lecture 6 raster data. raster layers it’s all cells

Lecture 6

Raster data

Raster layers It’s all cells

A matrix of cells

Row

Col

um

n

Grid cell 3, 6

Square cells

0 1 2 3 4 5 6 7

Grid origin0, 0

0

1

2

3

4

5

6

7

From Clarke

Resolution (cell size) ½ cell size = 2x

the space

Cell values Each cell has a value: Integer, real number, or NoData Cells can store raw numbers (elevation, temperature, slope)

1200 1130 1000 990 430

Or index values 1 = water 2 = land 3 = road 4 = building

1 1 1 1 1 4 4 4

1 2 1 2 2 4 4 4

1 1 1 3 3 3 3 3

1 1 2 2 2 2 2 3

2 2 1 2 2 2 2 3

4 4 2 1 2 2 2 3

8 4 3 2 1 1 2 3

3 3 3 2 2 2 1 1From Clarke

Categories of raster data Continuous

Elevation Rainfall

Discrete Landuse Tree type

Imagery Air photo Sateilte

Land

Water

Bridge

The mixed pixel problem

W GW

W W G

W W G

W GG

W W G

W G G

W GE

W E G

E E G

Water dominates Winner takes all Edges separate

From Clarke

Raster to vector conversion

Vector… to raster… back to vector

How can you improve these results?

From Clarke

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

Value in layer 1+ Value in layer 2+ Value in layer 3+ Value in layer 4

Rainfall 1998 10Rainfall 1999 21Rainfall 2000 9Rainfall 2001 11 equals 51

Use math operations +, -, \, and * Cells overlap each other Math performed on overlapping cells

More or less rainfall?7-4=3

Map algebra

7 9

49

6

4

1

2 5 8

4 8

94

3 3 6

6511

31

5

1

4 3

56

2

4

1

4 4 3

4 5

11

0 1 1

0005

47

2

5

3 6

-13

4

0

0

-2 1 5

0 3

83

3 2 5

-6-51-4

-1-6

3

-4

- =

Rain 1999 Rain 2000 Rain difference

Logical operations (and, or) Cells overlap each other Fast math – It’s all Zeros and ones 1- good, 0 - bad

Find best Ski areas

Map algebra

1 1

11

1

1

1

0 0 0

0 0

00

0 0 0

0000

00

0

0

0 0

10

1

0

1

0 0 0

1 1

11

0 1 1

0000

00

0

0

0 0

10

1

0

1

0 0 0

0 0

00

0 0 0

0000

00

0

0

AND =

Slope > 15% No trees

Multiple rasters and transparency

Metadata

Data about data Date information Who made it Location Scale Intended use Storage formats

FGDC FAQ ISO XML

TIN: Triangulated Irregular Network

3D vector data Triangles More efficient than a

grid

Elevations with TIN

3D Visualization

3D Visualization

World view – zoom to Mt Everest

Raster data exercise