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Online Data Visualization – Many Eyes

May 18, 2011

Today I tried out a web site called Many Eyes to consider as a tool for students to use to visualize their data.

This semester I taught a capstone that dealt with species endangerment and ethnolinguistic endangerment. One of the papers we read was a report by David Harmon and Jonathan Loh (2004) that looked at the correspondence of linguistic endemism and vertebrate species endemism by country. They actually had a map that showed that the top 25 countries for ethnolinguistic endemism usually happened to be one of the top 25 countries for species endemism. What stuck out at me was that the UNITED STATES (not known for species diversity) was in the top 25 for both. But of course, you have to consider the word “endemism” which refers to a species (or language) restricted to a particular geographic region, e.g. the United States which is a really large area and thus, easier to be endemic in.

So, since I do these types of things, I was interested in whether something like Many Eyes would be useful. When the Data Visualization group went to that web site, we were frustrated at some of the visualizations in that there was not enough information in the visualization of the data to get any useful information out of the graph.

During the Endangerment Course I had extracted the data from Harmon & Loh’s appendix and prepared a set of figures to evaluate whether the correlation between linguistic endemism and species endemism was any good considering that the nations of the world differ wildly in size. I imported these data into Many Eyes and created some visualizations (go to Many Eyes and search in the upper right hand corner of the page for People Klawinski to see the visualizations).

Now, when we look at these graphs, we can see that there is not a strong correlation between biological species richness and linguistic richness. We can also scroll over the data points and see which data point corresponds to which country and we see that the most speciose and linguistically rich nation is Papua New Guinea with 833 languages, 858 vertebrates in only 462,840 square km while the United States has 284 languages and 1078 species in 9,529,063 square km. Compare Cameroon’s 288 languages and 1099 species in 475,442 square km. So, which of these countries belong in the top 25? If you correct for area, probably not the United States.

So, this could be useful for students to explore the data. The most useful thing about the graphs is that they are interactive so that the student can change the variables being related by selecting the variables at the bottom of the page. The other advantage is that you don’t have to set up lots of different graphs for each pairing of data, you can just change th axes that the variables are on interactively. Being able to scroll over the points and weight them is also really good for data exploration. Downside, if you want to test these relationships, you have to go to a statistics package.

For the professor setting something like this up, the frustrating thing is that the character count for metadata (data about the data) is really limited which I discovered when I was describing my data set. So I really had to shorthand things and this may be why some of the more complicated data sets were not particularly informative. You could perhaps send the data to the students and have them generate their own visualizations and then discuss how different people chose to visualize the data and how well they documented the data in order to make the data useful.

In the end, I am way less down on this particular tool than I was this afternoon but it has taken me a few attempts and a few hours to put this together and get it right.

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