The Urban Social Pattern of Navi Mumbai, India
Malathi Ananthakrishnan
Thesis submitted to the Faculty of the
Virginia Polytechnic Institute and State University
in partial fulfillment of the requirements for the degree of
Master of Urban and Regional Planning
John Browder, Chair
Wendy Jacobson
Paul Knox
April , 1998
Blacksburg, Virginia
Keywords: urban social pattern, Navi Mumbai, Bombay, urban planning - India
Copyright 1998, Malathi Ananthakrishnan
The Urban Social Pattern of Navi Mumbai, India
Malathi Ananthakrishnan
(ABSTRACT)
This research thesis examines the emerging trends in urban social patterns in Navi
Mumbai, India. Unlike the other planned cities of India, Navi Mumbai was specifically built as
a planned decentralization of a large metropolitan city. The research focuses on explaining the
urban social pattern of this particular case study. An urban social pattern reflects the social
characteristics of the urban setting. In the case of Navi Mumbai, the government had a social
agenda of promoting a social pattern based on socioeconomic distribution rather than an ethnic
one. Analysis of the data provides an insight to the results of this social agenda, and provides a
basis to frame new ones. Thus, the study not only addresses a basic research question, but also
has policy implications.
The research involves a comprehensive review of secondary source material to establish
the theoretical framework for the research. The review also involves an extensive inspection of
urban social patterns across the world to better contextualize this particular case study. The
research puts forth a model that explains the social pattern of Navi Mumbai by social area
analysis using variables, which are drawn from social aspects of any city and indigenous factors
of Indian settlements. The model depends not only on statistical analysis but also on
interpretation of local conditions. This research situates the emerging social pattern in
geographic literature in developing countries.
This research was supported in part, by a grant from the College of Architecture and
Urban Studies, Virginia Tech.
Acknowledgment
I would like to take this opportunity to thank my Advisor and Chair of my committee, Dr. John
Browder. He was supportive of all my efforts to successfully complete this thesis. It would not
have been possible without his help. Thank you also to my committee members, Dr. Jacobson
and Dr. Knox, for the time and effort they contributed.
Thanks also due to everyone in Navi Mumbai who helped me collect the data and all relevant
information. Special thanks to Ms. Adusumilli, Senior planner, CIDCO, Mrs. Raje, Chief
statistician, CIDCO, Dr. Venkatachalam and Dr. Sengupta at IIT-Bombay and Dr. BanerjeeGuha at the University of Bombay. I would also like to thank Prachi and Avesh Tapde for their
hospitality in Navi Mumbai.
Dr. Dyck and Dr. Bohland clarified many of my conceptual and analytical queries. I would like
to give my appreciation for their support. I would also like to thank Dr. Randolph and Dr.
Schubert for having made a grant available for me to carry out the field research.
I am also grateful to my good friends Inga, Maneesha and Elda for not only helping me out with
proof reading and other mundane things, but also for being there during the ups and downs of the
entire process. I would like to thank my family for always encouraging me to think and my
fiancé for his patience.
Table of Contents
1. Introduction…………………………………………………………………..
1.1 Research Problem Statement
1.2 Significance of Thesis
1.3 Organization of the Thesis
1
2. The Research Setting…………………………………………………………. 3
2.1 Introduction
2.2 The Planning History of Bombay and the Greater Bombay region
2.3 The Creation of Navi Mumbai
2.4 The Draft Development Plan of 1973
2.5 Development Potential of the Site
2.6 Design Principles of Navi Mumbai
2.7 Social Agenda in the Planning of Navi Mumbai
2.8 Plan Implementation through the Public Administrative Framework
2.9 The Reality of Implementing the Plan
2.10 Conclusion
3. The Conceptual Framework………………………………………………….. 20
3.1 Introduction
3.2 Urban Form and Urban Pattern
3.3 Factors influencing Urban Form
3.4 The Evolution of the Urban Form of Indian Cities
3.5 Sociocultural Factors
3.5.1 Caste
3.5.2 Class
3.5.3 Religion
3.5.4 Language
3.5.5 Implications of the Sociocultural factors
3.6 The Built Form
3.7 Theories of Urban Social Patterns
3.7.1 Concentric Zone Theory
3.7.2 Sector Theory
3.7.3 Multiple Nuclei Theory
3.8 Case Study of Urban Social Patterns
3.8.1 Western Cities
3.8.2 Third World Cities
3.8.3 Indian Cities
3.9 Conclusion
4. Research Design……………………………………………………………… 38
4.1 Social Area Analysis
4.2 Hypothesis
4.3 Operationalization
4.4 Data Collection
4.5 Methodology
4.5.1 Descriptive Analysis
4.5.2 Cluster Analysis
4.5.3 Principal Component Analysis
4.5.4 mapping and Overlays
4.6 Data Analysis
5. Presentation of Data…………………………………………………………..
5.1 Introduction
5.2 Descriptive Analysis
5.3 Regional Scale – nodes
5.3.1 Principal Components Analysis
5.3.2 Cluster Analysis
5.3.3 Discussion
5.4 Sub-regional Scale – sectors
5.4.1 Principal Components Analysis
5.4.2 Cluster Analysis
5.4.3 Discussion
5.5 Conclusion
43
6. Interpretation / Discussion……………………………………………………
6.1 Regional Scale
6.2 Sub-regional Scale
6.2.1 Socioeconomic Status and Sector Theory
6.2.2 Family Status and Concentric Zone Theory
6.2.3 Ethnic Status and Multiple Nuclei Theory
6.3 Summary
6.4 Potential Utility of the Research
65
7. Conclusion…………………………………………………………………… 74
Bibliography……………………………………………………………………..
Glossary of Terms
Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
Appendix F
Appendix G
77
List of Tables
Table
number
2.1
2.2
2.3
2.4
2.5
2.6
Title
page
Population Density of Bombay
Immigrant population of Bombay
Land Fragmentation in 1970
Household Income and Capacity to Pay
Population Density in Various Sectors of Bombay
Land Use of Navi Mumbai
4
5
6
8
16
17
4.1
4.2
Constructs and Variables
Survey Sampling
39
40
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
5.12
5.13
5.14
5.15
5.16
5.17
5.18
5.19
5.20
5.21
Constructs and Variables
Work Force
Number of Earners
Occupational Classification of Workforce
Household Income
Location of Education Institutions
Level of Education
Male Population
Female Population
Family Size
Type of Housing
Ownership of House
Housing built by CIDCO
Housing built by Private Enterprise
Year of Occupation
Previous Place of Residence
Religion
Language
Spatial Pattern of Variables
Attributes of Principal Components
Attributes of Principal Components
43
44
44
45
46
47
47
48
49
50
51
52
52
53
53
54
55
56
57
60
61
List of Figure
Figure
Number
2.1
2.2
2.3
2.4
2.5
Title
Page
2
5
7
11
15
2.6
Expansion of Bombay
Twin City Across the Harbor
Development Potential of the Site
Nodes of Navi Mumbai
Institutional Hierarchy in Implementation of Development Plan for
Navi Mumbai
Land Use of Navi Mumbai
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
3.10
Circle and Swastika Town Plans
Concentric Zone Theory
Sector Theory
Multiple Nuclei Theory
Urban Social Patterns
Plan of Delhi and New Delhi
Asian Ports
Latin American Cities
Pattern of Indian Cities
Theories of Urban Social Patterns and Corresponding Case Studies
26
28
29
29
31
32
32
33
34
36
5.1
5.2
5.3
45
46
48
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
5.12
5.13
5.14
5.15
5.16
5.17
5.18
5.19
Distribution of Single-earner Families
Frequency of Families with Income range Rs. 2651-4450
Frequency of Families with at least one individual with Secondary
Education
Frequency of Male Population in the age group 25-45
Frequency of Households with 4 or 5 members
Frequency of Houses built by CIDCO
Frequency of Housing built by CIDCO
Frequency of Houses built by Private Enterprise
Frequency of Tenure
Frequency of Bombay as Previous Place of Residence
Frequency of Hindus
Frequency of Muslims
Frequency of Marathi
Frequency of Malayalam
Components in Rotated Space
Loadings of Principal Components
Dendrogram using Average Linkages between groups
Loadings of Principal Components
Dendrogram using Average Linkages between groups
6.1
6.2
6.3
6.4
6.5
Cluster of Nodes of Navi Mumbai
Average Linkage between Factor Scores
Average Linkage between Variables
Clustering of Sectors of Vashi
Average Linkage between Factor Scores
65
66
66
67
68
18
49
50
51
52
53
54
55
56
56
57
57
59
59
60
62
63
6.6
6.7
6.8
6.9
6.10
6.11
6.12
6.13
6.14
6.15
6.16
6.17
6.18
Average Linkage between Variables
Hypothetical Sector Pattern for Socioeconomic variables
Distribution of Number of Earners
Distribution of Income
Hypothetical Concentric Pattern for Family Status variables
Distribution of Ownership of Apartment
Hypothetical Multiple Nuclei Pattern for Ethnic variables
Distribution of Households speaking Marathi
Distribution of Households which follow Islam
Clustering of Sectors
Score 1
Score 2
Score 3
68
69
69
69
70
70
71
71
71
72
72
72
72
Chapter 1: Introduction
1.1 Research Problem Statement
The overall objective of this thesis is to determine what common patterns, if any,
exist in the urban social pattern of planned towns in India. The urban social pattern is one of
the many aspects of the urban form. The urban form of a city is primarily the result of the
characteristics of its physical and social design as well as socioeconomic and political forces.
