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For our final project,

Aaron, Abe, and I will be observing sharing behavior.  We’re interested in how visibility and accountability influence a system that would otherwise be opportunistically exploited by it’s users. We’ll be tracking both the giving and receiving of “cookies” (the term cookie will refer to anything that ends up in the “cookie jar”).  The scale of our experiment will be NYU wide.  An RFID reader will be used to grant students access to the jar with their student ID, which will be swiped once to unlock the jar and again to lock it back up.  A digital scale will measure and save the weight at both the locking and unlocking of the jar to measure the net weight change caused by a user adding or removing cookies.

The cookie jar will be a box with an electromagnetic locking mechanism, a HID mutliCLASS RFID card reader, and a digital scale below the floor of the box.  When a student ID is swiped the username, time of access, and current weight of the cookie jar’s contents are logged into the database.  When the user swipes a second time the cookie jar is locked and the username, time of locking, and net weight change are logged.  This completes all data logged for that session.

The cookies are free, however there is the currency of accessibility.  The net weight removed is directly proportional to the timeout before that user regains access to the cookie jar.  Additionally, there will be the cost of access to the jar to find out what’s inside.  The total timeout will be a sum of the cost for access plus the cost for the net weight removed.  If the net weight falls to zero at the closing of a session that user will lose access to the cookie jar indefinitely.

After a period of seemingly anonymous participation, visibility will be created when user histories are made public.  This feature will be released halfway through the experiment in order to allow for a period of perceived anonymity.  We’re interested in how this accountability will change user behavior.\

Each week, when the PBJ and fruit stocks are released, some of these physical resources will be placed in the cookie jar for safe keeping.


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CitiBike Stations

Team:  Aaron Arntz, Abe Rubenstein, and T.K. Broderick

Proposal:  We want to analyze the effect of both pruning and expanding the Citi Bike service graph in order to determine how these actions might improve self-regulation of the system. Using trip data, we will identify underperforming stations and experiment with network simulations that either remove such stations or add supplementary stations in an attempt to make all stations as efficient as possible. The goal of the simulation would be to help estimate the influence of station locations on the system, guiding decisions about station removal and relocation to better serve the needs of the users and the issues of reallocation. The simulation will use the CitiBike data together with some assumptions about how station removal or relocation will influence use.

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The nervous system is a cellular communication network that manages both the voluntary and involuntary functions of the body it serves using electrochemical signals through neural pathways.  With this in mind, I thought it would be relevant to consider the ways in which an “electronic nervous system” also operates both with and without the input or engagement of the user.  And like the central nervous system of our bodies, information networks are an integral part of our everyday functionality and survival, such as food, water, energy, transportation, money, and emergency response.

cellular |ˈselyələr| adjective

1 of, relating to, or consisting of living cells:

2 denoting or relating to a mobile telephone system that uses a number of short-range radio stations to cover the area that it serves, the signal being automatically switched from one station to another as the user travels about.

Besides this convenient coherence through shared terminology, the cellular communication networks that facilitate our massive web of communications are a perfect example of an electronic nervous system.  We are now almost universally depended on our phones to serve us information in order to successfully navigate the urban landscape, whether it’s the maps and routes that help us get where we need to go from services like Google maps or suggestions for where to go and what to do from services like FourSquare.  When I exit a subway station, rather than looking at street signs to orient myself I look at the map on my phone.  It tells me which direction I’m facing and with that information I determine which direction I need to walk to reach my destination.  When I’m in a neighborhood I’m unfamiliar with and looking for a good bar or restaurant I look to FourSquare for suggestions.

The location-based data that’s collected through the tracking of an individuals coordinates over cellular networks using GPS can help mobility systems designers better understand an entire city population’s collective habits of movement.  Prior to having access to this resolution of individual and collective position data, addressing the operational realities of a city had to be formed by assumptions extrapolated by the observation of smaller subsets of information.  Now there is a large-scale body of information that details where people go and when they go there.  Personal position information history, an involuntary collection of data, helps services learn about us by identifying our habits and preferences

If you own an iPhone, go to:

Settings > Privacy > Location Services > System Services > Frequent Locations

Then click on a city under History.  “New York, New York” will likely be at the top of the list for everyone in this class.  Here you’ll find a list of all the places you’ve stopped long enough to be registered as a “destination” along with a visualization with those destinations plotted on a map.

An intelligent urban system I started utilizing this summer is the GPS tracking of buses.  I live at the top of Greenpoint, which is accessed by the G train.  Due to maintenance, this train wasn’t operating in my area for most of the summer and for this reason I had to familiarize myself with the bus system.  In one particular instance I waited for nearly an hour for a bus that was scheduled to arrive much earlier.  I could have walked to the L in less time and this scheduling error caused me to arrive late for a meeting.  After finally arriving I was informed about the app Bus Time, which provides real-time information about the location of buses and their estimated arrival time for each stop.

I started using the app and it’s become a primary tool in planning my commutes.  Knowing the exact time of arrival (regardless of the posted schedule at the stop) helps me make informed decisions about when to leave the house, when a bus will be faster than a train, and which bus to take when there are several options within a small radius.  Even though the G train is now operating in my area again I’ve become much more comfortable using the bus system and the GPS information is a powerful tool for trip planning.

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