Spatial Outlier Detection for Mobility Profile Mining

With the rigorous growth of cellular network many mobility datasets are available publically, which attracted researchers to study human mobility fall under spatio-temporal phenomenon. Mobility profile mining is main task in spatio-temporal trend analysis which can be extracted from the location information available in the dataset. The location information is usually gathered through the GPS, service provider assisted faux GPS and Cell Global Identity (CGI). Because of high power consumption and extra resource installation requirement in GPS related methods, Cell Global Identity is most inexpensive method and readily available solution for location information. CGI location information is four set head i.e. Mobile country code (MCC), Mobile network code (MNC), Location area code (LAC) and Cell ID, location information is retrieved in form of longitude and latitude coordinates through any of publically available Cell Id databases e.g. Google location API using CGI. However due to of fast growth in GSM network, change in topology by the GSM service provider and technology shift toward 3G exact spatial extraction is somehow a problem in it, so location extraction must dealt with spatial outlier's problem first for mobility building. In this paper we adopt a methodology for the detection of spatial outliers from GSM CGI data, the LAC-clustering, which is a variant of a hierarchical clustering, based and used the basic GSM network architecture properties.