What is the primary function of data models in geospatial analysis?

The primary function of data models in geospatial analysis is to transform inputs into outputs using tools and functions. Data models serve as frameworks that structure how geographic data is organized, manipulated, and processed. They define the relationships between different data layers and facilitate the application of various analytical techniques to derive meaningful information from raw data.

In geospatial analysis, data models help in executing spatial operations, such as overlay, buffering, or network analysis, which are essential in producing results from simple inputs. By specifying not only the data type but also the processes that can be applied to the data, data models allow analysts to simulate real-world scenarios and test hypotheses more efficiently.

The other options focus on aspects that data models interact with but do not capture their primary purpose. Defining a study area, creating visual representations, or analyzing historical trends may all be parts of the broader analytical process, yet they do not encapsulate the main role of data models, which is fundamentally about transforming and processing data.