Looks like what3words [0], only less memorable.
The Louvre in Plus codes:"V86P+C8, Paris" [1]
The Louvre in what3words: "seasons.sharper.scan" [2]
I'm much more likely to remember "seasons.sharper.scan" than "V86P+C8"
0: https://map.what3words.com/
The Louvre in Plus codes:"V86P+C8, Paris" [1]
The Louvre in what3words: "seasons.sharper.scan" [2]
I'm much more likely to remember "seasons.sharper.scan" than "V86P+C8"
0: https://map.what3words.com/
On their map, you have to accept their non-GDPR-compliant cookie "consent" form in order to get access to the real interface, otherwise the cookie notice covers it.
Also, they have country-specific word lists. On one hand, that's nice because locals will be able to use it even if they're not familiar with the latin alphabet/the English language, on the other hand, it means you may be asked to search for misant.habiter.jaloux instead, at which point a plus code may become preferable.
Their algorithm also seems to be incredibly complex, requiring a 20 MB library (likely wordlists + workarounds to make offensive codes less likely). Their web site does not explain how it works, and varies between claims that the words were assigned randomly and claims that they weren't (shorter words in more populated areas, similar words far apart).
However, they've alleviated all need to discuss their technical benefits with their restrictive licensing approach, which guarantees that they will be unsuccessful while preventing anyone else from building a different approach based on words (both because it would create confusion and due to fear that they'll start costly lawsuits/claims).
I don't buy their claims. If they cared about human-friendliness and recognized the possibility of errors, the proper response would be to add an error detection scheme like barcodes. The use cases of what3words are even more diminished by requiring an app to see what's wrong.
> Also, they have country-specific word lists.
I do think the needs for localization is properly recognized (of course, one can get better geocodes by eliminating the possibility of natural languages in advance). But the resulting system is bad: they breaks down after the translation. And also as a native Korean speaker the word list is strange enough to me ;)
they also break down because systems like Siri are terrible about recognizing words in "foreign" languages (foreign being any language different from the one it speaks in, even if supported by the manufacturer).
Wow. We live in a world where people will drive off docks or down boat launches and end up in a lake because their GPS said that was the way to go.
In such a world purveyors of navigation aids really need to assume that there is no such thing as "obviously wrong".
I would think that what we would want in a system that assigns to physical locations elements of a sequence that has a natural ordering is to have the property that elements of the sequence that are near each other in that ordering correspond to physical locations that are near each other.
You can do such mappings using fractals. Here is an XKCD that shows such a mapping of the internet, associating with each internet address a place on the plane such that any group of consecutive IP addresses maps to a compact region in the plane: https://xkcd.com/195/
The same principle would work with a what3words-like mapping where the ordering is alphabetic ordering.
Besides, what3words makes you dependent on a proprietary database to map the words to coordinates, which as as far as I'm concerned is an instant deal breaker.