An improved cellular automata forecasting model for urban land use spatial structure changes

Abstract

Translate Article

Though the urban land use spatial dynamic simulation and forecasting based on cellular automata (CA) model have achieved remarkable progress, the CA model still has some problems and drawbacks in forecasting urban land use changes. In view of the deficiencies of traditional urban CA, an improved CA model based on spatial dynamic data mining and random forecast is proposed in this paper, which establishes an operable CA method to forecast and simulate the discrete status attribute. This improved CA model is examined in analyzing the urban land use structure changes in Jinan 2002-2006 and testified both feasible and effective. Based on the remote sensing images in Jinan 2002 and 2006, the urban land use spatial structures are classified into five types, commercial land, residential land, education facility, industrial land and the other. With the improved CA model, the urban land use framework in Jinan in 2010 was calculated, the result of which can be used as a reliable reference information for the following urban land use planning.