It is a synthesis of the spatial relationships of various elements. Different characteristics are
drawn from the factors influencing the physical design and cultural aspect of the city.
Physical and economic landscapes, land use and ownership, street patterns, planning
regulations, and political events may influence the physical design and pattern of a city.
Various processes influence the social pattern of the city. These include the ethnic
composition of the city, religion, race, migration, and the housing market.
Navi Mumbai (New Bombay) is one of the first planned new town developments
built for a diverse, middle class population in India. Traditional Indian cities have evolved
over the centuries, and their social pattern is characterized by residential segregation based
on ethnic, religious and linguistic classes. The purpose of this thesis is to delineate and
interpret the social pattern of Navi Mumbai.
Socioeconomic factors, housing characteristics, land use pattern and ethnic
classifications will be used as key variables to study the urban social pattern of Navi
Mumbai. Urban patterns occur because of repetition of these elements. The pattern of Navi
Mumbai will be studied at different hierarchical spatial levels: regional (node / township) and
sub-regional (sector / neighborhood).
1.2 Significance of Research
A holistic approach to the study of settlements involves understanding the
interrelationships between their constituent elements at a certain period of time. The study
of the physical form and structure of cities is the study of urban morphology. Why is such a
study significant? The urban form of the city influences behavioral, economic and social
processes within it (Vance, 1990). Thus, the study of human settlements has an
encompassing view of all the activities it supports.
The basic research here involves the search for an urban social pattern of Navi
Mumbai. This research determines how the present social pattern relates to various
theoretical frameworks. This research aspires to contribute to basic research in social
geography. The literature review shows that a specific study of Navi Mumbai has not been
previously documented. Therefore, this paper will augment existing knowledge about social
configurations of planned urban development in Asian regions.
A policy emphasizing a uniform distribution of the population is the ideological
orientation of the government. An interpretation of the emerging social pattern reveals
something of the social character of the city. The pattern suggests not only the outcome of
Malathi Ananthakrishnan
Chapter 1: Introduction
2
the policy, but also variables that influence this pattern. The urban social pattern also serves
as a framework for further research. Thus, the basic research has many applications in longrange planning in Navi Mumbai.
1.3 Organization of the Thesis
This thesis is divided into seven chapters. This first chapter is the introduction,
which provides the problem statement and the broader objectives of the thesis. The second
chapter provides the background to the particular case study used in the research. The third
chapter is a comprehensive review of the secondary sources to establish a context of the
research question. The fourth chapter outlines the methodology used for analysis of data and
explains the data source and method of data collection. The presentation of data and its
analysis is in the fifth chapter. Interpretation and discussion of the analysis and its
relationship to the theories discussed in the third chapter is done in the sixth chapter.
Chapter seven draws to conclusion the thesis with a review of the problem statement, the
research setting, its contextual framework, methodology, analysis and interpretation and the
broad outcomes of the thesis.
Chapter 2: The Research Setting
2.1 Introduction
Navi Mumbai (New Bombay), India, established in 1972, is a new planned city across
the harbor (of Bombay) from Bombay. This planned decentralization was the outcome of
efforts by the government to make Bombay more “sustainable” (Bombay Metropolitan
Regional Planning Board, 1973). The geographical area of Bombay is an island. The first
settlement was established in the southern most tip of the island. Urbanization and
subsequent suburbanization of Bombay
have created a linear city such that the
central business district (CBD) and
residential areas have become further
and further apart (Figure 2.1).
1965
Bombay’s high concentration of docks,
1957
trading posts, textile mills and
government offices have made it the
preeminent port of Western India.
South Bombay is the center of India’s
1950
banking and service industries. This
range of activities led to crowding at an
BOMBAY
NAVI
unprecedented scale. In Bombay, for
Arabian
MUMBAI
Sea
those who could not afford to make the
1910
long commutes, squatter settlements all
over Bombay became the way of life.
Navi Mumbai was designed to provide
a better quality of life, especially to the
middle and lower class of people.
2.2 The Planning History of Bombay
and the Greater Bombay region
Bombay is not a city built on Indian
traditional planning ideas. The city of
Source: Dwivedi and Mehrotra, 1995.
Bombay had its beginnings in a series
of fishing villages until it was taken over by the Portuguese in the 16th century. In 1661, the
King of Portugal gifted the Bombay islands to King Charles II of England when King
Charles married Catherine Braganza, a Portuguese princess. In 1668, the Crown rented
Bombay to the East India Company. Bombay was then established as a trading post. The
East India Company encouraged Indian and East India Company merchants to settle in
Bombay. By the 1780s, the East India Company had taken on the new role of ruler (Dwivedi
and Mehrotra, 1995).
Figure 2.1 Expansion of Bombay
The East India Company, now as rulers, was interested in developing the town in a
methodical manner, and providing efficient infrastructure (Dwivedi and Mehrotra, 1995).
The harbor was strengthened, the shipyard modernized and the city fortified. There was a
Malathi Ananthakrishnan
Chapter 2: The Research Setting
4
strong development of mixed land use settlements. Commercial and residential areas were
mixed because many merchants carried on business from home (Tindall, 1992).
In 1865, the Bombay Municipal Corporation was established, and, in 1896, the
Bombay Improvement Trust was created. These formal government bodies were the
beginning of a conscientious effort to regulate the growth of Bombay (Banerjee-Guha, 1995).
By the early 1900s, some thought was given to ’Greater Bombay’, which would encompass
the Fort area as well as the suburbs of Bombay. However, Greater Bombay came into
existence only after the Bombay High Court Act of 1945. This enclosed the Town and Island
of Bombay, the Port of Bombay, the suburbs and 42 villages within the definition of the new
city limit (Dwivedi and Mehrotra, 1995). The Post-War development Committee of 1945
and the ’Master Plan in Outline’ prepared by Albert Mayer and N. V. Modak influenced the
development of Greater Bombay for the next two decades (Dwivedi and Mehrotra, 1995).
The development acts of 1954 and 1964 emphasized the need to relocate industrial
activity from the island to the mainland (CIDCO, 1995). In the 1960s, various planning
committees were formed to develop a regional plan for Bombay. Land use zoning and the
concept of floor space index were incorporated for the first time. In 1966, the Gadgil
Committee strongly recommended a multi-nuclear growth using the creation of a new town
across the harbor. This committee appointed the Bombay Municipal Regional Planning
Board to develop the concept further (Gadgil Committee, 1965). In 1967, the Bombay
Municipal Regional Planning Board set up two committees to study the development of
Bombay. They recommended:
i the creation of a new town on the mainland across the harbor
i develop the suburbs of Bombay further
Bombay had reached a level of unmanageable growth by the 1960s. Bombay’s
infrastructure facilities were stretched to the limit. Commuter distances had become larger
because of increased suburbanization with no change in location of the CBD. The 1967
development plan estimated a housing shortage of 131,000 houses, and 24 percent of the one
and two room tenements were over crowded.
Table 2.1 Population Density of Bombay
1881
1891
1901
1911
1921
1931
1961
1971
Area in acres
14247 14281 14342 14575 15066 15480 16751 16720
Persons / Acre 54
56
54
67
78
75
165
184
(Various Census Reports for Bombay in Kosambi, 1986)
The Bombay Metropolitan Regional Planning Board in its report wrote
Bombay the Beautiful is no more beautiful. Many parts of it are not even tolerably
clean and healthy. Housing deficits are ever widening and slums like a cancerous
growth can be seen anywhere and everywhere. Adequate water is a serious problem.
