This is a demonstration of my love for thinking, for creating experiences, for living out of the box, and how that often means getting down & dirty with an array of skills including coding to say the least. Long story short instead of conducting research like we were told and left to do I lost several weeks of sleep to make a web-based system for entering and visualizing data over time. The ad-hoc system I developed became the focus of the research and in the end even the professor started thinking like a developer, "It's a shame this isn't on a mobile device!" (I didn't have any at the time.) --- To draw conclusions one needs to have solid data. But you don't want to stop at conclusions, you want to be able to keep going. Having a flexible, easy-to-use GUI for punching data in can open countless doors for inquiry. The base conclusions you were thinking to land at would barely even be the beginning if your data is not just solid, but can be brought to life varying in visualization in at least as much as you care to ask. This was our final presentation for PS3324: Environmental Psychology, Polytechnic Institute of NYU in the fall of 2008. The initial project was to gauge stress of commuters, but that wasn't happening. We chose to study territoriality in the University cafeteria and lounge. As we started conducting our research it hit us not only did we not know what we were looking for, our data was too volatile for figuring anything out. I took it upon myself to make a drag-and-drop, web-based interface for recording and retrieving data. Students could be colored and shaped to describe ethnicity and gender. While developing and using the GUI we realized the environment itself is also dynamic and could offer more to say about groupings and territoriality. So we added environmental attributes such as chairs and tables all scaled according to the given template. Undertaking developing a GUI, a usable system for punching in and visualizing data, was in and of itself an immensely rewarding and revealing experience. It completely transforms the entire nature of whatever one is researching and can bring so much to life.

1. Commuter Stress
by Abed Islam & Carlos Lima
Not really.
by Abed Islam & Carlos Lima

2. Come to Poly at 8 in the morning.
-That commute is way too stressful,
can we do something else? Please? Help?

3. Towards Territoriality
Learning to look for territoriality in Poly
by Abed Islam & Carlos Lima

4. Mission
• Proposed project on territoriality
• Go make observations in the
Cafeteria and Regna Lounge
• Look for signs of grouping, e.g.
Athletic teams via jerseys

5. Initial Method
Round 1
“We’re looking for…”
• Go to each destination:
• Sit down and record the general
location of people, their cumulative
activity, their gender via pencil &
paper
• Make note of grouping, i.e. obvious
student organization affiliation
• Next slide is first sample of cafeteria

6. Gathering data

8. Initial Method
Round 2
“What’re we looking for?”
• Go to each destination:
• Sit down and record the general
location of people, their cumulative
activity, their gender via pencil &
paper
• Where are the groups?
• Let’s be a little more accurate.

9. Cafeteria

10. • Realized:
• Didn’t know what to look for
• Scenes constantly change over time.
Problem trying to record placement,
people and activities by hand (takes
apx. 20 minutes)
• Didn’t find immediate signs of
grouping with limited data
• Data too limited? Perhaps doing
something wrong or not well enough...

11. Hypotheses
• The Observers and their recordings are
just as vital as the data being compiled.
• Just as vital as presentation of data
• Placing emphasis on presentation
aspect of data to gain insight into what
was unseen

12. Method
“What are[n’t] we looking at?”
• Pictures
• Take pictures of the scene and interpret this data once system is in
place
• Less human error. Photos are time-stamped, actions locked in
time
• Can identify people and behaviors across sets of data
• Entry and retrieval of all information to be done visually (GUI) against
a database
• Helps us realize the value of what’s missing and what’s present
• Can look at specific criteria (behaviors, people etc.)

13. Cafeteria
A Day in the Life: Typical cafeteria
usage. Date: 11/18/08 4:50PM

14. Cafeteria
A Day in the Life: Typical cafeteria
usage. Date: 11/18/08 5:00PM

15. Cafeteria

16. Cafeteria
Layout of gathered information. But wait. There’s
more:

17. Lounge
Lounge usage. Date: 11/18/08 5:10PM

18. Lounge
Lounge usage. Date: 11/18/08 5:10PM

19. Lounge
Lounge usage. Date: 11/18/08 5:10PM

21. Lounge
The Lounge. And, yes. There’s more

22. Observations
• Angle
• Orientation of furniture/people
wouldn't normally be recorded
while in the environment.
• A visible angle might create the
assumption that they are
engaging with a person adjacent
or across from them.
• Furniture (and to an extent
people), perpendicular/parallel to
walls

23. Observations
• Furniture
• The types and availability
of furniture differ from
location to location.
• Some people might like
that the same things are
in the same place, the
consistency is safe, a
stress-free no-brainer
choice.

24. Observations
• Furniture
• Movable: People can
adapt to the situation
• Group-based/-inclined
• Large groups can
divide/come together

25. Observations
• Placement
• Distance of “loners”: similar but
modifiable depending on the
circumstances
• Migration from pencil data to size-
accurate reveals the positioning of
earlier data might be arbitrary to the
point of uselessness
• Mathematical? Dependent on
crowding?

26. Demo
• Pretend this class is in Regna Lounge
• Data entry
• View/search

27. Demo
• No differentiation between
student/teacher.
• Does this matter?
• If it this area were a classroom and not
the lounge our angles and positions
would indicate that Abed and Carlos
were probably teaching the class at this
time, or plotting to overthrow the
professor (had we left him unnamed) or
might just be presenting.

28. And in the end...
• The system is incomplete:
• Didn’t create relations between people
• Didn’t create relations to pictures taken or have a system of viewpoints and timings
• The system can grow:
• Show things over time either via animations or actual video, more depth of data
• The notion of mathematical formula providing insight into the unseen is not alien, having more data
readily available might help such a thing “occur” to someone who is equipped to arise to such a task
• Immersion in the environment makes a fundamental difference:
• In the demo the professor became a student
• In practice as observers we are often too close to our environment to look for new things, presentation
helps take the observer away but it doesn’t give us an independent view

29. And in the end...
• Distraction/digression
• Presentation-emphatic method may end up making us ask too many
questions though while useful may be irrelevant, e.g. perpendicular
furniture
• But is this “junk” data/inquiry really junk or part of a bigger picture?
• Presentation from entry to ‘graph’ significantly influenced perspective
• We immediately went from a pseudo-deterministic view to something
objective
• With more data and precision we may see a relation between groups by
activity, ethnicity and the density of a public space and its available
furniture