How to map geographically-detailed survey responses?

January 17, 2012

(This article was first published on Statistical Modeling, Causal Inference, and Social Science » R, and kindly contributed to R-bloggers)

David Sparks writes:

I am experimenting with the mapping/visualization of survey response data, with a particular focus on using transparency to convey uncertainty. See some examples here.

Do you think the examples are successful at communicating both local values of the variable of interest, as well as the lack of information in certain places? Also, do you have any general advice for choosing an approach to spatially smoothing the data in a way that preserves local features, but prevents individual respondents from standing out? I have experimented a lot with smoothing in these maps, and the cost of preventing the Midwest and West from looking “spotty” is the oversmoothing of the Northeast.

My quick impression is that the graphs are more pretty than they are informative. But “pretty” is not such a bad thing! The conveying-information part is more difficult: to me, the graphs seem to be displaying a somewhat confusing mix of opinion level and population density. Consider, for example, the bright red color in Dallas. There must be areas in the countryside that are also heavily Republican but Dallas stands out because there are a lot of people there. In some ways this makes sense—that’s where the voters are—but to me it makes the map a bit confusing. I’m also bothered by the blurriness of the entire northeast—I assume this is happening because all the cities are in each others’ penumbras. That’s just one problem though; really, what’s bugging me more is the overlay of intensity with density.

I think I’d prefer something simpler such as putting a colored circle in each county with the size of the circle proportional to population. But, as I said above, “pretty” is important too.

The post How to map geographically-detailed survey responses? appeared first on Statistical Modeling, Causal Inference, and Social Science.

To leave a comment for the author, please follow the link and comment on their blog: Statistical Modeling, Causal Inference, and Social Science » R. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.


Mango solutions

RStudio homepage

Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training


CRC R books series

Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)