1. Lecture 14: More Raster and Surface Analysis in Spatial Analyst ------Using GIS-- Introduction to GIS By Weiqi Zhou, University of Vermont Thanks are due to Prof. Troy, upon whose lecture much of this material is based.

2. ©2007 Austin Troy Converting vector to raster ------Using GIS-- Introduction to GIS

3. ©2007 Austin Troy Converting vector to raster ------Using GIS-- Introduction to GIS

4. ©2007 Austin Troy Distance Analysis ------Using GIS-- Introduction to GIS Used to answer questions related to distance – Proximity – Straight Line Distance Measurement – Cost Weighted Distance Measurement – Shortest Path

5. ©2007 Austin Troy Proximity ------Using GIS-- Introduction to GIS Create zones based on proximity to features.

6. ©2007 Austin Troy Distance Measurement Calculate distance from each cell in the raster to the closest source (feature) ------Using GIS-- Introduction to GIS

7. ©2007 Austin Troy Cost Weighted Distance Measurement ------Using GIS-- Introduction to GIS Specify a cost raster to calculate cost weighted distance

8. ©2007 Austin Troy Density Functions Introduction to GIS

9. ©2007 Austin Troy Density Functions Introduction to GIS A raster density surface, based just on the abundance of points within a “kernel” or data frame.

10. ©2007 Austin Troy Neighborhood Statistics A “local” method of summarizing raster data within a neighborhood by a statistical measure, like mean, stdv. Introduction to GIS – Statistic types – Neighborhood shape – Neighborhood settings Window size Units

11. ©2007 Austin Troy Neighborhood Statistics Statistic type: Mean 3x3 cell squared neighborhood. Introduction to GIS Processing cell Neighborhood

12. ©2007 Austin Troy 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 Introduction to GIS

13. ©2007 Austin Troy Neighborhood Filters Improve the quality of raster grids by eliminating spurious data or enhancing features. Filter types –Low pass filters –High pass filters Introduction to GIS

14. ©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. Introduction to GIS

15. ©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 Introduction to GIS

16. ©2007 Austin Troy Why do we care about this? Low pass filtering: filtering out anomalies Introduction to GIS Bathymetry mass points: sunken structures

17. ©2007 Austin Troy Why do we care about this? Introduction to GIS We see sudden anomaly in grid Say we wanted to “average” that anomaly out Low pass filtering: filtering out anomalies

18. ©2007 Austin Troy Why do we care about this? Try a low-pass filter of 5 cells Introduction to GIS We can still see those anomalies but they look more “natural” now

19. ©2007 Austin Troy Why do we care about this? Try a low-pass filter of 25 cells Introduction to GIS The anomalies have been “smoothed out” but at a cost

20. ©2007 Austin Troy What about high pass filters? Find the wrecks Introduction to GIS All areas of sudden change, including our wrecks, have been isolated

21. ©2007 Austin Troy Applying a high pass filter Introduction to GIS Subtracting the mean grid from the original one. Applied a low pass filter: Summarizing the mean with a 20x 20 cell neighborhood

22. ©2007 Austin Troy Neighborhood Statistics We do this using the map calculator Introduction to GIS

23. ©2007 Austin Troy Neighborhood Statistics Using standard deviation is a form of high-pass filter. Introduction to GIS

24. ©2007 Austin Troy Neighborhood Statistics Here is the same function with 8x8 cell neighborhood. Introduction to GIS

25. ©2007 Austin Troy Neighborhood Statistics Applying filters on remote sensing imagery. Introduction to GIS