Abstract
We propose a method for modeling the time-series of local spatial association in geographical phenomena and implement a Web-based statistical GIS for the time-series analysis using client-provided dataset. In order to examine the pattern of time-series and classify similar ones on a cluster basis, we employ Moran scatterplot and extend it to time-series Moran scatterplot accumulated over a certain span of time. Using the time-series Moran scatterplot, we develop similarity measures of “state sequence” and “clustering transition” for the time-series of local spatial association. If we connect n corresponding points of a region on the time-series Moran scatterplot, the connected line composed of n nodes and n-1 edges forms a time-series signature of local spatial association for the region. From the similarity matrix of the time-series signatures, we generate a map of the clustered classification of changing regions. These analytical functionalities of cluster analysis on the time-series of local spatial association are implemented in a Web-based GIS using XML Web Services.
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Ahn, JS., Lee, YW., Park, KH. (2006). Web-Based Cluster Analysis for the Time-Series Signature of Local Spatial Association. In: Carswell, J.D., Tezuka, T. (eds) Web and Wireless Geographical Information Systems. W2GIS 2006. Lecture Notes in Computer Science, vol 4295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11935148_18
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DOI: https://doi.org/10.1007/11935148_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49466-9
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