地理加权回归及其在土壤和环境科学上的应用前景

Geographically Weighted Regression and Its Application Prospect in Soil and Environmental Sciences

地理加权回归(GWR)是近些年来出现的一种新的空间局部回归技术。它是将数据的空间位置嵌入线性回归模型中,以此来探测空间关系的非平稳性。在运用于空间数据分析方面,相对于传统的普通最小二乘回归法,具有明显的优势。本文首先介绍了GWR的理论起源并描述了该方法的基本原理、存在的不足以及后来的扩展;然后为了更准确地了解GWR的应用状况和研究进展,进行了一个文献调查;接着回顾了GWR在土壤和环境科学上的初步应用情况;最后对该方法在土壤和环境科学上的应用前景作了展望。目的是为我国土壤和环境科学领域的同行了解和应用GWR提供参考。经过国内外研究者多年的研究和实践,GWR方法已被证明是一个理论上较为成熟,能够应用到实际研究中的优秀空间统计学方法。因此,GWR在土壤和环境科学上将会有着广泛的应用前景。

Geographically Weighted Regression （GWR） is a new spatially local regression technology, which emerged in last decade and is attracting more and more attention in recent years. The technology is used for exploring spatial non-stationarity through embedding spatial locations in linear regression models. Compared with the traditional ordinary least squares regression method which uses global parameters, the GWR method uses local correlation coefficients to incorporate spatial heterogeneity which is usually non-stationary, thus more effectively dealing with spatial data. This paper first introduced the theoretical origin, principle, existing deficiencies and further expansion of GWR. At the meantime, in order to understand the research and application status of GWR more accurately, a literature survey was conducted. Then, the application situation of GWR in the soil and environmental sciences were reviewed and a look into the future was made. After years of development and practice, GWR has been proved to be a mature outstanding approach and should have a broad prospect of application in evaluation of resources and environment.