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Urban Growth SimulationUrban Growth Simulation A Case Study of Indianapolis A Case Study of Indianapolis

Sharaf Alkheder & Jie ShanSharaf Alkheder & Jie Shan

School of Civil EngineeringSchool of Civil Engineering

Purdue UniversityPurdue University

March 10, 2005March 10, 2005

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OUTLINEOUTLINE

IntroductionIntroduction

Data and preprocessingData and preprocessing

NN approach and implementationNN approach and implementation

Results and evaluationResults and evaluation

Concluding remarksConcluding remarks

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INTRODUCTIONINTRODUCTION

Urban growth is a complex processUrban growth is a complex process

Growth parameters include land use Growth parameters include land use suitability, city development level, suitability, city development level, economical phase, etc...economical phase, etc...

A functional model to describe such A functional model to describe such a process is impossiblea process is impossible

Neural Network (NN) is gaining Neural Network (NN) is gaining popularitypopularity

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INTRODUCTION (Cont’d)INTRODUCTION (Cont’d)

Li and Yeh (2002) integrate NN, GIS Li and Yeh (2002) integrate NN, GIS and CA to simulate different and CA to simulate different development patternsdevelopment patterns

Pijanowskia et al. (2002) integrate Pijanowskia et al. (2002) integrate Artificial NN and GIS to forecast the Artificial NN and GIS to forecast the change in land usechange in land use

Existing studiesExisting studies Atlanta growth simulation by Yang and Atlanta growth simulation by Yang and LO (2003)LO (2003) Urban growth prediction for San Urban growth prediction for San Francisco and Washington by Clarke and Francisco and Washington by Clarke and Gydos (1998)Gydos (1998)

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STATEMENT OF THE PROBLEMSTATEMENT OF THE PROBLEM

Indianapolis exhibited accelerated Indianapolis exhibited accelerated urban growth over the last three urban growth over the last three decadesdecades

Such a growth makes a small part of Such a growth makes a small part of Marion County in seventies to cover Marion County in seventies to cover whole Marion County and parts of whole Marion County and parts of neighbor counties in 2003neighbor counties in 2003

The objective of this work is to The objective of this work is to utilize NN algorithms to simulate utilize NN algorithms to simulate urban growth boundariesurban growth boundaries

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Simple Adaptive Linear NN (SALNN) Simple Adaptive Linear NN (SALNN) and Back Propagation (BPNN) and Back Propagation (BPNN) algorithms were usedalgorithms were used

Different city centers were selected Different city centers were selected

Three different datasets were used Three different datasets were used forfor trainingtraining

Short and long-term predictions were Short and long-term predictions were mademade

MethodsMethods

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STUDY AREA/ INDIANAPOLISSTUDY AREA/ INDIANAPOLIS

DATADATA PREPARATIONPREPARATION

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Six historical satellite images for Six historical satellite images for IndianapolisIndianapolis

over 30 years were used:over 30 years were used:

- One Landsat MSS 60-meter image - One Landsat MSS 60-meter image (1973)(1973)

- Five Landsat TM 30-meters image - Five Landsat TM 30-meters image (1982, (1982,

1987, 1993, 2000 and 2003)1987, 1993, 2000 and 2003) All images were rectified and All images were rectified and registered to Universal Transverse registered to Universal Transverse Mercator (UTM) NAD1983Mercator (UTM) NAD1983

DATADATA

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Although all images are Although all images are georeferenced, co-registration is still georeferenced, co-registration is still needed needed

Second order polynomial Second order polynomial transformation function was usedtransformation function was used

Projected images were resampled to Projected images were resampled to 30 meters30 meters

12 control points were used per 12 control points were used per imageimage

The Landsat TM 2000 georefrenced The Landsat TM 2000 georefrenced image was used as the reference image was used as the reference imageimage

IMAGE REGISTRATION IMAGE REGISTRATION

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EXAMPLE OF REGISTERED EXAMPLE OF REGISTERED IMAGESIMAGES

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A panchromatic 15-meter A panchromatic 15-meter resolution image was fused with resolution image was fused with the 2003 XS low resolution imagesthe 2003 XS low resolution images

Fusion is to produce an image with Fusion is to produce an image with high both spectral and spatial high both spectral and spatial resolutionresolution

Multiplicative method was used for Multiplicative method was used for fusion using all image bandsfusion using all image bands

