1. UCIME: The Urban Change Integrated Modeling Interface Keith C. Clarke Department of Geography UC Santa Barbara

2. Congratulations Mike Goodchild! UCSB Geography’s second National Academy member

3. Funding NSF Urban Research Initiative Los Alamos National Laboratories (UCOP, NSF) USGS (Menlo Park) Santa Barbara Economic Community Project

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

5. UCIME Objectives 1. 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

6. The goal Informed, participatory local decision making Simultaneous consideration of regional and local issues Multi-scale consistency Focus on issues based education

7. 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

8. The South Coast

9. Aerial Photos Years: 1929, 1943, 1954, 1967, 1986, 1997 1929

10. Assessors’ Digital Parcel Map

15. Roads input 1929 1999 2005

16. GIS/EM: The integration challenge Park & Wagner TGIS 1997 Isolated Loose Tight Integrated

17. 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)

18. Urban models in UCIME Population density structure SLEUTH (Urban form and land use) SCOPE By very loose coupling Hydrology Air quality Wildfire hazard

19. 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)

20. 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

21. 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

22. 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)

23. 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

24. Why SLEUTH?

25.  S lope  L and Cover  E xcluded  U rban  T ransportation  H illshade 1900 1925 1950 1975 2000

26. 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.html http://www.ncgia.ucsb.edu/projects/gig/ncgia.html

27. Project Web Site: Shareware C code and Documentation

28. Calibration Most essential element Ensures realism Ensures accountability and repeatability Tests sensitivity Required for complex systems models Conducted in Monte Carlo mode

29. 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

30. Calibration past “present” For n Monte Carlo iterations For n coefficient sets Predicting the present from the past

31. 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

32. 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

33. SLEUTH Outputs Statistics Logs Images Uncertainty maps Animations

34. Prediction (the future from the present) Probability Images Land Cover Uncertainty Alternate Scenarios

35. Model simulation

36. 2040 Scenarios

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

38. SCOPE South Coast Outlook and Participation Experience

39. 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.

40. Background Funding from Irvine Foundation, S.B. Foundation, local cities and SB County ECP and UCSB collaboration Developed 4 land use principles first

41. 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

42. 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 Worker availability Attractiveness to in-migration Attractiveness to in-migration Development Quality of Life SCOPE focuses on the interrelationships among five sectors Traffic congestion Climate and Setting Development Land availability Upper Rebuilding Build affordable Growth control Trans. policy Urban limit Tax/ subsidy Commuting Housing availability

43. Stella version of SCOPE

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

45. Scenario Basis RedRed Unrestrained Development GreenGreen Urban Growth Boundary honored YellowYellow No Commercial growth and unrestrained residential

46. Total Population

47. Population Density model P = f(Land Use, dRoads, Age, dCenter) Tested for Santa Barbara with 2000 census data Granularity = LU polygon INT census tracts Used to make forecasts from future maps

48. The UCIME Web Interface

49. Data/WorldScenarios Users/Decision Makers Individual Group Convergence Scenario as model/plan bridge

51. 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

52. Scenario Difference

53. 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