%0 Journal Article
%T Study on dimension-reduction of spatial economic statistics:A case study of economic statistial data of Sichuan<br>空间多维经济统计数据的降维方法——以四川省经济统计数据为例
%A DONG Cheng-wei
%A RUI Xiao-ping
%A DENG Yu
%A GUAN Xing-liang
%A <br>董承玮
%A 芮小平
%A 邓羽
%A 关兴良
%J 地理研究
%D 2012
%I 
%X There are more than three attributes in economic statistical data generally.When studying the inherent structural characteristics of these data such as clustering and distribution,researchers need to reduce multi-dimensional information to three-dimensional space or less to achieve multi-dimensional visualization.There are multi-dimensional reduction methods,whose results are different from each other because of different mathematics theories and application ranges,and the visualization results of these methods will vary.So evaluation of different methods can provide important references for the selection of methods in different areas.In the paper,the authors analyze economic statistical data of Sichuan province in 2007 based on county-unit by implementing four commonly used algorithms: the linear method PCA,nonlinear method NLM and SOFM,and a supervised classification method SVM,then obtain a series of classification results.Considering the status of economic development in Sichuan,the authors analyze the differences between the results of these methods,and draw some conclusions as follows.Although PCA can reveal the overall development trend,the result is not consistent with the real condition in Sichuan;NLM can well show the regional trend and core areas of economic development in Sichuan,and account for the development status;SOFM can also show the development status,but there are several classification errors in the northeastern part of the region.It is impossible for comparison within each cluster;as a supervised method,SVM needs a known sample set to train the classification process,which makes the sample selection subjective,and the search process for optimal parameters is complicated.The comparison of these methods and their application in economic statistics fields can provide a reference for the future relevant spatial dimension-reduction research.
%K dimension-reduction
%K multi-dimensional visualization
%K economic statistics data
%K Sichuan<br>降维
%K 多维可视化
%K 经济统计数据
%K 四川
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=869B153A4C6B5B85&jid=C0C75E88BA2EE501C8298896F64A711F&aid=21CB0722CA48644FFC96C6ABB1EDF2D6&yid=99E9153A83D4CB11&vid=4AD960B5AD2D111A&iid=5D311CA918CA9A03&sid=0F1312EB98113CF7&eid=ED51333671C94F12&journal_id=1000-0585&journal_name=地理研究&referenced_num=0&reference_num=0