UCIME: The Urban Change Integrated Modeling Interface

Keith C. ClarkeDepartment of Geography

UC Santa Barbara

Congratulations Mike Goodchild!

UCSB Geography’s second National Academy member

Funding

NSF Urban Research InitiativeLos Alamos National Laboratories (UCOP, NSF)USGS (Menlo Park)Santa Barbara Economic Community Project

www.geog.ucsb.edu/~kclarke/ucime

UCIME Objectives1. Develop a multi-scale infrastructure for

modeling the dynamics of US urban areas 2. Modeling and analysis of the growth

patterns of selected metropolitan regions 3. Predict land use intensity and population

distribution by coupling models of physical and human processes

4. Implement and disseminate an integrated GIS-based modeling environment for research and policy

The goal Informed, participatory local

decision making Simultaneous consideration of

regional and local issues Multi-scale consistency Focus on issues based

education

The foundation: Data Applications in many US cities Applications outside the US

(Australia, Portugal, Mexico, Brazil) Needed dense test case: Santa

Barbara Historical time series data Exclusions Land use Metropolitan data

The South Coast

Aerial Photos

Years: 1929, 1943, 1954, 1967, 1986, 1997

1929

Assessors’ Digital Parcel Map

Roads input

1929

1999

2005

GIS/EM: The integration challenge

Park & Wagner TGIS 1997

•Isolated•Loose•Tight•Integrated

Model integration

Share data from GIS Have common input/output layers Link inputs to outputs Have a single user interface (UCIME) Hide the models from the user Interact via scenarios (integrate via

planning/decision-making process)

Urban models in UCIME Population density structure SLEUTH (Urban form and land use) SCOPE By very loose coupling

Hydrology Air quality Wildfire hazard

SLEUTH archeology Clarke cellular automaton urban growth model

(UGM) Multiple applications (e.g. San Francisco,

Washington/Baltimore) Project Gigalopolis Applications: Chicago, New York, Portland,

Philadelphia, MAIA, Albuquerque, Detroit, Mexico City, Lisbon, Santa Barbara

1998/9 funding made model portable and web-based (USGS: EROS Data Center, EPA Collaboration)

1999-02 work extended and integrated model with other efforts (LANL and USGS collaboration, NSF Urban Research Initiative, SBECP)

EPA has provided significant input (MPI)

Gigalopolis: Project Goal Use historical data for urban areas to

understand present day urbanization Simulate using a Cellular Automaton

Model (SLEUTH) Run the model into the future Simulate alternative futures Compare across scale and cities Apply to Urban Dynamics cities

Cellular Automata Gridded world Cells have finite states Rules define state transitions Time is incremental Cells are autonomous, act as agents Self-replicating machines: Von

Neumann Classic example is Conway’s LIFE

Urban Cellular Automata Cells are pixels States are land uses Time is “units”, e.g. years Rules determine growth and change Different models have different rule sets Many models now developed, few

tested Requiem for large scale models (Lee)

Model tight couples land use change So far works at Anderson Level 1 and 2 Calibration for MAIA and Lower 48 States Needs two LULC layers Based on the concept of deltatrons Generates synthetic LU change based on

transition matrix and enforced spatial/temporal autocorrelation

Applies CA in change space

Why SLEUTH?

Slope

Land Cover

Excluded

Urban

Transportatio

n

Hillshade

1900 1925 1950 1975 2000

Project Web Site Set of background materials, e.g.

publications Documentation as web pages in HTML Model discussion list Source Code for model in C Version 3.0 now on web for download Uses utilities and GD GIF libraries Parallel version requires MPI Set of sample calibration data demo_city http://www.ncgia.ucsb.edu/projects/gig/ncgia.htmlhttp://www.ncgia.ucsb.edu/projects/gig/ncgia.html

Project Web Site: Shareware C code and Documentation

Calibration Most essential element Ensures realism Ensures accountability and

repeatability Tests sensitivity Required for complex systems

models Conducted in Monte Carlo mode

The Method “Brute force calibration” Phased exploration of parameter space Start with coarse parameter steps and

coarsened spatial data Step to finer and finer data as calibration

proceeds Good rather than best solution 5 parameters 0-100 = 101^5

permutations

Calibrationpast

“present”

