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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
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Overview Study Area
Salient features of City
Objective & Data Used
Results
Research Findings
Conclusions
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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)
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Change in Urban Area
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2000
Road Length Jaipur City
Year
0 1000 2000 3000 4000 5000
1995
Road Length
Km
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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
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Methodology
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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
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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
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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.
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Actual and Simulated Land Use 2002
Urban
Area
Forest
Crop
land
Barren
land
Legend
Simulated Land use Map 2002Actual Land use Map 2002
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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
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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
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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.
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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.
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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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