbook (2nd Edition), Taylor & Francis, 2008
raw
data
data
tables
visual
structs.
views 
data
transformations
visual
mappings
view
transformations
Data Visual Form
Human Interaction

Raw Data
unfiltered/unprocessed input data

Data Tables
suitable date structures: relations + meta-data

Visual Structures
convenient graphical elements

Views
(interactive) visualizations perceived by user(s)

Raw Data – data(sets) to be visualized,
available in different – binary/textual – formats
raw
data
data
tables
visual
structs.
views 
data
transformations
visual
mappings
view
transformations
Data Visual Form
Human Interaction

Data Transformations
provides document vectors
(normalized vectors in a N-dimensional space)
could imply different filtering operations
raw
data
data
tables
visual
structs.
views 
data
transformations
visual
mappings
view
transformations
Data Visual Form
Human Interaction

Data Tables – suitable data structures:
relations (depending on considered variables) + meta-data
raw
data
data
tables
visual
structs.
views 
data
transformations
visual
mappings
view
transformations
Data Visual Form
Human Interaction

Data Tables
tables of objects + their attributes

Data Tables
example for movie visualizations:
basic objects = instances of the “film” concept
attributes (properties) for each object
for each film: title, year of release, genre type, actors,…

visualization modelingFilmID 0083658 0088846 1189073 0103767
Title Blade Runner Brazil La piel que habito Baraka
Director Ridley Scott Terry Gilliam Pedro Almodóvar Ron Fricke
Actor Harrison Ford Jonathan Pryce Antonio Banderas N/A
Actress Sean Young Kim Greist Elena Anaya N/A
Year 1982 1985 2011 1992
Length 117 142 120 96
IMDB 8.2 8.0 7.6 8.6
Metascore 89 88 70 N/A
Genre Sci-Fi, Thriller Drama, Sci-Fi Drama, Thriller Documentary
could be considered
as meta-data

Data Tables
functional (abstract) representation:
f (input variables) = output variables
TopRatedMovies (FilmID = 0062622)91
2001: A Space Odyssey is a top rated SF film

Data Tables
variables implies a scale of measurement

Data Tables
a nominal variable N is an unordered set
e.g., film titles { Blade Runner, Brazil, The Wall,…}
(in)equality operators could be used

Data Tables
an ordinal variable O is a tuple (ordered set)
e.g., film ratings < G, PG, PG-13, R >
relational operators (like “<”) could be applied

Data Tables
a quantitative variable Q is a numeric range
example: film duration [0, 400]
arithmetic operators could be performed on them

Data Tables
subtypes regarding a certain nature of visualization
quantitative spatial
2D/3D spatial variables
commonly used in scientific visualization

Visualization of 112 million-atom reactive molecular dynamics
simulation to study high-temperature oxidation of a silicon-carbide
nanoparticle on 786,432 IBM Blue Gene/Q cores
cacs.usc.edu/education/cs596.html

Data Tables
subtypes regarding a certain nature of visualization
quantitative geographical
spatial variables
that specifically represent geophysical coordinates

3D map visualizations via Google Maps API
developers.google.com/maps/documentation/

Data Tables
variable subtype concerning similarity
quantitative similarity

Data Tables
temporal variables
quantitative time
ordinal time

visualizing the transaction dynamics of crypto-currencies
whalepool.io:8080/coinorama/
cryptomaps.org

Data Tables
unstructured scale
whose only value is present or absent (e.g., an error flag)

main classes of variables involved into data visualization

Data Tables
various scale types can be altered by transformations

Quantitative variables can be mapped
by data transformations into ordinal variables
film duration [0, 400] min. → <SHORT, MEDIUM, LONG>
classes of
values

Nominal variables can be transformed to ordinal values
film titles { Gattaca, Brazil, The Wall, Minority Report }
→ < The Wall, Minority Report, Gattaca, Brazil >
sorting

