A User-Centered Location Model
Personal and Ubiquitous Computing
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Extracting places from traces of locations
Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots
Discovering personal gazetteers: an interactive clustering approach
Proceedings of the 12th annual ACM international workshop on Geographic information systems
An experiment in discovering personally meaningful places from location data
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Extracting Semantic Location from Outdoor Positioning Systems
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Extraction of social context and application to personal multimedia exploration
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
Exploiting multiple radii to learn significant locations
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
Accurate GSM indoor localization
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
A quantitative method for revealing and comparing places in the home
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Practical metropolitan-scale positioning for GSM phones
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Ambient Compass: One Approach to Model Spatial Relations
ICDHM '09 Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009
SensLoc: sensing everyday places and paths using less energy
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Enriching location information: an energy-efficient approach
Proceedings of the 13th international conference on Ubiquitous computing
Challenges for social sensing using WiFi signals
Proceedings of the 1st ACM workshop on Mobile systems for computational social science
Contextual conditional models for smartphone-based human mobility prediction
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Checking in or checked in: comparing large-scale manual and automatic location disclosure patterns
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
Moving beyond the map: automated landmark based pedestrian guidance using street level panoramas
Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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Gathering and analyzing location data is an important part of many ubiquitous computing applications. The most common way to represent location information is to use numerical coordinates, e.g., latitudes and longitudes. A problem with this approach is that numerical coordinates are usually meaningless to a user and they contrast with the way humans refer to locations in daily communication. Instead of using coordinates, humans tend to use descriptive statements about their location; for example, "I'm home" or "I'm at Starbucks." Locations, to which a user can attach meaningful and descriptive semantics, are often called places. In this paper we focus on the automatic extraction of places from discontinuous GPS measurements. We describe and evaluate a non-parametric Bayesian approach for identifying places from this kind of data. The main novelty of our approach is that the algorithm is fully automated and does not require any parameter tuning. Another novel aspect of our algorithm is that it can accurately identify places without temporal information. We evaluate our approach using data that has been gathered from different users and different geographic areas. The traces that we use exhibit different characteristics and contain data from daily life as well as from traveling abroad. We also compare our algorithm against the popular k-means algorithm. The results indicate that our method can accurately identify meaningful places from a variety of location traces and that the algorithm is robust against noise.