. . "New York" . . "RIV/68407700:21230/10:00172398!RIV11-MSM-21230___" . "2010-11-08+01:00"^^ . "Spatial Extension of the Reality Mining Dataset"@en . "Spatial Extension of the Reality Mining Dataset" . . "288966" . . . . "[09AA37229C36]" . "Kencl, Luk\u00E1\u0161" . "The 7th IEEE International Conference on Mobile Ad-hoc and Sensor Systems" . "Data captured from a live cellular network with the real users during their common daily routine help to understand how the users move within the network. Unlike the simulations with limited potential or expensive experimental studies, the research in user-mobility or spatio-temporal user behavior can be conducted on publicly available datasets such as the Reality Mining Dataset. These data have been for many years a source of valuable information about social interconnection between users and user-network associations. However, an important, spatial dimension is missing in this dataset. In this paper, we present a methodology for retrieving geographical locations matching the GSM cell identifiers in the Reality Mining Dataset, an approach base on querying the Google Location API. A statistical analysis of the measure of success of locations retrieval is provided. Further, we present the LAC-clustering method for detecting and removing outliers." . . "S" . . "978-1-4244-7489-9" . "mobility; tracking; Reality Mining; GSM; Cell-ID; agglomerative clustering"@en . "RIV/68407700:21230/10:00172398" . . . . "Spatial Extension of the Reality Mining Dataset" . . "21230" . "San Francisco, CA" . "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC" . "8"^^ . "Spatial Extension of the Reality Mining Dataset"@en . . . "2"^^ . . "2"^^ . "Data captured from a live cellular network with the real users during their common daily routine help to understand how the users move within the network. Unlike the simulations with limited potential or expensive experimental studies, the research in user-mobility or spatio-temporal user behavior can be conducted on publicly available datasets such as the Reality Mining Dataset. These data have been for many years a source of valuable information about social interconnection between users and user-network associations. However, an important, spatial dimension is missing in this dataset. In this paper, we present a methodology for retrieving geographical locations matching the GSM cell identifiers in the Reality Mining Dataset, an approach base on querying the Google Location API. A statistical analysis of the measure of success of locations retrieval is provided. Further, we present the LAC-clustering method for detecting and removing outliers."@en . . . "Ficek, Michal" . .