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Urban Growth Modeling with 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. Outline (1). Introduction. Statement of the problem. Research objectives. Literature review.

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Urban Growth Modeling with Artificial Intelligence Techniques

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  1. 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 The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

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

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

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

  5. 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). The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  6. 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. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  7. 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. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  8. 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)]. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

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

  10. 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). The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  11. Literature review: Cellular Automata • 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. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  12. Literature review: Cellular Automata • 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. • Neural networks used by Li and Yeh (2001) to calibrate CA rules. • Wu (2002) development probability based CA model. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  13. Literature review:GeneticsAlgorithms • 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. • Wong et al (2001): GA for household and employment distributions’ parameters for Hong Kong. • Goldstein (2003):SLEUTH calibration. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  14. Literature review: Fuzzy Logic (FL) • Extend binary theory for continuous status. • FL for geographic boundaries with high spatial variability (Wang and Hall, 1996). • 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. • Liu and Phinn (2003) identify pixel state change with a fuzzy membership function. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  15. Literature review: Neural Networks(NN) • NN to mimic biological neural networks. • NN simulate geo-spatial complex systems (Openshaw, 1998). • 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. • NN with GIS to forecast land use change (Pijanowski et al., 2002). The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  16. Literature review: Unsolved issues • 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)]. • 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). • No effective search methods for calibration. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  17. Literature review: Unsolved issues • An effective evaluation scheme is needed to help select the best rules. • 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. • Calibration in fuzzy CA urban modeling needs to be clearly identified. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

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

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

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

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

  22. Crisp CA modeling: theory • Four CA components (cont’d) • Transition rules such as IF-THEN rules • Future state of a pixel: • Example: (Game of Life) 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. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

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

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

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

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

  27. Crisp CA model design: Rules • CA rules represent land use & constrains effect. • 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 (Pi) AND has neighboring residential pixels # ≥ threshold (Ri). • Population density ≥ threshold (Pi) AND has neighboring commercial pixels # ≥ threshold (Ci). Pi continuous [0:0.1:3], Ri & Ci [0:1:8] The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  28. Crisp CA model design:Evaluation • 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. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

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

  30. Crisp CA model design:Modeling • CA Modeling in ArcGIS through VBA. • Two multitemporal imagery sets: - Training images: calibration. - Testing images: prediction & validation. • CA runs for all combinations (P,R,C)ifrom 1973 till 1982, first calibration year, and evaluated. • 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. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

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

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

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

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

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

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

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

  38. Genetic algorithms calibration:initial populationdesign • 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: • Total modeling error (urban count and structure) per township to be minimized. deviation from 100%, urban count Modeling errors, urban pattern The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  39. Genetic algorithms calibration:Elitism and Rank Selection • 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. • Selection probability • Expected count: • Final count Sample calculation The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  40. Genetic algorithms calibration:Elitism and Rank Selection • 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). • This will end the selection process with a total of 30 strings. • Bad strings are not selected (search is directed to good strings). The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

  41. Genetic algorithms calibration: Crossover and mutation • 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: • 6 elitism strings crossedover to produce new 6 strings to be added with a total of 12 strings. • First 18 strings in the selection are crossedover. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006) Crossover point

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

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

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

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

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

  47. Fuzzy guided CA modeling:Theory • 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: • The notation of a singleton: x: element in the fuzzy set, : membership degree. • Fuzzy set for all x is: The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

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

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

  50. Fuzzy guided CA modeling:design • 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. The ASA-CSSA-SSSA International Annual Meetings (November 12-16, 2006)

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