Geospatial Data Models — Vector and Raster

1.2. Geospatial Data Models — Vector and Raster#

1.2.1. Learning Objectives#

By the end of this section, you should be able to:

Explain the vector data model and describe the three fundamental geometry types: Point, LineString, and Polygon.
Explain the raster data model and distinguish between thematic, continuous, and imagery raster data.
List the advantages and limitations of each model and select the appropriate model for a given analytical problem.

Section Goal

This section establishes the two paradigms every geospatial practitioner must master: the vector model for discrete features and the raster model for continuous fields. By the end you will be able to reason clearly about which model fits a given phenomenon and choose the right one for a given analytical problem.

1.2.2. Representing the World Digitally#

Every map and spatial dataset is an abstraction—a simplification of the infinitely complex real world into a manageable digital representation. Two complementary paradigms have emerged for this abstraction:

Vector data represents the world as discrete, precisely bounded features (a building, a road, a river basin).
Raster data represents the world as a regular grid of cells, each holding a single numeric value (elevation, temperature, land-cover class).

Neither model is universally superior. The right choice depends on the phenomenon being modeled and the analysis being performed.

1.2.3. Types of Geospatial Data#

In the context of Geographic Information Systems (GIS), real-world observations—encompassing any measurable objects or events in two or three dimensions—must be translated into simplified spatial representations. This process involves distilling complex real-world details into fundamental spatial constructs that can be effectively managed and analyzed within a GIS framework. These constructs are modeled in one of two primary ways:

Vector Data Model: This approach captures the geometry and location of spatial entities using points, lines, and polygons. It is well-suited for representing discrete features with clear boundaries and precise locations, such as buildings, roads, or administrative borders. An example of the vector data model is provided in Fig. 1.3.

Fig. 1.3 An example of the vector data model.#

Vector data models will be discussed in greater detail in Section 1.3.
Raster Data Model: In this model, spatial entities are depicted as a uniform grid of cells, with each cell containing a value that represents a specific attribute of that area, such as elevation or temperature. This model is ideal for continuous data that lacks distinct boundaries, like rainfall distribution or land surface temperatures. An example of the raster data model is provided in Fig. 1.4.

Fig. 1.4 An example of the raster data model.#

Raster data models will be discussed in greater detail in Section 1.4.

Both vector and raster models are fundamental to GIS and are chosen based on the nature of the data and the specific requirements of the analysis being performed.