Here’s an exciting new visualization from Nathan Yau at FlowingData. You can read the detail here, but essentially he set up a program to query Google Maps across a 20-mile grid and find the nearest “grocery store”. The distances are the red lines and in some zoomed-in versions there are dots for the stores.
It’s a bit like the fairly common approach to mapping wind direction and strength, which in a sense goes all the way back to hand-drawn synoptic charts. I have never got to grips with automated querying from Google Maps (on the basis that the results are not entirely trustworthy) but I guess it’s not that hard to loop through the co-ordinates on the grid, searching for “grocery stores near 34.813839,-87.029149” (or whatever), select the first result, find and save its co-ordinates. It gets a bit more laborious because Google’s result A is not the nearest, it’s a combination of location, popularity, relevance… but I guess you could query them all.
Then the visualization can be a leaflet.js combination of markers and lines, or some other interface from R back to Google.
What would be really interesting as a next step would be changing the geographical distances to transport time when zooming into urban settings. You then have two versions: one if you own a car and one if you don’t. I wonder if Google Maps would provide that easily? I know it’s there on the screen but might not be so easy for the computer to get it several thousand times…