Clustering Algorithms
Location-Aware Information Delivery with ComMotion
HUC '00 Proceedings of the 2nd international symposium on Handheld 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
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Mining call and mobility data to improve paging efficiency in cellular networks
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Proceedings of the 7th international conference on Mobile systems, applications, and services
Discovering semantically meaningful places from pervasive RF-beacons
Proceedings of the 11th international conference on Ubiquitous computing
Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications
Predicting user-cell association in cellular networks from tracked data
MELT'09 Proceedings of the 2nd international conference on Mobile entity localization and tracking in GPS-less environments
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Place-Its: a study of location-based reminders on mobile phones
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Human mobility modeling at metropolitan scales
Proceedings of the 10th international conference on Mobile systems, applications, and services
Estimation of urban commuting patterns using cellphone network data
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Human mobility characterization from cellular network data
Communications of the ACM
Are call detail records biased for sampling human mobility?
ACM SIGMOBILE Mobile Computing and Communications Review
Quantifying the potential of ride-sharing using call description records
Proceedings of the 14th Workshop on Mobile Computing Systems and Applications
Regularly visited patches in human mobility
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Trade area analysis using user generated mobile location data
Proceedings of the 22nd international conference on World Wide Web
Exploring sustainability research in computing: where we are and where we go next
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Human mobility and predictability enriched by social phenomena information
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Proceedings of The First ACM SIGSPATIAL International Workshop on Computational Models of Place
Generating storylines from sensor data
Pervasive and Mobile Computing
From big smartphone data to worldwide research: The Mobile Data Challenge
Pervasive and Mobile Computing
Time-clustering-based place prediction for wireless subscribers
IEEE/ACM Transactions on Networking (TON)
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People spend most of their time at a few key locations, such as home and work. Being able to identify how the movements of people cluster around these "important places" is crucial for a range of technology and policy decisions in areas such as telecommunications and transportation infrastructure deployment. In this paper, we propose new techniques based on clustering and regression for analyzing anonymized cellular network data to identify generally important locations, and to discern semantically meaningful locations such as home and work. Starting with temporally sparse and spatially coarse location information, we propose a new algorithm to identify important locations. We test this algorithm on arbitrary cellphone users, including those with low call rates, and find that we are within 3 miles of ground truth for 88% of volunteer users. Further, after locating home and work, we achieve commute distance estimates that are within 1 mile of equivalent estimates derived from government census data. Finally, we perform carbon footprint analyses on hundreds of thousands of anonymous users as an example of how our data and algorithms can form an accurate and efficient underpinning for policy and infrastructure studies.