Description: The spatial model is an approach within generative models that focuses on the representation and analysis of data considering the spatial relationships between data points. This type of model is fundamental in various disciplines, as it allows capturing the structure and dependency that exists between the locations of the data. Unlike traditional models that may treat data independently, spatial models incorporate proximity and interaction between points, resulting in a richer and more contextualized representation of information. The main characteristics of spatial models include the ability to handle geospatial data, the identification of spatial patterns, and the prediction of phenomena based on location. These models are especially relevant in fields such as geography, ecology, urban planning, and epidemiology, where location plays a crucial role in the dynamics of the phenomena studied. By integrating the spatial dimension into the analysis, spatial models enable a better understanding of complex interactions and variability of data based on their location, making them powerful tools for informed decision-making and resource management.
