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Spatial Information Systems (SIS) COMP 30110 Raster-based structures (1)

Spatial Information Systems (SIS) COMP 30110 Raster-based structures (1). Raster-based data structures. Unlike vector data, raster data are arrays of cells In simple raster structures there is a one-to-one correspondence between data value, cell and location

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Spatial Information Systems (SIS) COMP 30110 Raster-based structures (1)

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  1. Spatial Information Systems (SIS) COMP 30110 Raster-based structures (1)

  2. Raster-based data structures Unlike vector data, raster data are arrays of cells In simple raster structures there is a one-to-one correspondence between data value, cell and location In this case, all data values are stored as a simple array with a meta file including: number of rows and columns cell size minimum values of X and Y coordinates

  3. Simple raster-based data structures Example: Ncols 270 Nrows 476 Xcorner 391253.1875 Ycorner 4064188.25 Cellsize 3 NODATA_Value -9999 -9999 –9999 –9999 –9999 –9999 –9999 2321.5 2321.295 2320.653 2319.938 2319.385… -9999 –9999 –9999 –9999 2321.5 2321.5 2321.5 2321.093 2320.492 2319.851 2319.341… -9999 –9999 2321.5 2321.5 2321.5 2321.5 2321.421 2320.977 2320.449 2319.905 2319.438… -9999 –9999 2321.5 2321.5 2321.5 2321.5 2321.327 2320.94 2320.492 2320.024 2319.595… -9999 –9999 2321.5 2321.5 2321.5 2321.5 2321.281 2320.964 2320.588 2320.179 2319.777…

  4. Efficiency issues The simple raster-based structure of the example is inefficient in terms of data storage: regardless of the data distribution it uses the same amount of disk space This can also degrade data processing Two issues: compression methods (efficiently store data) scan order (how to scan the data in the array)

  5. Compression Geographic phenomena often show a degree of spatial autocorrelation: similar values near each other Therefore there are blocks of cells in the raster array with same data value Example: raster cells are used to represent an area with homogeneous property (e.g., colour, etc.) all cells covering that area will have same value These considerations are used in compression methods: run-length encoding quadtrees etc.

  6. A C B Run-length encoding It groups cells of the same value row by row Example: Row 1,5,1,3,3 Row 2,5,1,3,3 Row 3,7,1,1,3 Row 4,7,1,1,3 Row 5,4,1,4,3 Row 6,4,1,2,2,2,3 Row 7,6,2,2,3 Row 8,7,2,1,3 A=1, B=2, C=3 Useful when there are just a few attribute values Highly inefficient when there is high degree of spatial variability in the data

  7. A C B Quadtree (Samet 1989) It is a hierarchical data structure Based on the concept of recursive decomposition of space The quadtree data structure subdivides a grid into four quadrants: NW, NE, SW, SE NW NE SE SW

  8. A C B Quadtree (cont.d) Each quadrant is in turn subdivided into subquadrants if not homogeneous (i.e. contains only one attribute value)

  9. A C B Quadtree (cont.d) The process is repeated recursively to the obtained subquadrants Note that this method can only be applied to grids with both numbers of rows and columns equal to a power of 2

  10. A ROOT C B SE NE SW NW 1 NW NE SW SE 1 1 2 2 NW NE SW SE 3 1 NW NE SW SE 3 2 NW NE SW SE 1 3 1 3 NW NE SW SE 1 3 1 3 NW NE SW SE 3 3 2 2 Quadtree (cont.d) Represented as a tree where: the root node corresponds to the entire grid leaf nodes identify attribute values and quadrants without further subdivision intermediate nodes correspond to quadrants that are further subdivided A=1, B=2, C=3 NW NE SW SE 3 3 2 3

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