#meetup visualization

Silk.co bills itself as a place to publish data with interactive visualizations. This free tool was originally marketed as a great way for reporters and causes to share datasets. Unlike Tableau ($500–999 per user), Silk is currently free with the option for private, collaborative projects for teams. (Ed. Note - We will be charging soon). Silk’s team has also included user rights management and good privacy settings all in a platform with a lighter learning curve.

As an example, I created this public Silk project with members from a Docker Meetup group in New York City. I’ve always had issues dealing with Meetup groups. Curioiusly, location is not actually a requirement to join a group.

First, I created an image mosaic with thumbnail image links and data that I pulled using Meetup’s API via another tool I love, Blockspring. It’s possible to use the filters on the mosaic to dig into the dataset. Some datacards don’t have images because the users did not publish images on their Meetup profile. Here I filtered only for profiles with images.

Data from docker.silk.co

Clicking on any profile picture will bring up their “Datacard”. Each datacard is equivalent to a row on a spreadsheet. Each datacard is also a standalone Webpage. In my Silk, a datacard contains their user name with their profile link, Twitter ID (if listed in profile), total number of groups they are member of, and other topics of interests. Here is Harshil’s public meetup profile for reference. I can also easily convert the Twitter handle into a live Twitter stream on the page. This can be useful for providing additional personal context when reaching out to potential candidates.

Back on the homepage below the Mosaic, I created a map using Google’s natural language system. Silk uses Google Maps as its mapping engine. One of the data elements that the Meetup user profile contains is city/state combination. While Meetup does have longitude and latitude data in the API, Google Maps generates a lat-long pairing from the same city/state data the user enters.

You can also pick which data points are shown in the mini-datacard preview when you click on each pin. If you want, you can even display images on the pins. If you have more than one person per location pin because they have the same location on their profile, you can drill down into the datacards without leaving the page. Silk maps and all other visualizations also work well on mobile devices.

Mapping proved to be the most useful tool for my sourcing needs. I was able to hone in on the people who live nearby and compare that to their hometown. Any good recruiter will tell you that someone who has never left home is unlikely to move, even for the ideal position. Context from their interests and biography helped me craft personalized outreach messages.

Near the bottom of the page I added a column chart to show users’ membership counts to get a sense of their activity on Meetup. Then, I added a few easy-to-embed multimedia from other sources to help illustrate how Silk can be used in other ways. You can embed audio, video, slides, PDF docs, and images on any Silk.

Finally, I decided to play with the interests tags some more by creating a new page. The homepage was growing too long. Silk lets you create multiple pages, just like WordPress or Squarespace. Pages in a Silk are different than datacards. Datacards contain an embedded data table. Pages don’t have data tables. They are more like freehand canvases. All the elements on a page or a datacard, though, can be dragged and dropped around to customize your layout.

Using the same dataset, I grouped names by interest only and applied additional filters. Here I have filtered only group members interested in Openstack who live in New Jersey. I didn’t want to complicate this example so I can only select one specific topic at a time. You will see some duplicate names showing results sorted by their other Interests.

Data from docker.silk.co