버퍼링 방법에 기반한 공간내삽법 적용이헤도닉 분석에 의한 대기질의한계잠재가격 추정에 미치는 영향

Abstract

In this study, we assess the effects of using diverse types of spatial interpolation methods on the size of estimated marginal implicit prices of air quality in a hedonic analysis. By using spatial interpolation methods in a hedonic analysis, one can estimate the level of air quality at a location with no air quality monitoring station through using the recorded level of air quality from nearby air quality monitoring stations. There exist many types of spatial interpolation methods that can be used in a hedonic analysis such as IDW(Inverse Distance Weighted), Thiessen Polygons, Splines, Kriging, etc. One problem with these methods is that each method produces slightly different estimates of the air quality level at a location with no air quality monitoring station due to their differences in algorithm when they are applied to the same study area in a hedonic analysis. We use four types of spatial interpolation methods such as Thiessen Polygon, Buffering, Thin Plate Splines, and Thin Plate Splines with Tension. Findings from this study show that using different types of spatial interpolation methods in a hedonic analysis does substantially differentiate the resulting sizes and statistical significances of the estimated marginal implicit prices of air quality. We find that, among four spatial interpolation methods used, the Buffering method could seriously bias the size of the estimated marginal implicit prices of air quality because it has the side effect of biasing the samples included for a hedonic analysis. The major implication of this study is that one should employ diverse spatial interpolation methods in a hedonic analysis because the size and statistical significance of the marginal implicit price of air quality can vary according to spatial interpolation methods employed with knowing that each method has its own limitations.