DOI: 10.21227/tx6v-6s75
title: CRAWDAD uw/places<

description: Location-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.date/time of measurement start: 2004-06-07 date/time of measurement end: 2004-06-10 collection environment: 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). network configuration: 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. data collection methodology: 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. Tracesetuw/places/placelabPacket captures of BitTorrent traffic from Korea Telecom's mobile WiMAX network in Seoul, with payload cut off and with headers anonymized.files: campus.tar.gz, city-wide.tar.gzmeasurement purpose: Location-aware Computingmethodology: 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.sanitization: The payload was cut off by resampling with tcpreplay and tcpdump, and the header was anonymized by tcprewrite.uw/places/placelab Tracescampus: Two-hour long Place Lab trace collected on the campus of University of Washington, Seattle.configuration: The first trace segment, over a small area, is of an author'sdaily 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.format: 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 element to the trace. The element includes timestamp, coordinate computed with placelab using centroid tracker, and the list of detected access points.campus: 12-hour long Place Lab trace collected city-wide in Seattle, WA.configuration: The second trace is of an author's daily movementbetween home, work, lunch, school, and a friend'shouse 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 endof the day, he visited a shopping mall and hisfriend's house before returning home.format: 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 element to the trace. The element includes timestamp, coordinate computed with placelab using centroid tracker, and the list of detected access points.