Transportation is threatening to break down….
(BMRPB, 1973)
Population increase, concentration of industries and offices in certain pockets of Bombay,
lack of housing and infrastructure and high land values were the major problems identified.
The large migrant influx contributed to the overcrowding (Table 2.2).
Malathi Ananthakrishnan
Chapter 2: The Research Setting
5
Table 2.2 Immigrant Population of Bombay
1881
1891
1901
1911
1921
1931
1961
1971
Population
773196 821764 776006 979445 1175914 1161383 2771933 3070378
%
72
75
77
80
84
75
72
63
Immigrants
Males per
151
171
162
189
191
181
160
149
100 Females
(Various Census Reports of Bombay in Kosambi, 1986)
The concentration of industries and offices at the CBD and suburbs like Chembur and
Andheri created unequal development, air pollution and mixed land use (UNCHS, 1993).
Unhealthy and insanitary conditions for 1 million slum dwellers was the result of inadequate
housing stock. Lack of adequate water supply and sewage facilities worsened conditions.
Also, rocketing land prices prevented the acquisition of land for public purposes (BMPRB,
1973). In a final attempt, the Bombay Metropolitan Regional Planning Board recommended
considering a twin city across the harbor.
2.3 The Creation of Navi Mumbai
New Growth
Centers
Growth
Centers
of
Bombay
Town
Center
Arabian
Sea
Harbor
of
Bombay
Figure 2.2 Twin City Across the Harbor
Source: CIDCO, 1973.
1
The prominent authors of the
’twin city concept’ were Charles
Correa1, Pravina Mehta2 and Shirish
Patel3 who presented to the government
a proposal in 1964 for constructing new
growth centers across Bombay harbor
on the mainland (Figure 2.2). The
implementation occurred through
’correct’ political and bureaucratic
channels in 1969. This was in the form
of the Bombay Municipal Regional
Planning Board’s recommendation that
a new city be designed within the
Bombay Metropolitan region to
facilitate the decongestion of Bombay
(Correa, 1997). If the new city was too
far away, then this would not be
possible (BMRPB, 1973).
The site that was finally chosen
was across the harbor from Bombay
island. It is a narrow piece of land
bounded by the Western Ghat mountain
ranges on the north, south and east, and
Charles Correa is a prominent architect and urban designer in Bombay.
Pravina Mehta (late) was a structural engineer.
3
Sirish Patel, engineer and planner, was incharge of the planning and design of Navi Mumbai (1970-75).
2
Malathi Ananthakrishnan
Chapter 2: The Research Setting
6
the Arabian Sea on the west (CIDCO, 1973). Navi Mumbai covers an area of 344 sq. km. It
is a self-contained city independent of Bombay although there is still a visual connection to
Bombay.
It was hoped that the nearness to Bombay would facilitate the relocation of people
from Bombay (CIDCO, 1973). Correa, Patel and Mehta designed this regional plan based on
three basic objectives: a planned new development, financing physical and social
infrastructure through land sales, and improving Bombay by drawing off pressures for
growth into the new area (Patel, 1997).
The new town, comprising of a number of nodes (townships), was designed to
accommodate new industrial and commercial activity as well as for secure and affordable
housing to workers. The plan hoped to reduce homelessness in Bombay and provide slum
dwellers a better life as well as absorb migration from the countryside (Correa, 1985). The
regional plan was approved in 1970. The Bombay Municipal Regional Planning Board
created the City and Industrial Development Corporation (CIDCO) in 1970 to implement its
ideas.
2.4 The Draft Development Plan of 1973
The task of planning and developing Navi Mumbai was entrusted to the City and Industrial
Development Corporation (CIDCO), a government agency explicitly set up for this purpose.
CIDCO is a limited company, wholly owned by the State Government of Maharashtra
(CIDCO, 1973). The first task of CIDCO was to prepare a development plan for the new
town. CIDCO used certain development principles in its design. They were (CIDCO, 1973):
i polycentric pattern of development
i acquisition of all land to have better control of the environment and to use land as
the main resource for development.
The first step was to identify all the land that needed to be acquired for Navi Mumbai.
Owners were notified about the government’s proposal. The land notified for acquisition for
Navi Mumbai was under private and government ownership (Table 2.3)
Table 2.3 Land Fragmentation in 1970
Ownership
Area (sq. >500 sq. m. >1000 sq. m. >4000 sq. m. >10000 sq. m.
km)
(number)
(number)
(number)
Government
10137
All
Private
16677
18412
3338
1579
90
Marsh(wetlands)
84
(CIDCO, 1995)
CIDCO notified all private owners about the compulsory acquisition. The government
would acquire land under its power of eminent domain under Section 22, Maharashtra
Regional and Town Planning Act (MR&TP Act), 1966. Section 31(6) under the same act
gives the government the power to specify land use and proceed with development. The
finality of the approved Development Plan ensures that the pressure and friction which would
develop to obtain land use changes for particular land holdings would be largely eliminated
Malathi Ananthakrishnan
Chapter 2: The Research Setting
7
(CIDCO, 1973). This was not entirely true, and major law and order problems did occur.
Nevertheless, CIDCO acquired all the land after settling disputes about compensation
(CIDCO, 1995).
Although the main objective of the design of Navi Mumbai was to create a selfsufficient urban environment, it also hoped to improve the quality of life of Bombay. The
objectives were (CIDCO, 1973: 10):
1. Reduce the growth of population in Bombay city by creating a center that would absorb
immigrants, and also attract some of Bombay’s present population.
2. To support a statewide Industrial Location Policy which will lead eventually to an
efficient and rational distribution of industries over the State and a balanced development
of urban centers in the hinterland.
3. To provide physical and social services, raise the living standards and reduce the
disparities in the amenities available to the different sections of the population.
4. To provide an environment which would permit the residents of New Bombay to live
fuller and richer lives in so far this is possible, free from the physical and social tensions,
which are commonly associated with urban living.
5. To provide a physical infrastructure which prevents ethnic enclaves among the
population.
The Draft Development Plan gave only broad guidelines, leaving enough room for flexibility.
Although five minor amendments were made to this Draft Plan, no new document was ever
prepared. The Draft Development Plan remains the guiding document in use even today.
2.5 Development Potential of the Site
Turbhe
MIDC Industrial
Estates
Arabian
Sea
Creek
bridge
Taloja
Panvel
Nhava-sheva
The chosen site had various
development potentials (Figure 2.3).
These were (CIDCO, 1995):
• the Maharashtra Industrial
Development Corporation (MIDC)
Estates at Turbhe and Taloja;
• the plan for a modern, container
port at Nhava-Sheva;
• the existence of two municipal
corporations at Panvel and Uran;
• the newly commissioned bridge
across the Thane creek, and transport
corridors along Thane-Belapur;
• the Thane-Pune National Highway
4, Panvel-Uran rail and road links.
The success of Navi Mumbai was
thought to depend on the adequate
creation of jobs (CIDCO, 1995). The
development plan took into account the
Figure 2.3 Development Potential of the Site
Malathi Ananthakrishnan
Chapter 2: The Research Setting
8
provision of 750,000 jobs for a population of 2 million (CIDCO, 1995). This was necessary
to (CIDCO, 1995):
i make Navi Mumbai self-contained and not a dormitory;
i to decongest Bombay by shifting jobs that are concentrated in the southern part of
Bombay;
i to use the job centers with matching infrastructure provision as engines of growth
for the new city.
The employment base of Navi Mumbai was planned to encompass manufacturing
(industry), trade and commerce (wholesale and warehousing), as well as service sector
(office) jobs. The Industrial Location Policy issued in December 1974 posed various
restrictions on the start of new industrial units on Bombay island. A series of controls were
made for various regions within Bombay. No new, large or medium industrial units were
permitted on Bombay island. Only small-scale industries were allowed in place of old, large
industries. Industrial growth was encouraged only in the MIDC industrial estates of Navi
Mumbai (CIDCO, 1973).
Almost 87% of the office jobs of Greater Bombay are located on Bombay island with
62% in South Bombay. The plan called for the shifting of government offices from South
Bombay to Navi Mumbai. The authors of the regional plan cited the case of New Delhi to
emphasize their idea (Patel, 1997). A CBD was planned in Navi Mumbai with the aim of
creating 40,000 office jobs.
Although job opportunities were the driving force behind Navi Mumbai’s success, the
availability of cheaper, better quality houses was the biggest incentive (CIDCO, 1975). To
accommodate a population of 2 million, assuming a family size of five, 400,000 houses
needed to be built. Table 2.4 shows CIDCO’s estimates on the capacity to pay for housing by
different income groups.
