Urban growth modeling with artificial intelligence techniques
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Urban Growth Modeling with Artificial Intelligence Techniques. Dr. Jie Shan and Sharaf Al-kheder Geomatics Engineering School of Civil Engineering Purdue University [email protected] Outline (1). Introduction. Statement of the problem. Research objectives. Literature review.

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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

[email protected]

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


Outline 1

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)


Outline 2

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)


Introduction motivation

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)


Introduction 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).

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


Statement of the problem

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)


Research objectives

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)


Literature review 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)].

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


Literature review cellular automata

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)


Literature review cellular automata1

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)


Literature review cellular automata2

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)


Literature review cellular automata3

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)


Literature review genetics algorithms

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)


Literature review fuzzy logic fl

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)


Literature review neural networks nn

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)


Literature review unsolved issues

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)


Literature review unsolved issues1

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)


Problem solving approach

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)


Problem solving approach1

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)


Crisp ca modeling outline

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)


Crisp ca modeling theory

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)


Crisp ca modeling theory1

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)


Crisp ca modeling artificial city

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)


Crisp ca modeling artificial city1

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)


Crisp ca model design data

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)


Crisp ca model design data1

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)


Crisp ca model design rules

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)


Crisp ca model design evaluation

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)


Crisp ca model design calibration

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)


Crisp ca model design modeling

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)


Crisp ca model design modeling1

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)


Crisp ca model design modeling2

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)


Crisp ca modeling analysis

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)


Crisp ca modeling analysis1

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)


Genetic algorithms calibration motivation

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)


Genetic algorithms calibration design

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)


Genetic algorithms calibration initial population design

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)


Genetic algorithms calibration initial population design1

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)


Genetic algorithms calibration elitism and rank selection

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)


Genetic algorithms calibration elitism and rank selection1

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)


Genetic algorithms calibration crossover and mutation

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


Genetic algorithms calibration crossover and mutation1

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)


Genetic algorithms calibration modeling and evaluation

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)


Genetic algorithms calibration modeling and evaluation1

Genetic algorithms calibration: Modeling and Evaluation

  • Minimum GA objective

    value at early stage

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


Genetic algorithms calibration modeling and evaluation2

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)


Fuzzy guided ca modeling motivation

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)


Fuzzy guided ca modeling theory

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)


Fuzzy guided ca modeling design

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)


Fuzzy guided ca modeling design1

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)


Fuzzy guided ca modeling design2

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)


Fuzzy guided ca modeling modeling

Fuzzy guided CA modeling:modeling

Step#0

Step#25

Road effect

Step#50

Step#60

Elevation effect

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


Fuzzy guided ca modeling indianapolis

Fuzzy guided CA modeling:Indianapolis

  • 3 inputs beside the imagery are used.

  • Fuzzy rules:

  • Membership

    functions for inputs.

  • Fuzzy output represents neighborhood urban pixels for a test pixel to develop.

DEM

Roads

Population

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


Fuzzy guided ca modeling indianapolis1

Fuzzy guided CA modeling:Indianapolis

  • 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.

  • Fuzzy output approximate rule values.

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


Fuzzy guided ca modeling indianapolis2

Fuzzy guided CA modeling:Indianapolis

  • A small search range

    based on fuzzy output.

  • Spatial calibration (township).

  • Rule set with min. average

    error & close fitness to

    100% is selected.

  • CA run for calibration

    and prediction

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


Fuzzy guided ca modeling analysis

Fuzzy guided CA modeling:Analysis

  • Conclusions in crisp CA still valid.

  • Avg. error smaller at city townships.

  • Testing with 30m vs. 60m.

  • More restrict rules for 30m.

  • Smaller errors for 30m.

Avg. error, 60m

Rules, 30 vs. 60m

TypeII 30 vs. 60m

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


Neural networks for boundary modeling

Neural networks for boundary modeling

  • City boundary expansion indicates gross change of phenomena (e.g., political boundary).

  • Availability of historic imagery is problem.

  • City boundaries were digitized from classified satellite images.

  • 3 datasets were used for NN training.

  • Back Propagation (BPNN) algorithm for training.

  • Short (3 yr) and long-term (8 yr) predictions.

  • Directional NN training.

  • Evaluation (root mean square).

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


Neural networks for boundary modeling1

Neural networks for boundary modeling

  • 6 years boundaries of Indianapolis were digitized on classified satellite images.

  • 6 measurements

    at 3 degree radial interval.

  • A matrix of 120

    by 6.

  • 3 datasets

    (RBFN):

    Real data, 1 & 5

    year interpolation.

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


Neural networks for boundary modeling2

Neural networks for boundary modeling

  • Two-layer Back Propagation.

  • 2003 from 2000 (short-term).

  • Same results for 3 datasets.

  • RMS= 3095.37 m.

2003

  • Long term (8 yrs) prediction 2000

    from 1992 (long term).

  • Same performance for 3 datasets.

  • RMS= 3713.28 m.

2000

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


Neural networks for boundary modeling3

Neural networks for boundary modeling

  • Growth in all directions

    is not the same

    (directional growth).

  • Higher weights for higher

    growth directions.

Directional factors:

Lakes

Road

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

Population


Directional boundary modeling

Directional boundary modeling

  • Closer match.

  • 2003 from 2000.

  • RMS=1226.49m.

2003

  • 2000 from 1992.

  • Weights effect.

  • Better results.

  • RMS=1650.01m

2000

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


Concluding remarks

Concluding remarks

  • Artificial intelligence techniques fit the complex nature of urban process.

  • Model design reduces the need for large input data and modeling uncertainty.

  • Simple, yet accurate transition rules easily interpreted by end users.

  • Spatial calibration, township basis, took into account site specific features.

  • Temporal calibrationimportance.

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


Concluding remarks1

Concluding remarks

  • 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 modeling results close, quantitatively and qualitatively, to crisp CA results.

  • GA objective function optimal design.

  • FL reflect linguistic knowledge of urban process.

  • FL provides calibration initials.

  • NN simulate boundary with close urban match.

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


Recommendations and future work

Recommendations and future work

  • 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.

  • 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.

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


Urban growth modeling with artificial intelligence techniques

THANKS FOR LISTENINGQUESTIONS??

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


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