Transcript Raster data structure - test.scripts.psu.edu

Raster data models
Rasters can be different types of tesselations
Regular tesselations
Squares
Triangles
Hexagons
Raster data models
Irregular 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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ID
Land Use
Soil Type
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agricultural
sandy loam
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road
sandy loam
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agricultural
sandy loam
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industrial
sand
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:
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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
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North
East
North
East
South
West
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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
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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