Table 2.4 Household Income and Capacity to Pay (Figures estimated in 1971 income where
$1~Rs.7)
Household
% of
Monthly
Capacity to pay Affordable size
Income
Population
capacity to pay for housing (in of housing unit
(Rs. Per month)
(% of income)
rupees)
(in sq. m.)
Less than 200
20
10
1200
3
201-300
16
11
2580
5
301-400
15
12
4140
8
401-500
14
13
5940
12
501-600
12
14
7800
16
601-800
9
15
10800
22
801-1000
7
17
15600
31
Threshold of affordability
1001-1200
3
19
21000
43
1201-1500
2
22
30000
60
1501+
2
25
37800
75
Malathi Ananthakrishnan
Chapter 2: The Research Setting
9
(CIDCO, 1973)
The table shows the ability of each income group to contribute towards owned
accommodation. The average cost of construction was Rs. 550 per square meter and the cost
of development of land was Rs. 40 in 1970. Capacity to pay for housing divided by cost of
construction shows a very small (or no) house could be owned by most families. Otherwise,
each family could own only developed land.
The Government of India’s policy on publicly financed housing has been to build 21
sq. m. houses or larger (CIDCO, 1973). The housing has to be heavily subsidized to make it
affordable. This would have a great drain on the financial resources of the government. In
Navi Mumbai, it was proposed to use cross subsidies. The higher income groups would pay
a surcharge for housing, which would subsidize housing for the lower income groups.
CIDCO decided to use a maximum surcharge of 15% on housing for highest income group to
compensate for a maximum subsidy of 45% to the lowest income group (CIDCO, 1973).
CIDCO decided to build a large part of the housing as public housing. At the same time,
land would be leased under a 30-year repayment system to private cooperative housing
schemes and private owners.
2.6 Design Principles of Navi Mumbai
The conceptual design of Navi Mumbai was developed at the height of Modernism.
Le Corbusier had played an important role in the design of Chandigarh in Punjab in the mid1950s (Le Corbusier, 1961). Some of the highlights of the design elements of this plan were
sector planning, hierarchy of roads and important buildings of a gargantuan scale (Fry, 1977).
Le Corbusier explained "the plan is based on the main features of the 7V rule (Appendix B)
determining an essential function: the creation of sectors. The sector is the container of
family life" (Le Corbusier, 1961). The sector was based on the Spanish cuadra of 110 to 100
meters. Each of these cuadras was a self-contained unit with primary schools, community
centers and residential areas. The cuadra had a detailed zoning plan with single-use zoning
on all lots. No fast traffic was allowed in the sectors. V4 roads were designed for shopping
and commercial activity. Children were able to walk to school on the V7 through green belts
(Sarin, 1977). Many of these principles of Modernism were used in the planning of Navi
Mumbai. These were:
i decentralization by the design of self-sufficient townships(nodes),
i residential neighborhoods (sector),
i single-use zoning as opposed to the traditional multiple-use zoning
The result was a single-use zoning pattern with distinct areas for industrial, commercial,
residential and institutional activity. The total land of Navi Mumbai was divided into
thirteen townships. Each township had several sectors. Many of the sectors were residential
in character. The neighborhoods were self-sufficient and had their grocery store and primary
school. A sector centrally located within each node took on commercial activities.
The sector planning of Modernism is very similar to the grid planning of traditional
Indian cities. In India the square was used as the basic unit in the layout of traditional cities.
The square had a significance in Hinduism as this perfect geometric shape was thought to be
Malathi Ananthakrishnan
Chapter 2: The Research Setting
10
the abode of the gods (Henn, 1969). Even in the planning of Mohenjadaro (7th century
B.C.), main streets formed perfect rectangles dividing the city into separate residential areas
based on caste. All houses in a neighborhood were occupied by a particular caste. In India,
the four castes are Brahmin, Kshatriya, Vaishya and Sudra, which corresponds to the
professions priest, warrior/king, merchant and peasant.
The indigenous plans all started with a central focal point (either of political or
religious symbolism), and progressively moved outward depending on the natural landscape.
Many cities still reflect this street pattern. As the residential classification was based on the
caste, people were forced to work within that particular neighborhood. So each sector had
mixed use. Commercial and residential uses were adjacent to each other or one above the
other. This is significantly different from the single-use planning of Modernism.
The Bombay Municipal Regional Planning Board put forth the broad conceptual
regional plan of Navi Mumbai. The task of designing and detailing the physical design was
carried out by CIDCO. Mr. Parab, a true Gandhian, was the Chief Planner of CIDCO for 20
years (1970-90) (Engel, 1991). Under his leadership, the main philosophical design
principles of Navi Mumbai are based on Gandhian ideology (Parab, 1997). "Arguing to turn
any weaknesses into strength, Gandhi would have urged: If nature chooses not to
accommodate us, let us accommodate nature!" (Gandhi in Engel, 1991). This is the vision
that is the traditional Indian design inspiration for Navi Mumbai. Here in Navi Mumbai the
idea of a large “urban village” has been nurtured. The goal has been to create a city based on
Gandhian principles of swavalamban (self-reliance), swadeshi (fullest utilization of local
resources, both materials and human) and swatantrya (self-motivation and mutual self-help)
(Ganguli, 1973).
The functionality of the city is based on the principle of neighborhood design as seen
all over the Western world. Neighborhood planning in the West was a concept put forth by
Clarence Perry, an American designer of the 1920s. This was a model layout for an area
with specifications for residences, streets, amenities and utilities with segregation of
vehicular and pedestrian traffic (Banerjee, 1984). Each neighborhood unit was within a one
square mile radius. Neighborhoods could be placed near each other to form a larger urban
framework. This also facilitated the sharing of other, larger amenities by contiguous
neighborhoods. The neighborhood unit is used as a building block to build New Towns
across the world (Perry, 1929). This principle of neighborhood planning and its derivative
from Modernism was used in Navi Mumbai. In the case of Navi Mumbai, each
neighborhood was known as a sector (CIDCO, 1973).
Navi Mumbai consists of thirteen townships (or nodes). Each node is self-contained
for 100,000 to 200,000 people. Each node is divided into neighborhoods (or sectors). The
nodes contain residential, commercial, infrastructure and recreational uses (Figure 2.4). At a
larger scale, nodes share some common facilities such as water reservoirs and transport
facilities. Some of the nodes have special features. Vashi is the center of Navi Mumbai's
wholesale market. Airoli and Kopar-Khairane have industrial estates, while Nhava-Sheva
houses the new container port. Each node was planned to accommodate a range of income
groups. There would be no rich or poor nodes (CIDCO, 1973). The size of the node depends
Malathi Ananthakrishnan
Chapter 2: The Research Setting
BOMBAY
Airoli
Ghansoli
11
on walking distances to the
mass transit stop. The node should be
large enough to provide schools,
shopping areas and other facilities.
Kopar-Khairane
The Development Plan of Navi
Mumbai is an example of the new
consciousness for sustainable
Kharghar
Nerul
settlements (CIDCO, 1995). The plan
Jui
Belapur
Arabian
envisioned an ecologically friendly
Sea
city where products of nature would be
Panvel
used, and then unused portions would
Nhava-Sheva
be recycled. One of the ideas of
putting the environmental city into
Dronagiri
practice was the creation of woodland
corridors (Parab, 1997). The
Development Plan for Navi Mumbai
called for the planting of one hundred
thousand trees every year! (Engel,
1991). This would also ensure
reduction of soil erosion and the
development of woodlands for both
Figure 2.4 Nodes of Navi Mumbai
recreation and timber. The streams
Source: CIDCO, 1973.
flowing from the Western Ghats
mountain ranges would irrigate these trees. The plan called for the construction of holding
ponds to retain excess monsoon run-off, which would be used in the dry seasons. Holding
ponds would be used for pisciculture and recreation. Water treated from industrial and
sewage waste would be used to develop green areas (Parab, 1997).
Kalamboli
Vashi
Sanpada
The design concept of Navi Mumbai was very idealistic. This was partly because of
the scale and complexity of the project. There was also a high degree of uncertainty attached
to some of the policies and physical developments. It depended very heavily on external
factors, which were closely linked, for its success. For example, unless sufficient industrial
growth existed, a migration of population would not occur. For industrial growth large
finances were required. Private industries would not invest in this particular region unless
they were assured of workers and so on. As financial and economic considerations depended
on the government in office, the plan had a very important political component. Politicians
use the creation of jobs and better living environments as a common strategy for getting
votes. Hence, only activities, which ensured their re-election, would be strongly supported.
