A repost via repost. First, you should visit FlowingData a great blog dedicated to data visualization and analysis. Very interesting stuff and lots of great posts for those stodgy researchers like myself who would rather see data analysis (tables with coefficients) than pretty looking data. See one of my earlier posts on this subject. One of post-doc plans is work on data visualization of health inequities in Saskatchewan and Canada.

And via FlowingData a great article called “When Maps Shouldn’t Be Maps” by Matthew Ericson (deputy graphics director at The New York Times).

Snip: “But while maps like that are interesting to look at, it also forces readers who want to figure out the correlation between income and flooding to try and visually sum up all the colors on the map in their head. The map shows there’s low-income areas in the flooded areas and there’s also low-income areas outside the flooded areas. There’s middle- and upper-income areas in each, too. Unless the pattern is super clearcut, trying to figure out how much of a relationship exists is a tricky task.
So, instead, I used ArcView to select all the block groups that fell inside the flooded areas and calculate aggregate statistics for the area as a whole. Repeat the process for the block groups that hadn’t been flooded, and you have data for a simple table that clearly and effectively shows the difference between the two areas.”

I’ve been working on lots of maps for a paper recently and have to say that data visualization is very challenging work. Conveying the message you are trying to get across without overstuffing the graphic is an art. One that I definitely need to refine. But don’t forget sometimes you don’t need a map and tables work just fine. Happy mapping.

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