공간자기상관을 위한 이웃 정의 방법 비교

Abstract

For the identifying of spatial autocorrelation pattern, Moran’s Index (I) which has the range of value is -1 to +1 is common method for spatial autocorrelation measurements. When I is close to 1, all neighboring features have close to the same value, indicating clustered pattern. Conversely, if the spatial pattern is dispersed, I is close to -1. And I closing to 0 means spatially random pattern. However, this index equation is influenced by how define the neighboring features for target feature. To compare and understand the difference of neighbor definition methods, Fixed Euclidean Distance neighboring method and Gabriel Network method were used for I. In this study, these two methods were applied to two marine environments with water quality data. One is Gwangyang Bay which has complex geometric costal structure located in the South Sea of Korea. And for the Fixed Euclidean Distance method popular ArcGIS (ESRI) tool was used, but, for the Gabriel Network, Visual Basic program was developed to produce Gabriel Network and calculate Moran’s I and its Z-score automatically. According to those experimental results, different spatial pattern was showed differently for some data with the using of neighboring definition methods. Therefore, it is need to choose neighboring definition method carefully for spatial pattern analysis.