What type of GIS data model allows for precise location and labeling of objects or lines?

The vector data model is designed to represent geographic features with high precision through geometric shapes such as points, lines, and polygons. In a vector model, each feature can be defined by specific coordinates, allowing for accurate placement on a map. This model is particularly effective for capturing discrete objects like roads, buildings, and boundaries, where the precise location is critical.

Labels can also be easily associated with these geometric elements, providing descriptive information about each feature, which is essential for analyses that involve attributes, such as land use, zoning, and transportation networks. The clarity and detail that vector data offers make it the most appropriate choice when the objective is to represent and analyze specific locations and relationships between entities in a geographic context.

In contrast, contour data represents elevation changes using a continuous line rather than specific points or objects, while raster data uses a grid of cells to represent continuous data (like temperature or vegetation), which is less precise in labeling specific objects. Remote sensing refers to the technique of gathering data from a distance, often providing raster images of the Earth’s surface, but it does not inherently focus on defining objects with precise locations and labels like the vector model does.