[kybernetikos]

I did one of these for fun at the beginning of the year, and ended up needing to spend way more time on the wordlist than I'd expected. In the end I felt that a list of 4096 words was a decent compromise between accuracy and is still fairly managable for trying to remove words that are too easy to mistake for each other. It lets you do everywhere on earth to slightly more accuracy than what3words in 4 words.

Something that what3words does is not have an obvious hierarchy of words (e.g. where the first one covers a larger area, and subsequent words home in). I didn't like that, but I understand why they do it - if a single word is going to cover a large area, you have to be extra careful that you don't choose something offensive for a particular region. By having no obvious structure, they get away with being less careful on the wordlist.

[eterm]

The issue of hierarchy is that it's orthogonal to having very different results for nearby areas.

With a hierarchy you immediately run into, "Am I in gibbons.apple.banana or was it gibbons.apple.bandana" which is just down the road.

Without hierarchy it jumps out that one of those results is improbable if tallied with any knowledge of roughly where the person is.

[kybernetikos]

As you say, the issues are orthogonal. I randomise my wordlist so places next to each other don't have words necessarily close to each other in the alphabet. But because it's hierarchical e.g. most places in the uk start 'bishop'. I think this is good because you learn to recognise places,but imagine if loads of places in India started with the word 'colony'... it's a problem you don't have to worry about if you don't use a hierarchical scheme.

https://wherewords.id/