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Jorge Camoes

"Everything is related to everything else, but near things are more related than distant things." (First law of geography, Waldo Tobler)

Choropleth maps often leave many unanswered questions, but any serious analysis of a regional dataset must start by looking for spatial patterns.

You are "not particularly happy with the cluster analysis result". I used to feel that way too, exactly for the same reason: when applying the k-means algorithm to a dataset with a spatial dimension. You should try a spatially constrained cluster analysis algorithm instead.

Unless a bizarre randomness emerges from mapping the data, I don't think you can safely say that a panel chart is superior to a map (or the other way around). Each one tell us a different part of the story.


Excellent work.


Linked micromaps would be the way to go with this. By linking the trends and spatial arrangement you can simultaneously evaluate similarity in trend as well as spatial clustering.


I wonder what a Hans Rosling Gapminder-style movie would look like, with the bubbles (simple spots in this case, unless you add the changing state populations to the time series data) color-coded according to the results of your cluster analysis?


Ok I have redone the figure as a linked micromap plot (almost) with geographically constrained clustering of the mapped groups. Is there someplace I can post the plot?



Nicholas: send it to me and I'll post it. the user name of our gmail account is the name of the blog.

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