Modelling in Gis

8/8/2019 Modelling in Gis

1/34

.Modeling in GIS

12 April 2005


2/34

Begin working on Building A

Groundwater ProtectionModel


3/34

Types of models

A model may be a representation ofdata (e.g. a DEM)

A conceptual model is an idea of howsomething functions (often described with aflow chart)

Rule-based modeling uses rules andnumerical thresholds to interpretinformation represented in multiple data

themes


4/34


5/34

More types of modelsMathematical modeling involves use ofequations that may be implemented within

GIS or linked to GISStatistical mathematical models are basedon empirical observations and contain one ormore random variables

Deterministic mathematical models do notcontain any random variables

Environmental simulation models aremathematical models that represent

environmental processes


6/34

Even more modelsCartographic modeling involves GIS analysis ofspatial data with Boolean or mathematical

operationsStatistical GIS modeling involves developingrelationships between GIS-derived environmentalcharacteristics (independent variables) and

measures of ecological function (dependentvariables)

In coupled GIS/simulation modeling, GIS areused to derive input variables required by a

simulation model


7/34

Cartographic modeling example


8/34

Cartographic modeling

Cartographic modeling is often used toidentify suitable habitats for organisms fromenvironmental variables

E.g. maps of vegetation, food, roads, etc. can becombined to predict a species distribution

Has been used on Wild Turkeys, Golden-cheekedWarblers, Wood Storks, White-tailed Deer, GopherTortoise, California Condor, etc.


9/34

Cartographic modelingcontinued

It is also possible to combine the variables in amathematical model where each data layer

represents a separate variableFor example, it is possible to compute soil loss bass onsix variables:

1.) Rainfall erosion index (R) 4.) Slope length (S)

2.) Inherent soil erodibility (K) 5.) Cover & management factor (C)

3.) Slope percentage (L) 6.) Conservation practice factor (P)A = RKLSCP

A similar approach has been used to model non-pointsource pollution


10/34

Rule-based modeling

Expert systems are computer systemsthat help solve problems that wouldnormally require a human expertsinterpretation

Expert systems can be linked with a GIS

and thus made spatially explicitExpert systems utilize three types ofrules


11/34

Three types of ru

les for ru

le-based modeling1.) Database rule to evaluate numerical

information2.) Map rules to evaluate mappedcategorical variables

3.) Heuristic rules to evaluate theknowledge of experts


12/34

Inductive-spatial modeling

In inductive-spatial modeling, a GIS

learns relationships between datasetsin the geographic database, developingrules based on the analysis of the inputdata

This is a form of rule-based modeling

This approach has been used to modelhabitat suitability for Red Deer in Scotland


13/34

Spatial Decision Support System

SDSS is a type of rule-based modeling

A SDSS adds the ability to recommendmanagement solutions to environmentalproblems

It can also help evaluate theconsequences of various managementscenarios, aiding in decision-making


14/34

SDSS example


15/34

Statistical modeling

If the relationships needed fordevelopment of a model are not known,GIS can be used to:

Assemble spatial data on landscapeproperties

Derive new data that are syntheses of theoriginals

Statistically analyze the new data todetermine the strength of the interactions


16/34

Statistical modeling example


17/34

Avoiding spatial bias instatistical modelingHow do you minimize autocorrelation?

Random sample selectionChoosing sample points that areregularly spaced (at a distance that

meets an acceptable level of spatialautocorrelation)


18/34

Statistical models for continuousdata

In GIS-univariate statistical modelingwhat are dependent variables and whatare independent variables?

Dependent variables are typically fieldmeasurements (e.g. biomass, diversity,richness, etc.)

Independent variables are derived from adigital database containing continuous data(e.g. elevation)


19/34

Example of a univariate model


20/34

Examples of statistical models

Regression analysis to relate vegetationalteration by beaver dams to beaver colonydensity

ANCOVA to compare expansion rates of oakwilt fungus in urban vs. rural areas in TX

Stepwise multiple regression to relate the %of trees / cell damaged by spruce budwormto physical and vegetative site characteristicsrepresented by a number of GIS data layers


21/34

Statistical models for categoricaldataCategorical data requires a different

analysis than continuous data

Expected vs. observed outcomesBayesian statistics


22/34

Expectedvs. observed outcomes

Most rely on a chi-square (2) analysis

For example:Young et al. (1987) used 2 analysis todemonstrate that Northern Spotted Owls used old-growth more often than would be expected basedon its percentage of the landscape

Agee et al. (1989) used 2 analysis to examinehabitat preferences of grizzly bears


23/34

Expectedvs. observed outcomes2In addition to 2 analysis, it is also possibleto utilize logistic regression

Logistic regression compares the attributes ofthe locations where the phenomenon is presentwith those of the location where thephenomenon is absent

Pereira and Itami (1991) used logisticmultiple regression to model the potentialeffects of a proposed observatory on theMount Graham Red Squirrel


24/34

Mt Graham Red Squirrel

From http://medusa.as.arizona.edu/graham/envir.html


25/34

Squirrel numbers


26/34

Bayesian Statistics

Bayesian statistics provide a frameworkfor combining relative values of being rightor wrong (subjective probabilities) with theprobabilities of being right of wrong(conditional probabilities)

Relies upon state-conditional probabilitydensity functions, the a priori probability ofa state, and the a posteri probability of

each state, given certain conditions


27/34

GIS cou

pled with mathematicalmodelsGIS is most successful when coupledwith models that predict outcomes ofprocesses (e.g. succession, NDVI,nutrient cycling, etc.)

Often used as an iterative process to

simulate responses to newenvironmental conditions or to producenew maps of predicted ecosystem

properties along spatial gradients


28/34

Process1.) Hypotheses are formulated on how behaviorof organisms or ecosystems depends on their

spatial relation with systems & environment2.) Combinations of environmental variables areidentified

3.) Spatial distribution, coincidence, or proximityof variables identified with the GIS can be inputinto the computer models to examine thehypothesized consequences of spatial relations


29/34

Population simulation models

Population growth depends upon bothintrinsic and extrinsic factors

Intrinsic factors: birth rate, death rate,immigration, emigration

Extrinsic factors: Physical environment,interaction/competition with other species, etc.

Including spatial data (as an extrinsic factor)often produces more useful models


30/34

E

cosystem and landscapesimulation modelsEcosystem and landscape simulation modelsattempt to duplicate ecological function via

coupled differential equations that describekey ecosystem and landscape processes

For example, JABOWA and FORET forestmodels simulate the birth, growth, and death

of individual trees based on deterministic,intrinsic stand variables (e.g. shading,crowding) and stochastic environmentalvariables (e.g. heat sums, temperatureextremes, soil moisture)


31/34

E

cosystem and landscapesimulation models continuedThese models can be linked to a GIS in

two ways1.) Data from a GIS can be extracted andused to run a model

2.) The results of the model can be

displayed in GIS


32/34

Spatially dynamic ecosystemmodelsAlthough many of the models describedpreviously work fairly well, they all have

difficulty incorporating stochastic elements(e.g. fire and weather events)

In order to account for these things, you

need aM

onteC

arlo simulationTo date, few spatially dynamic models havebeen linked to a GIS, primarily due to thecomputational requirements


33/34

Time for scientificpaper

discussion


34/34

For nextThursday

Abstracts due in class

Will review them before lab

During lab, abstracts will be sent to ScientificSewanee coordinator

Read (and be prepared to discuss) UsingAtlas Data to Model the Distribution ofWoodpecker Species in the Jura, France

Read Campbell CH 16

Modelling in Gis

Documents

Transcript of Modelling in Gis