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Lecture 06
Raster and Vector Data Models Part Two
DATA MODEL OF RASTER AND VECTOR DATA MODEL OF RASTER AND VECTOR
REAL WORLD
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Vector Data Model
To prepare spatial data for computer processing, the vector data model first uses points and their x-, y-coordinates to represent spatial features as points, lines, and areas.
Then it organizes geometric objects and their spatial relationships into digital data files that the computer can access, interpret, and process.
Vector data
• Vector data are stored as a series of xy coordinates
– Points are stored as single xy coordinate – Lines are a string of xy coordinates
– Polygons are composed of one or more lines and a label
– Attributes are attached to each feature through a unique numeric code
– Many attributes may be stored in each vector file
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Types of Vector Data Models
Two main types of vector data models:
¾spaghetti vector data model
¾Topological vector data model
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The Spaghetti Model
• The spaghetti model is the most simple vector data model
• The model is a direct representation of a graphical image
• No explicit topological information
• Example: shapefiles
Spaghetti Model
• Description: direct line for line translation of the paper map (often viewed as raw digital data)
• Pros: easy to implement, good for fast drawing
• Cons: storage and searches are
sequential, storage of attribute data
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Spaghetti model
Topology
Topology is the mathematical procedure for defining spatial relationships, i.e., it is the mathematics of connectivity and adjacency for spatial features (how features join).
Topology is the programming that provides spatial relationships between nodes, chains, and geographic features, i.e., it is a special data structure that establishes connections and links for the nodes and chains in order to recognize spatial relationships among the geographic features.
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Intelligent Vector Data model - Topology
GIS analysis answers many questions:
Where is it?
What is it next to?
Is it inside or outside
How far is it from something else
The mathematical terms for these answers is:
Where is it? (location)
What is is next to (adjacency) Is it inside or outside (containment) How far is it (connectivity)
Topologyrepresents the structuring of coordinate data which clearly describes adjacency, containment, and connectivity.
Topology
¾With topology each feature has the following characteristics:
knows where it is.
Knows what is around it.
Has recognized spatial relationships with other features.
Has length, distance, perimeter, and area information.
Knows how to get around.
Understands its environment.
¾Example of topological data model: coverage
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Topological Vector Model
Storing Vector Data
Two common methods for storing vector data:
Georelational (e.g. shapefiles, coverage) Spatial and attribute stored separately
• “geo” – graphic/spatial part
• “relational” – database part (tabular)
• Classical approach to vector data
Object-based (oriented) (e.g. geodatabase)
• Spatial and tabular component are stored as a single system
• Spatial features can have properties and methods
• Recent trend towards object-based model
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Raster Vs. Vector
• Must Consider
– Discreteness of the entity being depicted – Intended application (efficiencies)
– Source data
– Storage considerations
• Resolution
• Color
Vector – Advantages and Disadvantages
• Advantages
– Good representation of reality – more efficient data storage
– Topology can be described in a network – Accurate graphics
• Disadvantages
– Complex data structures – Simulation may be difficult
– Some spatial analysis operations are
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Raster – Advantages and Disadvantages
• Advantages
– Simple data structure – Easy overlay
– Various kinds of spatial analysis – Uniform size and shape
– Cheaper technology
• Disadvantages
– Large amount of data – Less “pretty”
– Projection transformation is difficult
– Different scales between layers can be a nightmare – May lose information due to generalization
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Comparisons, raster v.s. vector
Characteristics
Positional Precision
Attribute Precision
Analytical Capabilities
Data Structures
Storage Requirements Coordinate conversion Network Analyses
Output Quality
Can be Precise Defined by cell size
Poor for continuous data Good for continuous data Good for spatial query, adjacency, area,
shape analyses. Poor for continuous data. Most analyses limited to intersections. Slower overlays.
Spatial query more difficult, good for local neighborhoods, continuous variable modeling. Rapid overlays.
Often complex Often quite simple
Relatively small Often quite large
Usually well-supported Often difficult, slow
Easily handled Often difficult
Very good, map like Fair to poor - aliasing
Vector Raster
