Selection of spatial regression model using point pattern analysis
- Authors
- Shin, H.S.; Lee, S.-K.; Lee, B.
- Issue Date
- 2014
- Publisher
- Korean Society of Surveying
- Keywords
- Kernel density; L-index; Meeting room business; Spatial error model; Spatial regression model
- Citation
- Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography, v.32, no.3, pp.225 - 231
- Journal Title
- Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography
- Volume
- 32
- Number
- 3
- Start Page
- 225
- End Page
- 231
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/13030
- DOI
- 10.7848/ksgpc.2014.32.3.225
- ISSN
- 1598-4850
- Abstract
- 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 confrm 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.
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