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calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
calibrating sleuth
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  • 1. SOUP: Self in regional planning Planning Oranizing Urban new directions CALIBRATING THE SLEUTH URBAN GROWTH MODEL IN A MULTI-MODAL FITNESS LANDSCAPE William Veerbeek Artificial Intelligence Section, Faculty of Sciences, Vrije Universiteit, Amsterdam
  • 2. SOUP: Self in regional planning Planning Oranizing Urban new directions EXPLODING URBAN GROWTH -1800: 3% of world population lived in cities -2000: 47% of world population lived in cities urbanization has a large impact on earth’s resources, yet no general theory or model exists!
  • 3. SOUP: Self in regional planning Planning Oranizing Urban new directions GAS: Geographic Automata Systems 1992: Urban growth models using Cellular Automata Cellular Automata: A CA is an array of identically programmed automata, or cells, which in- teract with one another in a neighborhood and have a definate state array cell interact neighborhood state starting condition
  • 4. SOUP: Self in regional planning Planning Oranizing Urban new directions early urban growth models using CA: -attention to transition rules -use spatially isotropic lattices D.P. Ward et. al, ‘An Optimized Cellular Automata Approach for Sustainable urban Development in Rapidly Urbanizing Regions (1999)
  • 5. SOUP: Self in regional planning Planning Oranizing Urban new directions CA: SPATIALLY ISOTRIPIC ENVIRONMENT spatial conditions of cities are almost never isotropic mountains river sea array cell interact neighborhood state starting condition
  • 6. SOUP: Self in regional planning Planning Oranizing Urban new directions 1994: Human Induced Land Transformation (HILT) model -first GAS to use geographic information as the envrionment for the CA Kirtland et. al, ‘An Analysis of Human Induced Land Transformations in the San Fransisco Bay/Sacramento area (1994)
  • 7. SOUP: Self in regional planning Planning Oranizing Urban new directions 1997: Slope, Land-use, Exclusion, Urban Extent, Transpor- tation and Hillshade model (SLEUTH) Two Papers: 1. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the histori- ca urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lisbon and Porto’ , Computers, Environment and Urban systems 26 , 525-552
  • 8. SOUP: Self in regional planning Planning Oranizing Urban new directions 1. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the his- torica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261 The paper presents the SLEUTH-model. Features include: -integration of GIS-layers as the operating environment -different cell states (not binary as in game of life) -complex set of transition rules -set of coefficients that dictate outcome transition rules -self-modifying rules -calibration method
  • 9. SOUP: Self in regional planning Planning Oranizing Urban new directions 1. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the his- torica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261 1. Integration of GIS-layers 2. Roads 3. Seeds 1. Slope 4. Excluded Areas -all layers except (roads layer) are cell-based (pixels)
  • 10. SOUP: Self in regional planning Planning Oranizing Urban new directions 1. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the his- torica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261 2. Different Cell-states 1. empty 2. seed cell 3. urbanized in current iteration 4. urbanized in previous iteration (any)
  • 11. SOUP: Self in regional planning Planning Oranizing Urban new directions over decentralisatie, kritische grenzen en ai 1. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the his- torica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261 3. Complex set of transition rules Composite rules composed of: -rules on interaction with GIS-layers -rules on cell-states of neighboring cells For every cell { count the #neighbors in the neighborhood for every cell { calculate individual_urbanization_probabilites of parameters } probability_of_urbanization = sum(normalized_parameter_values)/5 //(5 parameters) if probability_of_urbanization>0.5 { //probability > 50% cell becomes urbanized } } neighborhood used is classic MOORE (8 neighbors)
  • 12. SOUP: Self in regional planning Planning Oranizing Urban new directions 1. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the his- torica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261 4. Set of Parameters -diffussion (overall dispersiveness) -breed (control of new development) -spread (growth of urbanized areas) -slope resistance (probability of urbanization depending on slope values) -road gravity (controls urban development alongside roads) example spread: if (#neighbors>2 || random_number<spread_coefficient) { urbanize this cell }
  • 13. SOUP: Self in regional planning Planning Oranizing Urban new directions 1. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the his- torica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261 5. Self modifying rules Control of growth rate by positive feedback loops: -boost rapid urban growth (resulting in dispersed growth) -dampen slow urban growth (resulting in concentrated growth) Calculate growth_rate for a time cycle // Rapid growth: boost coefficients by 10% If growth_rate>high_growth_treshold{ DIFFUSION +* 1.1 SPREAD +* 1.1 BREED by +* 1.1 } -self modifying rules influnece effects of coefficients -influence of positive feedback rules is moderated over time
  • 14. SOUP: Self in regional planning Planning Oranizing Urban new directions 1. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the his- torica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261 Examples Remember this! Simulated growth pattern of Washington DC (2000) generated by SLEUTH-model
  • 15. SOUP: Self in regional planning Planning Oranizing Urban new directions 1. K.C. Clarke and S. Hoppen (1997), ‘A self-modyfying cellular automaton model of the his- torica urbanization in the San Fransisco Bay area’ ,planning and Design 24, 247-261 6. Calibration method Adapt the model to specific local conditions! 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 (description of the calibration process) Calibration: Optimization of coefficient values (diffusion, breed, spread, slope resistance, road gravity and self-modification)
  • 16. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 Brute force calibration (BFC): 3 steps: coarse, fine, final 1. generate permutation of coefficients 2. calculate simulations from seed-year 3. check if outcome is consistent with real data by using a set of 6 fitness criteria 4. coefficients of model with best fit is used in new phase (smaller incre- ments in permutations) differences in coarse, fine, final are: -amount of permutations used -resolution of the input layers (GIS)
  • 17. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 BFC is adaptive refinement 1 ������������� 0 .8 -take interval with best fitness value 0 .6 -use smaller increments within this 0 .4 interval for a new fitness calculation 0 .2 0 0 ��� �� �� �� �� �� �� �� �� �� ������������ ���� ������������� Assumptions: ���� -FITNESS FUNCTION IS MONOTONOUS! ���� -FITNESS IS UNI-MODAL! ���� ���� ���� �� �� �� �� �� �� �� �� �� �� �� ������������ adaptive refinement of a monotonous uni-modal fitness function
  • 18. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 Fitness criteria: 1. composite score (all scores together) 2. compare (ratio comparison urban areas) 3. r2 population (amount of urbanized cells) 4. edges r2 (total numer of edges) 5. cluster r2 (total numer of urban clusters) 6. LeeSalee (shape comparison) Remember that the scores are a result of the coefficient values that influ- ence the impact of the individual transistion rules ! (diffusion, breed, spread, slope resistance and road gravity) Assumption: NO INTERACTION EFFECTS!
