WSEAS Conference-Pran

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Markov Method Integration with Multi-layer

Perceptron Classifier for Simulation of Urban

Growth of Jaipur City

Pran Nath Dadhich ,Shinya Hanaoka

Department of International Development EngineeringTokyo Institute of Technology

6th WSEAS International Conference on6th WSEAS International Conference on

REMOTE SENSING, Iwate Prefectural REMOTE SENSING, Iwate Prefectural

Univ., Iwate, Japan,Univ., Iwate, Japan, October 4October 4- -6, 2010 6, 2010



Overview Study Area

Salient features of City

Objective & Data Used

Results

Research Findings

Conclusions



Salient Features of City

• Capital & Administrative headquarter of State

•Major tourist attraction point (4400/day Tourist visiting 2008)

•Population annual growth 6.5% in 2009

•The average annual growth rate of vehicles in Jaipur is

13%(2008)



Change in Urban Area



2000

Road Length Jaipur City

Year

0 1000 2000 3000 4000 5000

1995

Road Length

Km



Objectives

Ascertain spatial dimension of urban growth pattern by including policy

and factor which are influencing urban growth of developing city.

Assessment of simulated urban growth with actual urban area using

remote sensing and geographic information system (GIS).

Variable Dataata used

Land Use Data

1989

Satellite Data

& Topomaps

Land Use Data

2000

Road Data

2000

Proximity to road

Proximity from City

Urban

Distance from Forest Proximity to Urban

Centre

Hill Shade

Slope

DEM

Land Use Data

1989



Methodology



Create Sub model based

on Transition from each

land use Category

Create preliminary

spatial maps

Land use Map for 1989

and 2000

Creation of Variable Maps

Distance

Parameters

And Land Use

Categories

Remote Sensing and Topographic DataLand Use Roads Relif/ DEM

Urban, Forest, Crop, etc. Highway, Major Roads Slope, Hill Shade

Selection of

Variable usingCramer’s V

Research Frame work & Methodology

Transition Potential maps

for each sub model

Simulated Urban

Growth 2002

Cross Tabulation of Simulated

& Actual urban growth

MLP Neural

Network

procedure

Non-GIS

process

Spatial data

GIS & RS

Process

Markov’s

Model



What is MLPhat is MLP

MLP is a method based on Artificial Neural Network approach and the most

commonly used neural network method in remote sensing data analysis.

The perceptron concept first introduced in 1952 by Rosenblatt. Basic architecture has

shown in below figure.

11

Hidden Layer Output Layer Input Layer

The MLP algorithm

Input Layer- Input nodes are the elements of a feature vector. This feature vector

might consists of a data set, such as texture of image or other complex parameters.

Hidden Layer- According to theory, hidden layer represents Boolean function.

Out put Layer- This represents the out put data.

Source: Atkinson 1996



Markov Modelarkov Model

Transition potential area matrix created using MLP process used inMarkov model to simulate urban area.

Land use studies using Markov chain models have a proclivity to focus

on a large spatial level and engage both urban and non urban cover of

land.

Markov model have so many assumptions.

One important is that it considers land use and land cover as stochastic process and different categories of land use are as the states of chain.



Actual and Simulated Land Use 2002

Urban

Area

Forest

Crop

land

Barren

land

Legend

Simulated Land use Map 2002Actual Land use Map 2002



LandUse

Class

ActualLand use

Data (Ha.)

%fromTotal Study

Area

SimulatedLand Use

Data (Ha.)

%fromTotal

Study Area

% Change inAccuracy level

in Area

Forest

4444.47 4.10 3843.36 3.55 86.47

Urban

Table 1. Actual and Simulated land use of 2002 (Unit – Hectare)

Area 12630.69 11.69 12200.94 11.27 96.59

Crop

Land 21533.49 19.90 25958.70 23.99 82.95

BarrenLand 69357.69 64.10 66195.09 61.17 95.44



Actual Urban Total Pixels in

Simulated

Urban Non-

Urban

Urban

Simulated

Urban

Non-Urban 12919

Urban 8144 127422 135566

Table 2. Cross Tabulation of Actual Urban and Simulated Urban 2002

Total Pixels in

Actual Urban

140341

Pixel Matching for simulated data (Based on Actual

Data)

90.7%

Value = No. of Cell/Pixels 1 Cell/ Pixel =

0.09 hectare



Research Findings

Need of Prediction of Urban Growth

Urban Growth

Affect

Agricultural land

Forest Surface water bodies

Groundwater

For the sustainable planning, urban areas have to be properly monitored

from the past to present through different time series data to maintain an

internal equilibrium, which can be achieved with Remote Sensing data

interpretation.



Site Specific Findings

Actual and simulated urban growth are identical closely in total area for the

city except some density difference on periphery of city.

This density difference has been evaluated for these data by performing 1*1

pixel matching and found 93% pixel of simulated urban are matching with

actual urban area.

Research Findings

Although other classes like forest, cropland and barren land area, simulation

results have slight variation in simulated and actual data, this may be due to

seasonal variation of these two data.

The variables integrated to simulate urban have shown good results on

urban growth such as simulated urban following roads for growth.



Conclusions

The methodology used for prediction have provided useful

information about the trend of urbanization.

Simulated urban area differ only by 7% from the actual urban area of

2002.

Average annual growth rate of urban is 7.6% and 6.6% for actual and,

growth.

The variables applied in this study like slope, distance from roads and

proximity to city urban are deciding new developments of urban.

Prediction of urban growth-

Also important for optimal planning of the land and natural

resources.

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