Any change in political power would affect the policies and development strategies of this
new town.
2.7 Social Agenda in the Planning of Navi Mumbai
Considerations of social equity were very important in all aspects of development in a
country, which had been independent for only 20 years. The primary concerns were related
Malathi Ananthakrishnan
Chapter 2: The Research Setting
12
to providing better quality of housing, education and job opportunities, medical care and
social welfare. The design of a completely new city was a very good opportunity to
implement these national concerns. The Constitution of India also spells out the need for the
government machinery to facilitate social, economic and political equity.
The State shall not discriminate against any citizen on grounds of religion,
race, caste, sex, place of birth or any of them (Article 15, I).
The State shall strive to promote the welfare of the people by securing and
protecting as effectively as it may a social order in which justice - social,
economic and political - shall inform all the institutions of the national life
(Article 38).
The planners of Navi Mumbai thought this was a fortuitous occasion to provide social
justice to the millions of migrants and pavement dwellers of Bombay (CIDCO, 1973). In
1970, more than 30% of the population of greater Bombay could not afford a pucca (durable)
house (CIDCO, 1973). Thus, it was proposed that housing should be constructed so that this
income group could afford it. Incremental housing was suggested as the solution.
Housing would be built for the various income groups. For the lower income group,
cost-effective, ground floor houses would be possible initially. Construction would be made
with locally available, cheap material. More durable material could be used in the course of
time. The remaining two-thirds of the population could afford more expensive housing. For
them, walk-up apartments of three to four floors would be designed.
The plan took into account the fact that one-third of the housing in New Bombay
would be sites-and-services plots (CIDCO, 1973). The Gandhian principle of self-help
would be used to implement this agenda. The sites-and-services plots would have services
such as roads, water, electricity and sanitation (CIDCO, 1973). Individual families would
then have to build their own homes (swavalamban). The residents could design and
implement their construction in any way they chose (swatantrya). It recommended
construction using cheaper concrete, using bamboo instead of steel reinforcements and
setting up of local retail shops where residents would be able to buy inexpensive building
materials for building their homes (swadeshi) (CIDCO, 1973). To aid residents further,
CIDCO would sell the plot at a highly subsidized rate and with a twenty-year repayment
period. Housing for the middle income and high income groups would be in the form of
CIDCO housing, cooperative housing groups or private builders.
Navi Mumbai’s founders saw the construction of large amounts of new housing as an
opportunity to break down demographic divisions and to enhance social equity. The Draft
Development Plan spelled out
"there is a tendency in India that induces people to live in like groups,
enclaves or ghettos of age long tradition of ’birds of the same feather flocking
together’. In planned towns and cities this should be avoided to a great extent
by allocating housing in neighborhoods to members of different
communities."
(CIDCO, 1973)
Malathi Ananthakrishnan
Chapter 2: The Research Setting
13
To justify this consideration, planners cited the segregation of Bombay as an example. When
the East India Company encouraged merchants to establish residence in Bombay, merchants
from neighboring districts migrated into Bombay and constructed homes inside and outside
the Fort walls. This led to the development of ethnic enclaves. The Governor of Bombay
also encouraged this development because it reinforced the traditional panchayati (selfgovernment) system of administration by which the council of elders settled religious, and
law and order problems of the community (Dwivedi and Mehrotra, 1995). This further
contributed to the creation of ethnic enclaves within the settlement. Establishment of ethnic
enclaves has led to a number of problems in India. These are discussed further in the next
chapter. "In each node it is proposed that accommodation be made available for the entire
range of income groups expected in the city. It is expected that this accommodation of
residents from various social and income groups within the same physical area will not only
make for a healthier environment, but will also ensure a uniform standard of social and
physical infrastructure and see that no one class of residents is better served than another"
(CIDCO 1973: 17-18).
Provision of schools and colleges was a priority in the planning of Navi Mumbai. The
nodes (townships) were designed to provide one primary school per 5000 population, one
high school for 12,500 population and one college for 50,000 population (CIDCO, 1973).
These were the education facilities to be provided by the government. Other private
institutions would be encouraged also. Minimum standards for building construction were
developed by CIDCO.
Health planning was undertaken as public health projects, medical care, water supply
and sanitation, recreation and afforestation projects (CIDCO, 1973). The planning was for a
comprehensive coverage by taking the services to households, schools and colleges and
making health education a part of classroom education. The community health care center
would primary health care. It would have out-patient department, diagnostic and
investigation services. Mobile health care units would operate from this community health
center. The medical center would provide secondary health service. It would be a small
hospital and polyclinic where specialized health care would be provided to cases referred by
the community health care center and general practitioners. A large hospital for intensive
care and for teaching and research purposes would be set up (CIDCO, 1973).
The Greater Bombay region had some of the best social welfare programs in India.
Institutions for juvenile delinquents, handicapped children, exploited women and leprosyaffected persons would be developed in Navi Mumbai to accommodate the growing
population (CIDCO, 1973).
The planners of Navi Mumbai did not intend to create an identity for the city related
to physical objects. The Development Plan says (CIDCO, 1973: 17):
"CIDCO is anxious that the new city develop its own identity as quickly as
possible. It should contain its own jobs, shopping, recreational and other
social facilities an should not become a dormitory for Greater Bombay."
Malathi Ananthakrishnan
Chapter 2: The Research Setting
14
Thus, there was no aim to create a monumental city. Its identity is only that of a
spreading inkblot (Engel, 1991). It appears that the monumental style of Corbusier was not
an influence on this design. New, planned cities of India such as Chandigarh, Gandhinagar
can be described by their grid system or monumental scales. However, the identity of Navi
Mumbai is subtler. It is more of a philosophical identity - an identity based on the Gandhian
value of social equality.
The city of Navi Mumbai was planned to address the issue of social equality through
its physical design. The physical design would be the instrument to implement this objective.
In particular, the allotment of residential apartments would be governed by a policy, which
would help implement the objective. However, a strong institutional framework was
required for its success.
2.8 Plan Implementation through the Public Administrative Framework
The government authorities of Bombay realized that the effectiveness of regional
planning depended, largely, on the institutions responsible for the plan. In the very
beginning, the Gadgil Committee Report (1965) had recommended the setting up of a New
Town Development Authority (NTDA). CIDCO was appointed as the NTDA.
CIDCO undertook the task of (CIDCO, 1995):
i developing land and providing infrastructure such as roads, drainage, water
supply, electricity;
i developing residential plots for different income groups;
i promoting commercial and other employment activity;
i involving Government agencies for developing public transport and
telecommunications.
Other institutions have also been set up in the Greater Bombay region to facilitate planning
efforts in the region. These are (CIDCO, 1992):
i Bombay Metropolitan Regional Development Authority (BMRDA) in 1975
i Navi Mumbai Municipal Corporation (NMMC) in 1992.
i Specialized services provided by Maharashtra Housing and Area Development
Authority (MHADA),
i Bombay Electric and State Transport (BEST).
Before the creation of these different institutions, CIDCO had to coordinate all
planning and development programs. With the creation of these other agencies, CIDCO has
a more narrow and defined role. The role of CIDCO is to implement the plan of Navi
Mumbai. CIDCO has executed the implementation of the plan in various stages (CIDCO,
1992). These stages include:
i Draft Development Plan (programs and policies)
- Objectives
- Data base
- Other agencies
- Visualizing the future
i Action Plans
Malathi Ananthakrishnan
Chapter 2: The Research Setting
15
- Land use plans
- Residential layout plans
- Infrastructure plans
- Industrial location plans
- Environmental assessment
i Implementation
- Acquisition of land
- Finance
- Construction
- Relocation strategies
BMRDA took over such functions as coordination of metropolitan planning, funding,
execution of programs, development control and maintenance of the entire Greater Bombay
region including Navi Mumbai (UNCHS, 1993). Financial responsibilities and investment
decisions are made by a large number of agencies including the Government of India, State
Government of Maharashtra, CIDCO and firms in the private sector, but coordinated by
BMRDA.
