2. Arc Marine Data Model Exercise Exercise and data at dusk.geo.orst.edu/djl/arcgis/ArcMarine_Tutorial/ What to turn in: –Screen snapshot of what your ArcMap session looks like at the end of Section 4 (including dynseg referencing) –Answers to 2 simple questions at end of Section 4 (which cruise? which vehicle?) –Can put all of the above in a single MS-Word document, labeled with your NAME please! Due in Dropbox, April 28th, 6:00 p.m.

3. Midterm on Wednesday

4. Midterm May start off with multiple choice Bulk will be short answer/short essay Lecture PPTs and your notes Will not include –Labs or Arc Marine exercise –Journal articles

5. Major concepts Representations Object vs Field Model, data model, analysis model Data models –UML terminology, basic procedure from Reality --> conceptual --> logical --> physical –Customized ArcGIS data models For enterprise GIS Analysis Models –Binary, ranking, rating, weighted rating

6. Major concepts - cont. Geodatabase –what it is, why it’s important Topology, Spatial Analysis –what they are, why they are important, how they relate Web GIS from guest lectures Object orientation –Identity, inheritance, encapsulation

7. GIS Analysis Models

8. GIS Analysis Model Graphical modeling framework tied to actual GIS functions Functions, Data, Numerical Models, Tools, etc.

9. ArcGIS 9 Model Builder

11. From Designing Gdbs - Ch 7 Arc Hydro & HEC-RAS Hydrologic Engineering Centers River Analysis System See also “Demo 2” from Apr 12 lecture

12. Arc Marine & Model Builder From Brett Lord-Castillo, M.S. thesis, and Lord-Castillo et al., Transactions in GIS, in review, 2009

13. Arc Marine & Model Builder Models to automatically extract environmental data layers for spatio-temporal analysis Model: Get-SST AML to Modeler conversion at ArcGIS 9.x From Marine Data Model Technical Workshop, 2005 ESRI UC, Halpin et al.

14. The Anatomy of a GIS Analysis Model Berry, Chs. 24-26 compare several GIS models to illustrate different analysis modeling approaches compare varying levels of results from these models GIS is only as good as its data GIS is only as good the expression of its data

15. “It’s All Downhill from Here” the case for landslide susceptibility terrain steepness (high slope/low slope) soil type (unstable/stable) vegetation cover (bare/abundant)

16. BINARY model: codes cells 1 for susceptible 0 for unsusceptible multiplicative: cells must meet all 3 criteria

17. BINARY model: multiplies maps for Y/N solution RANKING model: adds maps for a range of solutions

18. RATING model: averages maps for an even greater range of solutions scale of 1 to 9 (most) for each condition

19. RATING model: for example one cell might be 9 in SL layer, 3 in SO, 3 in CO (9 + 3 + 3) / 3 = 5 or moderate susc.

20. Weighted Rating Model suppose SL is considered to be 5 times more important than SO or CO? so one cell might be: 9 * 5 in SL layer, 3 in SO, 3 in CO ((9*5)+ 3 + 3) / 3 = 17 –fairly high susc.

21. 4 Models for Landslide Susceptibility: Banana Bread to Fruit Cake! BINARY –1 for SL, 0 for SO, 0 for CO –1 * 0 * 0 = 0 NO susceptibility RANKING –1 for SL, 0 for SO, 0 for CO –1 + 0 + 0 = 1 LOW susceptibility RATING –9 for SL, 3 for SO, 3 for CO –(9 + 3 + 3) / 3 = 5 MODERATE susceptibility WEIGHTED RATING –9 for SL, 3 for SO, 3 for CO –((9*5) + 3 + 3) / 3 = 17 HIGH susceptibility

22. Banana Bread to Fruitcake data input to the models - constant logic of models or conceptual fabric of process - different rating models most “robust” –continuum of responses/answers –foothold to extend model even further from critical to contributing factors

23. Extension of Landslide Model to Risk: Consider Proximity to Features That we Really Care About

24. Extending a GIS Model ( cont. ) Risk –variable width road buffers as a function of SLOPE –buffer widens in steep areas Extending hazard to risk –weighted roads based on slopes –weight roads based on traffic volume, emergency routes, etc. –buildings: commercial, residential, etc. –economic value of threatened features, potential resource loss

