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Comment by ponderingHplus | Hacker News Reader

Yup that's definitely true, population centers rank highest for most topic lists (especially New York where meetup.com was founded)

The original use-case I envisioned for something like this is someone deciding which cities they might like to move to by choosing cities that have a lot of people with shared interests. So should the metric for that be total people that are part of a topic, or the % of total city population engaged in that topic? I've currently chosen the former, but interested in what others might think.

Thanks for the feedback,

-pH+

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I agree with erex78. The former measurement doesn't tell me much; I already know what cities have the most people.

The latter measurement better indicates the probability of a chance encounter with someone who has your shared interests.

Yeah that's a good point. I like scribu's point that it can be somewhere in between. I'll have to think on that a little.

Usually (taken from erf/error based on sample size), something grows proportional to the square root of the sample size. If using raw sample sizes, you can apply a square root to that sample size before using it (meters its impact).

> So should the metric for that be total people that are part of a topic, or the % of total city population engaged in that topic?

Ideally, both. You want to have a community with at least N people, but cities with a high % should rank higher.

Could also account for the number of total meetup.com members in that place.

Hmm interesting, I'll give that some thought. Thanks!

You could have a toggle where you divide by the overall population of each city. So basically let the user switch between percentage and absolute.

Another ranking to consider is the h-index metric for academic papers (https://en.wikipedia.org/wiki/H-index), where you use the number of members of each meetup group in place of citations. So a city with a score h has h meetups each of which has at least h members.

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