Macro-level Regional Planning
Micro-level Sub-regional
Inputs
Planning Inputs
Bombay Metropolitan Regional
Development Authority (BMRDA)
Navi Mumbai Municipal Corporation
Plan Implementation of Navi
Mumbai
City and Industrial Development
Corporation (CIDCO)
Figure 2.5 Institutional Hierarchy in Implementation of Development Plan for Navi Mumbai
In 1992, an amendment of the Constitution of India affected the functioning of
CIDCO. The 74th Amendment of the Constitution of India (the 1992 Amendment Act on
Municipalities) spells out the devolution of power to the local bodies and democratization of
development planning. This Act emphasizes that the management must be done by elected
representatives of the people who will account for two-thirds of the board. This committee is
responsible for the preparation of the draft development plan. This ensures a bottom-up
process with direct inputs from the citizens (UNCHS, 1993). These municipal corporations
will be responsible for their economic development and incorporate all ideas within the
Malathi Ananthakrishnan
Chapter 2: The Research Setting
16
Comprehensive Plan. The direct result of this Act is the creation, in 1992, of the Navi
Mumbai Municipal Corporation. This allowed CIDCO to give up its role as New Town
Development Authority (CIDCO, 1995).
A heavy-handed approach was used by the government to implement its social policy.
As most of the housing was built by CIDCO, a government agency, the government could
control, if not regulate, the distribution of the population on socioeconomic basis.
Households desirous of buying a house built by CIDCO had to submit an application that
stated the dwelling size they preferred. CIDCO allotted these houses, depending on when
construction was completed, on a rolling basis. This was intended to ensure a random
distribution of the various linguistic and religious groups of the population. The pattern
expected would now be one based predominantly on income.
2.9 The Reality of Implementing the Plan
The planning of Navi Mumbai began in 1971. The results of each of the planning
objectives can be studied now. The first objective of the Development Plan of Navi Mumbai
was to reduce congestion of Bombay by absorbing immigrants and attracting some of the
present population of Bombay.
Table 2.5 Population Density in Various Sectors of Bombay(BMRDA, 1978 in UNCHS,
1993)
1971
1981
1991
Population Density Population Density Population Density
(in ’000s) (pop/ha) (in ’000s) (pop/ha) (in ’000s) (pop/ha)
CBD
1120
1659
1031
1527
849
1258
Central Bombay
1950
1349
2254
1559
2309
1597
Bombay Island
3070
1447
3285
1549
3158
1489
Bombay Suburbs
2900
544
4958
930
6751
1266
Navi Mumbai1
128
600
328
617
Over the 1981-91 period, there was a considerable decline in the population of the CBD and
Bombay island. The increase in the population of the suburbs and Navi Mumbai accounts for
the decline in the CBD and Bombay island. Outmigration to other cities and countries is
negligible (BMRDA, 1978). The main reason for the shift was because of (UNCHS, 1993):
i dilapidation of older buildings in Bombay
i cheaper and better housing facilities in Navi Mumbai
i better employment opportunities in Navi Mumbai
i lesser commuter distances involved
The second objective of the development plan was to bring maximum jobs consistent
with the Gandhian principle of self-sufficiency (swavalambhan). CIDCO’s support of the
Industrial Location Policy brought more jobs to Navi Mumbai. The sectors that had
maximum growth in Navi Mumbai, were trade (39%), finance and services (27%) and
manufacturing (18%) (BMRDA, 1992 in UNCHS, 1993). The wholesale agriculture produce
1
residential area increased from 213 hectares in 1981 to 531 hectares in 1991.
Malathi Ananthakrishnan
Chapter 2: The Research Setting
17
market for vegetables, foodgrains, oil seeds, sugar and spices was moved from South
Bombay to Navi Mumbai (CIDCO, 1973). A separate railway siding and truck terminal were
constructed to facilitate effective relocation. This involved the relocation of 30,000 jobs
from Bombay and the reduction of 5000 truck trips per day. A new iron and steel stockyard
complex has been developed in Navi Mumbai. This means the relocation of 25,000 jobs and
a reduction of 1000 truck trips per day to Bombay. However, the economic agenda, which
was based on agriculture and cottage industries, is no longer effective because of the
government’s redoubled commitment to a policy of industrialization. Navi Mumbai
continues to be exploited as a major industrial zone (Engel, 1991).
CIDCO's third objective was to provide physical and social amenities in Navi
Mumbai. The land use of Navi Mumbai shows these amenities (Table 2.6 and Figure 2.6).
Table 2.6 Land Use of Navi Mumbai, 1993 (in sq. km.)
Land-use Zone
1979
1985
1986
1991
Residential
101.15 133.99 127.08 129.87
Commercial
6.51
6.51
6.51
5.75
Industrial
43.21
43.14
43.14
43.14
Port
12.00
22.7
22.7
22.7
Wholesale market
6.08
4.54
4.54
4.6
Woodlands / Park
90.26
61.24
68.15
69.35
Institutional
.76
1.09
1.09
1.09
Fishing and allied
6.14
3.44
3.44
3.44
Transportation
30.86
30.35
30.35
29.73
No development
46.73
36.70
36.70
34.03
Total
343.70 343.70 343.70 343.70
1992
128.71
5.75
43.14
22.7
5.76
69.35
1.09
3.44
29.73
34.03
343.70
1993
127.61
5.75
43.14
22.70
6.86
69.35
1.09
3.44
29.73
34.03
343.70
(CIDCO, 1997)
Primary, secondary and high schools have been provided in all sectors of Navi
Mumbai. All primary schools are within walking distance. This eliminates the need of
expensive transport for small children. There is at least one college in every node and Vashi
node has both medical and engineering colleges (CIDCO, 1995). Medical facilities are
provided by private medical practitioners. Every node has a hospital run by the Mahatma
Gandhi Medical Trust. Community health car centers are also there (CIDCO, 1995).
In its fourth objective to provide an ecologically friendly environment, CIDCO has
not been entirely successful. The area of woodlands has been constantly decreasing (CIDCO,
1995). Most woodlands are in the form of mango groves which form a part of neighborhood
parks. In the conceptual plan, streams flowing from the hillsides were to irrigate the
woodland corridors. No significant effort has been made to utilize this resource. However,
holding ponds have been constructed. Promenades have been built along them and they are
being used as recreation areas (Parab, 1997).
The fifth objective is the primary focus of this thesis. The objective to prevent ethnic
enclaves and to promote a pattern based on socioeconomic characteristics was fairly
ambitious. In order for its success, a perfect control of the market is required. The analysis
of the data will show the outcome of the objective.
Malathi Ananthakrishnan
Chapter 2: The Research Setting
NEW BOMBAY
BOMBAY
Arabian
Sea
Residential
Woodlands
Industrial
Port
Institutional
Trucking
Wholesale
Fishing
Wetlands
Figure 2.6 Land Use of Navi Mumbai
Source: CIDCO, 1995.
18
Though the Navi Mumbai
project was begun in 1970, the
development process has been slow.
The poor transportation links between
Bombay and Navi Mumbai has been
the main contributing factor. Growth
in other development sectors of
Bombay has also had an adverse effect
on Navi Mumbai’s growth. The
absence of a port and railway links
slowed growth. However, since 1990
there has been accelerated growth due
to the commissioning of Nhava-Sheva
port, the extension of the railway lines,
establishment of more industries and
construction of more houses. CIDCO
provides serviced sites for both
government and private ownership.
Houses have been constructed for
different sectors of society economically weaker section, lower
income group, middle-income group
and high-income groups. Commuter
services have become operational since
May 1992, and housing occupancy
rates are high. Hence, the city is no
longer a plan on paper, but a living and
working reality.
2.10 Conclusion
The Draft Development Plan of Navi Mumbai described many broad outlines for the
development of a city for the common citizen. The design principles described in the Draft
Development Plan were based on the philosophical reasoning of Mahatma Gandhi and the
functionalistic approach of Modernism. Many attributes of these two design principles are
not necessarily harmonious. While Modernism called for single-use zoning and a pattern
based on socioeconomic characteristics, the Gandhian principles supported cultural
heterogeneity and mixed use zoning.
Social aspects of city planning were given importance with special attention given to
considerations of employment opportunities, housing requirements, utilities, recreation and
commercial needs. Designing, development and implementation of ideas were done in an
incremental manner. Periodic socioeconomic and household surveys were used to determine
the status of constructed environment. Problems of design and development were identified,
and improvements made in the next phase of design.
Malathi Ananthakrishnan
Chapter 2: The Research Setting
19
This design also strongly supported the need to use the government’s power and
machinery to promote the uniform distribution of people and prevent ethnic enclaves. A
heavy-handed implementation strategy of this objective was done by taking complete control
of the residential allotment. The success of this strategy depended on maintaining this
control. This also implies that the urban social pattern was predetermined.
