1. Lecture 6 Raster data

2. Raster layers It’s all cells

3. A matrix of cells R o w Column Grid cell 3, 6 Square cells 01234567 Grid origin 0, 0 0 1 2 3 4 5 6 7 From Clarke

4. Resolution (cell size) ½ cell size = 2x the space

5. 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 11111444 12122444 11133333 11222223 22122223 44212223 84321123 33322211 From Clarke

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

7. The mixed pixel problem WGW WWG WWG WGG WWG WGG WGE WEG EEG Water dominates Winner takes all Edges separate From Clarke

8. Raster to vector conversion Vector… to raster… back to vector How can you improve these results? From Clarke

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

10. Use math operations +, -, \, and * Cells overlap each other Math performed on overlapping cells More or less rainfall? 7-4=3 Map algebra 79 49 6 4 1 258 48 94 336 6511 31 5 1 43 56 2 4 1 443 45 11 011 0005 47 2 5 36 3 4 0 0 -215 03 83 325 -6-51-4 -6 3 -4 - = Rain 1999Rain 2000Rain difference

11. 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 11 11 1 1 1 000 00 00 000 0000 00 0 0 00 10 1 0 1 000 11 11 011 0000 00 0 0 00 10 1 0 1 000 00 00 000 0000 00 0 0 AND = Slope > 15%No trees

12. Multiple rasters and transparency

13. Metadata Data about data Date information Who made it Location Scale Intended use Storage formats  FGDC  FAQ  ISO  XML

17. TIN: Triangulated Irregular Network 3D vector data Triangles More efficient than a grid

18. Elevations with TIN

19. 3D Visualization

21. World view – zoom to Mt Everest

22. Raster data exercise