# Introduction to GIS Slides by pptfiles

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```									Lecture 6

Raster data
Raster layers
n   It’s all cells
A matrix of cells

Column
Grid origin
0, 0   0   1   2   3   4   5    6       7
0
1
2
o
Rw    3
Grid cell
4                                          3, 6
5
6
7                                        Square cells

From Clarke
Resolution (cell size)
n   ½ cell size = 2x
the space
Cell values
n   Each cell has a value: Integer, real number, or NoData
n   Cells can store raw numbers (elevation, temperature, slope)
q   1200
q   1130
q   1000
q   990
1   1   1   1   1   4   4   4
q   430
1   2   1   2   2   4   4   4
n   Or index values
q 1 = water                  1   1   1   3   3   3   3   3

q 2 = land                   1   1   2   2   2   2   2   3
q 3 = road                   2   2   1   2   2   2   2   3
q 4 = building
4   4   2   1   2   2   2   3
8   4   3   2   1   1   2   3
3   3   3   2   2   2   1   1
From Clarke
Categories of raster data
n   Continuous
q   Elevation
q   Rainfall

n   Discrete          Land
Water
q   Landuse       Bridge
q   Tree type

n   Imagery
q   Air photo
q   Sateilte
The mixed pixel problem
Water dominates   Winner takes all   Edges separate
W W G            W G G               W E     G
W W G            W W G               W E     G
W W G            W G G               E   E   G

From Clarke
Raster to vector conversion

Vector…     to raster…   back to vector

How can you improve
these results?

From Clarke
Raster overlay
n   Cells from multiple layers
n   Same location
n   Like a shish kabob
n   Write equations with maps as variables – map algebra

Value in layer 1                     Rainfall 1998   10
+ Value in layer 2                     Rainfall 1999   21
+ Value in layer 3                     Rainfall 2000    9
+ Value in layer 4                     Rainfall 2001   11
equals     51
Map algebra
n   Use math operations +, -, \, and *
n   Cells overlap each other
n   Math performed on overlapping cells

More or less rainfall?
7-4=3
7   9   2   5   8         4    3   4   4   3        3   6 -2 1    5
9   4   4   4   8         6    5   4   4   5        3   -1 0 0    3
5
1
6
3
4
3
1
3
9
6
-     2
7
2
4
1
0
1
1
1
1
=    3 4 3
-6 -1 3
0
2
8
5
1   1   1   5   6         5    5   0   0   0        -4 -4 1   -5 -6

Rain 1999                     Rain 2000            Rain difference
Map algebra
n       Logical operations (and, or)
n       Cells overlap each other
n       Fast math – It’s all Zeros and ones
n       1- good, 0 - bad

Find best Ski areas

1    1   0   0   0           0   0   0    0   0       0   0   0   0   0
1    1   1   0   0           0   1   0    1   1       0   1   0   0   0
0    1   0   1   0   AND     0   1   1    1   1   =   0   1   0   1   0
0    0   0   0   0           0   0   0    1   1       0   0   0   0   0
0    0   0   0   0           0   0   0    0   0       0   0   0   0   0

Slope > 15%                          No trees
Multiple rasters and transparency
n   Date information
n   Location
n   Scale
n   Intended use
n   Storage formats
q   FGDC
q   FAQ
q   ISO
q   XML
TIN: Triangulated Irregular Network
n   3D vector data
n   Triangles
n   More efficient than a
grid
Elevations with TIN
3D Visualization
3D Visualization
World view – zoom to Mt Everest
Raster data exercise

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