25. Additional Factors in addition to or instead of SL, SO, CO other critical factors may be considered: –physical: bedrock type, depth to faulting –disturbance: construction areas, gophers? –environmental: storm frequency, rainfall patterns –seasonal: freezing and thawing cycles in spring –historical: past earthquake events

26. Benthic Habitat Example: Parameters Important to Benthic Species Water depth Sediment depth Substrate type Sediment type Exposure Rugosity/BPI Slope/Aspect Water chemistry Water temperature Voids/caverns (size & depth) Vegetation Biotic interactions Anthropogenic factors What can we measure directly, interpret, or derive? Deidre Sullivan, MATE Center, Monterey, CA

27. Bathymetric grid created from multibeam x,y,z data Monterey Bay data courtesy of MATE Center and Cal-State Monterey Bay

28. Slope grid derived from bathymetry

29. Aspect grid derived from bathymetry

30. Measure of surface area to planar area Rugosity grid derived from bathymetry using the Benthic Terrain Modeler

31. Rugosity Measure of how rough or bumpy a surface is, how convoluted and complex Ratio of surface area to planar area Graphics courtesy of Jeff Jenness, Jenness Enterprises, and Pat Iampietro, CSU-MB Surface area based on elevations of 8 neighbors 3D view of grid on the leftCenter pts of 9 cells connected To make 8 triangles Portions of 8 triangles overlapping center cell used for surface area

32. Bathymetric Position Index (BPI) derived from bathymetry using the Benthic Terrain Modeler dusk.geo.orst.edu/djl/samoa/tools.html

33. Bathymetric Position Index (from TPI, Jones et al., 2000; Weiss, 2001; Iampietro & Kvitek, 2002) Measure of where a point is in the overall land- or “seascape” Compares elevation of cell to mean elevation of neighborhood (after Weiss 2001)

34. Substrate type interpreted from Backscatter or Side Scan Sonar images

35. Building a Suitability Model What do we know about the species’ habitat requirements? Can we describe these habitat requirements using GIS data? Do we have enough information? Is it at the right scale? Does the model work?

36. Validate the model BPI Bathymetric Position Index Benthic Terrain Modeler &

37. Using Standard Deviation to Classify Values 68% 95% 99% 1 2 3

38. Binary Model (Multiplication) * Rugosity greater than 1.2 SD 10 = 1 0 * BPI greater than 1.5 SD = Areas that satisfy both criteria

39. Ranking Model (Addition) + Rugosity is greater than 1.2 SD 10 = 1 0 + BPI greater than 1.5 SD = Ranking because it develops an ordinal scale of increasing suitability 1 0 2

40. Rating Model + Rugosity is divided into 4 classes by SD then reclassified to values of 1, 2, 3, 4 10 = 1 0 2 BPI is divided into 4 classes by SD then reclassified to values of 1, 2, 3, 4 Rating because it develops a relative rating based on the simple average of the factors 1 Uses a consistent scale with more than two states to characterize the habitat (simple average)

41. Weighted Rating Model + Rugosity is divided into 4 classes by SD then reclassified to values of 1, 2, 3, 4 10 = 1 0 2 BPI is divided into 4 classes by SD then reclassified to values of 1, 2, 3, 4 Weighted rating develops a relative ranking with the most critical factors given more weight 1 Uses a consistent scale with more than two states to characterized the habitat, however it is a weighted average * 5 )(

42. Binary Ranking Rating Wt. Rating How do they compare?

43. Model Validation

45. “Mapematics”  Rating models considered most “mapematical” –how were weighting factors decided? –guess-timates? –derived from predictive statistical technique?  need right set of maps/data over a large area –based on an experiment in the field?  lots of time, funding, energy  Review literature for existing mathematical model and make them “mapematical” (i.e., use them!)

46. GEO 580 Example Predicting presence of the sensitive lichen Usnea longissima in managed landscapes Dylan Keon GEO 580 project

48. Gateway to the Literature Joerin, F., Using GIS and outranking multicriteria analysis for land-use suitability assessment, Int. J. Geog. Inf. Sci., 15 (2), 153-174, 2001. Jankowski, P., and T. Nyerges, GIS-supported collaborative decision making: Results of an experiment, Annals AAG, 91 (1), 48-70, 2001. Chau, K.T. et al., Landslide hazard for Hong Kong using landslide inventory and GIS, Computers & Geosciences, 30: 429-443, 204.