The research setting under consideration is the result of the hybridization of Indian
and Western ideas. Navi Mumbai is a modern, planned city within the context of a specific
historic and cultural setting. Very little analysis has been done on the outcome of CIDCO's
social agenda to ensure diffusion of ethnic groups and the urban social pattern that emerged.
The aim of this research is to examine the present urban social pattern of Navi Mumbai.
Chapter 3: The Conceptual Framework
3.1 Introduction
A human settlement is an establishment created by people for their inhabitation.
Human settlements contain people and societies in a physical environment consisting of
natural and man-made elements (Doxiadis, 1968). Such a human settlement is not just threedimensional, but four-dimensional, because it changes continuously in a temporal dimension.
A holistic approach to the study of settlements involves understanding the interrelationships
between its elements within the temporal context. The study of the physical form and
structure of cities is the study of urban morphology. The final outcome of a morphological
study is the formulation of a theory which connects facts to form hypotheses, principles and
existing theories for improving the design of cities (Doxiadis, 1968).
The aim of the thesis is to examine the urban social pattern of Navi Mumbai, (New
Bombay), India. Urban social pattern is the pattern formed by the interaction of various
social variables such as household characteristics, ethnicity, religion, language and housing
character. This literature review will first trace the human settlements in India. Most cities
in the Third World and India have been indigenous in origin and organic in growth. Many of
these cities have been under colonial rule, and bear characteristics of western influence. Navi
Mumbai is one of the first cities in India built for the common citizen. It is a city designed
with the design principles of the time. These design ideas seem to have a strong influence of
Modernism (CIDCO, 1973), and those of Mahatma Gandhi.
3.2 Urban Form and Urban Pattern
Every human settlement consists of certain elements. Interaction of these elements
form a pattern - the urban pattern. The urban pattern is a result of the relationships between
people and their social, economic and physical environments. Buildings and spaces are
created by people and quite often characterize them (Kostof, 1991). If the residents build the
buildings themselves, then they reflect their lifestyles. However, if government agencies or
contractors build them, they are more generic and may not represent the lifestyles of every
household.
Whatever the mode of construction, residents soon influence their urban environment,
changing and modifying it to suit their way of life (Lozano, 1990). Simultaneously, people
adapt to the physical environment around them. The human-environment relationship is a
two-way process termed as the socio-spatial dialectic (Knox, 1995). Thus, urban form is not
merely the architectural form of the city (Lozano, 1990). It is also a cultural manifestation.
Land ownership patterns, technology, transportation, communication and socioeconomic relationships influence urban patterns. Intricacies in relationships have increased
the complexity of the urban form over time. The pattern of spatial distribution is recognizable
in most contemporary cities (Alexander, 1987). Where market forces work, income is one of
the most important determinants. Education, occupation and values of housing influence the
spatial character. Socioeconomic factors have a very important contribution to the pattern.
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
21
Demographics, linguistics and ethnic background also influence urban patterns. Thus urban
social patterns are complex manifestations of underlying cultural values intermingled with
global economic forces (McGee, 1971).
Although details may not be identical, every city has certain elements. Doxiadis
defines five elements in the study of human settlements. They are nature, human beings,
society, buildings and infrastructure. Urban spatial patterns occur because of the repetitive
spatial distribution of these elements. The patterns have similarities, which may be universal
or local. “The typical sector represents the formal characteristics found throughout the area
and thus acquires some universality” (Lozano, 1990). Since the characteristics are universal
(within the frame of study) they may be studied by a spatial representative sector. This
representative sector is defined as the smallest area that exhibits the characteristics of the
urban settlement. In most studies this unit is the neighborhood which displays both physical
and social aspects of the whole urban development. They are the units of analysis of the
morphological study (Knox, 1995). Urban patterns represent a continuity of time and space.
Time and place may provide them with different characteristics making each city unique and
dynamic. In the study of Navi Mumbai, the node (township) and the sector (neighborhood)
will be used as the study areas using aggregated household survey data.
3.3 Factors Influencing Urban Form
Many factors influence the form of cities. Traditional settlements were shaped by (Lozano,
1990):
i the way in which nature and man-made features satisfy needs for protection and
defense
i the way in which physical and economic landscape allows for communication
with other regions
i the way in which the topography of a site suggests the construction of a human
settlement
i the way in which climate leads to building solutions
These factors influence the cultural and spiritual form of the cities as well. Traditional cities
have used physical forms to interpret cultural and religious beliefs (Lozano, 1990). For
example, a hill top site was the utilitarian response to any important building - a fort or a
religious building. These features contributed to a particular urban and social pattern.
The physical form is a variable of the social and built pattern of the city. The built
form is influenced by factors as (Alexander, 1987):
i land ownership
i street patterns
i existing land use
i economic considerations
i planning regulations
i political and historical events
The physical expansion of the city is always bound and guided by land ownership, and
natural and manmade obstacles. A city replaces existing land use. Thus, it is necessary to
determine existing land use as a pre-condition to urban growth and form. The change of land
use from rural to urban depends on the existing land use, and the ownership. Some farmers
may sell their land more easily than others may. The rural land may also have been
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
22
subdivided. Plots of varying sizes and shapes influence the layout of the streets and of
individual buildings (Knox, 1995). Planning controls influence development to a great
extent. Master plans and regional plans provide long-range strategies for development.
Various economic, social and political circumstances influence the social pattern
(Scargill, 1979). While some processes are culture-specific, others are global in scope.
These factors are (Alexander, 1987, Kosambi, 1986):
i ethnic composition of the city
i migration
i religion
i economic considerations
i race
i political and historical events
The housing market also influences the social pattern of the city. A household’s choice of
place to live is determined by its income level, personal preferences and many institutional
constraints. Owner-occupier, private rental and public sector housing operationalize housing
sectors.
A particular social pattern brings about a particular built form. Certain built forms
encourage certain social patterns. The social pattern and the built form are interrelated and
contribute to the urban morphology of a city.
3.4 The Evolution of the Urban Form of Indian Cities
The traditional theory of urban origin is generally attributed to Childe (Herbert, and
Thomas, 1990). Childe put forth a theory that urban centers were a result of agricultural
change. People as food gatherers advanced to become farmers. Domestication of animals
and cultivation of land created villages. Soon, surplus food production was achieved. This
allowed some of the people to develop other professions. Priests, craftsmen and merchants
were born. However, other scholars contend that it is doubtful that surplus can be attributed
as the single factor which caused the emergence of urban settlements (Jacobs, 1983).
Reasons such as trade and defense have also been used to explain the formation of cities.
For thousands of years, cities were very simple although they rarely served single
purposes. Instead, they supported a range of activities. Housing, commercial buildings,
government offices and warehouses formed the built environment of the city. Pedestrian
movement limited the size of the city. Clear differentiation between urban and rural existed,
often because of a city wall. However, within, a city contained social distinctions in terms of
class, race and religion (Vance, 1990). Urbanization took place at different chronological
periods. The factors influencing urbanization were also different. The variation in
influencing factors and historical circumstance gave rise to different urban forms in different
parts of the world. The evolution of the urban pattern of Indian cities is divided into the
social pattern and the built form.
3.5 The Sociocultural Factors
India is among the most stratified of all known societies in the world (Srinivas,
1992a). The caste system of India separates and hierarchies the Hindus. The external
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
23
manifestation of the separation and hierarchy through particular attributes of the castes brings
about social stratification of the urban social pattern (Marriott, 1992). Clothing, language,
rituals, marriage and death ceremonies distinguish one caste from another. In India, the
forms of social stratification are many. Along with the caste exist occupational stratification,
linguistic stratification and religious stratification. The social stratification is very deep and
varied. The Indian theory of social stratification depends on caste, linguistic, religious and
ethnic diversity of the country (Gupta, 1992).
Stratification implies a differentiation based on a set of criteria. The population may
be stratified based on income, language, religion or occupation (Bougle, 1992). Hierarchy
allows elements of the whole to be ranked with relation to each other (example: income and
prestige). However, all elements can not be arranged vertically. The differences may also be
placed in a horizontal system (example: language, religion). Thus, theoretically, vertical and
horizontal systems of stratification exist. The real world, unfortunately, differentiates itself
into only hierarchical status containing inequality (Gupta, 1992b).
The term ethnic group refers broadly to people “with some similar characteristics
which go beyond their mere place in a societal division of labor” (Brass, 1974:8). Ethnic
characteristics refer to language, culture, territory, diet and dress, and in the case of India,
sometimes reinforced by common work roles. The characteristics caste, class, religion and
language are discussed below. Berreman (1965) says "Caste systems rank people by birthascribed group membership rather than by individual attributes. Class systems by contrast
define the rank of their members according to their individual attributes and behavior".
