The most popular meet-up groups in Connecticut cities

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Hartford has more meet-up groups emphasizing socializing than any other groups, according to a data analysis of, a networking site that lets people coordinate groups and members in-person gatherings based on common interests. New Haven trends toward health and well-being groups, while Bridgeport has more gatherings for the career- and business-oriented, and Waterbury tends to focus more on parents and family.

We pulled data on the types of groups for the five most-populous cities in Connecticut to give a glimpse into its residents’ interests outside of work.

This is just a look at people who use to attend and organize get-togethers. It doesn’t take into account other means of coordinating, like Facebook or Eventbrite or paper fliers on a bulletin board.

Here’s a selection of categories by total members per city:

  • New Haven has the most members in social clubs for women (six groups, 2,000 members).
  • Stamford beats Hartford in the number of members subscribed to tech-related groups.
  • Hartford has the most people interested in meet-up groups dedicated to socializing.
  • For photography buffs, more people have signed up in groups around Bridgeport.
  • New Haven leads in the fine arts and culture category with 2,680 members. Waterbury has 9.

Select a different category to see how popular it is in each town.

Here’s a look at when groups in certain categories were started, showing the evolving interests over time. For example, Stamford has had a huge spike in meet-up groups focusing on career and business. Last year was a big year for health groups in all cities, though Hartford was the only city with a significant jump the year before in 2013.

There seems to be consistent interest in socializing for all cities, but only recently in New Haven. For tech groups, Hartford has had an early interest since 2009 but in 2014 more tech groups were formed in New Haven and Stamford.


Check by later this week for a walkthrough on how I used Python to pull data using’s API and analyzed it with R.

What do you think?