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Introduction to Geographical Information Systems - UNC Asheville

VIEWS: 6 PAGES: 56

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									Data Visualization Seminar
   NCDC, April 27 2011

        Todd Pierce

Module 1 Data Visualization
                Introduction
This seminar will look at visualization from the
  viewpoint of human perception and cognition

  How do humans perceive and use visuals?
  What are some principles that can be applied
  to visualizations to make them more effective?

The seminar is a summary of the first half of the
  UNC Asheville class “Tools for Climate Data
  and Decision-Making”
                     Outline
1 Data Visualization – history, uses, good and bad
  visuals
2 Human Perception – visual attendance, patterns, and
  working memory
3 The Eightfold Way – principles for effective
  visualizations                               THEORY
Lunch break
4 Best Practices – color, parts of a graph, picking the
  correct graph
5 Types of Graphs – types of analysis supported, do’s
  and don’t’s
6 Maps – (if time allows)                    PRACTICE
Sources
Sources
Sources
                 Let’s Get Started




Facebook Friends Graph

http://www.facebook.com/notes/facebook-engineering/visualizing-
friendships/469716398919
 Need for Climate Change Communication
Why are the skills in this course important?

- Climate Data needs to be a part of decision
  making as humans must start enacting climate
  mitigation and climate adaptation programs

- Climate Data is overwhelming in its quantity
  and needs to be better presented in
  visualizations – maps, charts, graphs – that
  can be used in decision making
 Need for Climate Change Communication
According to Global Climate Change Impacts in
  the United States
-Global warming is unequivocal and primarily
  human-induced
-Climate changes are underway in the US and
  are projected to grow
-Widespread climate-related impacts are
  occurring now and are expected to increase
-Future climate change and its impacts depend
  on choices made today
Need for Climate Change Communication
 Despite the need for choices to be made now,
  climate change skepticism abounds




                http://environment.yale.edu/uploads/SixAmericasJan2010.pdf
Need for Climate Change Communication
 There is a need to counteract the skeptics, but
   how? Climate Change is not a sound bite – it
   has complex concepts and counterintuitive
   findings as well as mountains of data.

 Some examples…
Need for Climate Change Communication
 Skeptics vs Scientific Consensus
 http://www.informationisbeautiful.net/visualizations/climate-change-
    deniers-vs-the-consensus/


 Increasing Sea Levels
 http://www.informationisbeautiful.net/visualizations/when-sea-levels-attack/
Need for Climate Change Communication
 Moscow Summer Heat Wave 2010
 http://www.climatecentral.org/gallery/graphics/how_unusual_was_the_russi
    an_heat_wave_of_2010/
Need for Climate Change Communication
 Increased US Snow
 http://www.climatecentral.org/gallery/graphics/arctic-paradox-warmer-
    arctic-may-mean-colder-winters-for-some/
             Data Visualization
So…data visualization can help explain climate
  change data (as well as many other things)

Let’s look at data visualization

  why use it?
  when did it get started?
  what makes a good or bad visualization?
       Why Use Visualizations?
To explain and to persuade

“picture is worth a thousand words”

Visuals help meet several objectives
       Why Use Visualizations?
Objectives for Visuals
-Clarity: make technical or numerical data easier
  to understand
-Simplification: break down narrative
  description into smaller parts (flow chart)
-Emphasis: draw attention to certain facts
-Summarization: show conclusions or main
  points
        Why Use Visualizations?
Objectives for Visuals
-Reinforcement: complement text and use
   repetition to help remember idea
-Interest: break up blocks of text
-Impact: grab reader’s attention and keep it
-Credibility: impress reader with data validity
   (“pictures don’t lie” ?)
-Coherence: help show how related parts of a
   document work together
                 Definition
Data visualization: the visual
  representations that support
  the exploration, examination,
  and communication of data.
• Information visualization:
  abstract data
• Scientific visualization:
  physical data, such as through
  X rays or MRI scans
                           History
    • Tables date to 2nd century CE, first ones in Egypt for
      astronomical data for navigation
    • Descartes created the Cartesian graph in the 17th
      century, but for mathematical analysis, not for
      information visualization




source: Stephen Few
                         History
    • In late 18th/early 19th century, William Playfair
      created or improved graphs for use in information
      visualization – invented the bar graph, used line
      graphs to show time trends, and invented the pie
      chart.




