Urban_Sprawl_Full_Paper

ANALYZING URBAN SPRAWL
USING GEOINFORMATICS: A CASE
STUDY OF PUNE
Submitted by:
Emtiaj Hoque, Sohini Kar, Varsha Yadav
Guided By:
Prof. Dr. Shamita Kumar
Institute of Environment Education and Research
Bharati Vidyapeeth University
Pune, Maharashtra, India
2013 - 14
The dissertation is submitted in partial fulfillment of the requirement for degree of Master
of Science in Geoinformatics

ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE STUDY OF PUNE Acknowledgement
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Acknowledgement
We worked with great number of people whose contribution in assorted ways to the thesis deserved
special mention. It is pleasure to convey my gratitude to them all in our humble acknowledgment.
We are gratefully acknowledging our guide Prof. Dr. Shamita Kumar, for her advice, supervision and
crucial contribution, which made her the backbone of the thesis. Her involvement with her originality
has trigged and nourished my intellectual maturity. We are grateful to her in every possible way. We
would like to show our sincere gratitude to Dr. Erach Bharucha, Dr. Kranti Yardi and Mr. Anand Shinde
for their guidance, instructions and support.
We would like to mention our sincere gratitude to Lakshmi KantaKumar N and Prachi Dev for their
extreme patience and support providing necessary help whenever we needed them and special thanks
goes to Mr. I. A. Khan who gave us the idea to work for urban applications.
Last but not the least, a special mention of our parents for their inseparable support, encouragement and
prayers.

ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE STUDY OF PUNE Abstracts
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Abstracts
ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE
STUDY OF PUNE
1. Emtiaj Hoque, Bharati Vidyapeeth University, Institute of Environmental Education & Research, Pune -43
Email: emtiajhoque123@gmail.com
2. Sohini Kar, Bharati Vidyapeeth University, Institute of Environmental Education & Research, Pune -43
Email: sohinikar08@gmail.com
3. Varsha Yadav, Bharati Vidyapeeth University, Institute of Environmental Education & Research, Pune -43
Email: vyadhav30@gmail.com
4. Shamita Kumar, Bharati Vidyapeeth University, Institute of Environmental Education & Research, Pune -43
Email: shamita@bvieer.edu.in
----------------------------------------------
Rapid industrialization and associated migration led Pune to become rapidly urbanized. Monitoring,
modeling and mapping urban sprawl is essential for better sustainable development. This paper focuses
on identifying the trend of urban sprawl in Pune city over the past one decades using temporal remote
sensing data (Landsat TM 1999 & Landsat 8 2013), using a buffer of 50km from city center to the
outskirt in five concentric circles and combining gradient analysis with spatial class metrics tools using
FRAGSTATS. A detailed quantitative analysis has been done showing the spatial changes. This study
performs Spatio-temporal analysis along with the extensive use of spatial class metrics to identify the
physical drivers of urban sprawl of Pune city.
Keywords: urban sprawl, spatial metrics, FRAGSTATS, Pune.

ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE STUDY OF PUNE Table of Content
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Table of Content
Contents Page No
Acknowledgement i
Abstracts ii
Table of content iii
List of figure iii
List of table iv
Chapter: 1 Introduction 1 - 9
1.1Introduction 2
1.2 Urban Areas and Urban Growth 2
1.3 Urban Sprawl 2
1.3.1 Causes of urban sprawl 3
1.3.2 Characteristics of urban sprawl 4
1.3.3 Consequences of urban sprawl 5
1.4 Research objective 6
1.4 Research Question 6
1.6 Context of Study Area 7
1.6.1 Regional setting and overview of Pune city 7
1.6.2.Demographic Profile 7
1.6.3. Socio Economic Profile 8
1.5.4 Land use and urban growth 8
1.5.5 Scope of the study 9
Chapter: 2 Literature Review 10 - 17
2.1 Introduction 11
2.2 Land use and Land cover change 11
2.3 Remote sensing of urban areas 11
2.4 Urban Pattern Analysis 13
2.5 Analyzing urban sprawl using spatial metrics 13
Chapter: 3 Data and Methodology 18 - 21
3.1 Introduction 19
3.2 Research design and methodology 19
3.3 Data source and type 19

ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE STUDY OF PUNE Table of Content
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Contents Page No
3.4 Method of Data analysis 19
3.4.1 Remote sensing image classification 21
3.4.2Accuracy assessment 21
3.4.3 Quantifying urban sprawl pattern using spatial metrics 21
3.5 Software used 21
Chapter: 4 Result and Discussions 22 - 34
4.1 Introduction 23
4.2 Land use analysis 23
4.3 Result of image classification and accuracy assessment 26
4.4 Urban expansion in Pune city over 13year 26
4.5 Spatio temporal analysis of urban sprawl using spatial metrics 27
4.5.1 Number of Urban Patches 27
4.5.2 Mean patch size 28
4.5.3 Total core area index 29
4.5.4 Core area density 30
4.5.5 Total edge 30
4.5.6 Edge density 31
4.5.7 Mean Shape index 32
4.6.8 Mean nearest neighbour distance 32
4.6.9 Proximity index 33
4.6.10 Interspersion juxtaposition index 34
Chapter: 5 Conclusions 35
Chapter: 6 Presentations and Publications 38 - 48
6.1 Research paper 39
6.2Indian society of Geomatics Conference 2013 47
6.2.1 Published abstract 47
6.2.2 Certificate 48
Chapter: 7 References 49

ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE STUDY OF PUNE List of Tables
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List of Tables
Table 1.1: Distribution of Workers in Pune 8
Table1.2: PMR Industries 8
Table 3.1: List of Satellite images collected for the study area 19
Table 3.2: List of spatial data used for the study 19
Table 4.1: Land use change of different categories between 2000 and 2013 22
Table 4.2: Accuracy assessment 26
Table 4.3: NP and it significance 27
Table 4.4: MPS and it significance 28
Table 4.5: TCAI and it significance 29
Table 4.6: CAD and it significance 30
Table 4.7: TE and it significance 30
Table 4.7: ED and it significance 31
Table 4.8: MSI and it significance 32
Table 4.9: MNND and it significance 32
Table 4.10: PROX and it significance 33
Table 4.11: IJI and it significance 34

ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE STUDY OF PUNE List of Figures
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List of Figures
Figure 1.1 Study area 7
Figure 3.1: General methodology of the study 19
Figure 4.1: Land use & Land cover change map 24
Figure 4.2: Areas acquired by various land-use features of 2000 24
Figure 4.3: Areas acquired by various land-use features of 2013 25
Figure 4.4: Diagram showing land use change detection rate 25
Figure 4.5: Land use change in sq.km 26
Figure 4.6: Urban Expansion 27
Figure 4.7: No of Patches 28
Figure 4.8: Mean patch size 29
Figure 4.9: Total core area index 29
Figure 4.10: Core area density 30
Figure 4.11: Total edge 31
Figure 4.12: Edge metrics 32
Figure 4.13: Mean shape index 32
Figure 4.14: Mean nearest neighbor distance 33
Figure 4.15: Proximity index 34
Figure 4.16: Interspersion juxtaposition index 34

ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE STUDY OF PUNE Chapter 1: Introduction
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Chapter 1
Introduction
1.1 Introduction:
Urbanization has been a universal and important social and economic phenomenon taking place all
around the world. This process with no sign of slowing down, could be the most powerful and visible
anthropogenic force that has brought about fundamental change in land cover and landscape pattern
around the globe. Rapid urbanization and urban growth especially in the developing world, is continuing
to be one of the critical issue of the global change in the 21st century affecting the physical dimensions
cities.
1.2 Urban Areas and Urban Growth:
The definition of an urban area is normally based upon the number of residents, population density,
percent of people dependent upon non-agricultural income and provision of public utilities and services.
The term ‘urban’ has originated from the Roman word Urbanus which adopted the meaning ‘city dweller’
in Latin. The precise definition of an urban area can vary from country to country. Some countries define
an urban area as any place with a population of 2,500 or more while some other countries set a minimum
population of 20,000 as a criterion. In general, there are no universal standards and therefore each country
develops its own set of criteria for recognizing urban areas. In India, an area is designated as urban if the
population is more than 5000 with a population density of more than 400 persons per sq. km and at least
75 percent of the population is involved in non-agricultural occupations (Shashidhar, H., 2001).
India’s urban population grew at an average rate of 2.35 percent per annum during 2000 to 2005 (United
Nations Population Division, 2007). It is projected that the country’s urban population would increase
from 28.3 percent in 2003 to about 41.4 percent by 2030 (United Nations, 2004). By 2001, there were 35
urban agglomerations (cities having a population of more than one million), as compared to 25 urban
agglomerations of 1991. This increased urban population and growth in urban areas is inadvertent due to
an unpremeditated population growth and migration. Urban growth, as such is a continuously evolving
natural process due to growth of population (birth and death). The number of urban agglomerations and
towns has increased from 3697 in 1991 to 4369 in 2001 (Census of India, 2001a). Among the 4000 plus
urban agglomerations, about 38 percent of its population reside in just 35 urban areas. This clearly
indicates the magnitude of concentrated growth and urban primacy due to urbanization.
1.3 Urban Sprawl:
Urban sprawl is one of the key issues today. Many western scholars have contributed to defining urban
sprawl in different ways. There are definitions and descriptions offered by western scholars or
organizations, and they vary in the subtle differences that can be found regarding to urban form, land
uses, impacts and density. In terms of urban form, sprawl is the opposite of the idea of the compact city,
with sprawl characteristics being scattered or leapfrog development. Linear urban forms, such as strip
development along major transport routes, have also been considered as sprawl. The second element of
the definition is the use of land use patterns to define sprawl. Spatial segregation of land uses, which
means different land uses are intentionally disconnected or located at a large distance to each other, is
most commonly used to define sprawl. However, sprawl cannot be defined clearly based only on urban