For n Monte Carlo iterations

For n coefficient sets

Predicting the presentfrom the past

The Problem Model calibration for a medium sized data set and minimal data layers requires about 1200 CPU hours on a typical workstation

CS calls problem tractability

Implementations to date DEC Alpha Silicon Graphics (Indy 10000 and O2) Silicon Graphics Origin 2000 cluster 32

processors: 2GB RAM SunBlade 1000 Rolla, MS MCMC Beowulf Linux Cluster Supercomputers (NESC EPA: NC)

Cray C-90 and T3D Cray T3E-1200

SLEUTH Outputs Statistics Logs Images Uncertainty maps Animations

Prediction (the future from the present)

Probability Images

Land Cover Uncertainty

Alternate Scenarios

Model simulation

2040 Scenarios

User requests Animations Probability-free forecasts Detail! “Report cards” More attributes

SCOPE

South Coast Outlook and Participation Experience

Economic Community Project

Board has representatives of non-profits, businesses, community activists, local government, UCSB, etc.

Mission:Mission: To act as a catalyst for creating a sustainable regional planning process for the South Coast which will support both a viable economy and preserve and enhance the quality of life over the next twenty years and beyond.

Background Funding from Irvine Foundation, S.B.

Foundation, local cities and SB County ECP and UCSB collaboration Developed 4 land use principles first

SCOPE archeology Originally called UGROW: Will Orr,

Prescott College with NASA funding Rewritten in Powersim for Santa

Barbara Ported to STELLA by Jeff Onsted Designed using stakeholder focus

groups, intensive collaboration and public outreach

• HH size

• Vacancy rates

Lower inc.

Middle

Business/Jobs

• Workers• Students• Retirees

Lower inc.

Middle

Upper

• Wage class jobs• Commuters

• Unemploy rate

Service/Retail

Office/Lt Mfg

• Density• Housing• Business• Ag/Open

Population

Housing

Land Use

Land availability

Workeravailability

Attractiveness to in-migration

Attractiveness to in-migration

Development

Qualityof Life

SCOPE focuses on the interrelationships among five sectors

Trafficcongestion

Climate and

SettingDevelopment

Land availability

Upper

Rebuilding

Build affordable

Growth control

Trans. policy

Urbanlimit

Tax/subsidy Commuting

Housing availability

Stella version of SCOPE

Stella Interface(WebSIM): zenith.geog.ucsb.edu

Scenario BasisScenario Basis

• RedRed Unrestrained Development

• GreenGreen Urban Growth Boundary honored

• YellowYellow No Commercial growth and unrestrained residential

Total Population

Population Density model

P = f(Land Use, dRoads, Age, dCenter)

Tested for Santa Barbara with 2000 census dataGranularity = LU polygon INT census tracts

Used to make forecasts from future maps

The UCIME Web Interface

Data/World Scenarios

Users/Decision MakersIndividual

Group

Convergence

Scenario as model/plan bridge

Environmentalist

0

10

20

30

40

50

1 4 7 10 13 16 19 22 25

Question

Sce

nar

io D

ista

nce

Unrestrained

Follow Current

Road Growth

Growth Boundary

EnvironmentFriendly

No Commercial

Neutral Responses

0

10

20

30

40

50

1 4 7 10 13 16 19 22

Questions

Sce

nar

io D

ista

nce Unrestrained

Follow Current

Road Growth

Growth Boundary

Environment Friendly

No Commercial

Good scenario sets Themes can be single or multiple How many? 7+-2 Relevance: Policy implication Comprehensive (drivers) Diverse Creative: Role for Visualization Transparent Coherent: properly formulated and plausible Consistent

Scenario Difference

Conclusion Sets of models when integrated are more

powerful than when used alone, or when one metamodel is formulated

Users want more than model results Users want credibility in modeling/ers Users don’t want control of models A single user interface for multiple

models must use a macro view Scenarios are key to bridging models and

views