A Dynamic Classification Pattern of Spatial Statistical Services Using Formal Concept Analysis. Issue 4 (19th February 2018)

• Record Type: Journal Article
• Authors: Chen, Yumin; Zhou, Jiang; Wilson, John P.; Wu, Jingyang; Wu, Qianjiao; Yang, Jiaxin
• Is Part Of: Geographical analysis. Volume 50:Issue 4(2018)
• Page Start: 454
• Page End: 476
• Publication Date: 2018-02-19
• Subjects: Geography -- Methodology -- Periodicals; Electronic journals; 910.00182
• DOI: 10.1111/gean.12154
• Languages: English
• ISSNs: 0016-7363

Abstract

Abstract : In ubiquitous computing environments, with advanced Information and Communication Technologies, the availability of geographical data is rapidly improving. Spatial statistical services which is based on mathematics and geographic principles provide powerful tools to mine effective information from the rapid improving data. But how to help users to find the appropriate spatial statistical service is a serious challenge. Classification which can help to organize and manage the service effectively might be the key to solve it. However, traditional classifications which start from a certain perspective such as a service function or data source usually aim at a certain application. It is fixed and does not consider both the link between the different attributes and the demands of different users. Formal concept analysis (FCA) utilizes mathematical order theory and particularly the theory of complete lattices to comprehensively express the interrelationships between attributes and objects. Based on FCA, this article provides a dynamic classification pattern which take the relationships between these services and the characteristics of ubiquitous environments into consideration to help different users to complete the expected classification that meets their demands. Moreover, with this pattern, any number of additional categories could be added to the classification scheme flexibly. Two kinds of classification results that use three kinds of sensors and data types are presented to prove the feasibility and validity of the dynamic classification pattern.