spatial modeling with gis longley et al., chapter 16
TRANSCRIPT
Spatial Modeling with GIS
Longley et al., Chapter 16
Spatial Modeling with GIS
• Introduction
• Types of Model
• Modeling Technology
• Multicriteria Methods
• Accuracy and Validity
Spatial modeling
• Modeling: An overworked term • data model a template for data
relational, object-oriented, coverage, shapefile
• Model concerned with how the world looks
• Model also a representation of some real-world process
• Concerned with how the world works
Spatial modeling
• Manipulation of geographic information in multiple steps
• Steps may represent stages in some complex analysis
• Calculation of indicators over space (potentials)• Steps may represent time • Dynamic model • Iterative analysis • Geocomputation (see www.geocomputation.org)
Analog or Digital Modeling?
• Analog use of a scale model
• Analogous process
• Varignon frame
• Need a digital process represented in 0s and 1s
• program in C • GIS script in VBA• Python
Scaled Real Models
Army Corps of Engineers:WES
Varignon Frame
“Live” table: Pollution demo
Scale in a digital model?
• Spatial resolution/extent
• Temporal resolution/extent
• Define what is left out of the model
• Leave out uncertainty about model data, predictions
• Model must run faster than the real world
• Ecological fallacy
Why model?
• Support some design process
• Allow the user to experiment with a replica
• Investigate what-if scenarios
• To understand change and dynamics
• Test sensitivity and confidence
Analysis vs. Modeling
• To analyze or model?
• Evacuation scenarios– Tom Cova's analysis – Church's simulations– LANL
Analysis
Modeling
LANL TRANSIMSIndividual vehicle-based traffic simulationof entire cities
Limits of Analysis
• Static, one point in time
• Search for patterns, anomalies
• Generating hypotheses
• Revealing what would otherwise be invisible
• Form vs. process
Modeling multiple stages
• Perhaps different points in time
• Implementing ideas and hypotheses
• Experimenting with policy options
• Scenario based planning
Types of Model
• Static models and indicators
• Combining GIS layers through overlay e.g., using ModelBuilder
• Universal Soil Loss Equation
• A = R x K x LS x C x P
• DRASTIC model of groundwater vulnerability
• Karst groundwater protection model
• DRASTIC
Santa Barbara Regional Impacts of Growth Study: 2040 forecasts
Karst groundwater protection model in Model Builder
Model result
Modeling Approach
• Individual vs. Aggregate models
• Is it possible to model every individual element in the system?
• Every molecule of groundwater? Every person in a crowd?
• Autonomous agent models
Mass Behavior: Problems
Twenty-one Hajj pilgrims trampledWednesday, February 12, 2003 Posted: 2:33 PM
EST (1933 GMT)MINA, Saudi Arabia -- Another 21 people were trampled to death Wednesday on their way to one of the rituals of the Hajj, the annual Muslim pilgrimage to Mecca, Saudi officials said. Wednesday's deaths happened on a bridge as the throngs of pilgrims were heading to throw stones at one of three pillars representing Satan's temptation of Abraham, the officials said. The stoning represents a rejection of evil deeds. On Tuesday, a similar incident killed 14 pilgrims.
Notting Hill Carnival
Cellular Models
• Work on a raster: Good match to GIS
• Initial conditions
• Each cell in one of a number of states
• Rules of state transition at each timestep based on states of cell and neighbors
• Conway’s Game of Life
• SLEUTH land use change model
(Universal) Turing machine
Cellular automata
• Framework for systems experiments
• Simplest way to demonstrate complex systems behavior
• Wolfram: Formal framework
• {Cells, States, Initial conditions, Neighborhood, Rules, Time}
• Conway’s LIFE
The game of life
• Grid of square cells extending infinitely in every direction. • A cell can be live or dead. • Each cell in the grid has a neighborhood consisting of the
eight cells in every direction including diagonals. • To apply one step of the rules, we count the number of live
neighbors for each cell. – A dead cell with exactly three live neighbors becomes a live cell
(birth). – A live cell with two or three live neighbors stays alive (survival). – In all other cases, a cell dies or remains dead (overcrowding or
loneliness).
Some examples
More examples
Urban Growth as a CA
Behavior Rules
T0 T1
For i time periods (years)
spontaneousspreading
center organicroad
influenced deltatron
f (slope resistance, diffusion
coefficient)
f (slope resistance,
breed coefficient)
f (slope resistance,
spread coefficient)
f (slope resistance, diffusion coefficient,
breed coefficient,road gravity)
SLEUTH applied to Santa BarbaraUrban growth to 2040
No new roads
Upgrade all local roads
Technology for Modeling in GIS
• Graphic user interface e.g. GISMO in ERDAS• ModelBuilder
– access to all ArcGIS functions – no looping at present
• Scripts ARC/INFO AML • ArcView 3.x Avenue • ArcGIS
– Visual Basic for Applications – Perl – Python – JScript – ArcScripts
Model Coupling
• linking model software to GIS • Loose coupling
– Exchanging files– Entering results
• Tight coupling – Common files– Common interface– Common code
• Modeling languages
Multcriteria Methods
• Multiple factors affect decisions• Weighted by difference levels of importance• Karst case
– slope > 5%– land use = cropping – distance from stream < 300m
• Simple binary decision • How to assign weights to each factor?• Stakeholders may disagree on weights • MCDM = multicriteria decision making
Analytical Hierarchy Process
• Devised by Thomas Saaty
• Each stakeholder compares each pair of factors
• Assigns comparative weights – e.g., slope 7 times as
important as land use – e.g., distance from stream
1/2 as important as slope • forming a complete matrix• Weights must sum to one
Slope Land use Distance from Stream
Slope 7 2
Land use 1/7 1/3
Distance from Stream
1/2 3
AHP example:Idrisi
Model accuracy and validity
• How do we know if the model is correct?• How do we know that forecasts are accurate?• Results from a computer are often trusted
implicitly • How to calibrate the model?
– Hindcasting– Boostrapping
• A model is never more than an approximation to reality but how good/bad is the approximation?
• Important to provide measures of confidence in results
Sensitivity testing
• Varying the inputs to observe effects on outputs • Some inputs affect outputs more than others • These are the inputs that most need to be
correct • Error propagation • Examining the impacts of input errors on outputs • Mostly by simulation