Visualisation?

Transcription

Visualisation?

Visualisa'on 2012 -‐ 2013 Lecture 1 Introduction
Brian Mac Namee Dublin ins1tute of Technology Applied Intelligence Research Centre Origins
This course is based heavily on a course
developed by Colman McMahon
(www.colmanmcmahon.com)
Material from multiple other online and
published sources is also used and when
this is the case full citations will be given
2011/12
2
Agenda
Preliminaries
Visualisation?
Module Overview
Books
Tools
Introductory Lecture
Short Group Exercise
2011/12
3
PRELIMINARIES
My Details
Names:
Brian Mac Namee
Email:
[email protected]
Website:
www.comp.dit.ie/bmacnamee
Office:
KE-G-026A
2011/12
5
Course Details
Course Materials: www.dit.ie/webcourses
Classes:
Thursdays 18:30 - 21:30
Room:
KE-1-004
Assignment (50%): A significant visualisation
project plus a presentation
Details will follow by week 4
Exam (50%):
2011/12
Module exam in January
6
VISUALISATION?
Drowning in Data (Again!)
The Large Hadron
Collider at CERN is
the largest scientific
instrument ever
constructed
When turned on, the
LHC generates 1GB of data per second – 15
PB per year
15 Minutes of Fame
In 2010, more than 13 million
hours of video were uploaded to
YouTube
In two months (60 days) more
video was uploaded than had
been created in the previous six
decades by the three major US television
networks (ABC, CBS, NBC)
By May 2011, more than 48 hours of video
were being uploaded per minute
"Where does all the data come from?", Geoffrey Fox, Tony Hey & Anne Trefethen
grids.ucs.indiana.edu/ptliupages/publications/Where%20does%20all%20the%20data%20come%20from%20v7.pdf
State of the Internet
www.jess3.com/the-state-of-the-internet/
Example: Taxi!
Consider a customer
ordering a taxi from
home to the airport
by phone and and
paying by credit card
What are the
opportunities for data
collection?
Example: Taxi!
- The taxi operator can record the time the
taxi was ordered, the pick-up and destination
locations, the customer’s details…
- The phone company can record the phone
call maker and receiver, the duration of the
call, …
- The credit card processor can record the
transaction amount, transaction time,
transaction type, …
- The credit card issuer can record the billing
record, the interest rate, the current
available credit …
Example: Taxi!
-  The car maintenance service can record the fuel
usage, the engine state …
-  The passenger can record the expense of the
taxi
-  The DAA can record the time the taxi drop off,
the number of passengers in the car…
-  Dublin City Council can record the passage of
the taxi across the city…
-  The taxi driver can record the time of the fare,
the type of fare, the amount of the fare…
-  The taxi company can record the path taken by
the driver, the duration of the trip…
Visualisation?
Well-designed visual representations can replace
cognitive calculations with simple perceptual
inferences and improve comprehension, memory,
and decision making
By making data more accessible and appealing,
visual representations may also help engage more
diverse audiences in exploration and analysis
The challenge is to create effective and engaging
visualizations that are appropriate to the data
"A Tour Through the Visualization Zoo", J. Heer, M. Bostock, & V. Ogievetsky, Communications of the ACM, vol. 53, no. 6, 2010
http://queue.acm.org/detail.cfm?id=1805128
Visualisation?
and decision making
Visualisation?
and decision making
Trends Over Time
Stock Charts
http://hci.stanford.edu/jheer/files/zoo/ex/time/index-chart.html
Unemployment Changes
http://hci.stanford.edu/jheer/files/zoo/ex/time/stack.html
Sparklines
Edward Tufte refers to a sparkline "as a
small intense, simple, word-sized
graphic with typographic resolution"
www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001OR
Comparisons
Box Plots
JDisk Report
JDisk Report
JDisk Report
Billion-Dollar-O-Gram