  • 19. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 For both Lisbon and Porto fitness values don’t gradu- ally increase AML AMP Calibration phase final fine coarse final fine coarse Score/resolution 784x836 392x418 196x209 347x563 173x281 86x140 Composite score 0.15 0.19 0.23 0.48 0.47 0.41 0.90 0.88 0.97 0.97 0.99 0.94 Compare 0.91 0.91 0.92 0.99 0.99 0.99 Population 0.78 0.99 0.98 0.98 0.99 0.98 Edges 0.85 0.85 0.93 0.99 0.95 0.97 Cluster LeeSallee 0.35 0.34 0.32 0.58 0.57 0.53 Diffusion 16 20 1 20 40 1 Breed 57 51 100 20 1 100 Spread 50 50 50 40 35 50 Slope 25 25 25 45 40 50 Roads 30 30 20 20 25 75 wrong assumptions? BFC is not an appropriate calibration method?
  • 20. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 Conclusions (Silva and Clarke): 1. model performance improved with increase spatial and parameter resolution 2. biggest gains in fitness were made during coarse calibration phase 3. non-linear behavior of fitness-values is result of different spatial resolution Critique: Increasing spatial resolution should lower scores since: -probability of false prediction increases (faulty urbanized cells) -differentiation of information of input layers becomes larger YET: SOME SCORES INCREASE, SOME SCORES DECREASE, SOME STAY FIXED AND SOME BEHAVE NON-LINEARLY
  • 21. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 Check the results again: AML AMP Calibration phase final fine coarse final fine coarse Score/resolution 784x836 392x418 196x209 347x563 173x281 86x140 Composite score 0.15 0.19 0.23 0.48 0.47 0.41 0.90 0.88 0.97 0.97 0.99 0.94 Compare 0.91 0.91 0.92 0.99 0.99 0.99 Population 0.78 0.99 0.98 0.98 0.99 0.98 Edges 0.85 0.85 0.93 0.99 0.95 0.97 Cluster LeeSallee 0.35 0.34 0.32 0.58 0.57 0.53 Diffusion 16 20 1 20 40 1 Breed 57 51 100 20 1 100 Spread 50 50 50 40 35 50 Slope 25 25 25 45 40 50 Roads 30 30 20 20 25 75
  • 22. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 Possibility: non-monotonous multi-modal fitness curve optimal value would not be found by using adaptive refinement! could be caused by interaction effects between parameters
  • 23. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 Alternative regression methods to optimize coefficient values: STOCHASTIC METHODS: -neural networks -evolutionary algorithms (advantage: distribution)
  • 24. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 evolutionary algorithms (EA): -population of candidate solutions moving through search space (inspired by principle of ‘survival of the fittest as found in nature’ 1 2 3
  • 25. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 evolutionary algorithms (general scheme): BEGIN INITIALIZE population iwth random candidate solutions EVALUATE each candidate REPEAT UNTIL (TERMINATION CONDITION is satisfied) 1 SELECT parents 2 RECOMBINE pairs of parents 3 MUTATE the resulting offspring 4 EVALUATE new candidate solutions 5 SELECT individuals for next generation; 0D END
  • 26. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 evolutionary algorithms: -information is stored in genes (different types of encoding) -problem of representation: genotype to phenotype (mapping) child1 parent1 child2 parent2 gray-coded bitstring sequence (7 bits = 128), 2-point recombination IN SLEUTH, COEFFICIENTS COULD BE STORED AS 7 BIT LONG BITSTRINGS (genotypes)
  • 27. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 WHAT ARE EA’S GOOD AT? -searching an non-monotonous multi-modal search space -providing a sub-optimal sollution at anytime -providing a sub-optimal sollution quickly anytyme behavior of an EA
  • 28. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 EA for the SLEUTH-model: Fitness criteria: child1 child1 1. composite score child1 child2 2. compare child1 child2 child1 3. r2 population child1 child2 child1 4. edges r2 child2 child1 child2 5. cluster r2 child2 child1 child2 child1 6. LeeSalee child1 child2 child1 child2 child1 child2 child2 child1 child2 child1 child2 child2 child2 genotypes: coefficients phenotypes: models
  • 29. SOUP: Self in regional planning Planning Oranizing Urban new directions 2. E. A. Silva and K. C. Clarke (2004), ‘Calibration of the SLEUTH urban growth model for Lis- bon and Porto’ , Computers, Environment and Urban systems 26 , 525-552 POSSIBLE ADVANTAGES: -quicker calibration (anytime behavior) -better sollutions than through linear refinement MODELS BECOMING MORE CONSISTENT WITH DATA FURTHER RESEARCH: -is search-space indeed non-monoutonous, multi-modal? (brute force) -are there indeed interaction-effects? -are fitness-functions bounded by different classes of cities?

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