An Extended Cellular Automata Model for Data Mining of Land Development Data
Gongzhu Hu, Yichun Xie · 2006
Land development is a critical task for municipalities and local governments. It demands a carefully crafted plan that takes factors of various aspects into considerations, including economical, financial, environmental, and regulatory. The Cellular Automaton (CA) model and several variations have been proposed and utilized to facilitate urban and regional land development, but elements in the models that help to find patterns in land development history to guide future planning are still lacking. In this paper, we propose an extended CA model (ECADM) to address this problem. The new model is multi-faceted extension of the CA model to include more attributes and transition rules so that data mining techniques can be applied to find relationships among various components in land development.