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form or land use. The third definition is based on the impacts of urban sprawl. Defining sprawl in terms of
its cost is a common way of discussing the impacts of sprawl. In addition, there is a consensus that part of
the definition of sprawl is low density.
The definitions of urban sprawl proposed by western scholars have been changing since the debate on
urban land use change started after World War II. There are two main changes: firstly, urban sprawl was
originally referred to neutrally, but scholars have increasingly referred to it derogatorily. Urban sprawl
was only considered to be a spatial expansion of the city in the early stages. However, with global
urbanization, most researchers believe that urban sprawl has given rise to a series of adverse
consequences, such as environmental damage, economic inefficiency, social injustice and therefore non-
sustainable development. Secondly, the definition of urban sprawl has increasingly been strengthened and
improved. Urban sprawl was initially described as discontinuous development of urban space, and then
further definitions have covered more descriptions of automobile dependency, single-use of land, low-
density development and so on.
Due to Galsters (2001) causes, conditions/characteristics and consequences/impacts of urban sprawl are
often confused, due to a semantic wilderness of the term sprawl and empirical deficits. He argues that ‘a
thing cannot simultaneously be what it is and what it causes’. Small (2000) point out that it is hard to find
solutions against sprawl if we don’t fully understand its causes. Given the current state of the literature, it
is a complex task to divide these three aspects. It is necessary, however, to understand the underlying
factors of sprawl before measuring their effects. In this section we attempt to divide these three aspects of
urban sprawl.
1.3.1 Causes of urban sprawl:
According to Siedentop (2005) there are two rivaling explanation patterns for causes of urban sprawl:
firstly sprawl is explained by the demand for urban land. Driving forces are land consumption of
households, companies, and public uses. Factors such as income, wealth and car use provide the
framework and location choices are made based on a comparison of utility effects and costs. Secondly
sprawl is explained by specific regulation patterns. The massive public subsidies for low density,
suburban forms of living and the publicly financed construction of street networks and local infrastructure
reinforce urban sprawl. According to this view, urban planning is the main cause of sprawl.
Conceptually, the arguments based on the demand for urban land relate to the ‘monocentric model’ of the
Alonso-Muth-Mills type. In this model, the externally given central business district (CBD) is the center
of the city and the location where all relevant interaction takes place. Households – and in some versions
also businesses – choose their location in the surrounding area on the basis of microeconomic constrained
optimization. They allocate their income optimally between land, consumer goods, and cost of
transportation to the CBD. Sprawl- like phenomena can arise from three factors: declining transport costs,
increasing income, and increases in total population. The first two factors yield the same effects. Since
households demand more land and can afford longer commutes, density declines near the CBD, but
increases at the outer parts of the city. The size of the city increases as agricultural land at the urban fringe
is converted to urban uses. As far as the footprint of the city is concerned, an increase in population has
the same effect. Density increases in all parts of the city as a reaction to population growth. In percentage
terms, this increase in density is much larger at the outskirts than near the CBD.
Since the causes are two fundamental economic trends – increasing incomes and declining transport costs
– the question arises, whether their logical consequences should be called urban sprawl. Particularly,
when taking into account the negative connotations of the term. Mieszkowski and Mills (1993) see these
factors as driving forces in a ‘natural evolution’ theory of what causes suburbanization. Gordon and

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Richardson (1996) speak about ‘natural economic factors’. Closely related are some social segregation
processes that are often related to sprawl. In the monocentric model, households with higher income will
locate at higher distance from the CBD than low-income households, since they allocate more money to
transportation to the CBD in their optimal allocation than. This effect is supported further by the lower
land prices at the urban fringe.
This process is in line with Tiebout’s argument that people sort themselves into different local
jurisdictions based on their preferences for local amenities. The income effect in the monocentric model
relaxes the constraint for Tiebout-type self selection and can itself be viewed as contributing to the pull
factor of the argument. The segregation process is possibly strengthened by some cumulative feedback
loops – sometimes called “flight from the bright” – that push certain social groups from central locations.
The loss of high-income population may lead to higher tax rates, higher crime rates, low performing
public schools, the habitation of poor and minorities in the centre etc.; all factors that will push high and
middle class population out of the centre. This factor may be strengthened further by the administrative
structure of the city and by the fiscal constitution. When urban core and ring belong to different local
jurisdictions which finance their public services main layout of local taxes, the spatial distribution of
income generates a corresponding distribution of public services, which reinforces the segregation
processes.
How much of this process is ‘natural’ and unavoidable as long as we welcome rising incomes and
declining transportation costs? In our view, given the state of discussion it would be severely misleading
to attribute all these changes to urban sprawl. On the other hand, there are substantial structural
differences between urban areas so that depending on certain factors these processes may work quite
differently. This suggests a concept like the one suggested by Mills (1999), who describes sprawl as
“excessive suburbanization”. Using such a definition of sprawl, however, raises the question of where the
“natural” ands and the “excessive” starts.
1.3.2 Characteristics of urban sprawl:
Burchell et al. (1998) characterize sprawl in two ways: on the one hand residential low-density scattered
development and on the other hand non-residential scattered commercial and industrial development.
Scattered development is a form that is commonly associated with urban sprawl. He further describes 10
points that characterize urban sprawl – these following characteristics are based on a review of research
findings:
 Low residential density
 Unlimited outward extension of new development
 Spatial segregation of different types of land uses through zoning regulations
 Leapfrog (discontinuous) development
 No centralized ownership of land or planning of development
 All transportation dominated by privately owned motor vehicles
 Fragmentation of governance authority over land uses between many local governments
 Great variances in the fiscal capacity of local governments because the revenue rising capabilities
of each are strongly tied to the property values and economic activities occurring within their own
borders
 Widespread commercial strip development along major roadways
 Major reliance upon the filtering or “trickle-down” process to provide housing for low-income
households.

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This categorization brings a lot of points into the discussion – the problem is that within this list, the
limits between causes, characteristics and consequences of sprawl are ambiguous and a clear distinction
between these categories is not entirely possible. The 10 points stated can be subdivided in spatial
patterns, main causes and main consequences of sprawl.
One of the most elaborated characterizations of urban sprawl is given by Galster et al. (2001). We will
find these dimensions again, when we talk about measuring sprawl, because he orientates along these
dimensions when quantifying the degree of sprawl. Within this section we present these 8 dimensions and
their meaning:
 Density: is a widely used indicator of sprawl whereby different types of density can be described
 Continuity: is the degree to which the unused land has been built densely in an unbroken fashion.
Sprawl can be continuous or discontinuous in other places.
 Concentration: describes the degree to which development is located disproportionately rather
than spread evenly.
 Clustering: sprawl is frequently clustered what means that it only occupies a small portion of the
respective land area.
 Centrality: the loss of centrality is one of the most serious concerns about sprawl.
 Nuclearity: describes the extent to which an urban area is characterized by a mononuclear pattern
of development.
 Mixed uses: sprawl is seen as a process that separates the different kinds of land uses (separation
of homes, workplaces, conveniences, income segregation along residential communities).
 Proximity: proximity is the degree to which land uses are close to each other (housing, work,
shopping, etc.).
1.3.3 Consequences of urban sprawl:
According to OECD (2000), urban sprawl has a range of negative consequences. Frequently mentioned
consequences are: green space consumption, high costs of infrastructure and energy, an increasing social
segregation and land use functional division. Furthermore, the need to travel, dependence on the private
car and as a consequence increased traffic congestion, energy consumption and polluting emissions are
associated with sprawl.
Due to Wassmer (2005) a lot of negative urban consequences can be attributed to sprawl, but sprawl also
has positive effects. When it comes to negative effects he mentions: the car and its polluting effects, a
lack of functional open space, air and water pollution, a loss of farmland, tax dollars spent on duplicative
infrastructure, concentrated poverty, racial and economic segregation, a lack of employment accessibility
etc. Talking about positive effects of sprawl there have to be considered increased satisfaction of housing
preferences, the convenience of car travel, the filling in of leapfrogging land, lower crime rates and better
public schools in suburban local governments.
Glaeser et al. (2003) analyze the impacts of sprawl in form of traffic congestion, environmental
consequences, infrastructure costs and social consequences. They conclude that cars are producing
externalities in form of congestion and pollution. However because of the decentralization of jobs, the
pollution problem is reduced. As people move to edge cites, commutes are getting shorter. Sprawl uses up
formerly undeveloped land. But, on the other hand only a small portion of (US) landscape is built- up
land, implying that there is no scarcity of land. He further argues that externalities decreased over time
per miles travelled. Moreover urban agglomerations economies may be reduced by sprawl and deter
overall productivity. However, this must not necessarily be the case. Sprawl cities differ substantially in

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productive, as a simple comparison of e.g. Detroit and Silicon Valley shows. The only true negative
consequences of sprawl are social. The segregation processes that we have discussed above lead to a
sharp social separation: Those who can afford cars live in the suburbs, those who can’t in the inner city.
 Ecological impacts: Building and sealing of land, as well as indirectly loss of natural potential of
soils and the expulsion of endangered animal and plants. According to him the problem is not that
agricultural space is used, but the fact that connected agricultural land is destroyed.
 Traffic impacts: It is argued that there is a negative correlation between built density and traffic
costs. Inhabitants of densely built cites have to bear lower traffic costs. Efficiency of public
transport is higher than in urban areas with lower density. However, critics say that density has
little influence on traffic behavior. Since households and firms suburbanize, radial commuting to
the city centre is more and more replaced by cross-commuting within the urban area. With jobs
nearby, transportation costs may actually be lower, even in a more decentralized structure. The
time cost of commuting would have increased even more without suburbanization.
 Social and health impacts: Sprawl leads to an erosion of functioning urban cores. This has not
only social and infrastructural consequences but also impacts on innovation capacity of regional
economies – in formless space, creative milieus may develop worse (Cervero et al. 1997). There
is a significant connection between broadening of settlements and concentration of poverty in city
cores. The degree of social interaction in sprawled areas has decreased (Putnam 1994). On the
other hand suburbia is not urban in form, but can be in terms of functions. Critics argue that social
heterogeneity and cultural diversity in suburbs is higher than alleged.
The Transportation Research Board (1998) defines consequences of sprawl in the form of costs. The
report divides effects of sprawl into five types of costs: public and private capital and operating costs,
transportation and travel costs, land/natural habit preservation, quality of life, and social issues. They
further argue that empirical or quantitative data is available in more or less detail concerning these
aspects. Benefits of sprawl are often ignored.
Concerning the costs of sprawl there are different debates in the literature: Ewing (1997) supports a
compact city form with development through planning while Gordon and Richardson (1997b) are
supporting the dispersed pattern of development with market led development.
1.4 Research Aims and Objectives:
General objective:
General objective of the study is to quantify spatio temporal trends and pattern in urban sprawl in Pune
city using remote sensing satellite image and spatial metrics.
Objectives:
The specific objectives of this research are
 To map and monitor the urban pattern using temporal spatial remote sensing data.
 To understand and assess the urban spatial growth at class level through spatial metrics.
1.5 Research Question:
 What are the changes in the spatial pattern of urbanization in Pune over the last decade?
 What are the physical drivers that have affected this urban expansion?

