Spatial Outlier Detection from GSM Mobility Data
Shafqat Ali Shad, Enhong Chen

TL;DR
This paper proposes a hierarchical clustering methodology to detect spatial outliers in GSM CGI data, addressing challenges in accurate location extraction due to GSM network topology changes.
Contribution
It introduces a novel hierarchical clustering-based approach specifically designed for identifying spatial outliers in GSM mobility datasets.
Findings
Effective detection of spatial outliers in GSM data
Improved accuracy in mobility profile building
Addresses topology change challenges in GSM networks
Abstract
This paper has been withdrawn by the authors. 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 building 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…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Opportunistic and Delay-Tolerant Networks · Geographic Information Systems Studies
