Location-Aware Information Delivery with ComMotion
HUC '00 Proceedings of the 2nd international symposium on Handheld and Ubiquitous Computing
Learning Significant Locations and Predicting User Movement with GPS
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
Extracting places from traces of locations
Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
On using existing time-use study data for ubiquitous computing applications
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
CenceMe: injecting sensing presence into social networking applications
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
Activity-aware map: identifying human daily activity pattern using mobile phone data
HBU'10 Proceedings of the First international conference on Human behavior understanding
Mining significant semantic locations from GPS data
Proceedings of the VLDB Endowment
On the semantic annotation of places in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Employing user feedback for semantic location services
Proceedings of the 13th international conference on Ubiquitous computing
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Distance-Based outlier detection on uncertain data of gaussian distribution
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
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Semantic place labels are labels like "home", "work", and "school" given to geographic locations where a person spends time. Such labels are important both for giving understandable location information to people and for automatically inferring activities. Deployed products often compute semantic labels with heuristics, which are difficult to program reliably. In this paper, we develop Placer, an algorithm to infer semantic places labels. It uses data from two large, government diary studies to create a principled algorithm for labeling places based on machine learning. Our labeling reduces to a classification problem, where we classify locations into different label categories based on individual demographics, the timing of visits, and nearby businesses. Using these government studies gives us an unprecedented amount of training and test data. For instance, one of our experiments used training data from 87,600 place visits (from 10,372 distinct people) evaluated on 1,135,053 visits (from 124,517 distinct people). We show labeling accuracy for a number of experiments, including one that gives a 14 percentage point increase in accuracy when labeling is a function of nearby businesses in addition to demographic and time features. We also test on GPS data from 28 subjects.