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Selection of Spatial Regression Model Using Point Pattern Analysis
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 Title & Authors
Selection of Spatial Regression Model Using Point Pattern Analysis
Shin, Hyun Su; Lee, Sang-Kyeong; Lee, Byoungkil;
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When a spatial regression model that uses kernel density values as a dependent variable is applied to retail business data, a unique model cannot be selected because kernel density values change following kernel bandwidths. To overcome this problem, this paper suggests how to use the point pattern analysis, especially the L-index to select a unique spatial regression model. In this study, kernel density values of retail business are computed by the bandwidth, the distance of the maximum L-index and used as the dependent variable of spatial regression model. To test this procedure, we apply it to meeting room business data in Seoul, Korea. As a result, a spatial error model (SEM) is selected between two popular spatial regression models, a spatial lag model and a spatial error model. Also, a unique SEM based on the real distribution of retail business is selected. We confirm that there is a trade-off between the goodness of fit of the SEM and the real distribution of meeting room business over the bandwidth of maximum L-index.
Spatial Regression Model;Spatial Error Model;Kernel Density;L-index;Meeting Room Business;
 Cited by
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