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GIS Analysis Models. GIS Analysis Model Graphical modeling framework tied to actual GIS functions Functions, Data, Numerical Models, Tools, etc. ArcGIS 9 Model Builder. ArcGIS 9 Model Builder.

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slide3
GIS Analysis ModelGraphical modeling framework tied to actual GIS functionsFunctions, Data, Numerical Models, Tools, etc.
from designing gdbs ch 7 arc hydro hec ras hydrologic engineering centers river analysis system
From Designing Gdbs - Ch 7Arc Hydro & HEC-RASHydrologic Engineering Centers River Analysis System

See also “Demo 2” from Apr 6 lecture

arc marine model builder
Arc Marine & Model Builder

From Brett Lord-Castillo, M.S. thesis, and Lord-Castillo et al., Transactions in GIS, in review, 2009

arc marine model builder8
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.

the anatomy of a gis analysis model berry chs 24 26
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
it s all downhill from here
“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)
slide11
BINARY model:codes cells 1 for susceptible0 for unsusceptiblemultiplicative: cells must meet all 3 criteria
binary model multiplies maps for y n solution ranking model adds maps for a range of solutions
BINARY model:multiplies maps for Y/N solutionRANKING model:adds maps for a range of solutions
slide13
RATING model:averages maps for an even greater range of solutionsscale of 1 to 9 (most) for each condition
rating model for example one cell might be 9 in sl layer 3 in so 3 in co 9 3 3 3 5 or moderate susc
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.
weighted rating model
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.
4 models for landslide susceptibility banana bread to fruit cake
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
banana bread to fruitcake
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
extending a gis model cont
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
additional factors
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
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

Benthic Habitat Example:Parameters Important to Benthic Species

What can we measure directly, interpret, or derive?

Deidre Sullivan, MATE Center, Monterey, CA

bathymetric grid created from multibeam x y z data
Bathymetric grid created from multibeam x,y,z data

Monterey Bay data courtesy of MATE Center and Cal-State Monterey Bay

rugosity grid derived from bathymetry using the benthic terrain modeler
Rugosity grid derived from bathymetry using the Benthic Terrain Modeler

Measure of surface area to planar area

rugosity
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

slide27
Bathymetric Position Index (BPI)derived from bathymetry using the Benthic Terrain Modelerdusk.geo.orst.edu/djl/samoa/tools.html
bathymetric position index from tpi jones et al 2000 weiss 2001 iampietro kvitek 2002

(after Weiss 2001)

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

building a suitability model
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?
validate the model
Validate the model

&

Benthic Terrain Modeler

Bathymetric

Position Index

BPI

binary model multiplication
Binary Model(Multiplication)

1

0

0

1

=

*

=

*

Areas that satisfy both criteria

Rugosity greater than 1.2 SD

BPI greater than 1.5 SD

slide34

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

slide35

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

slide36

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

how do they compare
How do they compare?

Ranking

Binary

Rating

Wt. Rating

mapematics
“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!)
slide41
GEO 580 ExamplePredicting presence of the sensitive lichen Usnea longissima in managed landscapesDylan Keon GEO 580 project
gateway to the literature
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.