Andy Kirk Malofiej 20 Presentation

Visualisation
Consultant

http://www.quoteaustininsurance.com/images/Consultant.jpg

Visualisation
Designer

http://gizmodo.com/5792960/paul-allen-dishes-out-gossip-on-bill-gates-and-his-yacht-on-60-minutes

Visualisation
Trainer

http://cathybretag.blogspot.com/2010/10/first-time-out-reflection-on-my-first.html

Hebden Bridge

London
2.5 hours

Hebden Bridge

2.5 mins

London

Curse of Knowledge

Absence of Knowledge

Surprise the novice,
get the expert to nod

Mirko Lorenz

Showcase of
data visualisation
techniques for thriving
in the age of
big data

Showcase of
data visualisation
techniques for thriving
in the age of
data that has thousands of
records and is quite complex
and makes life difficult

It’s not a technology problem;
it’s a people problem.

Aron Pilhofer (on data journalism)
Editor of Interactive News, New York Times

What is Big Data?

Why does it matter to you?

Context
Google Insights: “Infographic”

http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q

Context
Google Insights: “Big Data”

http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q

We are capturing,
creating and mobilising
unbelievable amounts of data
at an unbelievable rate.

And it is increasing.

Volume

Variety

Velocity

http://radar.oreilly.com/2012/01/what-is-big-data.html

Yahoo! C.O.R.E. Data Visualization | Periscopic

http://www.flickr.com/photos/visualizeyahoo/sets/72157629000570607/

http://www.zimbio.com/Ted+Danson/articles/13/TV+DVD+Cheers+Final+Season+4+DVD+Set

Running the Numbers II: Portraits of global mass culture | Chris Jordan

http://www.chrisjordan.com/gallery/rtn2/#gyre2

Visualisation should be
recognised as a discovery tool.

Manuel Lima

http://www.visualcomplexity.com/vc/blog/?p=644

Peer review wars | Nigel Hawtin

http://www.flickr.com/photos/nhawtin/5243787538/in/photostream/lightbox/

http://starwarsaficionado.blogspot.com/2011/12/classic-image-its-worse.html

http://v2.centralstory.com/about/squiggle/

1. Be clear about the visualisation’s
purpose and parameters

EXPLORE: facilitate reasoning of data EXPLAIN: convey information to others
Analysis Monitor/Signals
Familiarise with data Answer questions/Inform
Support graphical calculation Learn/Increase knowledge
Find patterns/Find no patterns Contextualise data
Discover questions Present arguments
Interact Assist with decisions
Shape opinion/Persuade

Emphasize issues
Tell a story
Inspire
Shock/Make an impact
Enlighten
Change behaviour
Entertain/fun
Art/Aesthetic pleasure

Jet Tracker | Wall Street Journal

http://projects.wsj.com/jettracker/

Brief? Open, strict, helpful, unhelpful
Format? Static, interactive, video
Pressures? Timescales, editorial
Audience size? One, group, www
Audience type? Domain experts, general
Resolution? Headlines, clusters, look-up
Rules? Structure, layout, style, colour
Capabilities? Design, technical, technology
People? Individual, team, collaboration

Analyst Politician Computer scientist Journalist
Researcher Designer Cognitive scientist
http://www.jasonnazar.com/2008/09/23/10-lessons-startups-can-learn-from-superheros/

2. Identify and develop questions
about the problem context

What questions are you hoping to
answer through this visualisation?

What stories should users/readers
be able to derive from this
visualisation?

Just Landed | Jer Thorp

http://blog.blprnt.com/blog/blprnt/just-landed-processing-twitter-metacarta-hidden-data

3. Acquire, prepare and explore
your data to begin familiarisation

Transforming Transforming Pre-prod.
Acquisition Examination Consolidating
for quality for purpose visualisation

System download
API
Web scrape
Scanned documents


Is it fit for purpose?
Is it complete?
Identify data types


Missing values
Erroneous values
Duplicates
Uncommon characters
Freak outliers?


Parsing
Merging
Normalisation
Conversion eg. Codify free-text

Inspired by Kim Rees’ talk at 2011 Wolfram Summit - http://www.wolframdatasummit.org/2011/attendee/presentations/Rees.pptx


Full resolution
Filter/Exclude (records & variables)
Aggregate/Roll-up
Sample
Statistics

Inspired by Kim Rees’ talk at 2011 Wolfram Summit - http://www.wolframdatasummit.org/2011/attendee/presentations/Rees.pptx

Yahoo! Mail Data Visualization | Periscopic

http://www.flickr.com/photos/visualizeyahoo/sets/72157627722660160/with/6235510547/


What other data do I need?

The United States of 2012 for Esquire Magazine | Stamen

http://content.stamen.com/united_states_of_2012


Patterns
Relationships
Range and distribution
Outliers

World Nuclear Reactor Sites | Nigel Hawtin/Peter Aldhous

http://public.tableausoftware.com/views/WorldNuclearReactorSites2/NorthAmerica?:embed=y

4. Conceive your visualisation
design solution

The 5 layers of a visualisation...

Data representation
Colour and background
Animation and interaction
Layout, placement and apparatus
The annotation layer

http://www.informationisbeautifulawards.com/2011/10/napkin-shortlist-for-the-1st-challenge/

138 years of popular science | Jer Thorp and Mark Hansen

http://www.flickr.com/photos/blprnt/6281316931/sizes/o/in/photostream/

My working process is riddled
with dead-ends, messy errors
and bad decisions
JerThorp

138 years of popular science | Jer Thorp and Mark Hansen

http://blog.blprnt.com/blog/blprnt/138-years-of-popular-science

Space Junk | Jen Christiansen and Jan Willem Tulp

Scientific American, April 2012

http://projects.nytimes.com/census/2010/explorer

Posted: Visualizing US expansion through post offices | Derek Watkins

http://derekwatkins.wordpress.com/2011/08/06/posted/

Max Planck Research Networks | Moritz Stefaner and Christopher Warnow

http://max-planck-research-networks.net/

Data Theft | Jen Christiansen

Scientific American, October 2011

The annotation layer is the
most important thing we do...
Otherwise it’s a case of
here it is, you go figure it out.
Amanda Cox
Graphics Editor, New York Times

http://eyeofestival.com/speaker/amanda-cox/

http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers

5. Construct, launch and evaluate
your visualisation solution

...you’ve started playing with the
visualization instead of debugging
... you hit some level of
engagement and it becomes
really interesting
Martin Wattenberg, "Big Picture" data visualization group, Google

http://queue.acm.org/detail.cfm?id=1744741

You know you’ve achieved
perfection in design, not when you
have nothing more to add,
but when you have
nothing more to take away
Antoine de Saint-Exupery

Sense of Patterns | Mahir M. Yavuz

http://casualdata.com/senseofpatterns/

Sense of Patterns | Mahir M. Yavuz

http://www.visualizing.org/full-screen/32596/embedlaunch

Thank you to…

Nigel Hawtin
Jen Christiansen
Moritz Stefaner
Alberto Cairo
Sarah Slobin
Derek Watkins
Kim Rees
Mahir M Yavuz
Jer Thorp
Stamen

www.visualisingdata.com
andy@visualisingdata.com
@visualisingdata

Andy Kirk Malofiej 20 Presentation

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