关键词: 城市土地, 经济增长, 自组织特征映射网络(SOFM), 城市分类

Abstract: Cities at prefectural level(area cities) are not only high-speed economic developing areas, but also the key areas of land supply, reorganization and active transaction. Five variables such as area of land transfer, average land prices, GDP, growth rate of GDP, and fixed assets investment are used to develop a self-organizing feature map(SOFM) artificial neural network model. The results show that 282 area cities in China are divided into the four categories: developed area of high land prices, developed area of low land prices, underdeveloped area of high land prices, underdeveloped areas of low land prices. According to the results, the characteristics of each region are analyzed and the current development situation is discussed. Classification results match the objective reality very well, indicating SOFM-based classification method is an alternative approach in research of socio-economic development.

Key words: urban land, economic growth, self-organizing feature map(SOFM), urban classification