data visualization seminar ncdc, april 27 2011 todd pierce module 1 data visualization
TRANSCRIPT
Data Visualization SeminarNCDC, 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
Lunch break4 Best Practices – color, parts of a graph, picking the
correct graph5 Types of Graphs – types of analysis supported, do’s
and don’t’s6 Maps – (if time allows)
THEORY
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 Consensushttp://www.informationisbeautiful.net/visualizations/climate-change-deniers-vs-the-consensus/
Increasing Sea Levelshttp://www.informationisbeautiful.net/visualizations/when-sea-levels-attack/
Need for Climate Change Communication
Moscow Summer Heat Wave 2010http://www.climatecentral.org/gallery/graphics/
how_unusual_was_the_russian_heat_wave_of_2010/
Need for Climate Change Communication
Increased US Snowhttp://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 understandCombines multiple data sourcesTells a storyEncourages aha! MomentsLeads to new insights and predictionsOften 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 wrongIgnores contextUglyConfusingObscures 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.