1. Models in GIS A model is a description of reality It may be: Dynamic orStatic Dynamic spatial models e.g., hydrologic flow Static spatial models (or point in time) e.g., land suitability analyses

2. Spatial Models Focus on computer based models of spatial phenomena Three classes: Cartographic models Spatio-temporal models Network models

3. Cartographic models Application of spatial operations such as buffers, interpolation, reclassification, and overlay to solve problems For example, suitability analyses; classification of land according to its utility for a specific use. (see Lab 9 and Lab 10) Often temporally static (features represented at a fixed period of time) Values don’t change during the model It may include a temporal component when it compares changethrough time (comparing vegetation in 1990 to vegetation in 2000)

4. Spatio-temporal models Dynamic in both space and time Time passes explicitly within the running of the model Changes in time-driven process operate on spatial variables

5. Network models May be temporally dynamic or static Constrained to modeling the flow and accumulation of resources through a network

6. Cartographic modeling Combine general data sets, functions, and operations in a sequence to answer questions, typically producing an output map from various input maps Examples: -distribution of suitable habitats, viable populations -migration route/corridor studies -water distribution systems, natural and constructed -species invasions -mill site selection -harvest scheduling -pollution response planning

7. Cartographic modeling

8. Cartographic models Often represented with flowcharts; graphically representing the spatial data, operations and their sequence

9. Combinations as an additive process

10. Discrete and Continuous Rankings

11. The Basis for Rankings

12. Example Weightings – Distance to Roads

13. Example – slope suitability ranking

14. Weightings Among Layers: How do you combine the rankings from various layers? The relative weights among your input layers will substantially affect the relative suitabilities you predict.

15. Determining Weights Unscaled Common scale Client or Expert Survey (iterative) Rank order weights Weights must be justified

16. Rank Order Determination of Weights

20. 20 operations 15 data layers

22. 20 operations 15 data layers

23. 20 operations 15 data layers

25. 20 operations 15 data layers

26. 20 operations 15 data layers

28. 20 operations 15 data layers

30. Points to Remember: Make sure the problem is clearly specified and appropriate forGIS analyses Determine the spatial and attribute accuracy required for theanalysis, and if existing data meet these requirements.Pay particular attention to inclusions and mmu Coordinate systems (including datums) have to be the samefor all layers Need to know the extent of each layer....where you have data.If extents are different, think about how this will affect analyses? If you have both raster and vector data, which will you convert,and how?

31. Ruffed Grouse Prime Habitat (This weeks Lab) Brushy/young aspen for summer and fall forage (< 12 years old) Mature Aspen for winter forage (> 61 yrs old) Intermediate aspen for brood cover (from 12 to 61 years old)

32. Ruffed Grouse Information Ruffed grouse have home ranges of 5 – 20 has. From this you deduce you want areas that are within 300 meters of all vegetation types You have a vegetation map of the management area that includes categories for aspen by age, as well as other vegetation types.

33. Draw a flowchart of the analysis that identifies suitable habitat areas Data set operation Vegetation layer Data set Reclass, Aspen < 12 Young aspen layer Example portion:

40. 1975 1981 1986 1991 1998 2000 Twin Cities Landsat Imagery

41. Agriculture Urban Water Forest Grass Wetland Extraction 1986 Overall accuracy: 95.2% Kappa: 94%

42. Agriculture Urban Water Forest Grass Wetland Extraction 1991 Overall accuracy: 94.6% Kappa: 93.2%

43. Agriculture Urban Water Forest Grass Wetland Extraction 1998 Overall accuracy: 95.9% Kappa: 94.9%

44. Agriculture Urban Water Forest Grass Wetland Extraction 2002 Overall accuracy: 92.1% Kappa: 90.2%

45. Change Map