Which data model is best suited to represent discrete features like city boundaries?

Which data model is best suited to represent discrete features like city boundaries?

• A. Vector
• B. Raster
• C. Graph
• D. Scalar field

Answer: (A)

Representing discrete geographic features with clear, defined borders relies on a data model that stores exact geometry for each feature and can attach attributes. Vector data does this by modeling cities as polygons (and boundaries as lines) with attributes like name and population. This allows precise borders, accurate area calculations, and clear spatial relationships such as adjacency or containment. Raster data, in contrast, uses a grid of cells and is geared toward continuous surfaces; boundaries become blurred as edges are approximated by pixel values. A scalar field assigns a value to every location, which suits continuous phenomena rather than distinct polygons. A graph focuses on connections and networks, like roads, rather than the explicit outlines of features. Therefore, vector is the best fit for discrete features like city boundaries.

Representing discrete geographic features with clear, defined borders relies on a data model that stores exact geometry for each feature and can attach attributes. Vector data does this by modeling cities as polygons (and boundaries as lines) with attributes like name and population. This allows precise borders, accurate area calculations, and clear spatial relationships such as adjacency or containment. Raster data, in contrast, uses a grid of cells and is geared toward continuous surfaces; boundaries become blurred as edges are approximated by pixel values. A scalar field assigns a value to every location, which suits continuous phenomena rather than distinct polygons. A graph focuses on connections and networks, like roads, rather than the explicit outlines of features. Therefore, vector is the best fit for discrete features like city boundaries.