1. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Urban Growth Modeling Artificial Intelligence Techniques Urban Growth Modeling with Artificial Intelligence Techniques Dr. Jie Shan and Sharaf Al-kheder Geomatics Engineering School of Civil Engineering Purdue University jshan@purdue.edu

2. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Outline (1) Introduction. Statement of the problem. Research objectives. Literature review. Problem solving approach. Crisp cellular automata modeling. Calibration with genetic algorithms.

3. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Outline (2) Fuzzy guided cellular automata modeling. Neural networks for boundary modeling. Discussion and analysis. Concluding remarks. Recommendations and future work.

4. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) motivation Introduction: motivation Athens urban growth 18 million in this area! Los Angeles City population excessive increase worldwide. infrastructure services demand. Cairo 1965 Cairo 1998 Urban modeling is a necessity! Mexico city!!

5. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Urban growth facts Introduction: Urban growth facts 1970 to 1990, more than 30,000 sq.m. of U.S. rural land became urban (Statesman Journal, 1991). 1969 to 1989, U.S. population increased by 22.5%, and VMT (vehicles miles traveled) by 98.4% (Federal Highway Administration, 1991). 1983 to 1987, U.S. population increased by 9.2 million, and # of cars and trucks increased by 20.1 millions (Statistical Abstract of United States, 1989).

6. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Statement of the problem Excessive unplanned urban growth. Absence of a standard urban growth model and a robust calibration module. Evaluation strategy. Satellite imagery availability with minimal cost.

7. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Research objectives Cellular automata, imagery, & other inputs for urban growth modeling. Imagery based design to minimize input data and modeling uncertainty. A spatiotemporal algorithm besides genetic algorithms to enhance calibration efficiency. Fuzzy logic theory for continuous urban growth modeling. Neural networks for boundary modeling.

8. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) General Literature review: General Two types of urban models:  Scale-based models: - Specific [e.g., BASS II (Bay Area Simulation System) for San Francisco, Landis(1992)]. - General [e.g., HILT (Human Induced Land Transformations), Kirtland, (1993) ].  Model’s applicability: - Physical aspects [e.g., Alonso, (1978)]. - Social aspects [e.g., Jacobs, (1961)].

9. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Cellular Automata Literature review: Cellular Automata Fastest emerging urban dynamic models. Fastest emerging urban dynamic models. Multi-dimensional discrete system. Multi-dimensional discrete system. Uses simple yet accurate transition rules for urban modeling. Uses simple yet accurate transition rules for urban modeling. Uses social and physical factors. Uses social and physical factors. Fits urban process spatially in imagery. Fits urban process spatially in imagery. Better in urban modelling than mathematical models (Batty and Xie, 1994a). Better in urban modelling than mathematical models (Batty and Xie, 1994a).

10. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Cellular Automata Literature review: Cellular Automata Earliest implementation of CA for geographic systems by Tobler (1979). Couclelis (1985) provided theoretical framework for CA in complex geographic problems [e.g., structure] CA first used for urban modeling by White et al. (White and Engelen, 1992a; 1992b) CA used by Batty and Xie (1994a) for modeling of Cardiff (UK) and Savannah (GA).

11. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Cellular Automata Literature review: Cellular Automata SLEUTH (Slope, Land use, Exclusion, Urban extent, Transportation, Hillshade), Clarke et al. (1997) SLEUTH (Slope, Land use, Exclusion, Urban extent, Transportation, Hillshade), Clarke et al. (1997)  Four types of data: land cover, slope, transportation, and protected lands.  Five factors for urban growth (e.g., SLOPE and ROAD-GRAVITY.  Complex transition rules.  Visual and statistical tests for calibration. Clarke and Gydos (1998) applied “SLEUTH” for urban growth in San Francisco region & Washington D.C/Baltimore. Clarke and Gydos (1998) applied “SLEUTH” for urban growth in San Francisco region & Washington D.C/Baltimore.

12. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Cellular Automata Literature review: Cellular Automata Yang and Lo (2003) used “SLEUTH” to test urban modeling scenarios in Atlanta, GA. Yang and Lo (2003) used “SLEUTH” to test urban modeling scenarios in Atlanta, GA. Wu and Webster (1998) used Multi Criteria Evaluation analysis to identify CA parameter values. Wu and Webster (1998) used Multi Criteria Evaluation analysis to identify CA parameter values. Neural networks used by Li and Yeh (2001) to calibrate CA rules. Neural networks used by Li and Yeh (2001) to calibrate CA rules. Wu (2002) development probability based CA model. Wu (2002) development probability based CA model.

13. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) GeneticsAlgorithms Literature review: Genetics Algorithms Recent direction in CA calibration. Recent direction in CA calibration. Colonna et al (1998) used GA to generate new rules for CA to simulate the land use changes of Rome, Italy. Colonna et al (1998) used GA to generate new rules for CA to simulate the land use changes of Rome, Italy. Wong et al (2001): GA for household and employment distributions’ parameters for Hong Kong. Wong et al (2001): GA for household and employment distributions’ parameters for Hong Kong. Goldstein (2003): SLEUTH calibration. Goldstein (2003): SLEUTH calibration.

14. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Fuzzy Logic (FL) Literature review: Fuzzy Logic (FL) Extend binary theory for continuous status. Extend binary theory for continuous status. FL for geographic boundaries with high spatial variability (Wang and Hall, 1996). FL for geographic boundaries with high spatial variability (Wang and Hall, 1996). Gradual change in land use conditions over time (Dragicevic & Marceau, 2000). Gradual change in land use conditions over time (Dragicevic & Marceau, 2000). FL in Wu (1996; 1998) work to define CA transition rules for land conversion. FL in Wu (1996; 1998) work to define CA transition rules for land conversion. Liu and Phinn (2003) identify pixel state change with a fuzzy membership function. Liu and Phinn (2003) identify pixel state change with a fuzzy membership function.

15. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Neural Networks(NN) Literature review: Neural Networks (NN) NN to mimic biological neural networks. NN to mimic biological neural networks. NN simulate geo-spatial complex systems (Openshaw, 1998). NN simulate geo-spatial complex systems (Openshaw, 1998). Liu (2000) used NN to detect the change from non-urban to urban land use. Liu (2000) used NN to detect the change from non-urban to urban land use. Yeh and Li (2002): NN with CA for urban simulation to model land use change. Yeh and Li (2002): NN with CA for urban simulation to model land use change. NN with GIS to forecast land use change (Pijanowski et al., 2002). NN with GIS to forecast land use change (Pijanowski et al., 2002).

16. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Unsolved issues Literature review: Unsolved issues A standard model for defining & calibrating CA transition rules is absent in literature. A standard model for defining & calibrating CA transition rules is absent in literature. Most models do not have an explicit transition rules [e.g., Wu model, 2002)]. Most models do not have an explicit transition rules [e.g., Wu model, 2002)]. CA models do not use multispectral imagery for urban extent or other data directly. They use cadastral maps instead. CA models do not use multispectral imagery for urban extent or other data directly. They use cadastral maps instead. Time consuming calibration (SLEUTH :135 days). Time consuming calibration (SLEUTH :135 days). No effective search methods for calibration. No effective search methods for calibration.

17. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Unsolved issues Literature review: Unsolved issues An effective evaluation scheme is needed to help select the best rules. An effective evaluation scheme is needed to help select the best rules. Spatial calibration is not included in most CA calibration algorithms to date. Spatial calibration is not included in most CA calibration algorithms to date. A fuzzy guided cellular automata model is needed, where CA rules can be designed as a function of the FL output. A fuzzy guided cellular automata model is needed, where CA rules can be designed as a function of the FL output. Calibration in fuzzy CA urban modeling needs to be clearly identified. Calibration in fuzzy CA urban modeling needs to be clearly identified.

18. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Problem solving approach Multitemporalsatellite imagery imagery Other input data (Population, DEM, road networks) Fuzzy CA model Simulated CA images Simulated Fuzzy CA images Ground truth imagery Calibration Urban growth modeling CRISP CA FUZZY CA Crisp CA model

19. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Problem solving approach Multitemporalsatellite imagery imagery Other input data (Population, DEM, road networks) Crisp CA model Simulated CA images Ground truth imagery Calibration Urban growth modeling CA-GA GA automated calibration NN model NN modeling Boundary modeling

20. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Crisp CA modeling: outline CA theory. CA theory. Artificial city modeling. Artificial city modeling. CA based urban growth model design. CA based urban growth model design. A spatiotemporal calibration algorithm. A spatiotemporal calibration algorithm. Design an evaluation scheme. Design an evaluation scheme. Indianapolis growth modeling. Indianapolis growth modeling. Integrate with GIS, such as ArcGIS (VBA). Integrate with GIS, such as ArcGIS (VBA). Analysis and discussion. Analysis and discussion.

21. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Crisp CA modeling: theory By Ulam and von Neumann in 1940s to study complex systems (von Neumann, 1966). By Ulam and von Neumann in 1940s to study complex systems (von Neumann, 1966). 2-dimensional CA for our work. 2-dimensional CA for our work. Four CA components: Four CA components:  pixels;  States (e.g., Water);  Neighborhood: Shape States function

22. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Four CA components (cont’d) Four CA components (cont’d)  Transition rules such as IF-THEN rules  Future state of a pixel: Example: (Game of Life) Example: (Game of Life) Crisp CA modeling: theory 1. IF 1 inactive pixel surrounded by 3 active pixels, activate. 2. IF surrounded by 2 or 3 pixels, remains active. 3. Else, become or stay inactive.

23. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Crisp CA modeling: Artificial city Effect of land use. E.g., roads drive urban growth. Effect of land use. E.g., roads drive urban growth. 200x200 pixels input image. 200x200 pixels input image. CA rules (3x3 neighborhood): CA rules (3x3 neighborhood):  IF test pixel is urban, river, road, lake or has pollution source in the lake or has pollution source in the neighborhood THEN no change. neighborhood THEN no change.  IF test pixel is non-urban, it changes to urban if in neighborhood changes to urban if in neighborhood  Three of more urban pixels.  At least one road AND one urban pixels.  At least one lake pixel AND one urban pixel.

24. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Crisp CA modeling: Artificial city CA simulates urban CA simulates urban growth at 0, 25, 50 growth at 0, 25, 50 and 60 iterations. and 60 iterations. Effect of road & Effect of road & lakes in driving lakes in driving growth. growth. Pollution source Pollution source buffer zones. buffer zones. Conservation of water. Conservation of water.

25. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Crisp CA model design: Data Indianapolis growth modeling Indianapolis growth modeling Excessive growth from 1973 to 2003. Excessive growth from 1973 to 2003. MSS/TM images (1973, 1982, 1987, 1992 and 2003) and population density input data. MSS/TM images (1973, 1982, 1987, 1992 and 2003) and population density input data. Images projected to UTM NAD1983 zone 16N. Images projected to UTM NAD1983 zone 16N. Ground reference data to classify images. Ground reference data to classify images. 7 classes : water, road, commercial, forest, residential, pasture and row crops. 7 classes : water, road, commercial, forest, residential, pasture and row crops. Commercial and residential as urban class. Commercial and residential as urban class.

26. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Crisp CA model design: Data 1990 & 2000 population census tract maps. 1990 & 2000 population census tract maps. Population density is computed per tract. Population density is computed per tract. Exponential model between density and distance from city center for 1990 and 2000. Exponential model between density and distance from city center for 1990 and 2000. Parameters (A & B) are updated Parameters (A & B) are updated yearly according to rate of change yearly according to rate of change (1990 & 2000). (1990 & 2000). population density grids as input. population density grids as input. 1990

27. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Crisp CA model design: Rules CA rules represent land use & constrains effect. CA rules represent land use & constrains effect. CA rules (3x3 neighborhood): CA rules (3x3 neighborhood):  IF test pixel is water, road OR urban (residential or commercial) THEN no change.  IF test pixel is nonurban (forest, pasture OR row crops) THEN It becomes urban if its:  Population density ≥ threshold (P i ) AND has neighboring residential pixels # ≥ threshold (R i ).  Population density ≥ threshold (P i ) AND has neighboring commercial pixels # ≥ threshold (C i ). P i continuous [0:0.1:3], R i & C i [0:1:8]

28. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Evaluation Crisp CA model design: Evaluation 3 evaluation measures for each rule combination (P,R,C) i : 3 evaluation measures for each rule combination (P,R,C) i : 1. Fitness: 2. Type I error: Pixels that urban in real but nonurban in simulated. 3. Type II error: Pixels that nonurban in real but urban in simulated.

29. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Calibration Crisp CA model design: Calibration Spatial & temporal Spatial & temporal calibration modules. calibration modules. Spatial calibration: Spatial calibration: - Site specific features. - Evaluation based on township. - Same rules, variable values. Temporal calibration: Temporal calibration: - Rule change over time. - Variable urban growth pattern.

30. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Modeling Crisp CA model design: Modeling CA Modeling in ArcGIS through VBA. CA Modeling in ArcGIS through VBA. Two multitemporal imagery sets: Two multitemporal imagery sets: - Training images: calibration. - Training images: calibration. - Testing images: prediction & validation. - Testing images: prediction & validation. CA runs for all combinations (P,R,C) i from 1973 till 1982, first calibration year, and evaluated. CA runs for all combinations (P,R,C) i from 1973 till 1982, first calibration year, and evaluated. Evaluation results arranged in descending order (ratio of Type I & II sum to total pixel count ). Evaluation results arranged in descending order (ratio of Type I & II sum to total pixel count ). Rule with min. avg. error & fitness closest to 100% (±10%) is selectedfor each township. Rule with min. avg. error & fitness closest to 100% (±10%) is selected for each township.

31. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Modeling Crisp CA model design: Modeling Recalibration at 1987. Recalibration at 1987. Best rules at 1987 to Best rules at 1987 to predict 1992 (5 years). predict 1992 (5 years). Calibration at 1992 Calibration at 1992 to predict 2003 (11 years). to predict 2003 (11 years). Final calibration at 2003 Final calibration at 2003 for future prediction for future prediction (2010 and 2020). (2010 and 2020). Close urban pattern match. Close urban pattern match. Simulation

32. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Modeling Crisp CA model design: Modeling Spatial calibration effect. Spatial calibration effect. Close Close fitness fitness to 100%. to 100%. Small Small average average errors errors 24-25%. 24-25%. 1992 Prediction sample Prediction

33. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Analysis Crisp CA modeling: Analysis Better connectivity for modeling Rule vary spatially Rule vs. class count 1992 calibration

34. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Analysis Crisp CA modeling: Analysis Rule redesign 1987 calibration Type I vs. urban Type II vs. nonurban Avg. vs. total count

35. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Motivation Genetic algorithms calibration: Motivation Introduced by Holland (1975) to mimic evolutionary processes in nature. Introduced by Holland (1975) to mimic evolutionary processes in nature. Manipulates a set of feasible solutions to find an optimal solution. Manipulates a set of feasible solutions to find an optimal solution. Effective for complex search spaces. Effective for complex search spaces. Why GA? CA is computationally extensive (lager # of combinations, need days). Why GA? CA is computationally extensive (lager # of combinations, need days). Increase calibration time with parameter #s. Increase calibration time with parameter #s. Assign higher weights for good solutions. Assign higher weights for good solutions.

36. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) design Genetic algorithms calibration: design GA extends CA model GA extends CA model to automate calibration to automate calibration while searching for while searching for optimal rule values. optimal rule values. GA operations: GA operations: - initial population design; - selection; - crossover and mutation.

37. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) initial population design Genetic algorithms calibration: initial population design CA transition rules design is used. CA transition rules design is used. Each combination of (R,C,P) i presents a string in the initial population pool. Each combination of (R,C,P) i presents a string in the initial population pool. 30 strings for each township. 30 strings for each township. Binary encoding. Binary encoding. String example

38. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) initial population design Genetic algorithms calibration: initial population design CA run for all 30 strings for evaluation (fitness, Type I and II errors). CA run for all 30 strings for evaluation (fitness, Type I and II errors). Objective function (based on modeling errors) to guide GA to optimal solution: Objective function (based on modeling errors) to guide GA to optimal solution: Total modeling error (urban count and structure) per township to be minimized. Total modeling error (urban count and structure) per township to be minimized. deviation from 100%, urban count Modeling errors, urban pattern

39. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Elitism and Rank Selection Genetic algorithms calibration: Elitism and Rank Selection Strings ordered based on GA objective function in ascending order (min. to max.). Strings ordered based on GA objective function in ascending order (min. to max.). String with lowest objective function has a rank of 30, the second one 29,etc. String with lowest objective function has a rank of 30, the second one 29,etc. Selection probability Selection probability Expected count: Expected count: Final count Final count Sample calculation

40. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Elitism and Rank Selection Genetic algorithms calibration: Elitism and Rank Selection The best 6 strings in terms of objective function are copied directly to next generation (elitism). The best 6 strings in terms of objective function are copied directly to next generation (elitism). The rest 24 strings are selected using rank selection (string count). The rest 24 strings are selected using rank selection (string count). This will end the selection process with a total of 30 strings. This will end the selection process with a total of 30 strings. Bad strings are not selected (search is directed to good strings). Bad strings are not selected (search is directed to good strings).

41. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Crossover and mutation Genetic algorithms calibration: Crossover and mutation Crossover: a pair of strings meet to produce offspring (same or better quality). Crossover: a pair of strings meet to produce offspring (same or better quality). Crossover probability is selected as 80% where 24 strings are crossed over: Crossover probability is selected as 80% where 24 strings are crossed over:  6 elitism strings crossedover to produce new 6 strings to be added with a total of 12 strings. 6 strings to be added with a total of 12 strings.  First 18 strings in the selection in the selection are crossedover. are crossedover. Crossover point

42. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Crossover and mutation Genetic algorithms calibration: Crossover and mutation After crossover: new 30 strings produced. After crossover: new 30 strings produced. Mutation: inversion of string bits for diverse structure and not stuck with bad solutions. Mutation: inversion of string bits for diverse structure and not stuck with bad solutions. Last stage in finalizing new population. Last stage in finalizing new population. Mutation for best 6 strings for (R,C) i by random addition of +1 or -1. Mutation for best 6 strings for (R,C) i by random addition of +1 or -1.

43. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Modeling and Evaluation Genetic algorithms calibration: Modeling and Evaluation CA run for new population CA run for new population for objective values. for objective values. Repeat GA process for Repeat GA process for 20 iterations. 20 iterations. Rules with minimum GA Rules with minimum GA objective function values objective function values are selected per township. are selected per township. Close match with reality & Close match with reality & crisp CA. crisp CA.

44. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Modeling and Evaluation Genetic algorithms calibration: Modeling and Evaluation Minimum GA objective Minimum GA objective value at early stage value at early stage

45. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Modeling and Evaluation Genetic algorithms calibration: Modeling and Evaluation Short running time for GA (6 hrs. avg.) compared to crisp CA (4 days). Short running time for GA (6 hrs. avg.) compared to crisp CA (4 days). Close modeling results to CA. Close modeling results to CA. 1987 calibration 1992 prediction

46. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Motivation Fuzzy guided CA modeling: Motivation Crisp CA is binary (develop/undeveloped), urban growth is continuous in space. Crisp CA is binary (develop/undeveloped), urban growth is continuous in space. A pixel might be partially developed. A pixel might be partially developed. Fuzzy logic identify pixel development potential. Fuzzy logic identify pixel development potential. Level of development identifies # of urban pixels in neighborhood for test pixel to develop. Level of development identifies # of urban pixels in neighborhood for test pixel to develop. Fuzzy logic to provide initial values for CA rule calibration. Fuzzy logic to provide initial values for CA rule calibration. Crisp CA is extended with fuzzy logic to achieve the continuous condition in space. Crisp CA is extended with fuzzy logic to achieve the continuous condition in space.

47. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Theory Fuzzy guided CA modeling: Theory Fuzzy logic first introduced by Zadeh from University of California, Berkeley in 1965. Fuzzy logic first introduced by Zadeh from University of California, Berkeley in 1965. Fuzzy set is a continuous interval bounded by 0 and 1 values: Fuzzy set is a continuous interval bounded by 0 and 1 values: The notation of a singleton: The notation of a singleton: x: element in the fuzzy set, : membership degree. : membership degree. Fuzzy set for all x is: Fuzzy set for all x is:

48. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) design Fuzzy guided CA modeling: design To design Fuzzy CA with artificial city. To design Fuzzy CA with artificial city. 3 Inputs: 3 Inputs: Membership function Input image Distance to city center DEM OUTPUT: urban neighborhood pixels # for development.

49. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) design Fuzzy guided CA modeling: design FUZZY RULES FUZZY RULES FUZZIFICATION FUZZIFICATION Min-max (Mamdani method) DEFUZZIFICATION (COA) DEFUZZIFICATION (COA) For every pixel: 1.DEM value. 2.Distance to city center OUTPUT min max DEM Distance Output yyy :Fuzzy output

50. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) design Fuzzy guided CA modeling: design Fuzzy output to design CA rules: Fuzzy output to design CA rules:  IF a pixel is urban, river, road, lake or has pollution source in neighborhood, THEN no change in its state.  IF a non-urban pixel has ≥ urban pixels in its neighborhood, THEN change it to urban.  IF a non-urban pixel has road or lake in its neighborhood AND has ≥ ( -2) urban pixels in neighborhood, THEN change it to urban.

51. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) modeling Fuzzy guided CA modeling: modeling Step#0 Step#0 Step#25 Step#50 Step#60 Elevation effect Road effect

52. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Indianapolis Fuzzy guided CA modeling: Indianapolis 3 inputs beside the imagery are used. 3 inputs beside the imagery are used. Fuzzy rules: Fuzzy rules: Membership Membership functions for inputs. functions for inputs. Fuzzy output represents neighborhood urban pixels for a test pixel to develop. Fuzzy output represents neighborhood urban pixels for a test pixel to develop. DEMRoads Population

53. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Indianapolis Fuzzy guided CA modeling: Indianapolis CA rules (function of fuzzy output): CA rules (function of fuzzy output):  IF a pixel is road, water, commercial or residential, THEN no change.  IF nonurban (forest, pasture or row crops) pixel has ≥ residential pixels in neighborhood, THEN change to residential.  IF non-urban has ≥ commercial pixels in neighborhood, THEN change to commercial.  IF commercial and residential pixels sum of non-urban pixel in neighborhood is ≥ pixels, THEN change to whichever is greater. THEN change to whichever is greater. Fuzzy output approximate rule values. Fuzzy output approximate rule values.

54. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Indianapolis Fuzzy guided CA modeling: Indianapolis A small search range A small search range based on fuzzy output. based on fuzzy output. Spatial calibration (township). Spatial calibration (township). Rule set with min. average Rule set with min. average error & close fitness to error & close fitness to 100% is selected. 100% is selected. CA run for calibration and prediction

55. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Analysis Fuzzy guided CA modeling: Analysis Conclusions in crisp CA still valid. Conclusions in crisp CA still valid. Avg. error smaller at city townships. Avg. error smaller at city townships. Testing with 30m vs. 60m. More restrict rules for 30m. Smaller errors for 30m. Rules, 30 vs. 60m TypeII 30 vs. 60m Avg. error, 60m

56. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Neural networks for boundary modeling City boundary expansion indicates gross change of phenomena (e.g., political boundary). City boundary expansion indicates gross change of phenomena (e.g., political boundary). Availability of historic imagery is problem. Availability of historic imagery is problem. City boundaries were digitized from classified satellite images. City boundaries were digitized from classified satellite images. 3 datasets were used for NN training. 3 datasets were used for NN training. Back Propagation (BPNN) algorithm for training. Back Propagation (BPNN) algorithm for training. Short (3 yr) and long-term (8 yr) predictions. Short (3 yr) and long-term (8 yr) predictions. Directional NN training. Directional NN training. Evaluation (root mean square). Evaluation (root mean square).

57. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Neural networks for boundary modeling 6 years boundaries of Indianapolis were digitized on classified satellite images. 6 years boundaries of Indianapolis were digitized on classified satellite images. 6 measurements 6 measurements at 3 degree radial interval. at 3 degree radial interval. A matrix of 120 A matrix of 120 by 6. by 6. 3 datasets 3 datasets (RBFN): (RBFN): Real data, 1 & 5 Real data, 1 & 5 year interpolation. year interpolation.

58. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Neural networks for boundary modeling Two-layer Back Propagation. Two-layer Back Propagation. 2003 from 2000 (short-term). 2003 from 2000 (short-term). Same results for 3 datasets. Same results for 3 datasets. RMS= 3095.37 m. RMS= 3095.37 m.  Long term (8 yrs) prediction 2000 from 1992 (long term). from 1992 (long term).  Same performance for 3 datasets.  RMS= 3713.28 m. 2003 2000

59. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Neural networks for boundary modeling Growth in all directions Growth in all directions is not the same is not the same (directional growth). (directional growth). Higher weights for higher Higher weights for higher growth directions. growth directions. Lakes Road Population Population Directional factors:

60. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Directional boundary modeling Closer match. Closer match. 2003 from 2000. 2003 from 2000. RMS=1226.49m. RMS=1226.49m.  2000 from 1992.  Weights effect.  Better results.  RMS=1650.01m 2003 2000

61. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Concluding remarks Artificial intelligence techniques fit the complex nature of urban process. Artificial intelligence techniques fit the complex nature of urban process. Model design reduces the need for large input data and modeling uncertainty. Model design reduces the need for large input data and modeling uncertainty. Simple, yet accurate transition rules easily interpreted by end users. Simple, yet accurate transition rules easily interpreted by end users. Spatial calibration, township basis, took into account site specific features. Spatial calibration, township basis, took into account site specific features. Temporal calibrationimportance. Temporal calibration importance.

62. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Concluding remarks Evaluation with 3 measures, fitness (urban count) and 2 modeling errors (urban pattern), is helpful to select the best rules. Evaluation with 3 measures, fitness (urban count) and 2 modeling errors (urban pattern), is helpful to select the best rules. GA reaches best solution in a timely manner. GA reaches best solution in a timely manner. GA modeling results close, quantitatively and qualitatively, to crisp CA results. GA modeling results close, quantitatively and qualitatively, to crisp CA results. GA objective function optimal design. GA objective function optimal design. FL reflect linguistic knowledge of urban process. FL reflect linguistic knowledge of urban process. FL provides calibration initials. FL provides calibration initials. NN simulate boundary with close urban match. NN simulate boundary with close urban match.

63. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Recommendations and future work There is a need to study effect of image classification on modeling uncertainty. There is a need to study effect of image classification on modeling uncertainty. Effect of fuzzy membership functions and rules for fuzzy guided CA on urban growth. Effect of fuzzy membership functions and rules for fuzzy guided CA on urban growth. There is a need to tune spatial calibration through using finer scale spatial units. There is a need to tune spatial calibration through using finer scale spatial units. Implementation of developed model to different case studies, representing cities with various size and urban growth behavior is needed. Implementation of developed model to different case studies, representing cities with various size and urban growth behavior is needed.

64. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) THANKS FOR LISTENING QUESTIONS??