Forecasting urban growth based on GIS, RS and SLEUTH … · Forecasting urban growth based on GIS, RS and SLEUTH model in Pune metropolitan area Lakshmi KantaKumar N, Nikhil G Sawant,

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 2, No 2, 2011

© Copyright 2010 All rights reserved Integrated Publishing services

Case study ISSN 0976 – 4380

Submitted on October 2011 published on November 2011 568

Forecasting urban growth based on GIS, RS and SLEUTH model in Pune

metropolitan area Lakshmi KantaKumar N, Nikhil G Sawant, Shamita Kumar

Institute of Environment Education and Research, Bharati Vidyapeeth University, Pune

[email protected]

ABSTRACT

Geoinformatics is today used extensively for managing the rapidly growing urbanization of

our cities and villages. Growing urban areas increasingly encroach on the surrounding rural

areas putting enormous pressure on the meager infrastructure, very often leading to the

unplanned and unsustainable development. The study area is Pune and its surrounding rural

areas. In this study, a self modifying cellular automated Slope, Land use, Exclusion, Urban

extension, Transportation and Hill shade - model (SLEUTH) has been used to simulate and

predict urban growth of Pune city by 2030 and its impact on the surrounding rural areas. The

study is based on 38 years (1973-2011) of multi-temporal data compiled and interpreted from

Landsat images, Survey of India toposheets and Aster GDEM. The predicted urban growth

shows that Pune city may expand mainly in the North and East, rather than in the South and

West directions. Rural areas in Urli Knanchan, Wagoli, Rahu, Talegoan Dhandhere,

Hadapsar and Thergaon revenue circles might be the most influenced areas of Pune’s

urbanization by 2030. This study showed that the combined approach of geographical

information system, remote sensing and SLEUTH model is very useful in modeling and

predicting the future urban scenarios, which help in planning of rural infrastructure

development.

Keywords: Urbanization, Geoinformatics, SLEUTH, Multi-temporal data, Rural-

infrastructure.

1. Introduction

Urban growth is the expansion of town and cities with respect to the increase of the size of a

built-up area. Urban expansion mainly depends upon the city requirement, facilities available

and industrialization. These causes migration of people from rural to the urban areas, putting

immense pressure on infrastructure, natural resources and lead to formation of slums,

increase traffic congestions, reduced farmlands, increase deforestation and threaten wild life.

Modeling is tool that can assist in analysis and especially the prediction of urban growth

dynamics (Silva & Clarke, 2002). Urban growth models provide an extra layer of insight to

policy makers and planners, who can use the model to anticipate and forecast future changes

or trends of development, to understand the impacts of future development, and explore the

potential impacts of different policies (Pettit et al., 2002, Verburg et al., 2002). Urban growth

dynamics are highly complex and nonlinear in nature. There are several models in existence

that model urban growth phenomena such as cellular automata modeling, agent based

modeling, spatial-statistics modeling, artificial neural network modeling, fractal modeling etc.

With the increased computational power and greater availability of the spatial data, micro

simulation such as the agent based and cellular automata simulation method have shown

great potential for representing and simulating the complexity of the dynamic process

involved in urban growth and land use change (Dietzel & Clarke, 2004). The collection and

Forecasting urban growth based on GIS, RS and SLEUTH model in Pune metropolitan area

Lakshmi KantaKumar N, Nikhil G Sawant, Shamita Kumar

International Journal of Geomatics and Geosciences

Volume 2 Issue 2, 2011 569

analysis of spatial data became easier with the advance of geographic information system

(GIS) and remote sensing (RS) techniques. Cellular Automata (CA) is particularly well suited

to model complex dynamics composed of large number of individual elements linked by non

linear couplings (Openshaw & Openshaw, 1997) such as urban system. CA was highly

emphasized and has been widely used in urban sprawl mechanism, urban theory and

urbanization effect (Batty, 1998, Batty et al., 1999 & Clarke et al., 1996) and it is closely

integrated with GIS. The power of this tool has become important for city planners,

economists, ecologists and resource managers oriented towards sustainable development of

regions (Clarke et al., 2002). The SLEUTH model is a cellular automaton model, developed

with predefined growth rules applied spatially to gridded maps of the cities in a set of nested

loops, and was designed to be both scalable and universally applicable (Silva & Clarke, 2002).

SLEUTH is an acronym of input layers Slope, Land use, Exclusion, Urban, Transportation

and Hillshade. SLEUTH is composed of four growth rules to simulate the urban growth;

spontaneous growth rule is responsible for urbanization of randomly selected pixels. The

urban pixels created by the spontaneous growth are then tested for the probability to become

new urban spreading centers under the new spreading center growth rule. Edge growth rule is

responsible to stem the organic growth of new urban spreading centers and finally road

influenced growth rules determines the urban growth along the transportation network.