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1.6 Study Area:
The study has been carried out for a rapidly urbanizing region in India. Pune is the cultural, commercial,
industrial, and knowledge city of the state of Maharashtra, India with an area of 741 sq. km and lies
between the latitude 18◦52’04’’and longitude 73°86’00’’. To account for periurban growth this study has
considered a 50 km. circular buffer from the Pune administrative boundary by considering the City
Business District (CBD) as center.
Figure 1.1 Study area
1.5.1 Regional setting and overview of Pune city:
Pune is one of the most renowned places among tourists to Maharashtra. The educational institutions,
presence of a number of industries and branches of virtually every array of economic activity have made
Pune a prosperous town. In 1987, the urban area of Pune was 138.36 sq.km. with an addition of 23
villages in 2001; the area has increased to 243.84 sq. km. The revised city development plan addresses the
urban area of Pune as a whole.
1.5.2 Demographic Profile:
The population of Pune city as per census 2011 is more than 3 million which has grown by more than six
times in the last 60 years. Migration has increased from 3.7 lakhs in 2001 to 6.6 lakhs in 2011. The
population density has increased from 10405.28 person per sq.km in 2001 to 12,770.25 person per sq.km.
Population densities especially in the core areas are very high. A fall in 0-6 year’s sex ratio in last decade
which is a negative indicator for social development has been observed. Pune’s rapid socio-economic
development has had a significant impact on the urbanization in the city; future growth is governed to a
large extent by the development patterns in the city and Pune Metropolitan Region (PMR). Thus, based

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on the statistical methods of population projection, the projected population for Pune city for the years
2041 is 8.59 million.
1.5.3 Socio Economic Profile:
The Workforce Participation Rate of Pune is approximately 34% and the non-workers contribute
66%indicating the dependency rate. The city has emerged as one of the major business centers in
Maharashtra. It is one of the main investment hubs of the state and comes under the Delhi Mumbai
Influence Corridor (DMIC) Project Influence area. It serves as a base for various large and small units
operating in sectors like auto components, engineering, IT, BPO, pharmaceuticals and food processing. It
also serves as the regional wholesale market, market center and a distribution center for agricultural
produce.
Table 1.1: Distribution of Workers in Pune
Selector
1991
2001
Nos. % Nos. %
Primary Sector 6,883 1.27 10,246 1.32
Household Industry 9061 1.68 25,430 3.28
Other worker 523607 97.05 739,943 95.40
Total main worker 539541 100.00 775,619 100.00
Source: Census of India 1981, 1991and 2001
Table1.2: PMR Industries
Industrial area
Completion
Status
Area
Distance
From
Pune Sector
Ha. Km
Pimpri Chinchwad MIDC
100% 1,225 18
Auto, Auto
components
Rajiv Gandhi InfoTech Park Hinjewadi Phase I 100% 87 15 IT, ITES
Rajiv Gandhi InfoTech Park Hinjewadi Phase I 80% 218 16 BT
Rajiv Gandhi InfoTech Park Hinjewadi Phase III (SEZ) 0% Land
Acquisition in Process
350 16 IT, ITES
Rajiv Gandhi InfoTech Park Hinjewadi Phase IV Proposed 400 16 IT, ITES
Kharadi Knowledge Park 100% 27 PMC Software
Talawade InfoTech Park 60% 75 18 IT
Talegaon Floriculture Park NA - 37 Floriculture
Ranjangaon Industrial Area 40% 925 55 White Goods
Chakan Industrial Area
40% 258 30
Auto, Auto
components
Source: Maharashtra Industrial Development Corporation
1.5.4 Land use and urban growth:
The Pune Municipal Corporation (PMC) is responsible for managing planned development in Pune city.
It is also the sole agency mandated to develop and dispose of land in the city. Over the years, the growth

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of the city has been on a ring and radial pattern, with reliance on road based transport. The Development
Plan of Pune 2001 envisaged the demands of housing hence the newly added 23 villages are mostly
utilized for residential use giving an increase from 37% in 1987 to 50% in 2001 of the land in the
residential use. The PMC, however, has been unable to meet the forecasted demands for housing,
commercial and industrial space, resulting in large scale unauthorized development, and areas with non
conforming land uses.
1.5.5 Scope of the study:
The present study aims to address the problem of increasing urban sprawl in the perspective of a
developing country with Pune city as the case under investigation. In the recent years, Pune has seen
unprecedented growth spatially and economically leading to sprawl. It is in this setting that the present
study aims to analyze problem of sprawl in Pune.

ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE STUDY OF PUNE Chapter 2: Literature review
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Chapter 2
Literature Review
2.1 Introduction:
The unprecedented growth of urban population and built up area worldwide have an enormous influence
on natural landscape at different spatial scale. Land use and land cover changes are the process in which
natural environments such as forest and grassland replaced by human induced activities such as intensive
agriculture and urbanization. This chapter reviews important literature regarding land use/ land cover
change, advances in remote sensing technology, spatial metrics and their application in monitoring urban
sprawl pattern.
2.2 Land use and Land cover change:
Land information is of prime importance to the researchers and planners at different levels. The land use
pattern reflects the character of the interaction between people and environment and the influence of
distance and resource base upon basic economic activities. The term land use is used here to describe the
function or use of an area of land is put to. Land use by definition is the use of land, usually with
emphasis upon its functional role with respect to economic activities. Land use refers to ‘Man’s activities
and the various use which are carried on land’(Clawson & Steward 1965). Land cover refers to ‘natural
vegetation, water bodies and rock/soil, artificial cover and other features resulting due to land
transformation’. The observed physical cover, seen on the ground or through remote sensing, includes
vegetation (natural/ planted) and human constructions (buildings etc.), which cover the earth’s surface.
Water, bare land or similar surfaces are included in land cover.
A city develops to perform a range of functions, which increase in size and complexity with urban
growth. The range of functions consists of a combination of industrial, commercial, service and
administration activities, the absolute and relative importance of which is associated with historical
development. As the functions of the city shift from secondary industry to tertiary industry in
development series, urban land use structure has undergone a profound change. Urban sprawl at the fringe
and urban renewal in the inner city appear at the same time, the spatial pattern of the city is transforming
from a uni-center to a multi nuclei one.
As the cities expand, through the continuous process of sprawling, prime agricultural land, open space
and forests (in and around the city) are transformed into land for housing, roads and industry. (E.Hardoy,
Diana Mitlin and David Satterthwaite 1992). Urban morphology of Indian cities have mostly evolved
through the process of intensification in the ancient urban core as well as by the current sprawling into
urban corridors and spill over into the rural fringe of peripheral areas.
2.3 Remote sensing of urban areas:
For decades the visual interpretation of aerial photography of urban areas has been based on the
hierarchical relationships of basic image elements. The spatial arrangement and configuration of the basic
elements (tone and color) combine to give higher order interpretation features of greater complexity such
as size, shape and texture, or pattern and association that are significant and characteristic for urban areas
and urban land use (Bowden, 1975; Haack et al., 1997). A number of urban remote sensing applications
to date have shown the potential to map and monitor urban land use and infrastructure (Barnsley et al.,
1993; Jensen & Cowen, 1999) and to help estimate a variety of socio-economic data (Henderson & Xia,

12
1997; Imhoff, Lawrence, Stutzer, & Elvidge, 1997). However, much of the expert knowledge of the
human image interpreter was lost in the transition from air photo interpretations to digital analysis of
satellite imagery.
The great strength of remote sensing is that it can provide spatially consistent data sets that cover large
areas with both high detail and high temporal frequency, including historical time series. Mapping of
urban areas has been accomplished at different spatial scales, e.g. with different spatial resolutions,
varying coverage or extent of mapping area and varying definitions of thematic mapping objects. Global
and regional scale studies are often focused on mapping just the extent of urban areas (e.g. Meaille &
Wald, 1990; Schneider et al., 2001). A basic difficulty these efforts encounter relates to the indistinct
demarcation between urban and rural areas on the edges of cities. Remote sensing provides an additional
source of information that more closely respects the actual physical extent of a city based on land cover
characteristics (Weber, 2001). However, the definition of urban extent still remains problematic and
individual studies must determine their own rules for differentiating urban from rural land (Herold,
Goldstein & Clarke, 2003).
Most local scale remote sensing applications require intra-urban discrimination of land cover and land use
types. Considering the land cover heterogeneity of the urban environment several studies have shown that
a spatial sensor resolution of at least 5m is necessary to accurately acquire the land cover objects
(especially the built structures) in urban areas (Welch, 1982; Woodcock & Strahler, 1987). Since 2000,
data from new, very high spatial resolution space borne satellite systems have been commercially
available. For example, IKONOS and QUICKBIRD may be considered the beginning of a new era of
civilian space borne remote sensing with particular potential for application in the study of urban areas
(Ridley, Atkinson, Aplin, Muller, & Dowman, 1997; Tanaka & Sugimura, 2001).
Investigations in local scale mapping of urban land use have shown that analysis on a per-pixel basis
provides only urban land cover characterization rather than urban land use information (Gong, Marceau,
& Howarth, 1992; Steinnocher, 1996). Based on the experience with visual air photo interpretation
(Haack et al., 1997) it is known that the most important information for a more detailed mapping of urban
land use and socioeconomic characteristics may be derived from image context, pattern and texture, also
described as urban morphology (Barnsley et al., 1993; Mesev, Batty, Longley, & Xie, 1995). There are
several versatile approaches for including structural, textural and contextual image information in land
use mapping. Some studies have used textural measures derived from spectral images to include this
information in the classification process (Baraldi & Parmiggiani, 1990; Forster, 1993; Gong & Howarth,
1990; Gong et al., 1992). Others have applied spatial post classification to estimate urban land use
information from remote- sensing derived land cover maps (Barnsley et al., 1993; Steinnocher, 1996). A
few studies have used remote- sensing derived discrete land cover objects or segments and described their
morphology and spatial relationships in a detailed mapping of urban areas (Barnsley et al., 1993; Mehldau
& Schowengerdt, 1990; Moller-Jensen, 1990). Barnsley and Barr (1997) further developed these ideas
and presented a complex GIS-based system for detailed contextual urban mapping on an illustrative
dataset. Many researchers believe that detailed spatial and contextual characterization of urban land cover
has high potential to result in detailed and accurate mappings of urban land uses and socioeconomic
characteristics (Barr & Barnsley, 1997; Herold et al.2002).
An emerging agenda in urban applications of remote sensing calls for a new orientation in related
research (Longley, Barnsley, & Donnay, 2001). The traditional remote sensing objectives emphasizing
the technical aspects of data assembly and physical image classification should be augmented by more
inter-disciplinary and application-oriented approaches. Research should focus on the description and
analysis of spatial and temporal distributions and dynamics of urban phenomena, in particular urban land
use changes. However, there is still a lot of resistance, especially among social scientists, against using

