Journal of Geographical Sciences ›› 2007, Vol. 17 ›› Issue (2): 234-244.doi: 10.1007/s11442-007-0234-3

• Climate and Environmental Change • Previous Articles     Next Articles

A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region

ZHANG Yang11,2, 21, ZHOU Chenghu13   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China|
    2. Graduate School of the Chinese Academy of Sciences, Beijing 100039, China|
    3. Department of Resources and Environment Sciences, Henan University of Finance and Economics, Zhengzhou 450002, China
  • Received:2006-12-01 Revised:2007-02-10 Online:2007-06-25 Published:2007-06-25
  • Supported by:

    National Natural Science Foundation of China; No.40301038

Abstract:

In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares (PLS) regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and in-fluencing factors demonstrate the land use character of rural industrialization and urbaniza-tion in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.

Key words: land use, multivariate data analysis, partial least-squares regression, Suzhou-Wuxi-Changzhou re-gion, multicollinearity