Tools - Proportional Ink
In this article we explore a basic rule for the design of data graphics, the principle of proportional ink. The rule is very simple: when a shaded region is used to represent a numerical value, the area of that shaded region should be directly proportional to the corresponding value. In other words, the amount of ink used to indicate a value should be proportional to the value itself. This rule derives from a more general principle that Edward Tufte set out in his classic book The Visual Display of Quantitative Information. The principle of proportional ink makes sense of, and extends, the arguments in our article about misleading axes. Bar charts We can see right away that the principle of proportional ink is violated by bar charts with axes that fail to reach zero. In this chart the value for 2014 is approximately 1.08 times the value for 2010, but because the vertical axis has been truncated, the bar for 2014 uses approximately 2.7 times as much ink as the bar for 2010. Line graphs
Notabilia – Visualizing Deletion Discussions on Wikipedia
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IEEE Conference on Data Mining
[April 22, 2009:] A companion book on The Top Ten Algorithms in Data Mining published in April 2009 [December 24, 2007:] A companion article in PDF for this top-10 algorithm initiative:Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand and Dan Steinberg, Top 10 Algorithms in Data Mining, Knowledge and Information Systems, 14(2008), 1: 1-37. In an effort to identify some of the most influential algorithms that have been widely used in the data mining community, the IEEE International Conference on Data Mining (ICDM) identified the top 10 algorithms in data mining for presentation at ICDM '06 in Hong Kong. As the first step in the identification process, in September 2006 we invited the ACM KDD Innovation Award and IEEE ICDM Research Contributions Award winners to each nominate up to 10 best-known algorithms in data mining.
Tools - Misleading axes on graphs
The purpose of a publication-stage data visualization is to tell a story. Subtle choices on the part of the author about how to represent a dataset graphically can have a substantial influence on the story that a visualization tells. Good visualization can bring out important aspects of data, but visualization can also be used to conceal or mislead. In this discussion, we'll look at some of the subtleties surrounding the seemingly straightforward issue of how to choose the range and scale for the axes of a graph. Bar chart axes should include zero We begin with a well-known issue: drawing bar charts with a measurement (dependent variable) axis that does not go to zero. It looks like Germany has a big edge over other nations such as Sweden, let alone France, right? (You might notice that in the redrawn graph we've removed the horizontal gridlines separating the countries. Line graph axes need not include zero What is the difference? When line graphs ought not include zero Well, not really.
How a Science Journalist Created a Data Visualization to Show the Magnitude of the Haiti Earthquake
On the one year anniversary of the Haiti earthquake, journalist Peter Aldhous created a data visualization that shows how the Carribean country’s relatively low seismic earthquake had as many fatalities as all but one earthquake over a time span of almost 40 years. The data visualization is striking but also a study in how journalists are increasingly telling stories that leverage datasets that are freely available to the public. Peter Aldhous, San Francisco Bureau Chief for New Scientist magazine, created the interactive graphics. We asked him to explain how he created the visualizations which compare seismic activity to fatalities caused by earthquakes over the span of four decades. Aldhous posted the data visualizations on his Web site with the following explanation: The earthquake that struck near the Haitian capital, Port-au-Prince, on 12 January 2010, was unremarkable in seismic terms — barely making the year’s top 20 most powerful quakes. The bar graph marks fatalities. dev.off()
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