source: Stephen Few
                          History
    • First college course in graphs in 1913 at Iowa State –
      today few courses offered outside of statistics classes
    • John Tukey in 1977 started exploratory data analysis
      as a tool for statistics – invented tools such as the
      box plot to help show trends in data and prove
      power of visualization for data exploration




source: Stephen Few
                         History
    • Edward Tufte in 1983 published The Visual Display of
      Quantitative Information, the first book to really
      show effective and beautiful ways existed to show
      data, and that most visuals did not use them




source: Stephen Few
                     History
• In 1984 the Apple Macintosh debuted – the first
  affordable PC with a graphical interface
• William Cleveland in 1985 published The Elements of
  Graphic Data – expanded on Tukey and improved use
  of visualization in statistics




                        source: Stephen Few
                         History
• The National Science Foundation started efforts in
  scientific visualization in 1986
• By 1999, information visualization was recognized as
  distinct discipline within visualization in general
• Two conditions needed for modern information
  visualization:
   – graphical computers
   – lots of readily accessible data.
   – Before, data was limited to the printed page, which can
     only be physically manipulated – the data is locked on the
     page and can’t be changed. With computers, users can
     interact with the data and explore ways to show it.
     What Makes a Good Visual?
Easy to understand
Combines multiple data sources
Tells a story
Encourages aha! Moments
Leads to new insights and predictions
Often used in unrelated areas

“forces us to notice what we never expected to see”
  – J W Tukey
    What Makes a Good Visual?
Easy to understand
    What Makes a Good Visual?
Easy to understand
    What Makes a Good Visual?
Combines multiple data sources
    What Makes a Good Visual?
Combines multiple data sources
     What Makes a Good Visual?
Tells a story
    What Makes a Good Visual?
Encourages aha! moments
What Makes a Good Visual?
     What Makes a Good Visual?
Leads to new insights and predictions
     What Makes a Good Visual?
Leads to new insights and predictions
     What Makes a Good Visual?
Often used in unrelated areas
     What Makes a Good Visual?
Often used in unrelated areas
     What Makes a Good Visual?
Often used in unrelated areas
     What Makes a Good Visual?
Often used in unrelated areas
     What Makes a Good Visual?
Often used in unrelated areas
     What Makes a Good Visual?
Often used in unrelated areas
      What Makes a Bad Visual?
Misleading or wrong
Ignores context
Ugly
Confusing
Obscures message

With computers, it is very easy to make a bad
 chart, graph, or map
      What Makes a Bad Visual?
Misleading or wrong (perspective issues)
      What Makes a Bad Visual?
Misleading or wrong (area used for linear value)
     What Makes a Bad Visual?
Misleading or wrong
     What Makes a Bad Visual?
Misleading or wrong
     What Makes a Bad Visual?
Misleading or wrong
     What Makes a Bad Visual?
Misleading or wrong – track removed
     What Makes a Bad Visual?
Misleading or wrong
      What Makes a Bad Visual?
Ignores context
      What Makes a Bad Visual?
Ignores context
      What Makes a Bad Visual?
Ugly (“chart junk”)
     What Makes a Bad Visual?
Confusing
     What Makes a Bad Visual?
Obscures message
     What Makes a Bad Visual?
Obscures message – better version
 Next Module: Human Perception
How can we make visuals better, so they show
 more of the ‘good’ qualities and less of the
 ‘bad’ qualities?

We can consider principles of human
 perception.

								
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