Which of the following best describes discrete data in GIS?

Discrete data in GIS refers to information that can be classified into distinct categories or counts, where individual entities or events can be identified and counted without ambiguity. This type of data typically represents features that have clear boundaries, such as the number of buildings in an area, the number of trees in a forest, or the locations of parking lots.

The main characteristic of discrete data is that it deals with finite and separate values; thus, it is possible to list or enumerate these data points. For example, a dataset that includes the number of schools in a city is considered discrete because each school is a separate, countable entity.

In contrast, the other options represent different types of data in GIS. Continuous data, for instance, is characterized by smooth transitions and does not have distinct separations, making it unsuitable for describing discrete phenomena. These types of data are often associated with measurements like temperature or elevation, where values vary gradually rather than in distinct, countable amounts.