What is a primary characteristic of a vector data model?

The primary characteristic of a vector data model is that it uses points, lines, and polygons for representation. This structure is fundamental to how vector data operates within geographic information systems (GIS). In vector models, spatial features are represented as discrete objects, allowing for precise modeling of geographic entities like roads (lines), lakes (polygons), and landmarks (points).

This representation is beneficial for various analyses, such as calculating distances or areas, and for accurately depicting the geometry of real-world objects. The ability to define shapes and boundaries with vector data enables detailed information about individual features to be captured, making it ideal for tasks that require high precision, such as urban planning or environmental monitoring.

The other options pertain to characteristics associated with other data models. Raster data, for instance, consists of grid cells and is used for continuous data representation (like elevation or temperature), which is distinct from the vector model’s focus on discrete entities. Also, while data visualization may include temporal elements, it is not a defining characteristic of the vector data model specifically, which is more concerned with spatial representation. Finally, stating that it only supports raster data directly contradicts the essence of vector modeling, which is inherently different from raster modeling.