1. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Lecture 14: More Raster and Surface Analysis in Spatial Analyst ------Using GIS-- By Austin Troy and Weiqi Zhou, University of Vermont

2. Fundamentals of GIS Reclassifying Raster Data ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011 Why? …. Here we reclass into 5 groups

3. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Reclassification with Grids ------Using GIS--

4. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Reclassification with Grids ------Using GIS--

5. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Reclassification with Grids ------Using GIS--

6. Fundamentals of GIS Reclassify: Soil moisture example Lecture Materials by Austin Troy except where noted © 2008 Slide courtesy of Leslie Morrissey

7. Fundamentals of GIS Reclassify: Soil moisture example Lecture Materials by Austin Troy except where noted © 2008 Slide courtesy of Leslie Morrissey

8. Fundamentals of GIS Neighborhood Statistics (Focal) A method of summarizing raster data within a neighborhood by a statistical measure, like mean, std dev. – Neighborhood shape – Neighborhood settings Window size Units – Statistic types

9. Fundamentals of GIS Neighborhood Statistics Statistic type: Mean 3x3 cell squared neighborhood. Processing cell Neighborhood

10. Fundamentals of GIS Neighborhood Statistics Neighborhood statistics creates a new grid layer with the neighborhood values This can be used to: –Simplify or “filter down” the features represented –Emphasize areas of sudden change in values –Look at rates of change –Look at these at different spatial scales

11. Fundamentals of GIS Neighborhood Filters Improve the quality of raster grids by eliminating spurious data or enhancing features. Filter types –Low pass filters –High pass filters

12. Fundamentals of GIS ©2007 Austin Troy Low Pass filtering Functionality: averaging filter –Emphasize overall, general trends at the expense of local variability and detail. –Smooth the data and remove statistical “noise” or extreme values. Summarizing a neighborhood by mean –The larger the neighborhood, the more you smooth, but the more processing power it requires. –A circular neighborhood: rounding the edges of features. –Resolution of cells stays the same. –Using median instead of mean, but the concept is similar.

13. Fundamentals of GIS ©2007 Austin Troy High Pass Filter Functionality: edge enhancement filter –Emphasize and highlight areas of tonal roughness, or locations where values change abruptly from cell to cell. –Emphasize local detail at the expense of regional, generalized trends. Perform a high pass filter –Subtracting a low pass filtered layer from the original. –Summarizing a neighborhood by standard deviation –Using weighted kernel neighborhood

14. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Why? …. filtering out anomalies Bathymetry mass points: sunken structures Low pass filter -- bathymetry

15. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 After turning into raster grid We see sudden anomaly in grid Say we wanted to “average” that anomaly out

16. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Try a low-pass filter of 5 cells We can still see those anomalies but they look more “natural” now

17. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Try a low-pass filter of 25 cells The anomalies have been “smoothed out” but at a cost

18. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 We can also do a local filter in that one area

19. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Low pass filter for elevation

20. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 A low pass filter of the DEM done by taking the mean values for a 3x3 cell neighborhood: notice it’s hardly different DEM Low pass

21. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 10 unit square neighborhood

22. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 20 unit square neighborhood

23. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 What about high pass filters? Say we wanted to find the wrecks All areas of sudden change, including our wrecks, have been isolated

24. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 In this high-pass filter the mean is subtracted from the original It represents all the local variance that is left over after taking the means for a 3 meter square neighborhood

25. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 We do this using the raster calculator

26. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 … or Math >> Minus

27. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 If we do a high-pass filter by subtracting from the original the means of a 20x 20 cell neighborhood, it looks different because more local variance was “thrown away” when taking a mean with a larger neighborhood Dark areas represent things like cliffs and steep canyons

28. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Using standard deviation is a form of high-pass filter because it is looking at local variation, rather than regional trends. Here we use 3x3 square neighborhood

29. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Note how similar it looks to a slope map because it is showing standard deviation, or normalized variance, in spot heights, which is similar to a rate of change -- emphasizing local variability over regional trends. The resolution of slope is quite high because it is sampling only every nine cells. When we go to a larger neighborhood, by definition, the resulting map is much less detailed because the standard deviation of a large neighborhood changes little from cell to cell, since so many of the same cells are shared in the neighborhood of cell x,y and cell x,y+1.

30. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Here is the same function with 8x8 cell neighborhood. Here, the coarser resolution due to the larger neighborhood makes it so that slope rates seem to vary more gradually over space

31. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Later on we’ll look at filters and remote sensing imagery, but here is a brief example of a low-pass filter on an image that has been converted to a grid. This can help in classifying land use types

32. Fundamentals of GIS Changing Cell Size (Focal) Lecture Materials by Austin Troy except where noted © 2008 Slide courtesy of Leslie Morrissey

33. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Slide courtesy of Leslie Morrissey Changing Cell Size

34. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Slide courtesy of Leslie Morrissey Change cell size – may cause data loss

35. Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Slide courtesy of Leslie Morrissey “Hidden” effect of Focal Functions on cell values

36. Fundamentals of GIS Change cell size WARNING Lecture Materials by Austin Troy except where noted © 2008 Slide courtesy of Leslie Morrissey

37. Fundamentals of GIS Distance Analysis ------Using GIS-- Used to answer questions related to distance – Proximity – Straight Line Distance Measurement – Cost Weighted Distance Measurement – Shortest Path

38. Fundamentals of GIS Proximity Can use raster distance functions to create zones based on proximity to features; here, each zone is defined by the closest stream segment ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

39. Fundamentals of GIS Distance Measurement ------Using GIS-- Can create distance grids from any feature theme (point, line, or polygon) Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

40. Fundamentals of GIS Distance Measurement Can also weight distance based on friction factors, like slope ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

41. Fundamentals of GIS Combining Distance and Zonal Stats Can also summarize distances by vector geography using zonal stats ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

42. Fundamentals of GIS Combining Distance and Zonal Stats Here we summarize by the mean ------Using GIS-- Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

43. Fundamentals of GIS Density Functions We can also use sample points to map out density raster surfaces. For pixels with no underlying sample point, the z value can simply be based on the abundance and distribution of points. Pixel value gives the number of points within the designated neighborhood of each output raster cell, divided by the area of the neighborhood Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

44. Fundamentals of GIS Density Functions Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

45. Fundamentals of GIS Density Functions Lecture Materials by Austin Troy, Brian Voigt and Weiqi Zhou except where noted © 2011

46. Fundamentals of GIS Spatial Interpolation Lecture Materials by Austin Troy except where noted © 2008 Slide courtesy of Leslie Morrissey

47. Fundamentals of GIS Spatial Interpolation Lecture Materials by Austin Troy except where noted © 2008 Slide courtesy of Leslie Morrissey

48. Fundamentals of GIS Pitfalls of Spatial Interpolation Lecture Materials by Austin Troy except where noted © 2008 Slide courtesy of Leslie Morrissey

49. Fundamentals of GIS Pitfalls of Spatial Interpolation Lecture Materials by Austin Troy except where noted © 2008 Slide courtesy of Leslie Morrissey

50. Fundamentals of GIS Spatial Interpolation in ArcMap Lecture Materials by Austin Troy except where noted © 2008