Vector Data Model
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Transcript Vector Data Model
GI Systems and Science
January 23, 2012
Points to Cover
What is spatial data modeling?
Entity definition
Topology
Spatial data models
Raster data model
Vector data model
Representing surfaces using
Raster approach
Vector approach
Spatial Data Modeling
GIS are computer representations of the real
world
These representations are necessarily simplified
Only those aspects that are deemed important are
included
The simplified representation of the real world
adopted by GIS is a model
Set of rules about how the spatial objects and
relationships between them should be represented
Spatial Data Modeling
A GIS model can be conceptualized in terms of
two aspects:
A model of spatial form: how geographical features
are represented
A model of spatial processes: how relationships
between these features are represented
Building a model of the world for your GIS is a
key stage in any GIS project
Formulating
research
question
Collecting
data
Creating
data model
Entering
data into a
GIS
Spatial Data Modeling
Creating a data model involves going through a
series of stages of data abstraction:
Indentifying the spatial features form the real world
that are of interest in the context of the research
question
Choosing how to represent the features (i.e., as
points, lines or areas)
Choosing an appropriate spatial data model (i.e.,
raster or vector)
Selecting an appropriate spatial data structure to
store the model within the computer
Formulating
research
question
Collecting
data
Creating
data model
Entering
data into a
GIS
Entity Definition
Figure 3.2
Source: Heywood et al., 2011
Entity Definition
Surfaces
used to represent continuous features or phenomena
Figure 3.3
Source: Heywood et al., 2011
Entity Definition
Networks
used to represent a series of interconnected lines
along which there a flow of data, objects or materials
Figure 3.5
Source: Heywood et al., 2011
Entity Definition
Issues associated with simplifying the
complexities of the real world
Identification of the proper scale for
representation
How much detail is required?
Dynamic nature of the real world
How to select the most appropriate representation of
the feature?
How to model change over time?
Identification of discrete and continuous features
Fuzzy boundaries
Entity Definition
Features with
fuzzy boundaries
Continuous canopy
and open woodland
Figure 3.7
Source: Heywood et al., 2011
Topology
A geometric relationship between objects
located in space
Adjacency
Features share a common boundary
Containment
A feature is completely located within another feature
Connectivity
A features is linked to another feature
Independent of a coordinate system
Independent of scale
Spatial Data Modeling
Creating a data model involves going through a
series of stages of data abstraction:
Indentifying the spatial features form the real world
that are of interest in the context of the research
question
Choosing how to represent the features (i.e., as
points, lines or areas)
Choosing an appropriate spatial data model (i.e.,
raster or vector)
Selecting an appropriate spatial data structure to
store the model within the computer
Formulating
research
question
Collecting
data
Creating
data model
Entering
data into a
GIS
Spatial Data Models
Data models and corresponding data structures
provide the information the computer requires to
construct the spatial data model in digital form
Two main ways in which computers can handle
and display spatial entities:
Raster approach
Vector approach
Spatial Data
Models
The raster data model
Based on principles of
tessellation
Cells are used as
building blocks to create
images of features
The size of the cell
defines the resolution
(degree of precision)
with which entities are
represented
Figure 3.8
Source: Heywood et al., 2011
Spatial Data
Models
The vector data model
The real world is
represented using twodimensional Cartesian
co-ordinate space
Points are basic building
blocks
The more complex the
shape of a feature the
greater number of
points is required to
represent it
Figure 3.8
Source: Heywood et al., 2011
Raster Data Model
Basic raster data structure
One layer stores and represents one feature
Presence-absence principle
Figure 3.10
Source: Heywood et al., 2011
Raster Data Model
Raster file structure for storing data on several
entities of the same type
Figure 3.11
Source: Heywood et al., 2011
Raster Data Model
One of the major problems with raster datasets is
their size
A value must be recorded and stored for each cell in an
image regardless of the complexity of the image
To address this problem a range of data
compaction methods have been developed
Run length encoding
Block coding
Chain coding
Quadtree data structures
Raster Data Model
Raster structure for storing data on several entities of
the same type
Reduces data volume on a row by row basis
Figure 3.12(a)
Source: Heywood et al., 2011
Vector Data Model
Basic vector data structure
A file containing (x,y) co-ordinate pairs that represent
the location of individual points
Figure 3.14(a)
Source: Heywood et al., 2011
Vector Data Model
Point dictionary vector
data structure
Allows to avoid
redundancy when areal
features share a
boundary (are adjacent)
But does not really
store information on
topology
Figure 3.14(b)
Source: Heywood et al., 2011
Vector Data Model
Topological vector
data structure
Informs the computer
where one feature is in
respect to its
neighbours
Withstands
transformations well
Figure 3.15
Source: Heywood et al., 2011
Vector Data Model
All topological vector data structures are
designed to ensure that:
Nodes and lines segments (arcs) are not duplicated
Arcs and nodes can be referenced to more than one
polygon
All polygons have unique identifiers
Island and hole polygons can be adequately
represented
Modeling Surfaces
Surfaces represent continuous features of
phenomena
Theoretically have an infinite number of data points
A model of a surface approximates continuous
surface using a finite number of observations
The issue of selecting a sufficient number
observations
Modeling Surfaces
Digital Terrain Models (DTMs) are digital
datasets recreating topographic surfaces
Created from a series of (x,y,z) data points
Resolution is determined by the frequency of
observations used
Are derived from a number of data sources
Maps (low to moderate accuracy, all scales, selected
coverage)
GPS (high accuracy, small areas)
Aerial photographs (high accuracy, large areas)
Modeling
Surfaces
Raster approach
DTM is a grid of height
values
Also known as Digital Elevation Model
(DEM)
Each cell contains a value representing the
height of the terrain covered by the cell
Accuracy depends on the size of the cell and
complexity of the surface
Figure 3.21
Source: Heywood et al., 2011
Modeling Surfaces
Vector approach
Grid
Triangulated Irregular
Network (TIN)
○ Triangles provide
area, gradient and
aspect of terrain
○ TINs use only surface
significant points to
reproduce a terrain
surface
Figure 3.22
Source: Heywood et al., 2011