Visual Mappings
creating analytic abstractions to be visualized
from spatial coordinates to surfaces
on an information 2D/3D landscape
raw
data
data
tables
visual
structs.
views 
data
transformations
visual
mappings
view
transformations
Data Visual Form
Human Interaction

various examples provided by Explained Visually
setosa.io/ev/

Visual Structures
use a vocabulary of visual elements:
spatial substrates + marks + graphical properties
raw
data
data
tables
visual
structs.
views 
data
transformations
visual
mappings
view
transformations
Data Visual Form
Human Interaction

Visual Structures
goal: the systematic mapping
of data relations onto visual form
visual encodings
could use
metaphors + idioms

Visual Structures
spatial substrate
marks
connection
enclosure
retinal properties
temporal encoding

Visual Structures
spatial substrate
empty space, as a container, can be treated as
if it had metric structure
scale type ↔ axis of space

Most important spatial axes:
U – unstructured no axis
N – nominal grid a region divided into sub-regions
O – ordinal grid sub-region ordering is significant
Q – quantitative grid a region has a metric

Axes can be linear or radial
can involve any of the various coordinate systems
for describing space
a common approach: Cartesian coordinates

Axes can be linear or radial
can involve any of the various coordinate systems
for describing space
example: using 2 orthogonal quantitative axes
to visualize movie popularity over the time
Year → QX Popularity → QY

Visual Structures
marks
visible “things” that occur in space:
points, lines, areas, volumes

types of marks
points  ⭐ ⚫
lines
areas
volumes
point & line marks take up space
and may have properties such as shape

Visual Structures
connection & enclosure
points and lines can be used to signify different
topological structures like graphs and trees,
showing relations among objects

Visual Structures
connection & enclosure
enclosure can be used for trees, contour maps,
and Venn diagrams

visualizing
a hierarchy of
entities belonging
to a class
enclosure diagram
bl.ocks.org/mbostock/4063530

Visual Structures
retinal properties
position, size, orientation, color, texture, shape

Visual Structures
retinal properties
example: using color as visual code denoting a film genre
FilmID (Genre) → P (Color)
possible conventions: Drama ⬛ Sci-Fi ⬛ War ⬛

podio.com/site/creative-routines

Visual Structures
retinal properties
position, size, orientation, color, texture, shape
aspects to be considered:
crispness, resolution, transparency, arrangement

aligned 2D positioning
unaligned 2D positioning
rendering according to value (length)
accuracy of
comparisons
various visualization techniques
could facilitate the user perception
(Cleveland & McGill, 1984) cited by Jerzy Wieczorek (2015)
civilstat.com/2015/10/statistical-graphics-and-visualization-course-materials/

Visual Structures
temporal encoding
temporal data to be visualized
versus
animation – mapping a variable into time

View Transformations
offer various views (graphical representations)
according to the user goals
raw
data
data
tables
visual
structs.
views 
data
transformations
visual
mappings
view
transformations
Data Visual Form
Human Interaction

Views
perceived by end-users
+
adjusted by graphical parameters
(position, scaling, clipping,…)
raw
data
data
tables
visual
structs.
views 
data
transformations
visual
mappings
view
transformations
Data Visual Form
Human Interaction

View—value distinction
regards how operations (transformations) are performed
at different places in the model

View—value distinction
regards how operations (transformations) are performed
at different places in the model
example:
when a point is deleted from the visualization,
has the point been deleted from the dataset?

