Spatial Data Mining and Analysis of the Distribution of Regional Economy

The aim of this paper is to study the regional economic difference with the spatial data mining theories. In this paper, we take the per capita agricultural total output value as index variable, and take the township as the basic analysis unit. Based on the ESDA methods (global and local spatial autocorrelation) of spatial data mining theory, including Moran I index, Moran Scatter Plot and LISA, we research and analyze the agricultural economy spatial distribution of Beijing townships in 2005 from the spatial interactive angel, and then reveal the spatial autocorrelation and spatial heterogeneity among townships. The results show that agricultural economy of Beijing townships has a strong spatial correlation generally, and there also exist spatial heterogeneity problems between local townships.