In my last post on data visualization, I had a couple tools recommended to me to try out. One had a limited trial period that I didn’t take advantage of in time, and the other, a tool by VisualizeFree, was too buggy to work. I uploaded my data easily enough (still had to clean it up first like some other ones), then I can’t view the actual visualization. Lame.  I could email support, but I’m too lazy to do that again.

I think I got more out of reading this article on what is (or should be) the point of data visualization.  Its based on a talk given by Manuel Lima of VisualComplexity.com, who curates that collection of data visualization examples and resources.  The main point from Lima was this:

“We need to make a transition from tools of curiosity to tools of functionality.”

Which is true, there are many tools that provide interactivity but not much substance. On the other hand tools for fun or aesthetics only can also drive innovation. I’m torn. It seemed like a lot of people were disagreeing over separating “information art” from data visualization, but this article states it more eloquently than I can. Manuel Lima also listed some key principles for data visualization that I did not know and should probably keep in mind:

form follows function

start with a question

interactivity is key

cite your source

the power of narrative

I’ve become much more judgmental toward charts and graphs thanks to blogs like Junk Charts, Flowing Data, and Simple Complexity.  So when I tried out one of the many Twitter measurement tools, Graph Edge, and received my first report, I was confused by the charts.

Followers and "Legitimate" Followers

This graph shows my Twitter followers and “legitimate” followers (they have a definition for it). Because the lower limit of my y-axis is the number of legitimate followers, it gives a false impression of having a very low number of legitimate followers. Why not start with 0?

Net Twitter follower change

This line graph shows the follows, unfollows, and net follower change over time for my Twitter account. But I thought it was strange to include negative numbers, because it looked like I had negative 1 unfollows. Anyway, there’s room for improvement here.

Misc.

Today I created a Twitter list for #measure and web analytics people (like @ABTests did) on TLists. I think the value is that its curated so you can see recommended people who actually tweet about web analytics on a regular basis, and you can follow many people at once/discover new people. It may not be worthwhile once Twitter implements lists though. And I successfully integrated my GWO data with GA thanks to this post from the GWO Tricks blog!

Lately I’ve been seeing a ton of new data visualization tools/experiments that I thought were worth sharing. One is not really applicable to web analytics or reporting, but others are.

Trendly

I saw a post about Trendly on the Google Analytics Blog, its an application that leverages GA data to display it in a way that is supposed to help you understand the data better. From what I can tell, they use statistically significant numbers (between the upper and lower control) to show a trend-line that is less jagged and crazy. I think that is helpful for noting the big picture of change over a longer time span, but less so if you want to do a deep dive. Which might be besides the point. And then their other graph shows time vertically (I’ve always had issues with that metaphor), and the percentage of traffic from that source correlates to thickness. Its easier to show than tell so here it is:

Trendly time graph

Darwin’s Origin of Species

This is the English major in me geeking out, but I think this changing textual visualization by Ben Fry (go to the site to see it in action) is a great way to show how different editions of an important book like this can change. I’d like to see it for Lyrical Ballads. This is from the information aesthetics blog again, but I think its cool.

Darwin visualization

California Stimulus Map

I should probably find some more data visualization blogs, but I like the information aesthetics finds so much. This interactive map by Stamen Design shows where stimulus money is being spent in California, and what kinds of projects it goes to. The subject matter is worthwhile, but mainly I liked the vertical graph on the right side, I find that visualization easier to duplicate (in Excel!) and translate to different data sets than the one in Trendly. I will try to sneak it into a report at some point, and I’m sure I will get a WTF reaction, but it would be more interesting than the standard Excel graphs for me and the reader.

CA stimulus map

Otherwise I am having fun with my new G1 phone, new running shoes, and I’m going to Arizona this weekend–good times! Hoping to get some hiking in if it is not over 100° outside.

So in my continuing quest to figure out new ways to visualize data, I came across this article through The Grok, (which I realize is from 2007) in Smashing Magazine on new and interesting approaches to data visualization.  I tested some of them out and I wanted to share and post pictures of the results (though some are better when in action than still).

1. Amaztype.  These people utilize Amazon Web Services to make a word of your choice based on book/album/dvd covers. You can sort of see the graphic design trends that people follow. I especially like that Goodnight Moon made it onto the Night visualization.
   

   Night

   

   Light

2. Death of the Pie Chart.  Using pixels instead of pie charts to demonstrate relative size. I like it.
   

   Pixel graphs

3. We Feel Fine.  This site does amazing things to get a sense of how the world is doing by “harvesting feelings from weblogs.” You can filter based on feeling, gender, age, weather, location, and date for 6 different ways of viewing the data. I tried out Madness, Montage, Murmurs and Mobs.
   

   Filtered Madness by the feeling "sad", possibly about Michael Jackson.

   

   An image from Montage.

4. Websites as graphs.  Graphs by the HTML code, so tables, links, images, etc. get different colored nodes. I made a graph of this site but I didn’t think it looked too interesting.
5. Book/essay word visualization.  A German Interaction Design Institute wanted to show the connection between words and ideas better and made an awesome sort of Venn diagram. One pretty much includes everything that I learned at the iSchool, and some extra German words tossed in.
   

   Visualization of words in an interaction design text.

6. The Shape of Song.  Graphical representations of music. I did one for Chopin’s Nocturne in E flat.
   

   Visualization of Nocturne in E flat.

7. Visual Complexity.  The mother lode of data visualization, a large gallery of different types. I was really fascinated by the transportation ones.  The ones I’m showing here are of the global internet distribution and the blogosphere from “inspirer” radiating to “inspiree.”
   

   Global internet distribution

   

   Blogosphere from inspirer in the center radiating to inspiree.