The robust principal component analysis （RPCA） is a technique of multivariate statistics to assess the social and economic environment quality. This paper aims to explore a RPCA algorithm to analyze the spatial hete...The robust principal component analysis （RPCA） is a technique of multivariate statistics to assess the social and economic environment quality. This paper aims to explore a RPCA algorithm to analyze the spatial heterogeneity of social and economic environment of land uses （SEELU）. RPCA supplies one of the most efficient methods to derive the most important components or factors affecting the regional difference of the social and economic environment. According to the spatial distributions of the levels of SEELU,the total land resources of China were divided into eight zones numbered by Ⅰ to Ⅷ which spatially referred to the eight levels of SEELU.展开更多
基金Supported by the National Scientific Foundation of China(70873118 70821140353 )+4 种基金the Chinese Academy of Sciences(KZCX2-YW-305-2 KZCX2-YW-326-1)the Ministry of Science and Technology of China ( 2006DFB919201 2008BAC43B012008BAK47B02)~~
文摘The robust principal component analysis （RPCA） is a technique of multivariate statistics to assess the social and economic environment quality. This paper aims to explore a RPCA algorithm to analyze the spatial heterogeneity of social and economic environment of land uses （SEELU）. RPCA supplies one of the most efficient methods to derive the most important components or factors affecting the regional difference of the social and economic environment. According to the spatial distributions of the levels of SEELU,the total land resources of China were divided into eight zones numbered by Ⅰ to Ⅷ which spatially referred to the eight levels of SEELU.