www.informationisbeautiful.net/2009/the-billion-dollar-gram/
www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization.html
Voting Data
www.usatoday.com/news/politics/presidential-poll-tracker
Age
Relationships
Risk Index
Scatter Plot Matrices
Parallell Coordinates
http://hci.stanford.edu/jheer/files/zoo/ex/stats/parallel.html
Maps
http://airomaps.nuim.ie/flexviewer/?config=Census2011.xml
AIRO
airomaps.nuim.ie/flexviewer/?config=Census2011.xml
Obesity Map USA
http://hci.stanford.edu/jheer/files/zoo/ex/maps/choropleth.html
Networks
http://visualization.geblogs.com/visualization/network/
Health InfoScape
http://visualization.geblogs.com/visualization/network/
Truthy
How #occupy
moved through
Twitter
http://truthy.indiana.edu/memedetail?id=179712&theme_id=5
Truthy
How
@barackobama
moved through
Twitter
http://truthy.indiana.edu/memedetail?id=783&resmin=45&theme_id=4
Fight Patterns, Aaron Koblin
www.aaronkoblin.com/work/flightpatterns
Hans Rosling (Legend!)
www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html
1854 London Cholera Epidemic
On the evening of August 31,
1854 Cholera broke out in
the Broad Street area of
central London
John Snow, a London doctor,
was asked to investigate
Visualisation was key to his
solution
Adapted from “The Visual Display of Quantitative Information”, Graphics Press USA, 2001
www.edwardtufte.com/tufte/books_visex
London Underground Map
Beck's London
Underground
map circa 1933
www.vam.ac.uk/vastatic/microsites/1331_modernism/highlights_19.html
London
Underground
map circa 1930
Draughtsman
Harry Beck,
thought, "If
you're going
underground,
why do you
need bother
about
geography? …
Connections are
the thing" and
designed a new
map.
An early sketch
of Harry Beck's
new map
Beck's London
Underground
map circa 1933
Visualisation?
and decision making
Visualisation?
Visualisation?
visualizations that are appropriate to the data,
the audience and the message
The Visualisation Trinity
Reader
Designer
Data
“Designing Data Visualizations”, N. Iliinsky & J. Steele, O'Reilly Media, 2011
http://shop.oreilly.com/product/0636920022060.do
The Visualisation Trinity
Reader
Informative
Data
Persuasive
Visual Art
Designer
“Designing Data Visualizations”, N. Iliinsky & J. Steele, O'Reilly Media, 2011
Ben Fry's Visualisation Steps
1.acquire
2.parse
3.filter
4.mine
5.represent
6.refine
7.interact
“Visualizing Data: Exploring and Explaining Data with the Processing Environment”, B. Fry, O'Reilly Media, 2007
1.acquire
2.parse
3.filter
4.mine
5.represent
6.refine
7.interact
Obtain the data,
whether from a file
on a disk or a source
over a network
1.acquire
2.parse
3.filter
4.mine
5.represent
6.refine
7.interact
Provide some
structure for the
data’s meaning, and
order it into
categories
1.acquire
2.parse
3.filter
4.mine
5.represent
6.refine
7.interact
Remove all but the
data of interest
1.acquire
2.parse
3.filter
4.mine
5.represent
6.refine
7.interact
Apply methods from
statistics or data mining
as a way to discern
patterns or place the data
in mathematical context
1.acquire
2.parse
3.filter
4.mine
5.represent
6.refine
7.interact
Choose a basic visual
model, such as a bar
graph, list, or tree
1.acquire
2.parse
3.filter
4.mine
5.represent
6.refine
7.interact
Improve the basic
representation to make
it clearer and more
visually engaging
1.acquire
2.parse
3.filter
4.mine
5.represent
6.refine
7.interact
Add methods for
manipulating the data or
controlling what features
are visible
Acquire: Obtain the data, whether from a file
on a disk or a source over a network
Parse:
Provide some structure for the
data’s meaning, and order it into
categories
Filter:
Remove all but the data of interest
Mine:
Apply methods from statistics or
data mining as a way to discern
patterns or place the data in
mathematical context
Represent: Choose a basic visual model, such
as a bar graph, list, or tree
Refine:
Improve the basic representation
