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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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slide1

Spatial Information Systems (SIS)

COMP 30110

Raster-based structures (1)

slide2

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

slide3

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…

slide4

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)

slide5

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.

slide6

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

slide7

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

slide8

A

C

B

Quadtree (cont.d)

Each quadrant is in turn subdivided into subquadrants if not

homogeneous (i.e. contains only one attribute value)

slide9

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

slide10

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