“Information visualization is about the not just creation of
visual images, but also the interaction with those images
in the service of some problem.”
Stuart Card, 2008

The Internet Map – interactive visualization of
Internet domains usage based on data
provided by Alexa – internet-map.net
case study

Expressiveness & effectiveness
a visualization is expressive if and only if
it encodes all the data relations intended
and no other data relations are considered

incorrect correlations – mapping from data to visual form
violates the expressiveness criterion

How about the processes
concerning data visualization?

visualization processes
Acquire
Parse
Filter
Mine
Represent
Refine
Interact
according to Ben Fry, 2008

Acquire
obtain the (raw) data to be analyzed and visualized

discussion
Tamara Munzner, Visualization Analysis & Design, 2017
www.cs.ubc.ca/~tmm/talks.html
aspects of interest concerning datasets:
data types
items
attributes
relations (links)
positions
grids

discussion
data and dataset types
tables – items & attributes
graphs (networks / trees) – items (nodes), links, attributes
fields – grids + positions, attributes
geometry (spatial) – items + positions
clusters, sets, lists – items

discussion

discussion
Tamara Munzner, Visualization Analysis & Design, PacificVis 2016
dataset availability
static
versus
dynamic

discussion
aspects of interest regarding the attributes:
attribute types
categorical

discussion
attribute types
ordered
ordinal
quantitative

discussion
ordering direction
sequential, diverging, cyclic

visualization processesOpen datasets available on the Web:
Academic Torrents – academictorrents.com
Awesome Public Datasets
github.com/caesar0301/awesome-public-datasets
Awesome JSON Datasets
github.com/jdorfman/awesome-json-datasets
Common Crawl – commoncrawl.org/the-data/
DataHub – datahub.io/dataset
Kaggle Datasets – www.kaggle.com/datasets

visualization processesOpen datasets provided by companies:
GitHub Archive
www.gharchive.org
Google BigQuery Public Datasets
cloud.google.com/bigquery/public-data/
Microsoft Azure: Public Datasets
docs.microsoft.com/azure/sql-database/sql-database-public-data-sets
Registry of Open Data on Amazon Web Services
registry.opendata.aws

Parse
deliver a certain structure for the data’s meaning,
and order it into categories

Filter
keep only the data of interest
could also imply noise reduction

Mine
apply methods from statistics or data mining
to discern patterns or
place the data in mathematical context
pragmatic approaches: G. Myatt, W. Johnson,
Making Sense of Data I, II, and III, Wiley, 2007, 2009, 2011

discussion
aspects of interest concerning data processing:
{ action, target }
discover distribution
compare trends
locate outliers
browse topology

discussion
actions
analyze
search (filter)
query

discussion
actions
analyze
consume
discover (explore) vs. present (explain)
produce
annotate, record, derive,…

discussion
Tamara Munzner, Visualization Analysis & Design, PacificVis 2016
http://www.cs.ubc.ca/~tmm/talks.html
casual, social,…

discussion
actions
search (filter)
How much data matters?

discussion
actions
query
identify, compare, summarize,…

discussion
target
all data
attributes
network data
spatial data

discussion
target
all data
trends, outliers, features

discussion
target
attributes
one – e.g., distribution

discussion
target
attributes
many – dependency, correlation, similarity,…

discussion
target
network data
topology + paths

discussion
target
spatial data
shape

Represent
choose a (set of) visual model(s)

discussion
aspects of interest concerning data visualization:
encode
arrange
+
map

discussion
encode
arrange
express
separate
order
align
use

discussion
encode
map
from categorical and
ordered attributes
to visual representations
color: hue, saturation
size, angle, curvature,…
shape
motion

Refine
improve the basic visual representation
to make it clearer and more visually engaging
applying techniques of perceptual optimization

Interact
add methods for manipulating the data
or controlling what features are visible

discussion
aspects of interest:
manipulate – change, select, navigate
facet – juxtapose, partition, superimpose
reduce – filter, aggregate, embed

Dr. Sabin-Corneliu Buraga – profs.info.uaic.ro/~busaco/Tamara Munzner (2017)

mash-ups
Your Life on Earth (BBC)
www.bbc.com/earth/story/20141016-your-life-on-earth
case study

adopting an iterative approach (Fry, 2008)

