I took a quick look at the FSQ data of the couple of hundreds of McDonald’s and Burger King restaurants in Switzerland. I applied a filter like this (the ‘date_refreshed’ attribute is unusable because it contains recent dates like 2024, even though it’s not checked…) and compared them with the OSM POI:

"date_closed" IS NULL AND "fsq_category_labels" NOT LIKE '[Event%' AND "fsq_category_labels" NOT LIKE '[Arts%' .

At least ~30% of the FSQ POIs were unusable. Mostly because they were hundreds of metres - even kilometres(!) - off, and that makes any automated matching almost impossible (please correct me if someone has a matching algo that is smart enough).

It may be that some of the nearly 12,000(!) FSQ categories are of better quality. But without the ‘confidence’ attribute - which is withheld in the free distribution - the FSQ data seems to be practically unusable for many main categories, according to my and other assessments - at least unusable for OSM IMHO.

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