Knowledge of the nature of phenomena in a given area is the basis for prediction of the changes resulting from a planned implementation of spatial decisions. A way to aid the prediction is a simulation, e.g. a dynamic model of a phenomenon. Repeating the simulation with changing parameters is one of the most effective ways for decision making. Spatial phenomena are characterized by repetition of local interactions between neighboring pieces of the surface. The sum of these local processes gives an image of a spatially varying phenomenon occurring sometimes over wide areas. A tool that best reflects these local interactions are cellular automata. In modeling natural phenomena a cell-automaton reflects a single part of the environment. Assuming the interactions between different components of the model, or their relationship with the environment, we can describe the operation of the slot to pursue them due to its susceptibility to external stimuli, or the impact on the state of the environment. As a part of this work, automata have been implemented aimed at transforming the environment, called Interactive Cellular Automata (ICA). While the data changes in a limited range of values, depending on their state and the state of the environment (modeled phenomena), they cause changes both in the space of the automata and the modeled environment as well. It has been shown that such formal action has a wide range of spatial applications that differ only by the form of the transition rule. This has been confirmed by the results of the adaptation of such formalism for modeling the site after classification, aggregation of object imaging and simulation of water circulation.