These four rules are influenced by the five growth coefficients i.e., Diffusion coefficient

(Diff): determines over all depressiveness of urban growth, Breed coefficient (Brd):

determines how likely a newly generated detached settlement is to begin its own growth cycle,

Spread coefficient (Sprd): controls the amount of infill and outward organic expansion, Slope

resistance coefficient (Slp): influences the likelihood of urban expansion on steeper slopes

and Road gravity coefficient (RG) impacts road influenced growth along with diffusion and

breed coefficients. Besides these initial growth rules a second level of growth rules termed

“self modification rules” applied to the model, are responsible to alter the values of growth

coefficients in order to achieve more accurate typical S-curve growth rate of urban expansion.

These self modification rules make the SLEUTH model more robust by preventing it from

projecting linear or exponential growth. SLEUTH has been successfully implemented in San

Francisco, Chicago, Sioux Falls and south coast California (NCGIA, 2011), Baltimore-

Washington (Jantz et al. 2003), Porto and Lisbon (Silva & Clarke, 2002) and Chiang Mai and

Taipei (Sangawongse et al. 2005). SLEUTH has been tested for more than 30 cities all over

the world (Clarke & Dietzel, 2004); one of the objectives of this paper is also to test the

applicability of SLEUTH in Indian context.

32.5% of the population of Pune Municipal Corporation (PMC) lives in slums and there are

477 slum pockets being found in the PMC area. This may be due to lack of efficient plans to

handle the rural migration and rapid urbanization of Pune. Thus, it is very important to

project the future scenario of urban growth of Pune in order to understand how the city can

expand; this may help the planners to carry out their planning more efficiently based on

computer simulation methods in additional to their expertise. This paper also aims to identify

the rural areas which would be mostly influenced by the Pune urbanization.

2 Materials and Method

2.1 Study Area

Pune is a plateau city situated near the Western margin of the Deccan plateau. The city

emerged from a tiny agriculture settlement called ‘Punnaka’ in the 8th century to become the





8th largest city in India. Pune noticed significant growth under the rule of Peshwas, after

which it became a municipality in 1858 and a municipal corporation in 1950. The climate of

Pune is monsoonal, moderate and salubrious. Pune is surrounded by the Sahyadri hills i.e.,

Western Ghats. Two rivers Mula and Mutha flow through center of the study area. The urban

area of Pune was hardly 5 sq. km. in 1818 and now it stands at about 700 sq. km., with the

total area of the Pune metropolitan region currently covering 1605 sq. Km. Provisional data

of the Census 2011 shows that 5.7 million people live in Pune metropolitan area.

Industrialization in Pune began in the early 1960’s with the establishment of industrial estates

in Pimpri – Chinchwad and was further boosted with the arrival of IT and multinational

companies in the last decade. The latitude of study area ranges from ‘18˚15' N to 18˚45' N’

and its longitude is between ‘74˚15' E to 73˚30' E’. It is covered by Survey of India (SOI)

1:50,000 toposheets numbered 47 F/10, 47 F/11, 47 F/14, 47 F/15, 47 J/2 and 47 J/3. The area

under Study is 4381 sq. Km.

Figure 1: Study Area

2.2 Input Datasets

Thirty eight years of multi temporal data sets from different sources dating back from 1973 to

2011 have been utilized in order to prepare the input data sets. For statistical calibration of

the model, at least four temporal urban extents, two road layers and two land-use layers along

with each slope, excluded and hillshade layer are required on three spatial resolutions with

same projected coordinated system and extent. Survey of India toposheets has been rectified

to the Everest 1956 (India and Nepal) datum, and then they are re-projected to the

UTM/WGS84 projection by using Molodensky datum transformation with Bursa-Wolfe shift

method. Atmospheric corrections have been carried out on the satellite images. The satellite

images have been geo-referenced with the help of rectified mosaic of SOI 1:50K toposheets

in UTM/WGS84 projection. The study area has been subsetted from these geo-referenced





satellite imageries. The subsetted imageries of Landsat-1 MSS of 1973 and Landsat-5 TM of

years 1992, 2000 and 2011 are classified by using the maximum likelihood supervised

classification method. Binary maps of urban extents showing urban/non-urban classes were

extracted from the classified images. Land use layers of 1973 and 2011 showing six classes

named as urban, agriculture, open scrub, forest, water and barren land have been prepared

with the help of classified images.

