Team Wifi: Refining the questions to get better answers

Being part of a community like Georgia Tech means that you quickly learn the ebbs and flows of the movement patterns around campus. You may quickly learn that a building is also busy between 7-10pm. But are these ebbs and flows substantiated by data? WiFi data provides neat insights into both people’s movements but other secondary characteristics that you wouldn’t consider!

Last week, we thought our group would use our data to analyze campus crime. After discussing it with our group and team advisors, we realized that we were limiting ourselves to one specific outcome of the data. What kind of broader trends could we see in our dataset that could inform decisions around campus?

This week, our group has worked on narrowing down from this big, broad goal (watching peoples’ movements) to a more narrow area of exploration for the current week: given a set few buildings, what are the movement patterns on the Monday/Wednesday/Friday and Tuesday/Thursday blocks of classes?

Here’s an example visualization of some of our raw data: this shows the increasing and decreasing volume of people connecting to specific access points in a building, organized by floor (1st through 5th).

5.30 culc

From this, you can see a few trends over the week it displays:

  • Even without labels, the weekends are distinct because they is much less activity.
  • Some access points people frequent a lot. Maybe those are the biggest classrooms in this building or a common gathering place in the building?
  • Some access points are infrequently occupied – perhaps near offices or more private labs?

We can use this data to make higher level conclusions about an area of campus that our personal heuristics are based on. For instance, if one of the bigger nodes is always popular on Mondays but not on Wednesdays, are we incorrect to assume that a particular study spot is always busy? What outside factors may influence that? Should we shift resources to accommodate the influx of people on Mondays? We hope our project will answer some of these questions and give others the tools to make similar discoveries about their campuses!

This week, we’re going to select a set of data to examine and a set of research questions to ask and begin the difficult task of cleaning/preprocessing our data.