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 Morans 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.

关键词： soil salinity spatial autocorrelation ordinary least square model spatial regression model Yellow River Delta

作者: Jianghao Wang Hong Wang Yong Ge

作者单位: College of Hydrology and Water Resources, Hohai University, Nanjing 210098 P.R.China State Key Laboratory of Resources and Environment Information System, Chinese Academy of Science, Be

会议类型: 国际会议

会议名称: 北京国际地理信息系统学术讨论会第七届会议（7th International Workshop Geographical Information System

会议地点: 北京

会议语种:英文

页码: 514-519

在线出版日期: 2007-09-14（万方平台首次上网日期，不代表论文的发表时间）