13
remote sensing techniques in urban studies. Rindfuss and Stern (1998) mention several reasons. First,
there is a general concern about pixelizing the social environment’, i.e. focusing too much on the physical
aspects of urban areas at the expense of social issues. Indeed, the socioeconomic variables of interest are
usually not directly visible from measurements taken from remote sensing observations. Secondly, the
social sciences outside of geography and planning are generally more concerned with why things happen
rather than where they happen, and accordingly, most social scientists tend to underestimate the value of
the detailed spatial data that remote sensing provides. It is not yet widely appreciated that remote sensing
can provide useful additional data and information for social science oriented studies, e.g., by quantifying
the spatial context of social phenomena and by measuring socially induced spatial phenomena as these
evolve over time. For example, by helping make connections across levels of analysis and between
different spatial and temporal scales, remote sensing has the potential to provide additional levels of
information about the links between land use and infrastructure change and a variety of social, economic
and demographic processes (Rindfuss & Stern, 1998). In terms of analyzing urban growth patterns, Batty
and Howes (2001) believe that remote sensing technology, especially considering the recent
improvements mentioned above, can provide a unique perspective on growth and land use change
processes. Datasets obtained through remote sensing are consistent over great areas and over time, and
provide information at a great variety of geographic scales. The information derived from remote sensing
can help describe and model the urban environment, leading to an improved understanding that benefits
applied urban planning and management (Banister, Watson, & Wood, 1997; Longley & Mesev, 2000;
Longley et al., 2001).
2.4 Urban Pattern Analysis:
Abstracting urban change across cities and scales has a long tradition in the field of geography.
Historically, geographers have examined how and why areas or spaces are the same or different. And
urban geographers have sought to understand and to identify regular patterns of urban development based
on demographic or socio-economic or political trends. These originally were expressed in the concentric
zone theory by Ernest Burgess (1925), the sector theory by Hoyt (1939), the multiple nuclei theory
describe by Harris and Ullman (1945), in addition to von Thuen‘s bid-rent theory (1826). Geographers
now test hypotheses derived loosely from the legacy of those models, analyzing census tract data with
multivariate statistical methods (Johnston, Gregory, Pratt and Watts, 2000). Recent studies have
conducted detailed spatio-temporal analysis on dynamic urban growth (Batty, 2002; Clarke et al, 2002;
Herold et al, 2001; White et al, 2001) with the concept of urban growth that characterize inconstant urban
growth over time and a-uniform spatial configuration of these areas.
The advent of high spatial resolution satellite imagery and more advanced image processing and GIS
technologies, has resulted in a switch to more routine and consistent monitoring and modeling of urban
growth patterns. Recently, landscape metrics have been used for urban applications in conjunction with
remote sensed imagery. Significant progress has been made in quantifying spatio-temporal urban patterns
using spatial metrics (Torrens et al., 2000; Herold et al, 2001; Hasse, 2003). Landscape metrics are
excellent vehicles for representing fractal measures in urban geography, and can be traced back to original
work of the mathematician B. Mandelbrot (1977). Fractal geometry is different from Euclidean geometry
which proposes only the integer dimensions of 0, 1, 2, 3 etc. Fractals are useful for describing spatial
forms which are not regular in the sense of Euclidean geometry but are characterized by alternate patterns
of continuity and fragmentation (Tannier, 2005). Urban geographers have applied this concept to similar
structures at different scales of analysis in urban settings.. One of the hot topics in urban application is
how to measure sprawling development by means of geo-spatial techniques.
2.5 Analyzing urban sprawl using spatial metrics:

14
Sprawl can be measured in relative and absolute scales. Absolute measurements are capable to create a
black-and-white distinction between a sprawled city and a compact city. Relative measures, in contrast,
quantify several attributes of urban growth that can be compared among cities, among different zones
within a city, or among different time for a city. In the later case, whether the city is sprawled or not is
generally decided by the analyst, or even left without characterizing the sprawl. It is important to mention
that most of the sprawl measurement techniques, in general, are relative measures or several measures of
urban growth that can be used as indicators of sprawl. Absolute identification of sprawl is never possible
with these measures unless we define a threshold towards the black-and-white characterization of
sprawling and non-sprawling. Defining a threshold, however, is not an easy task. Researchers have made
their own assumptions towards defining this threshold, which are even less clear to the scientists.
Important to realize that relative measures of sprawl or measures of urban growth pattern, most often, fail
to draw conclusion on sprawl and cannot be used universally. These measures may serve the scientific
purposes well, but, never can become a technology; because to interpret the results one has to be a
scientist. Therefore, how these techniques can become a tool for a city administrator is an obvious
question.
Many metrics and statistics have been used to quantify the sprawl. These metrics are generally known as
spatial metrics. Spatial metrics are numeric measurements that quantify spatial patterning of land-cover
patches, land-cover classes, or entire landscape mosaics of a geographic area (McGarigal & Marks, 1995).
These metrics have long been used in landscape ecology (where they are known as landscape metrics
(Gustafson, 1998; Turner, Gardner, & O’Neill, 2001, p. 401)) to describe the ecologically important
relationships such as connectivity and adjacency of habitat reservoirs (Geri, Amici, & Rocchini, in press;
Jim & Chen, 2009). Applied to the research fields outside of landscape ecology and across different kinds
of environments (in particular, urban areas), the approaches and assumptions of landscape metrics may be
more generally referred to as spatial metrics (Herold, Couclelis, & Clarke, 2005). Spatial or landscape
metrics, in general, can be defined as quantitative indices to describe structures and patterns of a
landscape (O’Neill et al., 1988). Herold et al. (2005) defined it as ‘‘measurements derived from the digital
analysis of thematic-categorical maps exhibiting spatial heterogeneity at a specific scale and resolution’’.
Spatial metrics have found important applications in quantifying urban growth, sprawl, and fragmentation
(Hardin et al. 2007). Based on the work of O’Neill et al.(1988), sets of different spatial metrics have been
developed, modified and tested (Hargis, Bissonette, & David, 1998; McGarigal, Cushman, Neel, & Ene,
2002; Ritters et al., 1995). Many of these quantitative measures have been implemented in the public
domain statistical package FRAGSTATS (McGarigal et al., 2002). Spatial metrics can be grouped into
three broad classes: patch, class, and landscape metrics.
Patch metrics are computed for every patch in the landscape, class metrics are computed for every class in
the landscape, and landscape metrics are computed for entire patch mosaic. There are numerous types of
spatial metrics that are found in the existing literature, for example: area/density/edge metrics (patch area,
patch perimeter, class area, number of patches, patch density, total edge, edge density, landscape shape
index, largest patch index, patch area distribution); shape metrics (perimeter-area ratio, shape index,
fractal dimension index, linearity index, perimeter-area fractal dimension, core area metrics (core area,
number of core areas, core area index, number of disjunct core areas, disjunct core area density, core area
distribution); isolation/proximity metrics (proximity index, similarity index, proximity index distribution,
similarity index distribution); contrast metrics (edge contrast index, contrast-weighted edge density, total
edge contrast index, edge contrast index distribution); contagion/interspersion metrics (percentage of like
adjacencies, clumpiness index, aggregation index, interspersion & juxtaposition index, mass fractal
dimension, landscape division index, splitting index, effective mesh size); connectivity metrics (patch
cohesion index, connectance index, traversability index); and diversity metrics (patch richness, patch
richness density, relative patch richness, Shannon’s diversity index, Simpson’s diversity index, Shannon’s

