[Dataset] uw/places (v. 2006-05-02)

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the initial version

@MISC{uw-places-2006-05-02,
  author = {Jong Hee Kang and William Welbourne and Benjamin Stewart and Gaetano Borriello},
  title = {{CRAWDAD} data set uw/places (v. 2006-05-02)}, 
  howpublished = {Downloaded from http://crawdad.cs.dartmouth.edu/uw/places},
  month = may,  
  year = 2006
}
					

Real, long-term data collected from three participants using a Place Lab client, 
from which the authors extract significant places

Location-aware systems are proliferating on a variety 
of platforms from laptops to cell phones. Though these systems 
offer two principal representations in which to work with 
location (coordinates and landmarks) they do not offer a means 
for working with the userlevel notion of "place".  A place is 
a locale that is important to a user and which carries a 
particular semantic meaning such as "my place of work", 
"the place we live" or "my favorite lunch spot". 

The authors propose an algorithm for extracting significant 
places from a trace of coordinates. The authors experimentally 
evaluate the algorithm with real, long-term data collected 
from three participants using a Place Lab client, a software client 
that computes location coordinates by listening for RF-emissions 
from known radio beacons in the environment (e.g. 802.11
access points, GSM cell towers).

As all the trace collectors typically stay 
within the Seattle city limits, and as most of this area 
is covered by the Place Lab AP database, there were few
problems with location data being unavailable.

The authors use Place Lab to collect traces of location
coordinates. Place Lab provides a way for a WiFi-enabled client 
device to automatically determine its location by listening to 
RF-emissions from known 802.11 access points (APs) in the environment.
Specifically, the system exploits the fact that each AP
periodically broadcasts its unique MAC address as part of 
its management beacon. A client holds a database of 
(MAC address, latitude and longitude) pairs which it uses 
to compute its location from heard beacons.
When the client device receives beacon messages
from nearby APs, it retrieves each AP's location
from the database and computes its own location
as the average of retrieved coordinates, using a simple
centroid tracking scheme.

[Traceset] uw/places/placelab (v. 2006-05-02)

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the initial version

@MISC{uw-places-placelab-2006-05-02,
  author = {Jong Hee Kang and William Welbourne and Benjamin Stewart and Gaetano Borriello},
  title = {{CRAWDAD} trace set uw/places/placelab (v. 2006-05-02)}, 
  howpublished = {Downloaded from http://crawdad.cs.dartmouth.edu/uw/places/placelab},
  month = may,  
  year = 2006
}
					

Place Lab traceset for extracting significant places

Location coordinates were generated and logged 
at a rate of once per second using Place Lab's centroid tracker. 
For initial evaluation, two day-length traces were collected 
during the daily routines of the first and second authors.
The traces were collected with wireless mobile devices 
(e.g. laptop, PDA), and corresponding place logs were also kept.

[Trace] uw/places/placelab/campus (v. 2006-05-02)

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the initial version

@MISC{uw-places-placelab-campus-2006-05-02,
  author = {Jong Hee Kang and William Welbourne and Benjamin Stewart and Gaetano Borriello},
  title = {{CRAWDAD} trace uw/places/placelab/campus (v. 2006-05-02)}, 
  howpublished = {Downloaded from http://crawdad.cs.dartmouth.edu/uw/places/placelab/campus},
  month = may,  
  year = 2006
}
					

Two-hour long Place Lab trace collected on the campus of University of Washington, Seattle

The first trace segment, over a small area, is of an author's
daily errands around the university campus and lasts 
for about 2 hours. The trace log was started in
the author's office. After about 10 minutes in his office, 
the author left to go home.
On his way off campus, the author ran errands in five
buildings across campus (places 2 through 6), staying
9 to 20 minutes in each place.

The traces are written in xml format. 
So, it is self-describing and easy to understand.

For example, in the campus trace, we measured the AP signals 
every second. And, for each measurement, our logging program 
appends a <position> element to the trace. The <position> element 
includes timestamp, coordinate computed with placelab using 
centroid tracker, and the list of detected access points.

[Trace] uw/places/placelab/city-wide (v. 2006-05-02)

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the initial version

@MISC{uw-places-placelab-city-wide-2006-05-02,
  author = {Jong Hee Kang and William Welbourne and Benjamin Stewart and Gaetano Borriello},
  title = {{CRAWDAD} trace uw/places/placelab/city-wide (v. 2006-05-02)}, 
  howpublished = {Downloaded from http://crawdad.cs.dartmouth.edu/uw/places/placelab/city-wide},
  month = may,  
  year = 2006
}
					

12-hour long Place Lab trace collected city-wide in Seattle, WA

The second trace is of an author's daily movement
between home, work, lunch, school, and a friend's
house with a total duration of about 12 hours. The
trace starts at the author's home in the morning. 
After about 30 minutes, he headed to his place of work. 
At work, he attended a meeting in a conference room 
in one corner of the building, and spent the rest of 
the time at his desk in the other corner. After 
a few hours, he left to attend two meetings in another 
building on campus - each meeting was held in a different 
room. After the second meeting ended, 
he returned to his pace of work. At lunch time, he went 
out to eat at a restaurant a few blocks away. At the end
of the day, he visited a shopping mall and his
friend's house before returning home.

The traces are written in xml format. 
So, it is self-describing and easy to understand.

For example, in the campus trace, we measured the AP signals 
every second. And, for each measurement, our logging program 
appends a <position> element to the trace. The <position> element 
includes timestamp, coordinate computed with placelab using 
centroid tracker, and the list of detected access points.

[Author] Jong Hee Kang

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[Author] William Welbourne

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[Author] Benjamin Stewart

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[Author] Gaetano Borriello

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[Paper] kang-extracting

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Location-aware systems are proliferating on a variety of platforms from laptops 
to cell phones. Though these systems offer two principal representations in 
which to work with location (coordinates and landmarks) they do not offer a 
means for working with the userlevel notion of 'place'. A place is a locale 
that is important to a user and which carries a particular semantic meaning 
such as 'my place of work', 'the place we live' or 'my favorite lunch spot'. 
Mobile devices can make more intelligent decisions about how to behave when 
they are equipped with this higher-level information. For example, a cell phone 
can switch to a silent mode when its owner enters a place where a ringer is 
inappropriate (e.g., a movie theater, a lecture hall, a place for personal 
reflection). In this paper, we describe an algorithm for extracting significant 
places from a trace of coordinates. Furthermore, we experimentally evaluate the 
algorithm with real, long-term data collected from three participants using a 
Place Lab client, a software client that computes location coordinates by 
listening for RF-emissions from known radio beacons in the environment (e.g. 
802.11 access points, GSM cell towers).