Applying Geovisualization Techniques in Enhancing Knowledge Discovery Framework for Geographical Databases-The Case Study of Traffic Flow in Taipei City
Date Issued
2010
Date
2010
Author(s)
Chen, Chih-Yuan
Abstract
Geovisualization is a method of exploring spatial knowledge hidden in multidimensional geographic and temporal data via interaction with map and graph. The visualization of Self-Organizing Map (SOM) is one of the most effective methods but still has problems of what size the network should be. This research proposed a novel method called “Divide and Regroup”, to integrate clustering analysis and two SOM algorithms (SOM and Geo-SOM) interactively and dynamically for finding hidden data relations and spatial patterns. Two different rush-hour traffic flow data of Taipei City were selected and two cases were done to demonstrate the effectiveness of this novel method. In the first case, the correlation coefficient and coefficient of determination of the unclassified data were low. Two major groups of traffic flow data were recognized using the Geovisualization approach. The correlation coefficients and coefficients of determination of the classified data have improved significantly. In the second case, six major spatial clusters and liner feature groups were recognized. Furthermore, these groups showed different data patterns indicate that the Geovisualization approach is useful for identifying spatial and data characteristics hidden in geographic data. The results demonstrated the effectiveness of the novel method of Geovisualization.
Subjects
Geovisualization
SOM
traffic flow
clustering analysis
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