Vector Data:
It is necessary to convert spatial data into a form which a computer can understand.
This can be modeled in two major ways – one is as a raster data model and the
other one is as a vector data model.
In the first form, data is represented as a matrix of cells, a grid containing the
feature at each location. This could be imagined as a spatial data mapped to a 2D
grid. An example would be the traditional maps. Vector data involves storing data as
geometric objects. For example, a road can be represented as a combination of lines.
In this case, the lines are the objects.
A given real world situation can be
represented as a raster or vector model. The choice between raster data model and
vector data model depends on how you conceptualize the feature. Vector data is
more useful for data which can be represented as objects. Vector data is easier to
handle on computers and it takes less space.
Representation of Vector data model:
A vector data model could be thought of as an object described using mathematical
notations. A vector data model is represented as a collection of simple geometric
objects like points, lines, polygons, arcs, circles, etc. For example, a city may be
represented by a point. A road may be represented by a collection of lines, and a
state may be represented as a polygon.
Image taken from *3
Vector Data Model and Topology:
Topology is one of the important features in spatial data. Topology can be defined as
relations that are relative. An example would be to find the two cities connecting a
road. In a vector based data model, such information is inherent in its representation
itself. In this example, a node could represent a city and an arc connecting the two
nodes could represent the road. Thus, by following the arc it would be easy to find
the two cities. If this was raster data model, each pixel would have to be scanned to
find the arc and then the cities. Hence, Vector data model is more useful when
operations related to topology are important.
On the downside, it would be computationally more expensive if there are too many
objects to be processed.
Vector Data Model and Scaling:
Because of the certain properties of vector data model, like the topology described
above, objects represented by vector data model could be scaled without any loss of
quality. Since, only the objects are represented by vectors, just scaling the vectors
could reproduce the actual object at different scales efficiently. This makes them
very useful to use in applications like Maps, where it is often required to zoom in and
out at different levels.
Vector Data Model and Storage:
Memory requirement for vector data model depends on the complexity of the
objects. They require very less storage for simple polygons. Generally, the amount of
memory required by a vector model would be less as compared to that of a raster
model because the data could be emulated by the vectors.
Vector Data Model and Computation:
Computation for vector data model could be expensive. This is because of the object
representation. Processing on a vector data model involves solving complex
geometrical problems; like finding intersection of one polygon with another, finding
distance across objects, etc. This problem is more evident when the data-set is
large.
Vector Data Model and Attributes:
In spatial applications, it is often required to associate an attribute to a geographical
object. Some examples would be population of cities, associating traffic on a road,
elevation at a given point on Earth, etc. Here population, traffic, elevation are
examples of attributes related to an object. Since the entities like cities, road, etc.,
are represented as objects, it is easy to associate them to these attributes. This
makes it easy to store in the database.
Vector Data Model and Applications:
Since the data is already represented as objects, it would be easy to expose them to
applications; and easy to write applications based on that. Application programmer
could just think of entities as objects and write programs based on that. In case
raster data model were used, end-users would have to deal with low-level details or
would have to be provided with an interface which converts the data from raster data
model to objects, and vice-versa.
Vector Data Model and Data Structure:
Vector data model requires more complex data structures to be represented in
computer. A raster data model could be represented using a grid; but for vector data
model each object would have to be treated differently. For example, to represent a
polygon, it would be required to have a count of the number of vertices, co-ordinates
of each vertex, and its relative position with respected to adjacent edges.
Vector Data Model and Data Quality:
Vector data is supposed to represent data with higher quality. This is preferred by
cartographers who would like to see straight lines in their maps instead of “jagged”
lines which usually occur in raster data model due to digitization. Some of the data
collected in vector format are coordinate geometry and legal boundaries. However,
data like that from remote sensing is in raster format.
Vector Data Model and Data Modeling:
Since data are represented as objects, it might be easy to convert logical models to
physical model. It would be easy to associate a physical data object to an object at
logical level; thereby making it easy to design the database.
References:
Internet:
*1) This site gives an overview of vector data models.
http://www.sli.unimelb.edu.au/gisweb/GISModule/GIST_Vector.htm
*2) Raster data and Vector data http://www.ce.utexas.edu/prof/maidment/giswr2003/visual/spatial.ppt
*3) About Vector data
http://www.geoplan.ufl.edu/giseducation/vector.html
Applications:
*4) Spatial Analysis of Raster and Vector data using ArcGIS –
http://www.esri.com/library/whitepapers/pdfs/arcgis_spatial_analyst.pdf