Geospatial Data Models, Vector And Raster Data Model

Geospatial Data
Model
(Vector And Raster Data Model)
IRS -502
Session 2024-25 (4th batch)
Institute of Remote Sensing and GIS
Jahangirnagar University
1

Vector (discrete) and Raster (continuous)
Models
GIS works with three
fundamentally different
types of geographic data
models:
Vector (discrete), Raster
(continuous) and
triangulated irregular
network (TIN)

4
Data models are at times interchangeable in that many phenomena
may be represented with either the vector or raster approach.
Pm 2.5
Source: Paul Bolstad
(2016)

5
 Vector data model and Raster data model can represent
same phenomena
E.g. Elevation represented as surface (continuous field) using raster
grid or as lines representing contours of equal elevation (discrete
objects), or as points of height (Z values).
 Data can be converted from one conceptual view to another
E.g. raster data layer can be derived from contour lines, point cloud
 Selection of raster or vector model depends on the
application or type of operations to be performed
E.g. Elevation represented as surface (continuous field) in raster - to
easily determine slope, or
as discrete contours if printed maps of topography
Data Model Concepts

In the vector model, information about points, lines and
polygons is encoded and stored as a collection of x,y
coordinates
The vector model is extremely useful for describing discrete
features, but less useful for describing continuously varying
features such as soil type
The raster model has evolved to model such continuous
features
Modern GISs are able to handle all three models
All three models (vector, raster & TIN) have unique
advantages and disadvantages
Data Model Concepts

7
 Data model is the objects in a spatial database plus the relationships
among them
 Coordinates are used to define the spatial location and extent of
geographic objects
 Attribute/non-spatial data are linked with coordinate data to define each
spatial object in the spatial database
 Most conceptualizations or models view the world as set of layers
 Each layer organizes the spatial and attribute data for a given set of
cartographic/spatial objects
 E.g. Lake, river, road, etc.
Data Model Concepts
Coordinates define spatial location and
shape. Attributes record the important
non-spatial characteristics of features in
a vector data model
Source: Paul Bolstad (2016)

8
Vector Data Model (Point)
 There are three basic types of vector objects: points, lines
and polygons
 Vector data model uses sets of coordinates and associated
attribute data to define discrete objects
 Point objects in spatial database represent location of
entities considered to have no dimension
 Simplest type of spatial objects
E.g. wells, sampling points, poles, telephone towers, and accident
location.

• Line objects are used to represent linear
features using ordered set of coordinate
pairs.A line is one-dimensional and has the
property of length, in addition to location.
E.g. infrastructure networks (transport
networks: highways, railroads, etc.);
utility networks: (gas, electric, telephone,
water, etc. natural networks such as river
channels
• Typically have a starting point, an ending
point, and intermediate points to represent
the shape of the linear entity.
• Starting points and ending points for a line
are referred to as nodes, while intermediate
points in a line are referred to as vertices
9
Source: Paul Bolstad (2016)
Vector Data Model (Line)

10
Vector Data Model (polygon)
 Polygon objects in spatial database
represent entities which covers an area.
A polygon is two-dimensional and has
the
 properties of area (size) and perimeter,
in addition
 to location.
E.g. lakes, Buildings, parcels, etc.
 Area entities are most often represented
 by closed polygons.These polygons are
 formed by a set of connected lines,
either
 one line with an ending point that
connects
 back to the starting point, or as a set of
lines
 connected start-to-end.

Sl.
No.
Characteristic Vector Structure Raster Structure
1. Data structure Complex Simple
2. Ease of learning Difficult – software is
complex
Easy - functions tend to be
more intuitive than in
vector
3. Positional
precision
Can be very precise and
thus accurate
Precision increased with
increased processing time
and data storage needs
accuracy. Limited by pixel
size
4. Attribute
precision
Good for polygon, point
and line data; not good
for continuous data
unless connected to TIN
or similar technology
Good for continuous data;
limited by size of pixels in
representing attribute
distribution in real world
5. Comprehensive
ness of analysis
capability
Good for spatial query
and relatively simple
data, analysis-limited to
Intersections
Not good for spatial query
but very good for spatial
analysis filtering, and
modeling

Sl.
No.
Characteristic Vector Structure Raster Structure
6. Overlay ability Limited, but overlaying
many layers can cause
many splinters, etc. in the
result which are difficult to
eliminate
Because all pixels line up,
overlay procedures do not
create problems
7. Storage
requirements
Relatively small but
complex
Relatively large and simple but
may be complex
8. Ability to work
with image data
Poor - data must be
vectorized first
Good - uses same kind of data
structure
9. Conversion to
other map
projections
Usually included in
package and relatively
simple to do
Difficult and quite often
creates warped images which
do not fill the raster, causing
problems with neighborhood
functions
10. Ability to work
with network data
structures
Good - because system can
handle lines
Poor - raster structure not
amenable to network
11. Cost Expensive Inexpensive
12. Output map
quality
Very good - looks like a
map
Poor - doesn't look like a map
to lay people

The measure of how closely pixels
can be resolved in an image is called
spatial resolution, and it depends on
properties of the system creating the
image.
For practical purposes the clarity of
the image is decided by its spatial
resolution.
Spatial resolution

19
TIN Data Model
 Triangulated Irregular Network (TIN) is data model
commonly used to represent terrain heights
 x, y, and z locations, used as measured points inTIN
 Result in TIN composed of nodes, lines and triangulated
faces
 TIN used for digital elevation models (DEM) or digital
terrain models (DTM)
 Very efficient way of representing topography

Geospatial Data Models, Vector And Raster Data Model

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