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GIS Analysis Models
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GIS Analysis ModelGraphical modeling framework tied to actual GIS functions
Functions, Data, Numerical Models, Tools, etc.
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ArcGIS 9 Model Builder
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ArcGIS 9 Model Builder
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From Designing Gdbs - Ch 7Arc Hydro & HEC-RAS
Hydrologic Engineering Centers River Analysis System
See also “Demo 2” from Apr 6 lecture
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Arc Marine & Model Arc Marine & Model BuilderBuilder
From Brett Lord-Castillo, M.S. thesis, and Lord-Castillo et al., Transactions in GIS, in review, 2009
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Arc Marine & Model Arc Marine & Model BuilderBuilder
Models to automatically extract environmental data Models to automatically extract environmental data layers for spatio-temporal analysislayers for spatio-temporal analysis
Model: Get-SSTModel: Get-SST
AML to Modeler conversion at ArcGIS 9.xAML to Modeler conversion at ArcGIS 9.x
From Marine Data Model Technical Workshop, 2005 ESRI UC, Halpin et al.
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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
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“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)
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BINARY model:codes cells 1 for susceptible
0 for unsusceptiblemultiplicative: cells must meet all 3 criteria
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BINARY model:multiplies maps for Y/N solution
RANKING model:adds maps for a range of solutions
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RATING model:averages maps for an even greater range of
solutionsscale of 1 to 9 (most) for each condition
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RATING model:for example one cell might be9 in SL layer, 3 in SO, 3 in CO
(9 + 3 + 3) / 3 = 5 or moderate susc.
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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.
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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
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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
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Extension of Landslide Model to Risk:Consider Proximity to Features That we Really
Care About
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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
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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
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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
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Bathymetric grid created from multibeam x,y,z data
Monterey Bay data courtesy of MATE Center and Cal-State Monterey Bay
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Slope grid derived from bathymetry
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Aspect grid derived from bathymetry
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Measure of surface area to planar area
Rugosity grid derived from bathymetry using the Benthic
Terrain Modeler
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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 onelevations of 8 neighbors
3D view of grid on the left Center pts of 9 cells connectedTo make 8 triangles
Portions of 8 triangles overlapping center cellused for surface area
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Bathymetric Position Index (BPI)derived from bathymetry using the
Benthic Terrain Modelerdusk.geo.orst.edu/djl/samoa/tools.html
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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)
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Substrate type interpreted from Backscatter or Side Scan Sonar
images
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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?
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Validate the
model
BPI
Bathymetric Position Index
Benthic Terrain Modeler&
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Using Standard Deviation to Classify Values
68%
95%
99%
1
23
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Binary Model(Multiplication)
*Rugosity greater than 1.2 SD
1 0
=1 0
*BPI greater than 1.5 SD =
Areas that satisfy both criteria
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Ranking Model(Addition)
+Rugosity is greater than 1.2 SD
1 0
=1 0
+BPI greater than 1.5 SD =
Ranking because it develops an ordinal scale of increasing suitability
1
02
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Rating Model
+Rugosity is divided into 4 classes by SD then reclassified to values of 1, 2, 3, 4
1 0 =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)
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Weighted Rating Model
+Rugosity is divided into 4 classes by SD then reclassified to values of 1, 2, 3, 4
1 0 =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)(
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Binary Ranking
Rating Wt. Rating
How do they compare?
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Model Validation
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“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!)
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GEO 580 Example
Predicting presence of the sensitive lichen Usnea longissima in managed landscapes
Dylan Keon GEO 580 project
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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.