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A study on the spatial neighborhood in spatial regression analysis
Journal of the Korean Data & Information Science Society 2017;28:505-13
Published online May 31, 2017
© 2017 Korean Data & Information Science Society.

Sujung Kim1

1Mibyeong Research Center, Korea Institute of Oriental Medicine
Correspondence to: Sujung Kim
Senior researcher, Mibyeong Research Center, Korea Institute of Oriental Medicine, Daejeon 1672, Korea. E-mail: sjkim@kiom.re.kr
Received April 12, 2017; Revised May 17, 2017; Accepted May 18, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Recently, numerous small area estimation studies have been conducted to obtain more detailed and accurate estimation results. Most of these studies have employed spatial regression models, which require a clear definition of spatial neighborhoods. In this study, we introduce the Delaunay triangulation as a method to define spatial neighborhood, and compare this method with the k−nearest neighbor method. A simulation was conducted to determine which of the two methods is more efficient in defining spatial neighborhood, and we demonstrate the performance of the proposed method using a land price data.
Keywords : Delaunay triangulation, small area estimation, spatial neighbor.


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