Most edit distances take into consideration number of "changes" required in one string to match with another string. And they give you a value that represent the distance between the words.
While it is helpful, when working with datasets and corpora that have been created with keyboards (e.g. SMS, e-mail, transcripts) it is common to have mistakes. In some cases a letter was accidentally mistyped. But the character used is normally close to the correct character.
For example, given the word "one", and two incorrect misspellings "onr" and "oni". The Levenshtein distance for both would be 1. But if you are aware that the keyboard layout is English with the QUERTY layout (notice the E and the R), so the distance between "one" and "onr", would be greater than the distance between "one" and "oni", because in the English keyboard the letter 'E' is neighbouring 'R'. Whereas 'I' is not even covered by the left hand, but by the right hand.
Here's some reference links for further research.
Ideally such edit distance would be extensible to support other keyboard layouts.
There is some indication that perhaps an existing edit distance like levenshtein could be extended to take into consideration the keyboard layout. So perhaps a new edit distance is not entirely necessary.
We could come with the the decision that it is too hard to implement, and it would be better done in a spell checker, or that it would require some statistics and would be out of the scope of Text. Or we could simply add documentation on how to do it, without adding any code. Or, perhaps we add a new edit distance.