Three classical model of geovisualization.

Taylor's theory on geovisualization emphasizes the importance of visual representation in understanding spatial data and enhancing geographic analysis. Key principles often associated with geovisualization, which may be reflective of Taylor's contributions, include:  

·Interactivity: Effective geovisualization allows users to interact with data, enabling them to manipulate variables and explore different scenarios. This interactivity helps in understanding complex spatial relationships. 

·Cognitive Processing: Visualizations are designed to align with human cognitive capabilities, making it easier for users to interpret spatial data. This involves using visual elements like color, shape, and size to convey information effectively. 

·Contextual Understanding: Geovisualization emphasizes the importance of context in interpreting data. Understanding the geographic, social, and temporal context of the data is crucial for accurate analysis and communication. 

·Storytelling: Good geovisualization tells a story about the data, guiding users through the analysis process and highlighting key insights. This narrative aspect helps in engaging the audience and facilitating understanding. 

·Collaboration: Geovisualization tools often support collaborative efforts, allowing multiple stakeholders to contribute to the analysis and interpretation of spatial data. This collaboration can enhance decision-making processes. 

DiBiase sees geovisualization not just as a means of representation but as a cognitive tool that can enhance the way users understand and analyze spatial data. He emphasizes the role of visualization in supporting human cognition, making complex data more accessible and interpretable. 、

He advocates for the use of interactive visualization tools that allow users to explore spatial data dynamically. This interactivity fosters a deeper engagement with the data and aids in discovering patterns, relationships, and insights that may not be immediately apparent through static representations. 

The Cartography Cube, proposed by Robert MacEachren and Chris Kraak in 1997, is a conceptual model designed to understand and analyze the various dimensions and components of cartographic visualization. This framework helps in discussing how different aspects of cartography interact and how they can be utilized effectively in geovisualization. 

This axis represents the types of data being visualized, such as qualitative versus quantitative data, or point, line, and area data. It emphasizes the nature of the data and how it can be represented on a map. The data dimension also encompasses aspects like data resolution, scale, and the inherent characteristics of the data being visualized (e.g., discrete vs. continuous data).

This axis refers to the cartographic representation itself, focusing on the design elements of the map. It includes aspects such as symbology, color schemes, and the overall layout of the map.The map dimension also addresses the design principles that affect readability and effectiveness, such as the use of visual hierarchy, contrast, and clarity.

The user dimension emphasizes the role of the map user in the visualization process. It considers the user's background, expertise, cognitive abilities, and the specific tasks they are trying to accomplish with the map. This dimension also explores how different users may interpret the same map differently based on their experiences and knowledge.