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Tom Morris

@Edent I've noodled around with trying to write some code to turn all of OSM into a Foursquare-style venues database but haven't had the time.

My rough plan—a script that parses the entire OSM dataset and:

- filters out the features one wants based on a predicate (pubs, restaurants, public buildings, etc.)
- flattens ways/relations into points (calculate centroids)
- emits lat, long, label, OSM type+ID

Stick result in DB (SQLite+SpatiaLite appeals to me), index on coords, query N closest.

7 comments
Tom Morris

@Edent Most of it is a data quality problem in deciding on the predicate to filter the data.

Do you wanna check into an untitled tree or water fountain in your local park? (Filter things without a name?)

What about checking into a whole city or country? Or a particular borough? Or an area that doesn't line up with a governmental/administrative area (Soho, say)? That's a thing people did on 4sq to tell friends they are visiting.

How about the M25? (This place is not a place of honour etc.)

Sam Sneddon 🏳️‍⚧️

@tommorris @Edent also answers aren’t necessarily consistent; you might not have enough data to filter it in the way people want. people might want to check in to Route 66, but not the M25.

Tom Morris

@Edent (Incidentally, this isn't a "don't do it", it's more a "I've played around with this in the past and you may will encounter fun weird stuff you didn't expect"—but weird stuff is good, please carry on.)

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