Vector data models are a subset of geographic information through which data are encoded as a geometric definition of a feature, organized within a framework that relates this geometry to the spatial location, context, and proximal relationship. Vector data models are often contrasted to raster data models, encoded in a regular grid spanning an extent with a fixed cell size. Vector data rely on geometric primitives, such as points, lines (sequences of linked points), and polygons (closed geometric forms), with variations on these and additional cases tailored to specific applications as they have emerged during the development of GIS. With the ability to define the location, placement, and interval of points that describe each feature, the resolution of the data are determined by the precision of placement of vertices from the sampled reality or when encoded in a processed form of the data. Due to their finite shapes, defined by their exact placement (point), the segment or overall length (line, polyline), or their area (polygon) vector data are commonly used to encode discrete, rather than continuous, features, with associated characteristics stored in an accompanying table of values corresponding to each shape feature.
Christman, Z. (2026). Vector Data Models. The Geographic Information Science and Technology Body of Knowledge (Issue 1, 2026 Edition), John P. Wilson (Ed.). DOI: 10.22224/gistbok/2026.1.1.
1. Vector Characterization of Space
Vector data formats are spatial features, described by geometric characteristics of a single
Vector data models rely on a characterization of space and spatial features defined by forms or composites thereof, based on these geometric primitives of points, lines, and polygons. Spatial relationships among the represented areas can be characterized based on their topological relationships or the modeling of the connectivity of the segments in a spatial network, in contrast to other types of entity-based models that could be used to represent spatial features and relationships in a Geographic Information System framework.
Vector data best describe spatial features with discrete limits, such that a point indicates the the precise location; a line follows an exact path; a polygon circumscribes a defined area. As many real-world phenomena exhibit a continuum of values, a threshold for these ranges must be defined to draw the precise geometric shapes. Thus, choices are made about what is inside or outside of a feature or along a path. The scale of vector data is determined by the ratio of distance/area of the vector feature to the landscape or feature it represents. Additionally, the sampling interval of the vertices of these shapes influences the degree precision with which they are rendered in the geographic information system (GIS) and the ensuing products generated with these data. Further, a choice may be made as to which type of vector data is used to represent a feature dependent on the level of generalization, the ultimate use, and the spatial characteristics of the data. For example, a feature like a river may be encoded as a polygon of the area of the water or as a (poly-)line of the path, and a landmark may be indicated by a polygon of its area or a single point of its centroid or entrance.
2. Frameworks of Vector Data Models
Early and basic forms of vector data models involved creating digital representations of analog map features on a Cartesian grid. In these, each point was defined with a unique combination of x- and y-coordinates, with a line defined by a series of these encoded points, and a polygon defined by a closed loop of coordinates with the same start and end point. In this schema, each feature was saved separately and associated with one set of attributes. Limitations of this model include redundancy of shared points or lines, and a lack of the encoding of any spatial relationships among the features. Regardless of these limitations, this method was adopted by initial GIS software programs, bolstered by the speed and agility at which users were able to reference and visualize the data.
Vector data models are enhanced by the encoding of topology, which defines the relationships among the features, enabling considerations of
Here are some key conceptual vector data models that have been utilized over time:
For an extensive comparison of spatial data models, including their uses and development, please see Peuquet (1984).
3. Specific Cases of Vector Data Models and Formats
These cases exist to encode complicated spatial information in data formats that leverage the geometric constraints of the vector data model with a specific application.