Figure 2: Methodology flow chart

Accuracy assessment has been carried out by using SOI 1: 50000 toposheets for 1973 land

use layer and field observation for 2011 land use layer. The overall kappa statistics for 1973

land use is 0.76 and for 2011 land use is 0.82. It was observed that confusion has been taken

place in classifying the open scrub and barren land. Reserved forests and water bodies such as

reservoirs, rivers and canals were excluded with 100% probability from the urban





development in the excluded layer, it has been prepared with the help of SOI toposheets and

classified image of 2011. Two road layers of years 1973 and 2011 have been prepared by

using different sources like SOI toposheets and Google earth etc. Slope layer in percentage

slope and hillshade layer has been prepared with the help of ASTER GDEM 2009. In this

Study the input layers are resampled to the three spatial resolutions i.e., 100m coarse, 50m

fine and 25m final resolutions which corresponds to the image size of 559 by 796, 1117 by

1591 and 2234 by 3182 pixels for the purpose of model calibration. All input layers prepared

have been converted into GIF format for use in the SLEUTH model (See Figure 3).

2.3 SLEUTH Implementation

SLEUTH Model is composed of two inbuilt models; first one is Clarke urban growth model

(UGM) used to simulate the urban growth of study area. Second one is the land cover

deltatron model (LCD) used to simulate non-urban land class transitions, and is loosely

coupled with UGM (NCGIA, 2011). The implementation of SLEUTH involves three steps

called as test, calibration and prediction. It is very important to perform test mode before

calibration phase. In the test mode model verifies the data sets and their initial reaction to the

input data, including assuring that they conform to data input specifications (Silva & Clarke,

2002). The test mode is followed by the calibration mode, which is the crux of SLEUTH

model. The goal of calibration is to derive a set of values for the growth parameters that can

effectively model the growth during the historic time period (Jantz et al., 2003).There are two

methods available to carry out the calibration phase i.e. brute force and genetic Algorithm.

Brute force method uses a predetermined order of stepping through the coefficient space and

genetic algorithm to search through the coefficient space in an adaptive manner (Goldstein,

2004).

In this study brute force calibration method has been adopted to sequentially narrow down the

ranges of coefficient values with respect to the increasing spatial resolution of datasets in the

three phase i.e., coarse, fine, final. At the end of each calibration run, the model produced 13

least squares regression metrics, such as compare (modeled final population), population

(number of urban pixels), cluster (urban cluster edge pixels), edges (urban perimeter), Lee

Sallee metric (a shape index), average slope, Xmean (average X values) and Ymean (average

Y values) etc. Each metric represents the goodness fit between the simulated growth and the

actual growth for the control years. Selection of the coefficient ranges to the subsequent

phase of calibration is based on these 13 metrics. Several approaches used so far to narrow

down the coefficient space include: sorting on all metrics equally, weighting some metrics

more heavily than others, and sorting only on one metric (NCGIA, 2011). Most of the

previous studies used Lee Sallee metric as a measure to narrow down the coefficient space in

the calibration process (NCGIA, 2011and Silva & Clarke, 2002). In this study, it was first

decided to use Lee Sallee metric, which is a measure of spatial fit between the modeled urban

growth to the known urban extent of control years, as a primary measure to narrow down the

coefficients. But it was observed that Lee Sallee metric associated with little growth in the

study area during coarse calibration (See Table 1). Hence Optimum SLEUTH Metric (OSM)

(Dietzel & Clarke, 2007; NCGIA, 2011) has been adopted to narrow down the coefficient

ranges. OSM is the product of compare, population, edges, clusters, slope, Xmean and

Ymean metrics. The calibration mode is followed by the prediction mode. SLEUTH requires

an urban extent for initialization, an initial transportation network, an excluded layer, slope,





Figure 3: Input data layers of Study area





and a hillshade for its prediction process. The averaged coefficient results of all Monte Carlo

iterations have been used to forecast the urban growth of Pune by 2030.

Table 1: Coefficients sorted by Lee Sallee Metric

Leesale Diff Brd Sprd Slp RG

0.35 1 1 1 80 1

0.35 1 1 1 80 20

0.35 1 1 1 80 40

3. Results and Discussions

3.1 Calibration

Coarse calibration has been performed by taking a step value of 20 with 5 Monte Carlo

iterations, while the fine calibration has been performed by taking step value 5 except for

Road gravity is 10 with 8 Monte Carlo iterations and the final calibration has been performed

by taking step value 1 except for road gravity is 4 with 15 Monte Carlo iterations. Top three

scores in coarse, fine and final calibrations are presented in Table 2. Average slope metric i.e.,

the least square regression of average slope for modeled urbanized cells compared with

average slope of known urban cells for the control years decreased rapidly and almost

reached to zero. This may be due to the presence of highly undulating terrain in the study area.

It was also noticed that most of the metrics decreased with the increase in spatial resolution.

