%0 Journal Article
%T A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region<br>偏最小二乘法在苏锡常地区土地利用研究中的应用
%A ZHANG Yang
%A ZHOU Chenghu
%A ZHANG Yongmin
%A <br>张旸
%A 周成虎
%A 张永民
%J 地理学报(英文版)
%D 2007
%I 
%X 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.
%K land use
%K multivariate data analysis
%K partial least-squares regression
%K Suzhou-Wuxi-Changzhou region
%K multicollinearity<br>江苏
%K 苏州-无锡-常州地区
%K 土地利用研究
%K 多变量数据分析
%K 最小二乘偏回归法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=869B153A4C6B5B85&jid=E7E6A89DD0CE36AA3756C02EB6F15CA7&aid=B1494191DA4739C172EDA6BD536C3503&yid=A732AF04DDA03BB3&vid=BCA2697F357F2001&iid=0B39A22176CE99FB&sid=D9AE183D3F5C3C75&eid=5B5B75F4854B8331&journal_id=1009-637X&journal_name=地理学报(英文版)&referenced_num=0&reference_num=20