文章基本信息

标题：Defining Spatio-Temporal Neighbourhood of Network Data
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作者：Tao Cheng ; Berk Anbaroglu
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2010
卷号：XXXVIII - Part 2
页码：75-79
出版社：Copernicus Publications
摘要：To improve the accuracy and efficiency of space-time analysis, spatio-temporal neighbourhoods (STNs) should be investigated and analysed in the classification, prediction and outlier detection of space-time data. So far most researches in space-time analysis use either spatial or temporal neighbourhoods, without considering both time and space at the same time. Moreover, the neighbourhoods are mostly defined intuitively without quantitative measurement. Furthermore, STNs of network data are less investigated compared with other types of data due to the complexity of network structure. This paper investigates the existing approaches of defining STNs and proposes a quantitative method to define STNs of network data in which the topology of the network does not change but the characteristics of the edges (i.e. thematic attribute values) change with time which requires dynamic STNs adapted to the properties of the network. The proposed method is tested by using London traffic network data
关键词：Spatio-temporal neighbourhood; spatio-temporal clustering; network complexity