Analyzing the Characteristics of the Expansion of the Metropolises in China from 1990 to 2010 Using Self-organizing Neural Network

This research employ remotely sensed data and explorative data mining tools to study the characteristics of urban expansion in China. We used 27 metropolises in China as samples，and acquired the urban expansion data and socioeconomic statistics of these cities from 1990 to 2010. We selected the indices in terms of urban expansion rate，land use intensity，and landscape pattern，and then analyzed the change of these indices across cities using self-organizing feature map(SOFM) as a visual data mining tool. By examining the component planes generated by SOFM，we found that the cities expanded very rapidly in a low intensity way from 1990 to 2010. The area of urban land in 2010 became 3. 81 times of 1990 in average，but the intensity of urban population deceased annually 3%to 4%in average. These cities expanded more rapidly in a lower intensity way from 2000 to 2010 comparing with the former decade. The biggest cities expanded in most expansion rate in the first decade，while the relative small cities leading the expansion in the second decade.