GIS Analysis Models

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GIS Analysis Models
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GIS Analysis ModelGraphical modeling framework
tied to actual GIS functionsFunctions, 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-RASHydrologic Engineering Centers River
Analysis System
See also Demo 2 from Apr 6 lecture
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Arc Marine Model Builder
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 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.
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The Anatomy of a GIS Analysis ModelBerry, 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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Its 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 modelcodes cells 1 for susceptible0 for
unsusceptiblemultiplicative cells must meet all
3 criteria
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BINARY modelmultiplies maps for Y/N
solutionRANKING modeladds maps for a range of
solutions
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RATING modelaverages maps for an even greater
range of solutionsscale of 1 to 9 (most) for
each condition
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RATING modelfor 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 be9 5 in SL layer, 3 in SO,
  3 in CO
• ((95) 3 3) / 3 17
• fairly high susc.

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4 Models for Landslide SusceptibilityBanana
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
• ((95) 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 RiskConsider
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 ExampleParameters 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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Rugosity grid derived from bathymetry using the
Benthic Terrain Modeler
Measure of surface area to planar area
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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

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
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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
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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

Benthic Terrain Modeler
Bathymetric Position Index
BPI
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Using Standard Deviation to Classify Values
1
2
3
68 95 99
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Binary Model(Multiplication)
1
0
0

1



Areas that satisfy both criteria
Rugosity greater than 1.2 SD
BPI greater than 1.5 SD
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Ranking Model(Addition)
1
0
0
0
2

1

1


Ranking because it develops an ordinal scale of
increasing suitability
Rugosity is greater than 1.2 SD
BPI greater than 1.5 SD
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Rating Model
Uses a consistent scale with more than two states
to characterize the habitat (simple average)


1
0
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
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Uses a consistent scale with more than two states
to characterized the habitat, however it is a
weighted average
Weighted Rating Model
)
(


5
1
0
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
Weighted rating develops a relative ranking with
the most critical factors given more weight
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How do they compare?
Ranking
Binary
Rating
Wt. Rating
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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 ExamplePredicting presence of the
sensitive lichen Usnea longissima in managed
landscapesDylan 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.