What form of data is considered continuous in GIS?

In the context of Geographic Information Systems (GIS), continuous data pertains to information that can take any value within a given range and represents phenomena that exist across a landscape. Raster data, which is composed of a grid of cells or pixels, is particularly well-suited for representing continuous surfaces such as elevation, temperature, rainfall, and other types of spatial data that vary smoothly across space.

Raster data can capture changes in these continuous variables by utilizing different values across the grid to represent variations in the phenomenon being measured. For example, in an elevation model, the height above sea level will gradually change from one cell to another, smoothly transitioning and providing a detailed representation of the terrain. This characteristic allows for the application of various analyses, including calculations of slope, aspect, and hydrological modeling, which are essential in understanding continuous spatial patterns.

In contrast, vector data types such as points, lines, and polygons are typically used to represent discrete entities or specific locations rather than continuous phenomena. Therefore, while vector data is valuable for representing certain types of geographic information, it does not inherently support the concept of continuity across a landscape in the same manner that raster data does. Thus, raster data representing surfaces serves as the definitive example of continuous data in GIS.