Geoscience Reference
In-Depth Information
ical relationships—adjacency, containment, connectivity—to the basic elements of the vector data.
These topological descriptions provide information in addition to the spatial relationships between
features. Some common vector data formats include GBF/DIME, TIGER, DLG, AutoCAD DXF,
IGDS DGN file, ArcInfo coverage, ArcInfo E00, shapefile, and CGM.
A raster data format is used to store data in a grid cell array. The size of a grid cell is fixed
throughout the entire data set with each cell equally spaced. A regular grid is used and can be the
shape of a square, rectangle, triangle, or hexagon. Because there is a fixed grid size, the spatial reso-
lution of the raster data layer will be equivalent to the size of the grid cell.
The use of raster and vector data has strengths and weaknesses. The decision to implement a
raster or vector data format relies on the convenience of implementation, the type of GIS operations
to be performed, the desired scale and accuracy, and the format of the original data sets. One should
be familiar with these data types and select an appropriate format. Table 10.1 provides a summary
of the advantages and disadvantages of raster and vector data.
Nonspatial attribute data in a GIS can be a set of tables or individual data records from a data-
base. These attribute data provide a description of features. Two methods are used to link spatial
and attribute data: attribute relationships and spatial relationships.
tAble 10.1
Advantages and disadvantages of Raster and vector data
Advantages
disadvantages
Vector
More compact data structure
Topological processing
Cartographic quality
Sophisticated attribute data handling
Applications that rely on individual spatial features
represented by points, lines, and polygons are much
easier (i.e., network analyses that rely on streets as
discrete features; land parcel-based applications, such as
land title registration and forest resource inventories that
rely on linear boundaries)
Mapping applications that rely on linear features, such as
roads, streams, coastline, building outlines, and parcel
boundaries are clearly defined using coordinates
Complex data model
Difficult to perform overlay processing, can be
computationally complex—involves geometric
intersection, topology building, and error checking
Difficult presentation of spatial variability
Expensive data collection
Use of expensive technology
Raster
Simple data model
Use of cheap technology
Ease of data collection and data processing of raster data
Ability to represent different types of continuous surfaces
(topography, land use/land cover, air quality)
Fast computer processing for overlaying operations
Fast display of surface data
Ability to handle very large databases
Suitable for applications that are difficult or impossible to
perform using vector data (hydrologic modeling,
portraying spatial processes such as spread of wildfire,
movement of pollutants from a point source, growth of
settlements)
No topological processing
Limited attribute data handling
Less compact data structure
Low cartographic output quality
Not suitable for applications that rely on individual
spatial features represented by points, lines, and
polygons (network applications, land parcel-based
applications)
Raster data processing restricted by the resolution
of the source data
Source:
From Lo, C.P., and Yeung, A.K.W.,
Concepts and techniques of geographic information systems,
Prentice Hall,
Upper Saddle River, NJ, 2002. With permission.
