I just had chance to play with a fantastic data visualization tool from IBM: Many Eyes. You can upload data sets or use data sets that others have uploaded to create all the regular visualizations (charts, etc), but also maps and treemaps, like this one that I built from party registration information in the early 2008 states:

The big blobs are states (Iowa, South Carolina, New Hampshire, Nevada). The size of the blobs represents the number of registered voters in 2004. You can see the small county boxes inside the states, showing the number of registered voters in each county. You can then zoom in on a state to see just the counties within that state. Within New Hampshire, I had town-level data readily available, so you can soom in to a particular county and see the towns in that county.

I was going to only post on projects I've worked on, but I saw this today and HAD to post about it. This chap has created a personal annual report, detailing things like 'airmiles traveled', 'number of emails sent', 'number of photos taken', 'animals eaten', 'books read', 'most frequented bar', 'best meal', 'miles run', 'plants killed' or 'beverages drunk by type'. And he has done so with tremendous clarity of design. Seriously impressive stuff! I have no idea how he kept track of all of this.

Its great to have a team of people to do analysis for you.
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I've been keeping my eye open for a good example of a PowerPoint slide that needed some love, and in a meeting today, I finally found one. I definitely don't pretend to be an expert on how to make this chart in to the perfect slide, but here are some things I would do to help it on its way.

Here is an anonomized, but otherwise un-altered version of the original slide:

The first thing I want to do when I see something like this is to remove the chart junk.

This post is less of an example of good practice, and more of an illustration of how technology is currently hampering best practice in one important area.

Politics is all about people, but its also about geographic neighbourhoods and communities.

To use data to help to understand voters and to understand where they are and how their neighbourhoods and communities work, data needs to be mapped out. And here is the problem. Mapping out data is tough.

I find a table of numbers the most inaccessible thing in the world. It just doesn't tell you anything without you having to read the table. You must literally read the numbers and remember the relative positions of big ones and small ones to make sense of it.

... my mind really doesn't work that way, and so I was VERY happy when I saw Edward Tufte talk about what he calls sparklines.
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I have always hated pie charts. I hated them even before L.E.K. trained me to use a stacked bar chart instead. This was a step forward, but wasn't ever quite right. Thanks to the guys at Juice Analytics, I've now (perhaps temporarily) fallen in love with the square pie chart.

For example, I was taking a look at the population of Iowa today. There were several layers to the problem.

I watched Hans Rosling's inspiring presentation at TED and HAD to apply at least some of what he did to some data I was playing with that day. I was looking at patterns of voter registration data in Presidential years in New Hampshire. There were four variables that I was playing with: geography, three types of party registration (Democrat, Republican, Independent), number of registered voters and time. I didn't have a good way to show this until Hans' talk inspired me.

I was in a bar making a list of friends I'd lost touch with and, inspired by Garofalo's Genealogy of Pop/Rock Music chart, I started sketching a chart of my friends over time. A little odd I agree, but for sure its an interesting way of showing a pattern in something that you don't usually see patterns in. Goes to show that even in areas where there doesn't appear to be any 'data', there is plenty there if you choose to look for it!

I got home and knocked out some quick charts in Excel.
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