In fused imageIn fused image spatial resolution is improvedspatial resolution is improved spectral resolution may deteriorates in spectral resolution may deteriorates in

certain areas such as roads and certain areas such as roads and residential areasresidential areas

IMAGE FUSION IMAGE FUSION

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FUSION RESULTSFUSION RESULTS

OriginalOriginalFusedFused

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FUSION RESULTSFUSION RESULTS Spatial resolution improvement examplesSpatial resolution improvement examples

Fused OriginalFused Original

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FUSION RESULTSFUSION RESULTS Spectral resolution deterioration examplesSpectral resolution deterioration examples

Fused OriginalFused Original

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Fused images and original images are Fused images and original images are respectively used for classificationrespectively used for classification Same training and testing conditions with Same training and testing conditions with 1:4 ratio were implemented for both 1:4 ratio were implemented for both classificationsclassifications Classification method: maximum likelihood; Classification method: maximum likelihood; supervisedsupervised High resolution orthophotographs and USGS High resolution orthophotographs and USGS land classification maps were used as ground land classification maps were used as ground referencesreferences Seven classes were specified in the images:Seven classes were specified in the images:

- Water - Road - Residential - - Water - Road - Residential - Commercial Commercial

- Forest - Pasture/grasses - Row crops- Forest - Pasture/grasses - Row crops

IMAGE IMAGE CLASSIFICATIONCLASSIFICATION

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CLASSIFICATION RESULTSCLASSIFICATION RESULTS

OriginalOriginalFusedFused

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Some areas in the fused images Some areas in the fused images were classified better than the were classified better than the original images, e.g. forest class original images, e.g. forest class Other areas were deteriorated e.g. Other areas were deteriorated e.g. commercialscommercials Classification accuracy of the Classification accuracy of the original 2003 images was 89.14%, original 2003 images was 89.14%, while it was 84.00% for the fused while it was 84.00% for the fused imagesimages Higher overall classification Higher overall classification accuracy is achieved for original accuracy is achieved for original imageimage

CLASSIFICATION RESULTS CLASSIFICATION RESULTS (Cont’d)(Cont’d)

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The six years historical urban growth The six years historical urban growth boundaries of Indianapolis area were boundaries of Indianapolis area were measured.measured.

DATA FOR NN SIMULATIONDATA FOR NN SIMULATION

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Two centers were selectedTwo centers were selected For every configuration, six measurements For every configuration, six measurements were recorded at each 3 degrees angle were recorded at each 3 degrees angle intervalinterval A matrix of 120 by 6 measurements was A matrix of 120 by 6 measurements was preparedprepared

DATA FORDATA FOR NN SIMULATIONNN SIMULATION

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Three datasets were prepared for NN Three datasets were prepared for NN training:training:

- Real data set without - Real data set without interpolationinterpolation

- 5 year interpolated data set- 5 year interpolated data set

- 1 year interpolated data set- 1 year interpolated data set RBFN algorithmRBFN algorithm

NEURAL NETWORK ALGORITHMSNEURAL NETWORK ALGORITHMS

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Two of the well-known NN algorithms Two of the well-known NN algorithms were trained using the three prepared were trained using the three prepared datasets for every center configurationdatasets for every center configuration The adaptive linear NN as well as BP The adaptive linear NN as well as BP algorithms were usedalgorithms were used Radial growth distance was predicted Radial growth distance was predicted as a function of angular distribution as a function of angular distribution and yearsand years Short (3 years, for 2003) and long Short (3 years, for 2003) and long term (7 years, for 2000) predictionsterm (7 years, for 2000) predictions

NEURAL NETWORK ALGORITHMSNEURAL NETWORK ALGORITHMS

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SALNN StructureSALNN Structure

NEURAL NETWORK ALGORITHMSNEURAL NETWORK ALGORITHMS

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BPNN StructureBPNN Structure

NEURAL NETWORK ALGORITHMSNEURAL NETWORK ALGORITHMS

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For every center configuration, we For every center configuration, we produced the following outputs:produced the following outputs:

- SALNN & BPNN long term - SALNN & BPNN long term prediction prediction (2000 based on 1973 to (2000 based on 1973 to 1993)1993)

- SALNN & BPNN short term - SALNN & BPNN short term prediction prediction (2003 based on 1973 to (2003 based on 1973 to 2000)2000)

NEURAL NETWORK GROWTH NEURAL NETWORK GROWTH SIMULATIONSIMULATION

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SALNN vs. BPNN long term prediction SALNN vs. BPNN long term prediction (2000)/Center(a)(2000)/Center(a)

