Lecture 6 Raster data

Raster layers It’s all cells

A matrix of cells R o w Column Grid cell 3, 6 Square cells Grid origin 0, 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 From Clarke

Categories of raster data Continuous  Elevation  Rainfall Discrete  Landuse  Tree type Imagery  Air photo  Sateilte Land Water Bridge

The mixed pixel problem WGW WWG WWG WGG WWG WGG WGE WEG EEG 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 Rainfall Rainfall Rainfall equals 51

Use math operations +, -, \, and * Cells overlap each other Math performed on overlapping cells More or less rainfall? 7-4=3 Map algebra = Rain 1999Rain 2000Rain 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 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

World view – zoom to Mt Everest

Raster data exercise