Exploring Spatial Variation of Soil Salinity in the Yellow River Delta

摘要

In this paper, spatial autocorrelation analysis, ordinary least square (OLS) and spatial regression model are applied to explore spatial variation of soil salinity based on samples collected from the Yellow River Delta. Generally, spatial data, like soil salinity, elevation etc., are characterized by spatial effects such as spatial dependence and spatial structure. Inasmuch as these effects exist, the utilization of OLS model may lead to inaccurate inference about predictor variable. Moreover, the traditional regression models used to analyze spatial data often have autocorrelated residuals which violate the assumption of Guess-Markov Theorem. This indicates that conventional regression models cannot be used in analyzing spatial variation of soil salinity directly. To overcome this limitation, spatial regression model is introduced to explore the relationship between soil salinity and environmental factors (including elevation, pH and organic matter concentration, etc.). By verifying Moran's I scatterplot of regression residuals, we find no autocorrelation in spatial regression model compared with high positive autocorrelation in the OLS model;besides, the spatial regression model has a significant (p ＜ 0.01) estimations and good-fit-it in our study. Finally, an approach of specifying suitable spatial weight matrix is put forward.