GSM移动性数据的空间离群点检测
Spatial Outlier Detection from GSM Mobility Data
摘要：
这篇论文已被作者撤回。随着蜂窝网络的迅猛发展，大量的移动数据集被提供，这吸引了研究人员将人类的移动行为纳入时空现象的研究范畴。移动剖面的建立是时空趋势分析的主要任务，它可以从数据集中可用的位置信息中提取。位置信息通常通过GPS、服务提供商辅助的伪GPS和小区全球身份（CGI）来收集。由于GPS相关方法中的高功耗和额外的资源安装要求，小区全球身份是用于位置信息的最便宜的方法和容易获得的解决方案。CGI位置信息是四个集头，即移动国家代码（MCC）、移动网络代码（MNC）、位置区域代码（LAC）和小区ID，位置信息以经度和纬度坐标的形式通过任何可用的小区ID数据库（例如使用CGI的Google位置API）来检索。然而，由于GSM网络的快速增长，由GSM服务提供商和技术转移到3G的拓扑结构的变化在某种程度上是一个问题，在它的精确的空间提取，所以位置提取必须首先处理的空间离群点的问题移动性建设。本文提出了一种从GSM CGI数据中检测空间异常值的方法，该方法基于层次聚类，并使用基本的GSM网络结构属性。
英文摘要：
This paper has been withdrawn by the authors.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 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 proposed a methodology for the detection of spatial outliers from GSM CGI data, the proposed methodology is hierarchical clustering based and used the basic GSM network architecture properties.
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