How is discrete data typically characterized in GIS?

Discrete data in GIS is characterized as vector data with attributes for specific locations. This type of data represents distinct, separate occurrences or features in the geographic space, which can be accurately represented as points, lines, or polygons. Each feature has associated attribute data, meaning it carries additional information relevant to the specific location, such as demographic data for a point representing a city or the land use type for an area represented as a polygon.

In contrast to continuous data, which can vary smoothly across space (like elevation or temperature), discrete data focuses on distinct items in a map's context. The attributes linked to these discrete features provide detailed context, enabling complex analyses and visual presentations based on these specific locations. This is essential for tasks such as urban planning, environmental management, and resource allocation.

Other types of data mentioned, such as raster data, represent continuous surfaces and do not usually equate to discrete data. Linearly distributed data might imply a uniform distribution which is not typical in the context of discrete features, as they can be randomly or unevenly distributed across the geographic area. Thus, understanding that discrete data aligns with vector representation and specific attributes is key in GIS applications.