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Suitability Modeling in GIS RESM 440 Wednesday October 20, 2010

2. Today. Today's topic:Suitability modeling in GIS This week reminders in lab:Remember to complete any late labs by end of next weekThis week in lab: Work on your final projectRequired reading: Collins et al. 2001 (Land Use Suitability Analysis paper). 3. Review from Monday: Modeling in GIS.

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Suitability Modeling in GIS RESM 440 Wednesday October 20, 2010

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    1. Suitability Modeling in GIS RESM 440 Wednesday October 20, 2010

    2. 2 Today Todays topic: Suitability modeling in GIS This week reminders in lab: Remember to complete any late labs by end of next week This week in lab: Work on your final project Required reading: Collins et al. 2001 (Land Use Suitability Analysis paper)

    3. 3 Review from Monday: Modeling in GIS What is modeling? Attempting to simulate real-world processes in GIS Static model: One point in time Dynamic model: Several points in time

    4. 4 Suitability modeling Suitability modeling: integration of multiple criteria and criteria weights to find best solution(s) to a spatial ranking question Used to identify / rank locations for many purposes Spatial decision support: Using GIS based models to aid in decision making Used by: Land managers (USFS, BLM, NPS, states, counties) Municipalities and local governments (cities, towns etc.) Many others

    5. 5 Examples of suitability modeling/ranking Community/land use planning: (Example: Areas suitable for development) Habitat suitability modeling (areas suitable for wildlife species) Site selection (areas suitable for industrial park) Vulnerability assessment (areas vulnerable to exotic species) Other assessment or ranking (comparisons of areas for conservation)

    6. 6 Goals of suitability modeling Identify best/worst locations Provide supporting maps and information Help review plans and compare alternatives Identify areas where further study is needed (information gaps)

    7. 7 History of suitability modeling (Collins et al.) Manual overlay of hand-drawn maps (early 1900s) Inclusion of ecological inventory (human made, natural aspects) (1960s) Computer mapping, map algebra overlays (late 60s, early 70s) Multi-criteria evaluation and spatial decision support (1990s-today) New directions: expert systems, complex spatial problem solving

    8. 8 Process of suitability modeling Define the overall problem or goal What are you ranking? What is required result? Develop flow chart or plan for analysis Determine evaluation criteria and scoring within criteria Determine weights for individual criteria Calculate result (ranking or suitability) Evaluate and present results

    9. 9 Basic ideas behind suitability/ranking models

    10. 10 Step: Criteria development Criterion: one element of a suitability model that can be represented by a spatial layer Steps in criteria development: Determine best criteria to represent your need Obtain relevant GIS dataset(s) Interpret data: Translate into mappable, quantifiable elements Assign criterion scores/ranks Normalize criterion scores across all criteria (same scale)

    11. 11 Ranking/scoring within criteria Assigning values for criteria Discrete: Good/bad, Yes/No, 0/1 Continuous: Criteria scores vary along a numeric scale Generally higher values mean more suitable Scores can be assigned incrementally (e.g. 0 to 10) Scores can vary according to a user-defined relationship function

    12. 12 Step: Weighting/combining multiple criteria Determining criteria weights: How important is each criterion? Expert knowledge Solicit weights from stakeholders (surveys, meetings) Determining method to combine criteria & weights Additive linear model is most common (see previous slide) Other models also can be used GIS processes Usually performed using cell-based raster overlay Map each criteria, determine score, multiply by weight Uses map algebra Grid overlays

    13. 13 Additional considerations/limitations Practical limits/potential problems Difficult to translate some criteria into meaningful scores Subjectivity in assigning scores, criteria weights Applying pixel-based results to the real world Data limitations Data not always equally available or of equal quality for all criteria Highly susceptible to assumptions & limitations of individual map layers

    14. 14 Examples of suitability models Land development suitability model: Site selection model (Bolstad, pp 487-495) Habitat suitability model: Beaver Introduced species vulnerability model: Emerald Ash Borer Watershed ranking model: Conservation Success Index for Eastern Brook Trout

    15. 15 Example 1. Site suitability example (Bolstad) Model: Suitability for site to build a home Base layers: Elevation, soils, roads

    16. 16 Example 1. GIS steps used Spatial Analyst menu: Surface Analysis (aspect, slope) Spatial Analyst menu: Distance ArcToolbox: Clip, Dissolve, Buffer tools Spatial Analyst menu: Reclassify Spatial Analyst menu: Convert features to raster Spatial Analyst menu: Raster Calculator (to combine results)

    17. 17 Example 2. Habitat Suitability Index (HSI) HSIs developed for hundreds of wildlife species Some are applicable spatially, some are not Aquatic and terrestrial species Based on literature, field studies Apply different spatial aspects of species biology: Limiting resources for species Food Cover

    18. 18 Example 2. HSI for beaver habitat Habitat layers: Slope < 15% Preferred land cover (forested wetland is best) Proximity to water Distance from roads/bridges Assign numeric score to each layer Final map indicates habitat suitability

    19. 19 Example 3. Introduction of forest pest species GIS can be used to model / rank susceptibility to introduction of forest insect pests Uses: Identify areas with risk factors Identify areas for future monitoring or prevention efforts Example: Emerald Ash Borer Introduced in Midwest Severe economic damage Specific to ash trees Likely introduced in shipping materials from overseas

    20. 20 Example 3. Emerald Ash Border (EAB) EAB introduction risk mapping (MN example) Risk factors: Sawmills Campgrounds Nurseries Urban areas Firewood dealers Seasonal homes Dont move firewood!

    21. 21 Example 4. Conservation Success Index (CSI) Trout Unlimited Ranks watersheds based on probability of success for supporting cold water fish species (trout) Used to guide policies and priorities related to species conservation, based on best probability of success

    22. 22 Example 4. CSI scoring framework example

    23. 23 Example 4. Brook trout CSI Brook trout conservation potential model based on: % of original range still occupied Degree of flow modification # Introduced species Land use conversion potential

    24. 24 Example 4. Brook trout CSI by watershed

    25. 25 Advantages of suitability modeling in GIS Quantifiable, mappable results for sometimes abstract concepts Ability to adjust models, see how results are affected (sensitivity analysis) Process of assigning weights may increase awareness & promote consensus-building among stakeholders for given policy issue Greater awareness of future data needs and gaps

    26. 26 Summary Suitability Modeling Criteria, weighting Examples of application of suitability modeling Limitations and advantages of this approach Coming up next week: Test review Future directions in GIS 3D or virtual modeling

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