Cellular network data has become a hot source of study for extraction of user-mobility and spatio-temporal trends. Location binding in mobility data can be done through different methods like GPS, service provider assisted faux-GPS and Cell Global Identity (CGI). Among these Cell Global Identity is most inexpensive method and readily available solution for mobility extraction; however exact spatial extraction is somehow a problem in it. This paper presents the spatial extraction technique of mobile phone user raw data which carries the information like location information, proximity location and activity of subjects. This work mainly focuses on the data pre-processing methodology and technique to interpret the low level mobility data into high level mobility information using the designed clustering methodology and publically available Cell-IDs databases. Work proposed the semi- supervised strategy to derive the missing locations thorough the usage of semantic tag information and removal of spatial outliers for precise mobility profile building.
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