1. Re: CPSC 344 David Casperson just confirmed that CPSC 344 (CRN 11211) is now scheduled and open for registration for the Winter 2017 semester. This is one of the course options for the GIS minor. I know the challenges of getting the CPSC courses needed to complete the GIS minor - so David worked to ensure that this one is made available this year. It is scheduled for Thursday evenings 6:00 - 8:50pm with Garth Frizzell as instructor. Scott and Neil, please advise students in your courses this semester and encourage them to sign up as one more step towards completing the GIS minor.

2. Raster Analysis Euclidean Distance in practice Raster Calculator –Boolean Operators –Parentheses Reclassify Histograms Euclidean Allocation Surface interpolation with Kriging Viewshed Analysis

3. Local: Operations done on cell by cell basis Neighborhood (focal): Operations performed using an adjacent group of cells e.g. 3 x Zonal: Operations performed using zones (groups of cells with the same value) Groups of Raster operations

4. Euclidian Distance: Examples Units are only round in X and Y directions 0 1 2 3 4 5 6 7 1.4 2.8 4.2 5.6

5. Euclidean Distance Datasets: Streams and Roads

6. Euclidean Distance Settings: – Input = raster or vector (one at a time) – Output = the name you want, in your K: – Output cell size = 20 (could be 10, 50…)

7. Output (Roads Distance) Raster stops at boundary (easily changed)

8. Changing Output Extent 1.Environments 2.Processing Extent 3.Same as… Default/any layer/ current display

9. Extent same as “Clip” Now you get data beyond original vector

10. Now the same for Streams

11. Raster Calculator Not exactly the same as SQL Many similar elements (& vs. And) Parentheses (always parentheses)

12. Boolean Operators in RasterCalc & And | Or ^ XOr ~ Not != not equal

13. Red Text… No parentheses… With parentheses…

14. Output of

15. Work-Through: Bear Survey Say you want to count the bear cubs As a proud citizen of the North(ish), you know: – Bears like to eat blueberries Blueberries best in fresh clearcuts – Bears also like mature conifer cover – Streams are good – And you want to stay in your car

16. Step 1: Data Preparation Select spruce over 50 (SQL—stands for?) – Generate Euclidean distance raster Polygon to raster for FCover, using age – Because we want to be in or out, not a distance

17. Step 2: Build the Expression Stand age under 10 Distance to river under 100 Distance to spruce stand under 100 Distance to roads under 50 ("standage_full" < 10) & ("Spruce50_dis" < 100) & ("streams_eucdi" < 100) & ("Roads_Eucdi" < 50)

18. Step 3: Output Take your pick…

19. Output of “==“ means equals (never single =). But what’s wrong here?

20. Straight-Line Distance Units are only round in X and Y directions 0 1 2 3 4 5 6 7 1.4 2.8 4.2 5.6

21. Selecting Within a Range ("Roads_Eucdi" 75)

22. Reducing Frustration Keep a text document open All scripts go in the text doc before running

23. Reclassify: Stand Age Unintelligible:

24. Reclassify Stand Age Default not what I want (too many classes) Classify>5 classes, equal interval

25. Reclassified Stand Age Age classes categorized, easily interpreted

26. Histograms Customize>Toolbars>Spatial Analyst

27. Euclidean Allocation Which source is closest to each cell? Example with “Plots” data from Aleza

28. Euclidean Allocation Identify gaps in distribution Practical applications?

29. Spatial Pattern in Allocation Size Say we want to visualize the average allocation size across the map, and don’t like to just look at the plots themselves

30. Kriging Interpolates values for the space between points for every pixel

31. Step 1: Zones to Polygons Must be integer type raster – If not, Raster Calculator: Int(rasterX)

32. Step 1: Zones to Polygons 1a: calculate area of polygons in ha

33. Step 2: Join Poly data to Points Area attribute to points Points>Joins and Relates Based on spatial location It falls inside

34. Step 2 Output Each point has an area Area is going to be used in a kriging function

35. Step 3: Kriging In our case, we want to interpolate a surface showing the average area of the plot represented by each point

36. Kriging Output

37. Another Kriging Application Growth rate of trees (points) Barren soil at top of hill Deep soil downslope Translates to growth rate Problems with this map? (I have 5 noted)

38. From the UCLA Statistics Department: Discuss.

39. No information given… But probably a kriging function Udmurt

40. Viewshed Analysis http://www.innovativegis.com/basis/mapanalysis/topic15/topic15.htm

41. Viewshed from Two Points

44. Observer Points Who can see what?

46. Watershed Analysis Assigns each pixel to the point to which it will flow, creating watersheds

47. Pixel vs. Voxel Voxels are 3-D pixels Think Minecraft… Dr. Anthony Jjumba will be guest lecturing – November 22 – Landscape change modeling https://carestreamdentalblogdotcom1.wordpress.com/2014/02/19/three-dimensional-basics-pixels-and-voxels/

48. Summary All tools will be encountered in lab, except: – Kriging – Observer points – Watershed analysis The above are good for final projects Labs focus heavily on raster calculator – Get used to using parentheses Map critique…

49. Map Critique (level = hard)