raster data models rasters can be different types of tesselations squarestriangleshexagons regular...
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Raster data modelsRasters can be different types of tesselations
Squares Triangles Hexagons
Regular tesselations
Raster data modelsIrregular tesselations
Raster data models
– but most common raster is composed of squares, called grid cells
– grid cells are analogous to pixels in remote sensing images and computer graphics
Raster data models
• A raster representation is composed a series of layers, each with a theme
Raster data models
• Raster layer can be attached to a RDBMS
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1 agricultural sandy loam
2 road sandy loam
3 agricultural sandy loam
4 industrial sand
ID Land Use Soil Type
Raster data models
• Resolution of a raster is the distance that one side of a grid cell represents on the ground
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= grid cell resolution
The higher the resolution (smaller the grid cell), the higher the precision, but the greater the cost in data storage
Raster data models
• Compression of raster data:
– run length encoding– value point encoding– chain codes– block codes– quadtrees
Raster data models
• Run length encoding and value point encoding
Raster data models
• Raster chain codes– directions around the boundary of a region
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Start
North 1 East 1 North 2 East 2 South 3 West 3
Value 4
Raster data models
• Raster block codes– two dimensional run length encoding
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Raster data models• Quadtrees
– a partitioning of heterogeneous space into quarter sections
Raster data models
• Quadtrees– node is a quadrant that is heterogeneous– leaf is a quadrant that is homogeneous– quadrants are assigned an ID number according to their position
and level
Raster data models• Quadtrees
– advantages• efficient• variable resolution, can generalize data display
– disadvantages• complex• difficult to modify/update• not efficient if data is hetergeneous
Raster data models
• Orderings of two dimensional data
• Goal is to store data that are ‘close’ in physical space close on the disk
Raster data models
• Raster data input– conversion from vector data
• Presence/absence• Dominant type• Percent occurance
Raster data models
• Raster data input
Raster data model• Raster data input
– interpolation from point data to surface
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Raster data model
• Direct data capture in raster format– classified satellite remote sensing– aerial photography– scanned maps (from a drum scanner)
• must be rectified and registered for integration with other geographic data (corrected for distortions and georeferenced to a coordinate system)
Raster vs. Vector• Raster
– Advantages • simple to understand• overlay operation is straightforward• can represent high spatial variability• similar format for digital images
– Disadvantages• typically less compact storage than vector• hard to represent topological relationships• output graphics are often ‘blocky’ inappearance
Raster vs. Vector• Vector
– Advantages • more compact storage than raster
• efficient encoding of topology and therefore more efficient topologic operations (I.e. network)
• graphic output approximates hand drawn maps
– Disadvantages• more complex than raster
• overlay operations are complicated
• representation of high spatial variability is inefficient
• cannot handle image data