Table 2: Calibration results

Coarse Calibration

Compare Pop Edges Cluster Slope Xmean Ymean Diff Brd Sprd Slp RG

0.95 0.9 0.94 0.77 0.75 0.92 0.91 60 20 20 80 40

0.95 0.9 0.94 0.77 0.75 0.92 0.91 60 20 20 80 60

0.95 0.9 0.94 0.77 0.75 0.92 0.91 60 20 20 80 80

Fine Calibration

0.93 0.9 0.96 0.67 0.3 0.94 0.87 65 30 30 70 50

0.93 0.9 0.96 0.67 0.3 0.94 0.87 65 30 30 70 60

0.93 0.9 0.96 0.67 0.3 0.94 0.87 65 30 30 70 70

Final Calibration

0.62 0.9 0.94 0.61 0 0.95 0.45 67 30 30 70 50

0.62 0.9 0.94 0.61 0 0.95 0.45 67 30 30 70 54

0.62 0.9 0.94 0.61 0 0.95 0.45 67 30 30 70 58

This indicates that the SLEUTH model may appear impotent in dealing with those areas

which have very high undulations and the model is more appropriate for coarse resolutions. It

was noticed that the population metric, which is the goodness fit of modeled urban pixels

with the control years and Edge metric had high correlation while all other coefficients except

the average slope and Ymean had significant correlation with modeled data with real world





data. Since a model is an abstraction of reality, SLEUTH model can be used to predict the

future urban scenario of Pune city.

3.2 Prediction

Models are often judged by their predictive power (Silva & Clarke, 2002). Hence urban

growth of year 2011 has been predicted by using the urban extent layer of 2009 as the seed

layer, which is prepared from Landsat-5 TM by using the maximum likelihood supervised

classification method. It was observed that the visual interpretation (See Figure 4 and 5) of

modeled urban growth with the actual urban growth says that the model was successful to

generate the overall urban growth of Pune by 2011. The urban layer of year 2011, road layer

of year 2011, slope and hillshade layers of year 2009 have been used as seed layers to predict

the urbanization by 2030. For this forecast 100 Monte Carlo iterations were performed. The

prediction best fit values have been derived from the avg.log file, which was produced during

the final calibration; the values for prediction best fit are presented in Table 3.

Table 3: Prediction best fit values

Diff Brd Sprd Slp RG

93 43 43 44 53

Figure 4: Comparison of modeled and actual urban growth of the Study area

Figure 5: Comparison of modeled and actual urban growth





The probability map of urban growth of Pune by 2030 i.e., Figure 6, shows that the chances

of Pune’s expansion towards the North and East direction may be higher than the other

directions. This may be due to the presence of the Western Ghats in the South and West

direction. Road influenced growth may be much higher along the NH-9 and SH-27 than the

NH-4 towards the Bangalore.

Figure 6: Probability Map of urban growth

Figure 7: Increase in Built-up area map from 2011-2030





Figure 7 shows that rural areas in Urli Knanchan, Wagoli, Rahu and Talegoan Dhandhere

revenue areas might be the main centers of attraction for Pune urbanization in the future; this

may be due to presence of Pune- Ahmednagar State Highway (SH-27), Pune-Hyderabad

National Highway (NH-9) and greater availability of flat ground compared to the West and

South sides of Pune. Figure 8 shows that maximum percentage of non built-up land in the

rural areas of Hadapsar, Kothurud and Thergaon revenue areas might be converted in to built-

up areas by 2030.

Figure 8: Percentage of Built-up area by 2030

MIDC’s development of the Rajiv Gandhi IT and Bio-tech Park in Hinjewadi and Talegaon

Industrial Areas (MIDC, 2011) supports the predicted urban growth in the North direction.

MIDC developed Kharadi Knowledge Park, Ranjangaon Industrial area (MIDC, 2011) and

new upcoming townships on the Pune- Ahmednagar State Highway supports the predicted

urban area in Wagoli and Talegaon Dhandhere Revenue Circles. Kurkumbh Industrial area,

Magarpatta and upcoming townships support the predicted urban growth along Pune-

Hyderabad National Highway.

4. Conclusion

This study has shown that remote sensing is an ideal data source and GIS as an efficient tool

for urban and regional modeling. The SLEUTH model is an appropriate model for coarse

resolution in India. Minor changes to the SLEUTH model are required to improve

performance in the Indian context on large scale. SLEUTH is an urban growth model; in fact

this study has shown that it can be useful in gauging the potential of rural areas to become

new urban centers of rapidly expanding cities like Pune. Our future research is concentrating





on rigorous calibration methods in order to improve SLEUTH performance for the Indian

scenario.

5. Acknowledgement

We gratefully acknowledge Professor Keith C. Clarke, University of California Santa-

Barbara for providing necessary help and special thanks goes to Dr. Calrie A Jantz,

University of Shippensburg, David Rodrigues, BVIEER, Anuj K Singh, Bentley Systems and

Maj. Gen. P K Choudhury, Survey of India.

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Forecasting urban growth based on GIS, RS and SLEUTH … · Forecasting urban growth based on GIS, RS and SLEUTH model in Pune metropolitan area Lakshmi KantaKumar N, Nikhil G Sawant,