15
evenness index, Simpson’s evenness index). One may refer the manual of FRAGSTATS for detailed
discussion.
Tsai (2005) has classified the spatial metrics that are used in urban sprawl studies into three classes’
density, diversity and spatial-structure pattern. However, density and diversity may also relate to spatial
structure, such as, built-up density or patch density, and land-cover diversity. Therefore, distinct
classification may not be possible in general.
Galster et al. (2001) identified eight conceptual dimensions of land-use patterns for measuring the sprawl
(Table 2). These dimensions are density, continuity, concentration, clustering, centrality, nuclearity,
mixed uses, and proximity. Under the name of sprawl metrics, Angel et al. (2007) have demonstrated five
metrics for measuring manifestations of sprawl (Table 3) and five attributes for characterizing the sprawl
(Table 4). Under each attribute they have used several metrics to measure the sprawl phenomenon.
However, they have not recommended any standard threshold that can be used for distinguishing a
sprawling city from a non-sprawling city. Furthermore, interpretation of results from these metrics is also
difficult and confusing since metrics are huge in number and one may contradict with other.
Sierra Club (1998) ranked major metropolitans in USA by four metrics, including: population moving
from inner city to suburbs; comparison of land-use and population growth; time cost on traffic; and
decrease of open space. USA Today (2001) put forward the share of population beyond standard
metropolitan statistical area (SMSA)4 as an indicator for measuring the sprawl. Smart Growth America
(Ewing, Pendall, & Chen, 2002) carried out a research to study the impacts of sprawl on life quality in
which four indices were used to measure urban sprawl: (1) residential density; (2) mixture of residence,
employment and service facilities; (3) vitalization of inner city; and (4) accessibility of road network. All
of these metrics are useful for relative comparison of urban growth pattern; however, they cannot be
directly used for black-and-white discrimination of sprawling and non-sprawling.
Some of the researchers also have contributed to measuring sprawl by establishing multi-indices by GIS
analysis or descriptive statistical analysis (Batisani & Yarnal, 2009; Feranec, Jaffrain, Soukup, & Hazeu,
2010; Galster, Hanson, &Wolman, 2000; Glaeser, Kahn, & Chu, 2001, pp. 1–8; Hasse, 2004; Kline,
2000; Nelson1999; Torrens, 2000). These indices cover various aspects including population,
employment, traffic, resources consumption, architecture aesthetics, and living quality, etc. Commonly
used indices include: growth rate such as growth rate of population or built-up area; density such as
population density, residential density, employment density; spatial configuration such as fragmentation,
accessibility, proximity; and others such as per-capita consumption of land, land-use efficiency, etc. (e.g.,
Fulton, Pendall, Nguyen, & Harrison, 2001; Jiang, Liu, Yuan, & Zhang, 2007; Masek, Lindsay, &
Goward, 2000; Pendall, 1999; Sutton, 2003; The Brookings Institution, 2002; USEPA 2001). However,
no one has provided straight answers to the questions like: what should be the built-up growth rate in a
non-sprawling city, or what should be the per-capita consumption of land in a non-sprawling city.
Torrens (2008) argues that sprawl should be measured and analyzed at multiple scales. In his approach of
measuring sprawl, he has declared some ground-rules in developing the methodology. Measurements
have been made to translate descriptive characteristics to quantitative form. The analysis is focused at
micro-, meso-, and macro-scales and can operate over net and gross land. The analysis examines sprawl at
city-scale and at intra-urban levels that the level of the metropolitan area as well as locally, down to the
level of land parcels. Although inter-urban comparison and use of remote sensing data are not focused on
in this paper, the methodology would be sufficient to be generalized to other cities using remote sensing
data. The research has devised a series of 42 measures of sprawl, which have been tracked longitudinally
across a 10-year period. Although the author claims that this approach can provide a real insight of urban

16
sprawl, however, the methodology became complex and resulted in confusion owing to the use of many
scales and metrics.
Jiang et al. (2007) proposed 13 attributes (Table 5) under the name of ‘geospatial indices’ for measuring
the sprawl in Beijing. Finally, they proposed an integrated urban sprawl index that combines the
preceding 13 indices. This approach, indeed, minimizes the interpretation effort. However, their approach
requires extensive inputs of temporal data such as population, GDP, land-use maps, land-use master
planning, floor-area ratio, maps of highways, and maps of city centers. Many developing countries lack
such type of temporal data; and therefore, most of these indices are difficult to derive. Furthermore, they
did not mention any threshold to characterize a city as sprawling or non-sprawling. However, this type of
temporal analysis is useful to compare among cities or different zones of a city or status of a city at
different dates. Whether a city is becoming more sprawling or not, with the change of time, can be well
depicted by this type of analysis.
The main problem associated with most of the available sprawl measurement scales is the failure to define
the threshold between sprawling and non-sprawling. Although relative comparisons can provide us some
insights into sprawl phenomenon and the associated city, but often these measures are not adequate and
we need black-and-white characterization of sprawl. The second greatest problem is the number of
metrics used for the measurement of sprawl. The preceding discussion shows that many scales and
parameters are being used for the measurement of sprawl. The question is what the most stringent tools
are or how effective they are. The answer is still awaited. Alberti and Waddell (2000); Geoghegan,
Wainger, and Bockstael (1997); Herold, Goldstein, and Clarke (2003), and Parker, Evans, and Meretsky
(2001) propose and compare a wide variety of different metrics for the analysis of urban growth.
However, their comparisons do not suggest any standard set of metrics best suited for use in urban sprawl
measurement as the significance of specific metric varies with the objective of the study and the
characteristics of the urban landscape under investigation.
Important to mention, many metrics are correlated and thereby contain redundant information. Riitters et
al. (1995) examined the correlations among 55 different spatial metrics by factor analysis and identified
only five independent factors. Thus, many typical spatial metrics do not measure different qualities of
spatial pattern. The analyst should select metrics that are relatively independent of one another, with each
metric (or grouping of metrics) able to detect meaningful structure of urban landscape that can result in
reliable measures of sprawl. It is often necessary to have more than one metric to characterize an urban
landscape because one metric cannot say about all. However, the use of many metrics results in many
measures those are often difficult to interpret resulting in difficulties for reaching to a black-and-white
conclusion. Use of highly correlated metrics does not yield new information, rather makes interpretations
more difficult. ‘‘Just because something can be computed does not mean that it should be computed’’
(Turner et al., 2001, p. 401). Often, different metrics may also result in opposite conclusions; for example,
in Herold et al., 2003, ‘number of patches’ within the time span 1929–1976 was increasing (an indication
of sprawl); however, if one considers ‘mean nearest neighbor distance between individual urban patches’,
it was decreasing (an indication of compactness).
Another challenge is the spatial resolution of remote sensing data. Many metrics, for example patch or
spatial heterogeneity analyses, are dependent on spatial resolution. In a low spatial resolution image,
individual objects may appear artificially compact or they may get merged together. In an area of low-
density development where houses are relatively far apart, a spatial resolution of 30 m will produce an
estimate of developed land four times that produced using the same underlying data but a spatial
resolution of 15 m. Apparently, the most preferred spatial data are those that are sufficiently fine scale to
represent individual units, e.g., individual land parcels or houses. Important to realize that although higher
spatial resolution provides better interpretability by a human observer but a very high resolution leads to a

17
high object diversity which may end up in problems when a classification algorithm is applied to the data;
or it may produce a very high number of patches resulting in complications in metric analysis. Owing to
the increased heterogeneity in high resolution images, analysis of spatial association or spatial
heterogeneity will also be influenced at a high degree. Furthermore, temporal analysis of sprawl or
measurement of sprawl as a process may include images from sensors having different resolutions. In
such cases, resolution-dependent metrics are no longer usable.
Some of the researchers proposed several metrics that are simpler and claimed to be capable of black-and-
white characterization of sprawl from remote sensing data. Many of those sprawl measurements are
devised to reflect the relationship between population change and land conversion to urban uses. A
hypothetical black-and-white sprawl determination approach explains if the built-up growth rate exceeds
the population growth rate, there is an occurrence of sprawl (Barnes et al., 2001; Bhatta, 2009a; Sudhira,
Ramachandra, & Jagdish, 2004). However, it is often difficult to distinguish population change in a given
jurisdiction as either the cause or effect of urban development; therefore, ‘the population factor should not
be used as a sole indicator of urban sprawl’ (Ji, Ma, Twibell, & Underhill, 2006). In the developing
countries, population densities in cities are very high compared to developed countries. With the
development of economic base, urban residents in developing countries generally seek some more living
space and extended urban facilities. Therefore, if the growth rate of built-up exceeds the population
growth rate, it may not indicate a sprawl. In some of the instances, the growth rate of population may be
negative but the built-up area may remain unchanged (built-up is generally irreversible). In this case, the
preceding analysis will artificially show the area as dispersing. Further, a low built-up growth rate in an
area does not guarantee a compact development.
In a recent effort, the concept of ‘housing unit’ has been used as a proxy for population and combined
with digital orthophoto data to generate urban sprawl metrics (Hasse & Lathrop, 2003). In most cases, an
increasing (or diminishing) number of built-up activities like housing and commercial constructions can
be more effective to indicate sprawl as consequences of land consumption because usually construction
activities, as compared to population change, reflect directly economic opportunities as the major driving
force of land alteration (Lambin et al., 2001). Bhatta (2009a) has considered proportion of households in a
zone to the total households of the city (A) with the proportion of built-up areas within the respective
zone to the total built-up areas of the city (B). The relation between these two proportions (A–B) shows
the compactness/dispersion of a zone. If 0 is considered as ideal condition, then positive values show the
compactness and negative values show sprawl. However, this approach is useful for the intra-city analysis
of built-up area and the relative compactness among zones. This index cannot be used to identify whether
the city is sprawled or not in absolute sense. Therefore, we need to consider the absolute growth rate of
household and built-up within a zone. Important to realize that growth in impervious surfaces generally
includes all developmental initiatives like transportation network, commercial, industrial, recreational,
and educational establishments; not only the residential housing units. Built-up areas that are actually
occupied by residential housing units and their related growths are not easily identifiable from the remote
sensing data (Bhatta, 2010).

