As a matter of fact, all maps are misleading in some way or another. It’s all in the development, display and interpretation of the data. That’s why there’s been so much growth in the geospatial technologies field in the past five years or so.

There’s a science behind standardizing those statistics according to the information you are trying to convey. A classic starting point for learning about this topic is the book “How to Lie With Maps” by Mark Monmonier.

Do you want to see percentage per capita tweet figures? What about sprawling cities versus consolidated populations? Then you might want to look at tweets per square mile. But, what about Twitter participation density? That information would alter those figures as well.

Just a few more flowingdata points to ponder…

http://ejohn.org/blog/processingjs/

As more and more of these maps come out, my concern with these them is that no matter the subject, the size of the bubbles tends to always reflect the size of the city and not the intensity of the interest. Each new map is starting to look like every other map. New York, Chicago, LA will always yield big bubbles; Maine, Wyoming not so much.

Anybody out there have a thought as to how to normalize the data on these maps so they show per capita tweets? Is the quality of the data good enough to normalize? For instance, does Minneapolis, MN represent downtown? the county? southern MN? Is this something worth pursuing?

Food for flowingdata thought.