An improved spatial-temporal data model defined on the cellular space

Typical Cellular Automata (CA) driven by deterministic rules can´t fit well the environment changes. Thus introduce rules adjustment, which requires recording the change of CA pattern by establishing a new spatial-temporal data model. Based on the property of forestry fire spreading and ESTDM model, an improved spatial-temporal data model CAESTDM which is defined on the cellular space is proposed. The new model is driven by events and is spatial and temporal scalable, which better fits for the operation of the cellular automata with less data redundancy.