Data and
Methodology
Chapter 3

ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE STUDY OF PUNE Chapter 3: Data and Methodology
19
Chapter 3
Data and Methodology
3.1 Introduction:
This chapter presents the available data, the overall method, techniques, approaches and material used to
achieve the research objectives. It mainly explain the data sources and types, methods of field data
collection, image classification technique employed, accuracy assessment, selection of spatial metrics and
list of software packages used in the research.
3.2 Research design and methodology:
This research is conducted in three phases. The first phase is a preparation phase which consists of
research proposal development, including problem definition, formulation of research objective and
associated research questions, defining methods, identifying required data types. The second phase is
information and data gathering phase. In this phase, important data required to carrying out the research
including primary and secondary data are collected. In the end the collected data is processed, analyzed
and the finding is presented so as to meet the predefined objective of the research, which is followed by
conclusion and recommendation.
The methodology in this study involves remote sensing classification technique as well as spatio temporal
analysis of spatial metrics. Creation of base layers: base layers like district boundary layers and road
layers are created from SOI maps of scale 1:25000 and 1:50000. Image preprocessing included
georeferencing of the remote sensing data of the two years and then creating a 50 km buffer around the
Pune CBD. Supervised classification using Anderson classification scheme level-1 is done for finding out
the variation of urban growth that took place in Pune district. Land cover analysis and change detection is
necessary for analyzing the difference of the two years. Built up area extraction is done in two parts:
Class level pattern analysis using spatial metrics (FRAGSTATS) and analyze urban expansion map.
3.3 Data source and type:
Different remote sensing and GIS data from different sources has been used in this research. Landsat TM
images of 2000 and Landsat 8 20113 were used to detect urban land cover change pattern of this study
area. These images were obtained from the United States Geological Survey (USGS) website as standard
product i.e. geometrically and radio metrically corrected. Most GIS data such as administrative
boundaries, CBD, major roads are obtained from SOI maps. All dataset used in this study are
geometrically referenced to the WGS 1984, UTM 43N projection system.
3.4 Method of Data analysis:
After collecting all the relevant primary and secondary data, the next task was to process and analyzing
the data. As discussed earlier this research applies remote sensing and spatial metrics techniques to
quantify urban growth processes and patterns. Remote sensing image classification is a relevant method
that can provide information on the extent and rate of urban growth whereas spatial metrics are computed
based on the remote sensing image classification result to quantify the pattern of growth.

20
Figure 3.1: General methodology of the study
Table 3.1: List of Satellite images collected for the study area
Satellite data Resolution Year Source
Landsat TM image
30m 2000
http://glcf.umiac
s.umd.edu)
Landsat 8 image
Band 1 to 7 – 30 m
Band 8 – 15 m
Band 9 – 30
Band 10 and 11 – 100 m
2013
http://glcf.umiac
s.umd.edu)
Table 3.2: List of spatial data used for the study
Spatial data Format/type Source
Major roads Shape file Survey of India
Administrative boundary Shape file Survey of India
2000
2013
Image pre-processing
(Create a 50 km buffer)
Multi-Temporal Images
(Landsat TM & Landsat 8)
Signature extraction
Supervised Classification
Accuracy assessment
Land use & Land cover map
Built up area extraction
Class level
pattern analysis
Analyze urban
expansion map

21
3.4 Method of Data analysis:
After collecting all the primary and secondary data, the data was processed and analyzed. As discussed
earlier this research applies remote sensing and spatial metrics techniquies to quantify urban growth
processed and patterns. Remote sensing image classification is a relevant method that can provide
information on the extent and rate of urban growth whereas spatial metrics are computed based on the
remote sensing image classification result to quantify the pattern of growth.
3.4.1 Remote sensing image classification:
Two multi-temporal Landsat images are used to analyze the urban growth trends and patterns of Pune for
the past 13 years. In remote sensing image classification, supervised maximum likelihood classification
algorithms was applied in to different land cover classes which finally ended up generating two different
year land cover maps of the study area. In Maximum Likelihood classification method, pixels with
maximum likelihood are categorized into the corresponding class.
The land cover maps are composed of six major classes namely; urban area, agriculture land, barren land,
scrubland, forest, water bodies. Each land cover classes comprise different land uses classes.
3.4.2 Accuracy assessment:
In remote sensing land cover mapping study, classification accuracy is most important aspect to assess the
reliability the final output maps. The main purpose of assessment is to assure classification quality and
user confidence on the product. In this study accuracy of the classification results for the year 2000 and
2013 are assessed using 400 randomly sampled ground truth points with the help of Google earth.
3.4.3 Quantifying urban sprawl pattern using spatial metrics:
Spatial metrics are useful tools to quantify the dynamic patterns of ecological processes. Change in urban
landscape pattern can be detected by using spatial metrics that quantify and categorize complex landscape
structure into simple and identifiable pattern.
For this specific study a group of nine metrics are selected and potential of each metrics to best describe
urban pattern. These are: No of patches (NP), mean patch size, total edge, edge density (ED), mean
nearest neighbor distance, proximity index, Juxtaposition index total core area density, mean core area,
core area index, mean shape index.
3.5 Software used:
ArcGIS 10.1, QGIS, Erdas Imagine 2010, FragStats 4.1, Google earth, are used in this study.

Results and
Discussions
Chapter 4

ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE STUDY OF PUNE Chapter 2: Results and Discussions
23
Chapter 4
Results and Discussions
4.1 Introduction:
In this chapter the outcomes of this research are presented and discussed in detail sequentially. Starting
from characteristics information extraction, dynamic change analysis, spatio-temporal quantification of
urban growth to finding the growth pattern analysis using spatial metrics. Most of discussions are
supported by maps, tables and illustrative graph.
4.2 Land use analysis:
Land use dynamics of Pune during 2000 to 2013 and details are given in table 4.1.
Table 4.1: Land use change of different categories between 2000 and 2013
Land use
Area in sq.km
Change in %
2000 2013
Built up area 6.4756 9.91497 - 149.573
Agricultural land 58.2096 71.17905 - 564.034
Barren land 6.95808 11.23788 - 186.126
Scrub land 152.3367 142.9713 407.2958
Vegetation 30.47514 15.97941 630.4108
Water bodies 6.23268 7.10586 - 37.9741
The region with rich vegetation of 12% (2000), gradually loses vegetation to 6% (2013) at the cost of
increase in built-up from 3% (2000) to 4% (2013). 2000 and 2013, the major change was detected in the
scrub land use category and significant change in agricultural and commercial use. In the 2000 land use
map of Pune, agricultural land use was 22% of the total area of the city and in 2013 it became 32%. The
increase in agricultural land use in the year 2013 was, mainly due to some area, which was not covered in
2000 shown as agricultural use in 2013. In 2000, barren land was 3% and in the land use map of 2013, it
was shown as 4%. In 2000, water bodies were 2% shown and land use for water bodies appeared in 2013
as 3%.

24
Figure 4.1: Land use & Land cover change map
Figure 4.2: Areas acquired by various land-use features of 2000

25
Figure 4.3: Areas acquired by various land-use features of 2013
The classified images clearly illustrates the percentage of urban land is increasing in all the directions due
to setting up the new industries(IT&BT), economic development and high-rise buildings coming up in the
periphery. Concentric pattern of urban growth is observed with the aggregations at the center and
dispersed growth in the periphery. Open spaces and vegetation have been converted to built-up. At some
locations linear pattern is observed along the national highways and the local roads leading to the
formation of the typical ‘urban corridors’ mainly consisting of commerce and small industrial activities
Figure 4.4: Diagram showing land use change detection rate

26
4.3 Result of image classification and accuracy assessment:
According to the results of the accuracy assessment the extraction of the water bodies has relatively
higher accuracy in all images. Conversely built up class has lower accuracy due to the mixed pixels in the
classes. In addition to this, the Landsat 8 sensor offers numerous improvements than earlier generation of
Landsat sensor such as Landsat 4&5. The improved spectral content of Landsat 8 could record small
bright surface missed by Landsat TM that has been manifested on Landsat 8 imagery in this study.
Accuracy assessment of the classified images of 2000 and 2013 shows an overall accuracy of 82.73%,
and 80.12%.
Table 4.2: Accuracy assessment
Year Kappa coefficient Overall accuracy (%)
2000 0.70 82.73
2013 0.72 86.12
4.4 Urban expansion in Pune city over 13 year:
The total urban area of Pune increased from 6.47568sq.km to 9.91497sq.km during 2000-2013 (Figure
4.5), with an annual urban expansion area of 0.26sq.km per year, which also means the expanded area
was 6.67 times of the original urban area in 2000. The rate of urban expansion, however, was not
homogeneous spatially and temporally. Generally speaking, Shanghai’s urban expansion experienced
continuously increases if every ten years is taken into consideration as interval. This rule is consistent
with the overall one in the whole India (Liu et al. 2005a; Liu et al. 2005b).
As to the trajectory of urban land in Pune, it was closely related to the national and regional policies and
development strategies.
Figure 4.5: Land use change in sq.km

27
Specifically, after the economic reform starting 2000, Pune’s urban development started obviously. Not
only the total urban area increased, but also the annual urban expansion area increased from 2000 to 2013,
which is the representative of powerful economic engines.
Figure 4.6: Urban Expansion
4.5 Spatio temporal analysis of urban sprawl using spatial metrics:
The classification of multi-temporal satellite images into built up, non built up and water body for two
different time period of 2000 and 2013 has resulted in highly simplified abstracted representation of the
study area. These study area shows a clear pattern of increased urban expansion prolonging both from
urban center to adjoining non-built up areas along major transportation corridors. The spatial metric are
used to describe trend and changing pattern of actual built up extracted Landsat images.
4.5.1 Number of Urban Patches:
Table 4.3: NP and it significance
Formula
NPU = n
NP equals the number of patches in the landscape.
Range NPU>0, Without limit.
Significance/ Description
It is a fragmentation Index. Higher the
value more the fragmentation

28
Number of patches (NP) of a particular habitat type may affect a variety of ecological processes
depending on the landscape context. For example, the number of patches may determine the number of
subpopulations in a spatially-dispersed population. In the following figure 4.7, the concentric zones of
10km each represent the X axis and the Y axis represents the number of patches. There is a positive
correlation in both the years, the number of patches in 2013 increases with increase from the center C1
towards the fifth zone C5 in almost a straight line, while C3 from where it increases but slightly less than
in 2013.
Figure 4.7: No of Patches
4.5.2 Mean patch size:
Table 4.4: MPS and it significance
Formula
MPS =
∑
,
i = patch; a = area of patch I; n = total number of patches
Range MPS>0,without limit
MPS is widely used to describe landscape structure. MPS is a measure
of subdivision of the class or landscape. Mean patch size index on a
raster map calculated, using a 4 neighboring algorithm.
The range in mean patch size is ultimately constrained by the grain and extent of the image and minimum
patch size; relationships cannot be detected beyond these lower and upper limits of resolution. Mean
patch size at the class level is a function of the number of patches in the class and total class area. In the
figure 4.8, there is a huge difference between the two years, though the trend of the line for both the years
shows a strong negative correlation. In 2000, the mean patch size decreases in a slightly bend curve from
3 hectares in C1 to 1 hectare in C5 much below the size of 2013 where patch size decreases from 6
hectares in C1 to 2.5 hectares in C3 to 1.5 hectare in C5. The patch size is more towards the center and
reduces as it reaches the peripheral boundary.

