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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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 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 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.
 Keywords
Spatial Regression Model;Spatial Error Model;Kernel Density;L-index;Meeting Room Business;
 Language
English
 Cited by
 References
1.
Anselin, L. (2005), Exploring Spatial Data with GeoDa: a Workbook, Center for Spatially Integrated Social Science

2.
Bailey, T. C., and Gatrell, A. C. (1995), Interactive Spatial Data Analysis, Longman, London

3.
Borruso, G. and Schoier, G. (2004), Density analysis on large geographical databases, Search for an Index of Centrality of Services at Urban Scale, ICCSA 2004, LNCS 3044, pp. 1009-1015

4.
Brunsdon, C. (1995), Estimating probability surfaces for geographical point data: an adaptive kernel algorithm, Computers and Geosciences, Vol. 21, No. 7, pp. 877-894 crossref(new window)

5.
Chi, G. and Zhu, J. (2008), Spatial regression models for demographic analysis, Population Research and Policy Review, Vol. 27, pp. 17-42 crossref(new window)

6.
Cressie, N. (1991), Statistics for Spatial Data, John Wiley and Sons, New York

7.
Diggle, P. J. (1983), Statistical Analysis of Spatial Point Patterns, Academic Press, London

8.
Diggle, P. J. (1985), A kernel method for smoothing point process data, Applied Statistics, Vol. 34, No. 2, pp. 138-147 crossref(new window)

9.
Goodwin, M.T. and Unwin, D. (2000), Defining and delineating the central areas of towns for statistical monitoring using continuous surface representations, Transactions in GIS, Vol. 4, No. 4, pp. 305-317 crossref(new window)

10.
Hwang, S. (2004), Temporal extensions of K function, UCGIS Fall 2004, pp. 1-18

11.
Jin, C.-J., Park, H.-S., and Kang, J.-M. (2012), An empirical analysis of locational tendency of coffee shops around Hongik university, Journal of the Urban Design Institute of Korea, Vol. 13, No. 5, pp. 71-82. (in Korean with English abstract)

12.
Kloog, I., Haim A., and Portnov, B. (2009), Using kernel density function as an urban analysis tool: investigating the association between nightlight exposure and the incidence of breast cancer in Haifa, Israel, Computers, Environment and Urban Systems, Vol. 33, pp. 55-63. crossref(new window)

13.
Lee, B. (2008), Applying the L-index for analyzing the density of point features, The Journal of GIS Association of Korea, Vol. 16, No. 2, pp. 237-247. (in Korean with English abstract)

14.
Lee, H. Y. and Sim, J. H. (2011), GIS Geomatics, Bobmunsa, Paju. (in Korean)

15.
Lee, S.-K. and Lee, B. (2013), Assessing the appropriateness of the spatial distribution of Standard lots using the L-index, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 31, No. 6-2, pp. 601-609. (in Korean with English abstract) crossref(new window)

16.
Ripley, B. D. (1976), The second-order analysis of stationary point processes, Journal of Applied Probability, Vol. 13, pp. 255-66. crossref(new window)

17.
Yim, P. and Lee, S. (2013), Estimation of prices and rents of knowledge industrial centers in Seoul metropolitan area considering spatial autocorrelation, Journal of the Korea Real Estate Analysts Association, Vol. 19, No. 2, pp. 5-20. (in Korean with English abstract)