uw/places2006050213200605022006-11-14uw/placesLocation-aware dataset for extracting significant places.Real, long-term data collected from three participants using a Place Lab client,
from which the authors extract significant places.the initial version2006-05-022004-06-072004-06-1051525349http://www.cs.washington.edu/homes/jhkang/http://www.crawdad.org/wiki/pmwiki.php?n=Main.Dataset.uw-placeslocationwardriving802.11GPSsignal strengthLocation-aware Computing802.11 infrastructureLocation-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.20200605022006-11-14uw/places/placelabPlace Lab traceset for extracting significant places.Place Lab traceset for extracting significant places.the initial version2006-01-012004-06-072004-06-10Location-aware ComputingLocation 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.uw/places54200605022006-11-14uw/places/placelab/campusTwo-hour long Place Lab trace collected on the campus of University of Washington, Seattle.Two-hour long Place Lab trace collected on the campus of University of Washington, Seattle.the initial versionfalse2006-05-022004-06-072004-06-07The 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./download/uw/places/campus.tar.gzuw/places/placelab55200605022006-11-14uw/places/placelab/city-wide12-hour long Place Lab trace collected city-wide in Seattle, WA.12-hour long Place Lab trace collected city-wide in Seattle, WA.the initial versionfalse2006-05-022004-06-102004-06-10The 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./download/uw/places/city-wide.tar.gzuw/places/placelab51uw/placesJong Hee Kangjhkang@cs.washington.eduUniversity of WashingtonComputer Science and EngineeringGraduate studentComputer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195-2350http://www.cs.washington.edu/homes/jhkang/52uw/placesWilliam Welbourneevan@cs.washington.eduUniversity of WashingtonComputer Science and EngineeringStudent53uw/placesBenjamin Stewartstewartb@cs.washington.eduUniversity of WashingtonComputer Science and EngineeringStudent49uw/placesGaetano Borriellogaetano@cs.washington.eduUniversity of WashingtonComputer Science and EngineeringProfessorDepartment of Computer Science and Engineering, University of Washington, Box 352350 Seattle, WA 98195-2350+1-206-685-9432+1-206-543-2969http://www.cs.washington.edu/homes/gaetano/gaetano.borriello@intel.comIntel Research SeattleResearch member (Founding Director)Intel Research Seattle, 1100 NE 45th Street, Seattle, WA 98105-4615+1-206-545-2530+1-206-633-6504kang-extractingJong Hee KangWilliam WelbourneBenjamin StewartGaetano BorrielloExtracting places from traces of locationsSIGMOBILE Mob. Comput. Commun. Rev.932005http://www.cs.washington.edu/homes/jhkang/papers/kang05mc2r.pdf1559-166258-68http://doi.acm.org/10.1145/1094549.1094558ACM PressNew York, NY, USALocation-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).measurementwirelessuw_placescrawdaduw/places20050001