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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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

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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 • 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