Cellular automata are a widely used form of spatially explicit simulation model, where complex processes evolve over space and time through a lattice of cells, each linked to its neighboring cells. Typically, this spatial lattice is structured as a two-dimensional grid of square cells. Each cell holds a set of states that change over time according to transition rules, which depend on the state of the cell and its neighbors. That is, a cellular automata model allows the exploration of how local interactions lead to the emergence of global patterns, governed by clearly defined rules. A cellular automata model is defined by six key components: a lattice or framework, individual cells, neighboring cells, transition rules, initial conditions (states), and an update sequence (time). These models are well-suited to geographic information systems (GIS) due to their simple data structures and ability to represent spatial changes and patterns in an intuitive way. This has made cellular automata in simulating phenomena such as land use changes and the spread of diseases.