WheterPeopleSwear

by Patrick Appel

Flowing Data explains how to read it:

The brighter the red, the more profanities used in the area, and the more black, the less swearing. Words looked for were (pardon my language): fuck, shit, bitch, hell, damn, and ass, and variants such as damnit. 

How it was made:

Isolines are based upon an interpolated surface generated from approximately 1.5 million geocoded public posts on Twitter between March 9th and April 12th, 2010. These data represent only a sample of all posts made during that period. Isolines are based upon the average number of profanities found in the 500 nearest data points, in order to compensate for low population areas.

Larger version here.

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