eh - I vaguely disagree. I think most of the people in the US are also off (or at least historically "dated").

Even without looking, I could pretty immediately tell that this was wikipedia data, since most of the names are "textbook famous".

So really - we're both saying that the word "famous" was probably the wrong pick. Should have just said "Map of wikipedia's most known people from every city" and then the skew is clear.

It's very much western lense. It's what would a foreigner know in this part of Asia

Yes, and for much of the US it's also "What would a foreigner know in this part of the US".

Ex: Atlanta is "Martin Luther King Jr". It's a great pick if you're going for "internationally recognized". But it's not a great pick for "famous" (at least in my opinion).

Just off the top of my head, more valid alternatives might have been:

- Kanye West

- Spike Lee

- Jeff Foxworthy

- Pat Conroy

- Julia Roberts

I agree with what you are saying but you might have picked the single worst example for your point I could imagine. MLK less famous than Spike Lee? C'mon.

Kanye West does show up for Atlanta if you pick the culture filter.

And Julia Roberts wasn't born in Atlanta; she shows up in the Atlanta suburb of Smyrna, where she was born.

Besides that, though, MLK is certainly more famous than anyone you listed. Not even close.

Aren't you agreeing with me here?

You're saying that we should be defining famous as "known far and wide" - which is fine, but that makes "what would a foreigner recognize" a great fit for the pick.

I'm rebutting the idea that this is limited to non-western countries - It's not. If you want a pick who people from a location would identify with more as a local... this data isn't that: every person on my list is in my news much more often than MLK jr.

Let me know when I can celebrate the federal holiday Jeff Foxworthy day and go down to my locality's Kanye West Boulevard

You can tell Asian history isn't that well documented in English. The Western stuff is much more balanced

Ok, how about this: scrape Wikipedia in multiple languages, combine the notability data, and assign weights to appropriate locations. For example notability in Japanese Wikipedia would have more weight in locations where they speak Japanese.

Yes, I realize location != language and it would get tricky in locations with multiple languages like Switzerland or countries with big diasporas. The dataset would still be biased as not all languages have Wikipedia.

It would be a small step into the right direction.

What? It seems directly related to me.