WiFi in Practice

In setting up the platform for our final deliverable, our tasks are now more technical than conceptual – the questions and software pieces are defined, so we are now fully concentrated on plugging everything together.

It is still a good time to consider, more or less retrospectively, some of the benefits that our system has to offer. Namely, we have framed WiFi data for usability, which has absolutely been the greatest challenge of the past weeks. Working to define a clearly bounded question has been at the center of our discussions – in dealing with millions of wireless network requests, what information do you keep, what information do you hide, how do you present it to the people that need it, and why?

As mentioned previously, we have decided to frame WiFi data in the context of coverage optimization, which means that we are developing a system that tell its users, fundamentally, where to put things and when places are congested based on network usage. This problem is particularly interesting because the need for this kind of information resurfaces every time we receive feedback from a new field or profession. The problem is also supported by a very high level of information, as even a ranking of locational use over hours is very helpful at justifying the use of a workspace, or diagnosing hazardous crowd behaviors.

Applying this general overview to a map will help to bring out even stronger correlations. Although the data in this demonstration is not yet based on real network usage, the image hopefully leaves you wanting to see the actual data in this display as much as we do:

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The intent with an interface of this kind is to not only show network usage overall, but also to differentiate between the usage and mobility patterns of different demographics, days, and events. These kinds of comparisons are already representable in this working prototype, and we are now working to siphon the data into it.

Acknowledging that overlaying data on locations is meaningful but not everything, we are also taking care to enable the exploration of crowd behaviors from other perspectives. By investigating the relationships between locational pairs, we can produce a language for policymakers to understand and communicate which geographic areas are ‘tied’ togethers. This soup of building relationships helps to describe what this might look like:

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Just by displaying the top most significant adjacencies between wireless access points as crowds move across campus, telling patterns are already revealed. If a strand of buildings is comprised totally of dorms and the library, for example, we can uncover some immediate information about student studying habits, as well as optimal locations for campus security at night.