Raster data models Rasters can be different types of tesselations SquaresTrianglesHexagons Regular tesselations

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 agriculturalsandy loam 2roadsandy loam 3agriculturalsandy loam 4industrialsand IDLand UseSoil Type

Raster data models Resolution of a raster is the distance that one side of a grid cell represents on the ground = 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 Start North 1 East 1 North2 East2 South3 West 3 Value 4

Raster data models Raster block codes –two dimensional run length encoding

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

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