29
Figure 4.8: Mean patch size
4.5.3 Total core area index:
Table 4.5: TCAI and it significance
Formula TCAI = ∑ ∑
,
= ( ) ℎ ℎ ( )
Range TCAI ≥ 0, ℎ
Significance/ Description TCA equals the sum of the core areas of each patch (m2), divided by 10,000.
Figure 4.9: Total core area index
In the following figure 4.9 65% of the core area is found to be in the first class or zone C1 of 2013 while
it reduces in a straight line to nearly 52% in C5 and in 2000 the total core area was 50% in C1 which
reduced to about 40% in C5. Same is the case of mean core area, except that in 2013, there was an abrupt
decrease of the core area in C3 which slightly increased to C4 and then reduced in C5

30
4.5.4 Core area density:
Table 4.6: CAD and it significance
Formula
CAD =
∑
(10,000)(100)
= number of disjunct core areas in patch ij based on specified edge
depth (m)
A= total landscape area
Range
CAD > 0, without limit.
CAD equals the sum of number of disjunct core areas contained
within each patch of the corresponding patch type, divided by total
landscape area (m2), multiplied by 10,000 and100.
The core area density (CAD) does a much better job of characterizing the differences in landscape
structure among landscapes. The core area density was more in 2000 in C1 as compared to C1 in 2013.
The core area density of both the years merged in C2, C4 and C5, while C3 of 2000 has a dense core as
compared to the C3 of 2013.
Figure 4.10: Core area density
4.5.5 Total edge:
Table 4.7: TE and it significance
Formula
TE = ∑ e
e = total length (m)of edge in landscape involving patch type ( class) i;
includes landscape boundary and background segments involving patch type i
Range TE≥ 0, ℎ
Significance/
Description
TE equals the sum of the lengths (m) of all edge segments involving the
corresponding patch type. If a landscape border is present, TE includes landscape
boundary segments involving the corresponding patch type and representing ‘true’
edge only (i.e., abutting patches of different classes).

31
Total amount of edge in a landscape is important to many ecological phenomena. In the following figure
4.10, the total edge positively increases for both the years till 30 km zone after which it increases more for
2013 and in 2000 the trend remains the same but the number is lesser than 2013.
Figure 4.11: Total edge
4.5.6 Edge density:
Table 4.8: ED and it significance
Formula
ED = (10,000)
E = total length (m) of edge in landscape.
A = total landscape area (m2).
Range ED = 0, without limit.
Significance/
Description
ED equals the sum of the lengths (m) of all edge segments in the
landscape, divided by the total landscape area (m2), multiplied by
10,000 (to convert to hectares).
In the figure 4.11, there is a negative correlation in both the years. The edge density for both the years is
more towards the center and reduces towards the boundary of the study area. The difference between the
two years is very less.
Figure 4.12: Edge metrics

32
4.5.7 Mean Shape index:
Table 4.9: MSI and it significance
Formula SHAPE =
.
P = perimeter (m) of patch ij
a = area of patch ij
Range SHAPE > 1, without limit.
SHAPE equals patch perimeter (m) divided by the square root of patch
area (m2), adjusted by a constant to adjust for a square standard.
Figure 4.13: Mean shape index
Shape is a difficult parameter to quantify concisely in a metric. FRAGSTATS computes 2 types of shape
indices; both are based on perimeter-area relationships. Mean shape index (SHAPE) measures the
complexity of patch shape compared to a standard shape. Mean shape index is minimum for circular
patches and increases as patches become increasingly noncircular. Mean shape index (MSI) measures the
average patch shape, or the average perimeter-to-area ratio, for a particular patch type (class) or for all
patches in the landscape. The trend of the figure shows a negative correlation in both the years, In
2000,the mean shape index was higher i.e. nearly 1.27 in C1 which indicates noncircular patches while in
2013 it reduced to 1.25 in C1. The mean shape index of both the years intersected in C2 and C3 while it
reduces to C5 steadily but the index being still higher in 2000 than in 2013 indicating lower shape index
has circular patches. Hence patches near the boundary indicate almost circular patches.
4.6.8 Mean nearest neighbour distance:
Table 4.10: MNND and it significance
Formula
ENN = ℎ
ℎ = distance (m) from patch ij to nearest neighboring patch
of the same type (class), based on patch edge-to-edge
distance, computed from cell center to cell center.

33
Range MNN > 0, without limit
MNN equals the distance (m) to the nearest neighboring
patch of the same type, based on shortest edge-to-edge
distance.
Figure 4.14: Mean nearest neighbor distance
Nearest-neighbour distance is defined as the distance from a patch to the nearest neighbouring patch of
the same type, based on edge-to-edge distance. Nearest-neighbour metrics quantify landscape
configuration. FRAGSTATS computes the nearest-neighbour distance (NEAR) and proximity index
(PROXIM) for each patch. The index distinguishes sparse distributions of small habitat patches from
configurations where the habitat forms a complex cluster of larger patches. Mean nearest neighbour
distance is the same for both the years in C1 which increases gradually to C5 in both the years but more in
2013 than in 2000
4.6.9 Proximity index:
Table 4.11: PROX and it significance
Formula
PROX = ∑
= area of patch ijs within specified neighborhood of patch ij
ℎ = distance between patch ijs and patch ijs based on patch edge to edge distance, computed from cell
center to cell center
Range PROX > 0.
Significance/
Description
PROX equals the sum of patch area (m2) divided by the nearest edge-to-edge distance squared (m2)
between the patch and the focal patch of all patches of the corresponding patch type whose edges are within
a specified distance (m) of the focal patch.
The proximity index for both years show a negative correlation trend, more in 2013 than in 2000. In 2013
C1, there are 25000 patches which abruptly fall to 14000 patches in C2, bends and gradually reduce to
5000 patches in C5. The proximity index in 2000 shows a gradual decrease in C1 5000 patches to nearly
1000 patches in C5.

34
Figure 4.15: Proximity index
4.6.10 Interspersion juxtaposition index:
Table 4.12: IJI and it significance
Interspersion juxtaposition index for both the years shows a positive correlation. 2013 shows a higher
increase than 2000 though both have the same trend rising from C1 and increasing to C5.
Figure 4.16: Interspersion juxtaposition index
Formula
IJI =
∑
∑ ∑
( )
(100)
Range 0 < IJI ≤ 100
Significance/
Description
IJI equals minus the sum of the length (m) of each unique edge type involving the corresponding
patch type divided by the total length (m) of edge (m) involving the same type, multiplied by the
logarithm of the same quantity, summed over each unique edge type; divided by the logarithm of the
number of patch types minus 1; multiplied by 100 (to convert to a percentage).

ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE STUDY OF PUNE Chapter 5: Conclusions
37
Chapter 5
Conclusions
Conclusions
Urban sprawl is still a controversial issue among scholars, who argue over its impact as well as the way it
should be measured. Hence, employing public policy in order to restrain the phenomenon is hampered, in
particular, by the lack of empirical evidence. This lack affects the ability to convince the authorities to
adopt such policies.
Questions about exactly what sprawl is, how it affects the urban environment, and how it should be
measured remain unanswered. Efforts have been undertaken recently to deal with these issues. This study
focused on the measurable question: how can the various aspects and characteristics of sprawl be
measured and what are the indices that should be implemented empirically in a unit of investigation at the
town scale?
The integrated sprawl index introduced in this paper is an unusual combination, making use of sprawl
measures from different disciplines: urban studies, fractal geometry, and ecological research. We note,
however, that there are some measures that are more effective in measuring sprawl on a municipal scale
(e.g. density, shape/fractal,
Residential, commercial, and industrial land-use composition) and other measures that are less effective
or less relevant (e.g. leapfrog, mean patch size, other built-up land uses). The latter group seems to be
more effective in measuring sprawl on a regional or metropolitan scale. For example, the appearance of
new scattered settlements in a region will be considered leapfrogging development, whereas this index is
not noticeable at a town scale.
Urban land-use composition is less heterogeneous than is open land-use composition, because of the
dominance of residential uses within the urban built-up area. Hence, a residential land-use measure
represents well the urban land-use composition, obviating the need to compute the percentages of all other
land uses. The integrated sprawl index according to land-use mixture suggested by this study represents
the land-use composition and the diversity or the equilibration that exists between residential land use and
all other land uses in the built-up area of the urban municipality. However, the spatial distribution of land
uses as defined by the level of segregation and access between residential and other land uses is another
aspect of mixed land use that was not tested in this study. Therefore, further investigation of sprawl, in
terms of land-use mixture, on the municipal scale is still needed; for example, distances, travel time, and
the accessibility of different land uses; geometry of the spatial distribution of land uses; and the
population distribution at different distances from these land uses. An urban landscape that is
characterized by a high level of land-use mixture, as well as by heterogeneity of its spatial land-use
distribution is considered to be compact. On the other hand, an area in which land uses are segregated and
distant from one another is considered to be sprawling.
The various sprawl measures used in this study show clearly that urban sprawl is a phenomenon that
should be described and quantified by a combination of several measures. Each group of measures
represents different features or characteristics of this phenomenon and does not necessarily depend on
other dimensions. We especially found differences between the two characteristics of sprawl and their
effect on the urban landscape pattern. The configuration characteristic of sprawl is linked to accelerated
urban growth and increased land consumption more than is the composition characteristic. Thus, sprawl
that resulted from a scattered, less-dense configuration of the built-up area is more responsible for the
waste of land than is sprawl that emanated from a homogeneous land-use pattern. On the other hand,
sprawl that resulted from the composition characteristic is tied to the socioeconomic level of the
population. The latter is characterized by a high level of car-based transportation systems and the

ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE STUDY OF PUNE Chapter 5: Conclusions
38
increased use of private vehicles for work travel. This finding leads to the hypothesis that different sprawl
patterns have different impacts on the urban form and should be studied in the future.
Since urban sprawl appears to be a multidimensional phenomenon, we hypothesize that its implications
probably emerge from different urban patterns of development. Apparently, this complexity is linked to
the disagreements that exist between scholars and planners on this issue. Our finding implies that different
sprawl patterns have diverse implications for urban form that should be investigated. Some settlements,
especially quasirural ones, were found to be more sprawling than others. This fact implies that sprawl
rates may be higher in rural settlements than in urban settlements. Therefore, we highly recommend
continuing the investigation of rural sectors, as this might be more relevant to sprawl and its impacts on
land consumption.
Higher sprawl rates were found to be significantly correlated with higher population and land-
consumption growth rates. This finding implies a higher consumer preference for residing in more
sprawling patterns, meaning that sprawling settlements are probably more attractive to new residents who
seek housing improvement than are compact settlements. A definite conclusion on this matter requires
further investigation of consumer preferences and the alleged positive impacts of sprawling patterns
perceived by consumers. We believe that this possible consumer preference for sprawling patterns, along
with the lack of available land in Pune, fully justifies attempts to regulate and restrain sprawl in Pune.