SALNN SALNN BPNNBPNN

Better Performance for SALNN for real data onlyBetter Performance for SALNN for real data only Close performance at the third dataset with SALNN Close performance at the third dataset with SALNN being betterbeing better BPNN didn’t perform well at real dataBPNN didn’t perform well at real data Noticeable discrepancy between real and long-term Noticeable discrepancy between real and long-term predicted boundariespredicted boundaries

RESULTS (1)RESULTS (1)

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SALNN vs. BPNN short term prediction SALNN vs. BPNN short term prediction (2003)/Center(a)(2003)/Center(a)

SALNN SALNN BPNNBPNN

Better match between predictedBetter match between predicted and real boundariesand real boundaries

SALNN perform better than BPNN for all of the SALNN perform better than BPNN for all of the three data setsthree data sets

RESULTS (2)RESULTS (2)

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SALNN vs. BPNN long term prediction SALNN vs. BPNN long term prediction (2000)/Center(b)(2000)/Center(b)

SALNN SALNN BPNNBPNN

Some effect of center is clear on the Some effect of center is clear on the predicted resultspredicted results Third dataset produce the best resultsThird dataset produce the best results SALNN performs betterSALNN performs better

RESULTS (3)RESULTS (3)

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SALNNSALNN vs. BPNN short term prediction vs. BPNN short term prediction (2003)/Center(b)(2003)/Center(b)

SALNN SALNN BPNNBPNN

Better performance for SALNNBetter performance for SALNN Center effect is less than for long term Center effect is less than for long term predictionprediction

RESULTS (4)RESULTS (4)

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Urban growth rate is faster in certain Urban growth rate is faster in certain directions due to driving factors such as directions due to driving factors such as development probabilitydevelopment probability Weighted radial growth as a function of Weighted radial growth as a function of radial measurement and growth direction was radial measurement and growth direction was usedused Threshold should be met to implement the Threshold should be met to implement the weighted growth modification weighted growth modification Better results were obtained were the real Better results were obtained were the real boundaries match the predicted ones very boundaries match the predicted ones very closelyclosely

WEIGHTED NN URBAN GROWTHWEIGHTED NN URBAN GROWTH

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Weighted SALNNWeighted SALNN short term prediction short term prediction (2003) (2003)

Center (a) Center (a) Center (b)Center (b)

Very close match between real and Very close match between real and predicted boundariespredicted boundaries The effect of center on prediction results The effect of center on prediction results minimizedminimized

RESULTS (5)RESULTS (5)

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The best growth prediction for the two The best growth prediction for the two algorithms and centers achieved using algorithms and centers achieved using the third dataset the third dataset For both centers, the results showed For both centers, the results showed that SALNN gave better results compared that SALNN gave better results compared to the BPNN results to the BPNN results Under the limitation of the availability Under the limitation of the availability of the data the SALNN works better than of the data the SALNN works better than BPNN.BPNN. Results of predictions is somewhat Results of predictions is somewhat independent on the centers location independent on the centers location especially for the third datasetespecially for the third dataset Weighted NN results are the best in Weighted NN results are the best in term of matching the real and predicted term of matching the real and predicted boundariesboundaries

RESULTSRESULTS SUMMARYSUMMARY

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SALNN algorithm produced better SALNN algorithm produced better results than BPNN given the limited results than BPNN given the limited size of the available datasize of the available data Prediction results improved as the Prediction results improved as the interpolation interval between the real interpolation interval between the real data points gets smaller. data points gets smaller. City center location has certain effect City center location has certain effect on the predicted urban growth patternon the predicted urban growth pattern Weighted NN improved the prediction Weighted NN improved the prediction results and minimized the effect of results and minimized the effect of center locationcenter location

CONCLUSIONSCONCLUSIONS

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CURRENT AND FUTURE CURRENT AND FUTURE WORKWORK Urban growth prediction using (X,Y) Urban growth prediction using (X,Y)

coordinates of the boundariescoordinates of the boundaries Weighted NN simulation for the fact Weighted NN simulation for the fact

that growth is not the same in all that growth is not the same in all directionsdirections

Urban growth errors NN trainingUrban growth errors NN training Growth errors statistical modeling as a Growth errors statistical modeling as a

function of the radial distance, time and function of the radial distance, time and angles of growthangles of growth

Cellular Automata and Fuzzy Logic Cellular Automata and Fuzzy Logic simulationsimulation

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QUESTIONSQUESTIONS