Visualizations

Examples

• Eurozone debt - http://www.bbc.co.uk/news/business-15748696
• Wikileaks US embassy cables - http://datavisualization.ch/datasets/wikileaks-us-embassy-cables/
• Stopping SOPA and PIPA - http://visual.ly/stop-sopa
• Auto accident statistics in Britain - http://www.bbc.co.uk/news/magazine-16631597
• A snapshot of the rapidly changing world of computing, communications and technology - http://www.nytimes.com/interactive/2011/12/06/science/1206-world.html?ref=science

Press

NPR did a couple of interesting segments on Big Data, visualizations, and the search of mathematicians and others who can do that stuff. (December, 2011)

• Part 1 - http://www.npr.org/2011/11/29/142521910/the-digital-breadcrumbs-that-lead-to-big-data?ps=rs
• Part 2 - http://www.npr.org/2011/11/30/142893065/the-search-for-analysts-to-make-sense-of-big-data

New York Times article from December, 2011 on bioinformatics and visualization, MicJ

Other

• http://www.r-bloggers.com/how-the-new-york-times-uses-r-for-data-visualization/
• At some point nyt.com supported a "viz lab" where people could use their data sets to build their own visualizations. I can't find a current reference to this now. 20 January 2012
• IBM's Many Eyes -
• http://www.cc.gatech.edu/~stasko/7450/syllabus.html

Presentations

• David McCandless: The beauty of data visualizations (TED) - http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization.html
  • Military spending - http://www.guardian.co.uk/news/datablog/2010/apr/01/information-is-beautiful-military-spending
• What we learned from 5 million books (TED) - http://www.ted.com/talks/what_we_learned_from_5_million_books.html
  • Google's ngram interface: http://books.google.com/ngrams/

Keywords

• infographics
• Big data

The Plan

1) Planning items

• Are there any field trip opportunities?
• Figure-out what books to order
• Figure-out what are the likely conference opportunities?
• Are there any other tools besides R that we should be considering? GRASS?

2) Things to learn

• Is there a somewhat canonical process or technique that one can reliably apply to go from readings -> data -> information -> visualization?
• How to utilize geocoding attributes?
• How to utilize timestamp attributes?

3) Things to read

4) Things to do during the class

5) Questions

• Which parts of statistics do people need to know?

6) Tools

• R under Linux/OSX