Which model is characterized by static spatial datasets combined for problem-solving?

Cartographic models are designed to represent spatial information visually and integrate various datasets to facilitate problem-solving in geographic contexts. These models leverage static spatial datasets, meaning that they depict information that doesn't change over time, allowing for the analysis and visualization of spatial relationships and patterns.

In cartographic modeling, different layers of information—such as land use, elevation, and transportation networks—are combined to create a comprehensive picture that helps in making informed decisions or understanding spatial phenomena. For instance, a cartographic model might overlay population density data on land use maps to visualize and analyze urban planning scenarios.

The nature of static datasets is crucial here. They provide a stable framework for analysis, making it easier to understand spatial interactions without the complexities introduced by changing conditions found in other types of models. This distinguishes cartographic models from spatio-temporal models, which handle data that varies over time, and network models, which focus on the flow through interconnected nodes and links. Vector models, while representing spatial data, primarily define shapes and boundaries rather than integrating multiple datasets for complex problem-solving.

Thus, the characterization of cartographic models as reliant on static spatial datasets for solution-oriented analysis is what makes this choice accurate in the context of the question.