Geographic Information System (GIS) Models:

Background

A data model in geographic information systems is a mathematical construct for representing geographic objects or surfaces as data. For example, the vector data model represents geography as collections of points, lines, and polygons and the raster data model represent geography as cell matrixes that store numeric values. ^[1]

Raster Model

The raster data model is an abstraction of the real world where the basic unit of data (points, lines and areas) is represented using a matrix of cells or 'pixels'. The raster model uses the grid-cell data structure where the geographic area is divided into cells identified by rows and columns. The following information must be known when using raster data

• Grid extent (number of rows and columns)
• Grid resolution (size of grid cell)
• Georeferencing information (e.g. corner coordinates)

In the simplest form, each cell contains a value for the element. Any cell not containing a feature would have the value of "0". In more sophisticated systems, the cell value is a label that links to the record as an attribute

Vector Model

• A point is defined by a single pair of coordinate values. A point normally represents a geographic feature that is too small to be represented as a line or area. For example, a port, a dock, or a hatchery can be represented as a point depending on the scale of the map on which it is be shown.
• A line is defined by an ordered list of coordinate pairs defining the points through which the line is drawn. Linear feature include contour lines, ship tracks and streams. At most mapping scales these features will retain their linear form, although the degree of detail and generalisation will vary with scale. A line is synonymous with an arc.
• An area is defined by the lines that make up its boundary. Areas are also referred to as polygons. Examples include ocean basins, lagoons, mangroves, lakes, etc. When shown on maps at a very small scale these features may also eventually become points.

Comparison of Raster and Vector Methods

Advantages and Disadvantages

There are several advantages and disadvantages for using either the raster or vector data structure to store spatial data. These are summarized below:

• Raster Model
  • Advantages
    • Simple data structure
    • Efficient for remotely sensed or scanned data
    • Simple spatial analysis procedures
  • Disadvantages
    • Requires greater storage space on computer
    • Depending on pixel size, graphical output may be less pleasing
    • Projection transformations are more difficult
    • More difficult to represent topological relationships
• Vector Model
  • Advantages
    • Data can be represented in its original resolution without generalisation
    • Requires less disk storage space
    • Topological relationships are readily maintained
    • Graphical output more closely resembles hand-drawn maps
  • Disadvantages
    • More complex data structure
    • Inefficient for remotely sensed data
    • Some spatial analysis procedures are complex and process intensive
    • Overlaying multiple vector maps is often time consuming