Presentations and
Publications
Chapter 6

ANALYZING URBAN SPRAWL USING GEOINFORMATICS: A CASE STUDY OF PUNE Chapter 6:Presentations and
Publications
39
Chapter 6
Presentations and Publications
7.1 Research paper:
Spatial Pattern Analysis of Urban Sprawl in Pune District using
Geoinformatics
Sohini Kar, EmtiajHoque, VarshaYadhav, Shamita Kumar
Institute of Environment Education & Research, BharatiVidyapeeth University, Pune 411043, India
Rapid industrialization and associated migration led Pune to become rapidly urbanized. Monitoring, modeling and mapping
urban sprawl is essential for better sustainable development. This paper focuses on identifying the trend of urban sprawl in Pune
city over the past one decade using temporal remote sensing data (Landsat TM 2000 & Landsat-8 2013), using a buffer of 50km
from city center to the outskirt in five concentric circles and combining gradient analysis with spatial class metrics tools using
FRAGSTATS. A detailed quantitative analysis has been done showing the spatial changes. This study performs spatio-temporal
analysis along with the extensive use of spatial class metrics to identify the physical drivers of urban sprawl of Pune city.
Introduction:
Urbanization is a very important issue in India.Urban sprawl is also referred as irresponsible, and often poorly planned
development that destroys green space, increases traffic, contributes to air pollution, leads to congestion with crowding and does
not contribute significantly to revenue, a major concern. Increasingly, the impact of population growth on urban sprawl has
become a topic of discussion and debate. Typically conditions in environmental systems with gross measures of urbanisation are
correlated such as population density with built-up area (Smart Growth America, 2000; The Regionalist, 1997; Berry, 1990). The
relation of population growth and urban sprawl is that the population growth is a key driver of urban sprawl.The study on urban
sprawl (The Regionalist, 1997; Sierra Club, 1998) was attempted in the developed countries (Batty et al., 1999; Torrens and
Alberti, 2000; Barnes et al., 2001, Hurd et al., 2001; Epstein et al., 2002) and recently in developing countries such as China (Yeh
and Li, 2001; Cheng and Masser, 2003) and India (Jothimani, 1997 and Lata et al., 2001).
In India alone currently 25.73% of the population (Census of India, 2001) live in the urban centres, while it is projected that in
the next fifteen years about 33 % would be living in the urban centres. . Pune is the second largest city in Maharashtra and 8th
in
the country. Pune is one of the fastest growing city in India which is growing at an alarming rate. The growth of the city is
peripheral. The growth rate in the core part of the city is about 2 – 2.5% per year and the annual growth rate in peripheral wards
is about 4.4%. The driving force for growth is mainly the development of IT industry as well as the economic boom in the
automobile sector which forms a major portion of the industries in and around Pune. The peripheral growth has resulted into the
increased residential areas and area under transportation network and facilities. Hence, spatial pattern of its growth is an
important issue for analytical study using remote sensing and GIS applications.The spatial patterns of urban sprawl over different
time periods, can be systematically mapped, monitored and accurately assessed from satellite data along with conventional
ground data (Lata, et al., 2001). The physical expressions and patterns of sprawl on landscapes can be detected, mapped, and
analysed using remote sensing and geographical information system (GIS) technologies (Barnes et al., 2001). The patterns of
sprawl are being described using a variety of metrics, through visual interpretation techniques, all with the aid of software and
other application programs. The earth scientists with the Northeast Applications of Useable Technology In Land Use Planning for
Urban Sprawl (NAUTILUS) program are using techniques of statistical software to characterise urbanising landscapes over time
and to calculate spatial indices that measure dimensions such as contagion, the patchiness of landscapes, fractal dimension, and
patch shape complexity (Hurd et al.,2001; NAUTILUS 2001). Hurd et al, (2001) focused on a method to generate images
depicting the pattern of forest fragmentation and urban development from the derived classifications of satellite imagery.
The impacts of urban patterns on ecosystem dynamics should focus on how patterns of urban development alter ecological
conditions (e.g. species composition) through physical changes (e.g. patch structure) on an urban to rural gradient. The use of

Publications
40
gradient analysis for studying urban-to-rural gradient of land-use intensity to explain the continuum of forest change from city
centre to non-urban areas might help to explore ecosystem effects of different urban configurations, but current applications do
not differentiate among alternative urban patterns (Alberti et al., 1999). Most studies of the impacts of urbanisation do not
differentiate among various urban patterns. Planners need this ecological knowledge, so that their decisions can minimise impacts
of inevitable urban growth. Decisions by urban dwellers, businesses, developers, and governments all influence patterns. Spatial
pattern is one (of very few) such environmental variable, which can be controlled to some extent by land-use planning. Design
strategies for reducing urban ecological impacts will remain poorly understood and ineffectual if spatial pattern issues are not
addressed in ecological studies of urban areas. Hence Remote sensing and GIS technology is used for analytical study of
urbanization and to get a clear picture of the alarming growth of urbanization with the aid of spatial and statistical software.
The objectives of the current study are:
 To map and monitor the urban pattern using temporal spatial remote sensing data.
 To understand and assess the urban spatial growth at class level through spatial metrics.
Study area:
The study has been carried out for a rapidly urbanizing region in India. Pune is the cultural, commercial, industrial, and
knowledge city of the state of Maharashtra, India with an area of 741 sq. km and lies between the latitude 18◦52’04’’and
longitude 73°86’00’’. To account periurban growth we have considered 50km. circular buffer from the Pune administrative
boundary by considering the City Business District (CBD) as center (Figure 1). The predominant land cover primarily consists of
grassland, cropland, and bare land with forests, urban zones and scattered water bodies.
Figure 1: Study area
Pune district is among the highest in the state with over 57.39 lakh people living in cities according to the Census of India.
Population is one of the main factor of the urban growth as well as migration from rural to urban areas. According to the Census
of India 2001 and 2011, the actual population of Pune in 2001 was 7,232,555 which had an increase in urban growth to
9,429,408. The population growth per sq. km reduced from 30.73% in 2001 to 30.37% in 2011. The density per sq.km in 2001
was 462 which increased to 603 in 2011. This depicts that there has been a huge growth in a span of 10 years contributing to
urban growth.
Invest in manufacturing, IT and overall growth in economic activity has led to an influx of people into Pune. Pune has established
itself as the ‘Academic Corridor’ of India and is also the emerging InfoTech Hub. It is the place of huge IT investments. Hence
migration due to employment opportunities as well as educational purpose has been major ‘pull factors’. Due to close proximity
to the economic region of the country i.e Mumbai, rapid growing infrastructure and enchanting climate makes Pune a favorable
place to settle. Urban sprawl is the scattering of new development of land use pattern causing loss of productive agricultural land,
forest covers and other forms of greenery, loss in surface water bodies, depletion in ground water aquifers and increasing levels

Publications
41
of air and water pollution causing environmental problems. The process of urbanization is affected by population growth and
migration. The PMPC (Pimpri - Chinchwad Municipal Corporation) has the record of the growth rate which has been mentioned
above.
Data used:
The materials used for the spatial analysis are as follows:
DATA YEAR PURPOSE
Landsat TM 2000 Land cover analysis and change detection
Landsat 8 2013 Land cover analysis and change detection
Survey Of India (SOI) toposheets of scales
1:50000 and 1:25000
Recent To generate boundary and road layer maps
Methodology:
The methods adopted in this analysis involved:
1. Creation of base layers: Base layers like district boundary layers and road layers are created from SOI maps of scale
1:25000 and 1:50000.
2. Image preprocessing included georeferencing of the remote sensing data of the two years and then creating a 50km
buffer around the Pune CBD.
3. Supervised classification using Anderson classification scheme level-1 is done for finding out the variation of urban
growth that took place in Pune district.
4. Land cover analysis and change detection is necessary for analyzing the difference of the two years.
5. Built up area extraction is done in two parts: Class level pattern analysis using spatial metrics (FRAGSTATS) and
analyze urban expansion map.
Figure 2: Methodology

Publications
42
Result & Discussion:
1. Land use analysis: The following figure represents the final analyzed output data for Pune district. Results are obtained by
classification and reclassification of the raster images of 2000 and 2013. The reclassified image of 2013 was subtracted from the
reclassified image of 2000 to find out the changes in the land use giving more attention to the urban growth. In 2013 there has
been a substantial increase in urbanization from the center to the periphery of the boundary of the study area. Loss of agricultural
and forest lands due to urban growth has taken place over the years.
Figure 3: Land use & Land cover change from 2000 to 2013
Figure 4: Urban Expansion map from 2000 to 2013

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