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Commuter Stress
by Abed Islam & Carlos Lima
Not really.
by Abed Islam & Carlos Lima
Come to Poly at 8 in the morning.
-That commute is way too stressful,
can we do something else? Please? Help?
Towards Territoriality
Learning to look for territoriality in Poly
by Abed Islam & Carlos Lima
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
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
Gathering data
Presentation on Territoriality for Environmental Psychology
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.
Cafeteria
• 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...
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
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.)
Cafeteria
A Day in the Life: Typical cafeteria
usage. Date: 11/18/08 4:50PM
Cafeteria
A Day in the Life: Typical cafeteria
usage. Date: 11/18/08 5:00PM
Cafeteria
Cafeteria
Layout of gathered information. But wait. There’s
more:
Lounge
Lounge usage. Date: 11/18/08 5:10PM
Lounge
Lounge usage. Date: 11/18/08 5:10PM
Lounge
Lounge usage. Date: 11/18/08 5:10PM
Presentation on Territoriality for Environmental Psychology
Lounge
The Lounge. And, yes. There’s more
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
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.
Observations
• Furniture
• Movable: People can
adapt to the situation
• Group-based/-inclined
• Large groups can
divide/come together
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?
Demo
• Pretend this class is in Regna Lounge
• Data entry
• View/search
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.
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
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

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Presentation on Territoriality for Environmental Psychology

  • 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
  • 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.
  • 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
  • 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