to make it clearer and more
visually engaging
Interact:
Add methods for manipulating
the data or controlling what
features are visible
1.acquire
2.parse
3.filter
4.mine
5.represent
6.refine
7.interact
1.acquire
2.parse
3.filter
4.mine
5.represent
6.refine
7.interact
Computer
Science
1.acquire
2.parse
Graphic
Design
3.filter
4.mine
Statistics &
Data Mining
5.represent
6.refine
7.interact
HCI
Module Overview
Module Aim
The central aim of this module is help
students attain the ability to take complex
data, process it and extract value from it
through visualisation
2011/12
75
Learning Outcomes
Create and deploy successful data
visualisations using leading software tools
Design effective visualizations based on
principles from perceptual psychology,
cognitive science, graphic design and visual art
Select, formulate and integrate metaphors to suit
particular data-driven tasks
Analyse and evaluate how mental models aid in the interpretation of
complex visual displays
Demonstrate understanding of how humans perceive the world around them on a
general level and absorb complex data/information on a specific level
2011/12
Analyse and evaluate how metaphors are used to convey unfamiliar information
Content
Programming
in
Visual
Perception
& Memory
Creating
Visualizations
Using
Psychology
Graphic
Design
Visualization
Practice
Visualization
Case Studies
Visualization
Theory
Computer
Science
Creating
Visualizations
Using
Mathematics
Graph
Theory
Statistics
Module Content
Fundamentals of
visualisation
Visualisation types:
- Trends
Data characteristics
& dimensions
- Comparisons
Use of encodings
- Maps
Colour theory
Graphical perception
& communication
Interaction design
- Correlations
- Networks
- Hierarchies
- Visualising Text
- High-dimensional
data
Software
&
Tableau is an enterprise level
visualisation-based business analytics tool
R is a statistical programming language
with extensive visualisation features
Tableau Public is its little brother!
R Studio is a rich GUI interface to R
www.tableausoftware.com/products/
public
www.r-project.org
www.rstudio.org
Software
And some other bits
and pieces along the
&
way...
Tableau is an enterprise level
visualisation-based business analytics tool
R is a statistical programming language
with extensive visualisation features
Tableau Public is its little brother!
R Studio is a rich GUI interface to R
www.tableausoftware.com/products/
public
www.r-project.org
www.rstudio.org
Tableau Public Visualisations
A gallery of visualisations
created using Tableau
Public
www.tableausoftware.com/public/gallery
R Visualisations
A gallery of visualisations
created using R with
source code!
addictedtor.free.fr/graphiques/thumbs.php
Everyone Can Draw...
“Those who believe they cant draw and
those who would never assign themselves
the label of "creative" shy away from
visualization for this reason.”
“And that’s too bad - because you don’t
have to be a Rembrandt to have an
idea that can be understood by
scrawling a stick figure or two.”
Jessica Hagy, Beautiful Visualisations, p. 353-367
2011/12
Visualization of the Week
www.pinterest.com/brianmacnamee/great-visualisation-examples/
(Un)Visualization of the Week
www.pinterest.com/brianmacnamee/terrible-visualisation-examples/
Warm-up Exercise
Suggest a visualisation of Dublin's traffic
congestion consider 3 variables:
- Time
- Volume
- Geography
Instructions:
- In groups of 3
- Take 20 mins
- Sketch on board
2011/12
when ready
Remember!
and decision making
visualizations that are appropriate to the data,
the audience and the message
For Next Time
Post at least one thing to one of
- www.pinterest.com/brianmacnamee/
great-visualisation-examples/
- www.pinterest.com/brianmacnamee/
terrible-visualisation-examples/
Maybe visit the R and Tableau Public
galleries for ideas

Visualisation?

Transcription

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