Location-based notification as a general-purpose service
WMC '02 Proceedings of the 2nd international workshop on Mobile commerce
The Journal of Machine Learning Research
Proceedings of the 12th ACM international conference on Ubiquitous computing
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Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Recommending friends and locations based on individual location history
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Sharing location in online social networks
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Extracting social and community intelligence from digital footprints: an emerging research area
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IEEE Pervasive Computing
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Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Understanding user spatial behaviors for location-based recommendations
Proceedings of the 22nd international conference on World Wide Web companion
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In this paper, we illustrate how User Generated Mobile Location Data (UGMLD) like Foursquare check-ins can be used in Trade Area Analysis (TAA) by introducing a new framework and corresponding analytic methods. Three key processes were created: identifying the activity center of a mobile user, profiling users based on their location history, and modeling users' preference probability. Extensions to traditional TAA are introduced, including customer-centric distance decay analysis and check-in sequence analysis. Adopting the rich content and context of UGMLD, these methods introduce new dimensions to modeling and delineating trade areas. Analyzing customers' visits to a business in the context of their daily life sheds new light on the nature and performance of the venue. This work has important business implications in the field of mobile computing.