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Spatial Models

Spatial Models. Can help determine areas best for: People Reserves Species Starbucks Suitability Index Models 0 = Unsuitable 1 = Suitable. Raster-Based Models. Raster-based Models: Combination of raster operations Part of a much larger set of modeling methods Includes: Raster Math

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Spatial Models

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  1. Spatial Models • Can help determine areas best for: • People • Reserves • Species • Starbucks • Suitability Index Models • 0 = Unsuitable • 1 = Suitable

  2. Raster-Based Models • Raster-based Models: • Combination of raster operations • Part of a much larger set of modeling methods • Includes: • Raster Math • Comparisons • Boolean Operations • Statistics

  3. Raster “Models” • Topography: Elevation/depth, slope, aspect, contours • Statistics: min, max, mean, std. dev. • Distance • Density • Interpolation from points • Classification from remotely sensed data • And lots more…

  4. Pika Habitat Suitability • Land cover: • Rock: 0.9 • Snow and ice: 0.7 • Herbaceous veg: 0.5 • Evergreen: 0.1 Monthly Minimum Temperature Elevation 1 1 Suitability Suitability 0 0 0 5 2500 3600 Degrees C Meters

  5. ArcGIS Approaches • Spatial Analyst Tools • Spatial Analyst drop-down menu is gone in ArcGIS 10 • Model Builder • Scripts: Python

  6. Analysis Environment • Spatial Reference (Coordinate System) • Make them the same • Extent • Area of interest • All rasters should overlap • Cell Size • Largest of all rasters or larger

  7. Toolbox → Spatial Analysis Tools → Map Algebra → Raster Calculator Raster Calculator

  8. Environment Settings

  9. Raster Math 1 + 12 = 13 = +

  10. Common Functions • Local: • Arithmatic: +,-,/, *, • MOD (Modulo): returns the remainder • Boolean: • OR: If either input is true, output is true • AND: If both inputs are true, output is true • CON (Conditional)

  11. Comparisons • <> (Not Equals) • == (Equals) • < (Less than) • <= (Less than or equal to) • > (Greater than) • >= (Greater than or equal to)

  12. Raster Math: Comparisons 1 > 2 = 0 = >

  13. Conditional Operator • Con(<condition>,<true>,<false>) • Given a raster “condition”: • Puts the true value where true and false value where false • Example: • Find the elevations in Rocky over 3000 meters • HighElevations=con(RockyDEM>3000,1,0)

  14. Elevations over 3000 meters • Con("W100N40.DEM“>3000,0,1)

  15. Elevations over 3000 meters

  16. 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?

  17. Flow Diagrams • Control Flow Diagrams – “Flow” Charts • Data Flow Diagrams • Data Structure or “Hierarchy” Diagrams

  18. Bathymetric Raster • Created from multi-beam sonar data • DEM for surface under the water

  19. Slope Raster from Bathymetry

  20. Aspect Raster from Bathymetry

  21. Benthic Terrain Modeler (BTM) • Collection of ArcGIS Tools • Benthic Position Index (BPI) • BPI = Depth – Mean Depth of Surrounding Pixels

  22. BPI – Focal Mean

  23. BPI – Ridges and Valleys

  24. BPI – Slopes and Flat Areas

  25. Bathymetric Position Index

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

  27. Rugosity from Bathymetry

  28. Substrate Type from Sonar

  29. Standard Deviations to Classify 1 2 3 68% 95% 99%

  30. Binary Model (Operations) • Multiplicative 1 0 1 0 1 0 1 0 0 = * Areas that satisfy both criteria Rugosity greater than 1.2 SD BPI greater than 1.5 SD = *

  31. Ranking Model (Operations) • Additive 1 0 2 0 1 0 1 0 1 = + Ranking because it develops an ordinal scale of increasing suitability Rugosity is greater than 1.2 SD BPI greater than 1.5 SD + =

  32. Rating Model = + 1 0 1 1 2 Rugosity is divided into 4 classes by SD then reclassified to values of 1, 2, 3, 4 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

  33. Weighted Rating Model * 5 + = 0 1 1 2 Rugosity 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 BPI is divided into 4 classes by SD then reclassified to values of 1, 2, 3, 4

  34. Types of Models • Binary Models: • Areas that satisfy all criteria • Ranking Models: • Ordinal ranking of areas • Rating Models: • Relative rating • Weights add to give different layers more influence on outcomes • Can use continuous data

  35. Unified Modeling Language • UML • General-Purpose Graphical Language • Lots of symbols • Most folks do not understand them • ArcGIS doe there own version of “Activity Diagrams” • Heavily used in the Computer Science & Engineering field

  36. UML Class Diagram

  37. Model Builder • Form of “Graphic Programming” • Provides a precise, repeatable series of steps • By changing the inputs, you can repeat the model on different input datasets

  38. Model Builder Input (Layer or File) Data: File, Layer Process (ArcGIS Tool) Process: Tools Iterator: Loops Iterator

  39. Model Builder Input (Layer or File) Process (ArcGIS Tool) Output (File, Layer)

  40. Old Model Builder

  41. General GIS Flow Chart Analysis Define Task Automate Find Data Document • Prep Data • Download • Un-compress • Define Projections • Convert File Formats • Project to desired SRS Disseminate Cartography Web Publishing Ok? Load in ArcMap Paper

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