How about
a taxonomy
of information
visualization?
bl.ocks.org/mbostock

visualization taxonomy
Simple visual structures
direct reading
1-variable [X]:
lists, 1D scatterplots, pie charts, distributions, box plots,…

also stydy eagereyes.org/techniques/pie-charts
commons.wikimedia.org/wiki/File:World_Oil_Reserves_by_Country-pie_chart.svg

direct reading
2-variable [XY]:
2D object charts (histograms) – for continuous values
2D scatterplots – in the case of discrete values

2D visualizations generated by Google Data Studio
www.google.com/analytics/data-studio/
example

direct reading
3-variable
[XYR]: retinal scatterplots, Kohonen diagrams
[(XY)Z]: information landscapes, information surfaces
[XYZ]: 3D scatterplots

studying the neural network behavior
– here, data classification – in the Web browser
playground.tensorflow.org

retinal scatterplot – here, a heat map
secviz.org/content/user-behavior-a-heatmap
visualizing user-behavior (Y) over time (X); color (retinal
variable R) is used to indicate the intensity of the activity

direct reading
4-variable [XYZR]:
3D retinal scatterplots, 3D topographies

a 3D visualization of tectonic topography
(context: Vrancea region’s seismicity)

articulated reading
n-variable
[XYRn-2]: 2D retinal scatterplots
[XYZRn-1]: 3D retinal scatterplots

a 3D visualization of the relations between text translations
with Tensor Flow (Google) – projector.tensorflow.org

articulated reading
n-variable
[XYRn-2]: 2D retinal scatterplots
[XYZRn-1]: 3D retinal scatterplots
may present
a barrier of
perception

scatterplot of attractiveness versus age, colored by gender
O’Connor & Biewald, 2009

articulated reading
trees (used for hierarchical data):
node and link trees, enclosure trees, hyperbolic trees,
TreeMaps, cone trees

treemap of terms occurring in geography titles and
comments for 6 selected scene types
T. Segaran & J. Hammerbacher (Eds.), Beautiful Data, O’Reilly, 2009

articulated reading
networks

Flight Patterns – using air traffic GPS data to visualize
commercial flight patterns and density (Koblin, 2005)
www.aaronkoblin.com/work/flightpatterns/
explore also graphofthings.org

articulated reading
time
aspects of interest:
discrete vs. continuous values
moments vs. intervals

articulated reading
time
typical solutions:
calendar, timeline, alternative views

Here Is…
hereistoday.com
case study

Definitive Daft Punk
themaninblue.com/writing/perspective/2011/05/12/
case study

Composed visual structures
single-axis composition [XYn]:
permutation matrices, parallel coordinates

a parallel coordinate view of a firewall log file
context: security visualization – secviz.org

double-axis composition [XY]: graphs
ontology visualization
owlgred.lumii.lv

recursive composition
2D in 2D [(XY)XY]:
scatterplot matrices, hierarchical axes,…

visualizing correlated information
www.andreykurenkov.com/writing/fun-visualizations-of-stackoverflow/

marks in 2D [(XY)R]:
stick figures, color icons, shape coding, Keim spirals,…

100 Years with the San Francisco Symphony by Adobe
thewhyaxis.info/music/
case study

3D in 3D [(XYZ)XYZ]:
(virtual) worlds within (virtual) worlds

holographic visualizations – HoloLens (Microsoft)
www.microsoft.com/hololens/developers

Interactive visual structures
dynamic queries
imagery (“magic”) lens
overview + detail
brushing and linking
extract & compare
attribute explorer (multi-faceted)

Poem Viewer – imagery lens for visualizing corpora
ovii.oerc.ox.ac.uk/PoemVis/
case study

brushing
performing a data selection task (e.g., click/tap and drag)
linking
highlighting the matching data samples in the other views
for a demo, visit mbostock.github.io/protovis/ex/brush.html

Focus + context attention-reactive
visual abstraction
data-driven methods:
filtering, selective aggregation

