Continuous data in GIS typically represents which type of phenomena?

Continuous data in GIS represents phenomena where values can vary across a surface, making it essential for modeling and analyzing geographically variable attributes. This type of data is characterized by its smooth and uninterrupted transitions, meaning that instead of being confined to specific points, continuous data seeks to capture a gradient of values across a defined area. For instance, temperature, elevation, and precipitation are all examples of continuous phenomena, as they change gradually rather than being limited to discrete values at fixed locations.

In contrast, fixed locations typically align more closely with discrete data, where phenomena are measured at specific points rather than across a surface. Patterns that change over time can relate to both continuous and discrete data, but they primarily reflect temporal dynamics rather than spatial variability. Static, unchanging features are typically represented by discrete data as well, which conveys specific, identifiable locations without the variation found in continuous datasets.