Gapminder – www.gapminder.org/tools/
case study

Focus + context attention-reactive
visual abstraction
view-based methods:
micro-macro readings, highlighting, visual transfer
functions, perspective distortion, alternate geometries

micro-macro readings
presenting large quantities of data at high densities
goal: to see the bigger picture
medium.com/@dominikus/the-superpower-of-interactive-datavis-a-micro-macro-view-4d027e3bdc71
Selfiexploratory – selfiecity.net/selfiexploratory/

remarks
Each visualization project (solution)
has unique requirements
“If each data set is different, the point of visualization
is to expose that fascinating aspect of the data
and make it self-evident.” – Stephen Fry

remarks
Pragmatic advice: apply KISS principle
less detail can actually convey more information
beware of chartjunk
Tufte (1983)

conclusions
chartjunk using a large area and a lot of “ink”
(many symbols and lines)
to show only 5 hard-to-read numbers
real-life examples: junkcharts.typepad.com

remarks
Pragmatic advice: use proper labeling
provide explanations, legends, visual conventions, etc.

concluzii
History of Rock Music
www.svds.com/rockandroll/

remarks
Pragmatic advice: know your audience
different types of visualizations
for different (types of) users

concluzii
3D visualization of human body (available in the Web browser)
BioDigital Human – human.biodigital.com

remarci
“Never trust summary statistics alone;
always visualize your data” – Alberto Cairo (2016)
the datasets have the same statistics features
(e.g., standard deviation, Pearson correlation),
but their visualizations are different
details in J. Matejka, G. Fitzmaurice, “Same Stats, Different Graphs: Generating Datasets
with Varied Appearance and Identical Statistics through Simulated Annealing”,
ACM SIGCHI Conference on Human Factors in Computing Systems, 2017
www.autodeskresearch.com/publications/samestats

tools
JavaScript libraries for 2D visualizations:
D3.js (Data-Driven Documents) – d3js.org
D3.js extensions – github.com/wbkd/awesome-d3
Leaflet (mobile-friendly interactive maps) – leafletjs.com
Raphaël (vector graphics) – raphaeljs.com
Recline (building data apps) – okfnlabs.org/recline/
Sigma (graph drawing) – sigmajs.org
Vis (handling large amounts of dynamic data) – visjs.org

instrumente la nivel de client
JavaScript libraries for 3D visualizations:
Cesium (3D globes and maps) – cesiumjs.org
PhiloGL – www.senchalabs.org/philogl/
Three.js – threejs.org

visualization taxonomyremark:
visual representations of
data could be also physical
(i.e. generated by a 3D printer)
www.3dprintmath.com

conclusions
Main purposes of information visualization:
exploratory visualization
discover patterns, trends, or sub-problems in a data set
explanatory visualization
transmitting information or a point of view to the user

conclusions
case study
Food Poisoning Outbreaks (Ruslan Kamolov, 2015)

conclusions
VOWL (Visual Notation for OWL Ontologies)
+ interactive visualization tools for desktop and Web
vowl.visualdataweb.org
case study

conclusions
How You Will Die (Nathan Yau, 2016) – a visualization experiment
on life expectancy based on World Health Organization data
flowingdata.com/2016/01/19/how-you-will-die/
case study

conclusions
case study
explaining an election law by using data visualization
RAW Graphs – github.com/densitydesign/raw

online resources
Data + Design – infoactive.co/data-design
Information is Beautiful – www.informationisbeautiful.net
Awesome Visualization Research
github.com/mathisonian/awesome-visualization-research
Data Visualization Catalogue – datavizcatalogue.com
Data Viz Done Right – www.datavizdoneright.com
Flowing Data – flowingdata.com

“Conclusion”
data visualization
definitions, classification, methods, examples
datavizproject.com

next episode:
affective factors & persuasive technologies

HCI 2018 (8/10) An Introduction to Data Visualization. Design, processes, techniques, use cases

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HCI 2018 (8/10) An Introduction to Data Visualization. Design, processes, techniques, use cases