3.5.1 Caste
Castes are the hierarchical divisions of people based on professional and family
membership. The spirit of the caste system is determined by the attitudes of each caste to the
other. Repulsion between castes forced isolation and the creation of distinct residential
enclaves (Bougle, 1992). The dominant caste legend is the Purushasukta legend whereby the
Brahman, Kshatriya, Vaishya and Sudra are said to have come from the mouth, arms, thighs
and feet of the Creator. Although no hierarchy is mentioned in the Sukta, a hierarchy from
Brahman to Sudra has been interpreted (Bougle, 1992). However, this popular caste
hierarchy is not clear throughout the Indian subcontinent (Srinivas, 1992b). Various
combinations of the hierarchy have come about due to regional differentiation in certain
attributes of social living. Vegetarian castes occupy higher positions. Certain occupations
such as butchery and cobblery lower the rank. Certain customs lower or raise the status of
the caste. The caste system varies from village to village and is a local phenomenon.
3.5.2 Class
"Class refers to a system of stratification which is economic in character" (Gupta,
1992b:14). The criteria for the differentiation can normally be translated into money or
wealth. However, these single criterion hierarchies can be misleading as they depend on cutoff points related to individual analysis (Gupta, 1992a). As many individual criteria are
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
24
linked to other attributes, it may be better to create a composite index of education,
occupation, prestige and income to form a socioeconomic status.
3.5.3 Religion
Religion and language have provided the motive power for nationalism in India
(Brass, 1974). There are many religions in India. India is the birthplace of two major
religions –Hinduism and Buddhism – and two minor religions - Jainism and Sikhism.
Buddhism, Jainism and Sikhism stemmed off from Hinduism and are very similar to
Hinduism. However, Islam was a religion that came to India from outside and is culturally
very different from Hinduism. From the beginning Islam has been a conquering and
proselytizing faith (Hodson, 1985). A certain degree of animosity between Hindus and
Muslims has existed since the first Muslim ruler of 1018 AD. “In most folk-memory the
Muslims of India had been ruler, not subjects” (Hodson, 1985:11). During the Mughal rule
(16th to 18th century), the Muslims were in power over most of India. After the decline of the
Mughal Empire and the loss of political power to the British, Muslims became apprehensive
of Hindu domination. An overwhelming view of Hindu-Muslim relations in the nineteenth
and twentieth centuries is that Hindus advanced due to their enthusiasm to take up western
education and government employment (Kaura, 1977).
The Hindu religion has always been a pacifist and tolerant religion, absorbing other
religious doctrines and never proselytizing. A Hindu revival period in the late nineteenth
century to arouse enthusiasm for political action made the Muslims more insecure. At this
time they felt the need for a political party of their own. In 1906 they formed the All-India
Muslim League. While the Congress party represented the majority of the Indian population,
the Muslim League represented only the Muslim population (Brass, 1974). The League
demanded for a separate electorate and for more employment in public service. Hindus and
Muslims drifted apart in the issue of independence from British rule, which culminated in the
partition of united India into India and Pakistan. The wake of Independence brought with it
violence and terror in the Indo-Pakistan borders in Punjab and Bengal. Anger and frustration
broke out as violence as Hindus moved from Pakistan into India and Muslims moved from
India to Pakistan (Hodson, 1985).
3.5.4 Language
A systematic inventory of Indian languages began in the mid-eighteenth century. The
census of India 1951 (immediately after Independence) recorded a total of 179 languages and
544 dialects in India. The major languages of India are Hindi, Bengali, Tamil, Gujarati,
Marathi, Malayalam, Kannada, Telugu, Urdu and Punjabi. The linguistic distribution is not
only diverse but also very complex (Das Gupta, 1970). The characteristics of the
population regarding bilinguals, degree of control over the language and relationship between
the languages affect their social communication.
The framers of the Indian Constitution chose Hindi and English as the official
languages of the government (King, 1997). Hindi was chosen because it was the language
spoken by the largest percent of the population while was a result of the British legacy.
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
25
However, a demand for a national language also arose. In a multilingual society there may
be a plurality of national languages. The Eighth Schedule of the Constitution of India
declared the fourteen major languages listed as national language (Gumprez, 1971).
However, confusion has always existed about the status of Hindi as official or national
language. Writers in Hindi commonly refer to Hindi as Rashtrabasha (state language) which
may signify language used by the state, a synonym for official language and like state
religion, a state language with an unique status (Das Gupta, 1970). This confusion in
terminology is the basis for most language-related problems in Independent India. Although
a majority of the rivalry has been for and against Hindi, there also been conflict between
other regional languages.
3.5.5 Implications of the Sociocultural Factors
The implications of caste and class are closely related to those of power and wealth
(Dumont, 1988). Certain castes are dominant in a society. Traditionally these castes had
either wealth or power. In many places, the Brahman priests had more power because it was
believed that they were the representatives of the Creator on earth. In some villages, all
castes looked up to the farmer caste because they were important landowners and were
wealthy (Srinivas, 1992a). The inequality and economic differentiation cause conflict
between the castes and classes. The separatism movements seen all over India are all based
on ethnicity and inter-caste rivalry (Bose, 1989).
The partition of United India into India and Pakistan came with many problems.
Pakistan officially declared itself as a Muslim state. Although a minority of Hindu leaders in
India felt that India should be declared as a Hindu state, a majority of the leaders preferred a
composite nationalism. This rationale of composite nationalism influenced policies related to
religion and language (Das Gupta, 1970). When the ethnic groups occupy distinct
neighborhoods, ethnic conflicts are easily targeted towards these select neighborhoods. This
issue can not only be seen at the time of partition in 1947 but also was seen during the recent
communal violence in 1993. The Babri Masjid in Ayodhya was broken down by Hindu
fundamentalists. Repercussions were felt all over the country. Hindu-Muslim riots broke out
even in Bombay which has normally been a very peaceful city. Small Muslim enclaves
within a majority Hindu neighborhood were targeted, and vice versa. This was not seen in
more heterogeneous neighborhoods, as it was difficult to isolate only one family.
Language conflicts have also occurred in India. In the early 1950s, many political
leaders advocated for the use of Hindi as a national and official language. The union
government declared that fifteen year deadline after Independence would be given for
transition of official language from English and Hindi to only Hindi. There was strong
opposition from non-Hindi areas in general and South India in particular (Hindi is a IndoAryan language while the languages of South India belong to the Dravidian group). The
South Indian state of Tamil Nadu was most vocal in the Anti-Hindi agitation. The Tamilnad
Students’ Anti-Hindi Agitation Council objected to both the removal of English as an official
language and the declaration of Hindi as the sole official language. The better control the
Tamil people had over English, they believed, had led them to better job opportunities.
Agitation and violence broke out in many non-Hindi states over this issue. Compromise was
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
26
finally reached in 1963 under the Official Languages Act. Despite the Act, violence sparked
off by language issues has continued to occur in India.
The ethnic segregation and conflict has existed from the beginning of the Indian
Civilization. In the initial stages it was in the form of caste differentiation as prescribed by
the Hindu/ Vedic texts. The caste system over the next ten to fifteen centuries became
deeply rooted in the Hindu population and became a part of life. The multi-dimensional
society was soon complicated by the emergence of other religions, both from within and
without the country. Hinduism, Buddhism, Jainism, Sikhism were born in India while Islam,
Judaism and Christianity found their way into India. Stratification of the society had to
accommodate these religious factors. The Indian society was also stratified horizontally by
language. A number of languages coexisted in all parts of the country. Related to castes,
class, religion and language is the issue of group identity which is the cause of most ethnic
conflicts. While some groups spoke of an all-India nationality other speaks of a regional
nationality (Brass, 1974). This does not imply that social assimilation does not occur. Social
assimilation and mobilization are a part of any evolving civilization. However, the
differentiation and assimilation in progress in a multi-ethnic society receives a prominent
place in any political conflict.
3.6 The Built Form
The historical evolution of the built form of Indian cities can be divided into three
distinct phases. The earliest is the Hindu phase (3000 B. C to 12th century AD), which
contributes many elements to the urban form. These characteristics are derived from the
need for defense and administration and the importance of religion (Kopardekara, 1986).
The temple as the symbol of religion dominates the urban form. The temple also influences
the siting of other land uses. Prime commercial and residential land was located near the
temple. The science of architecture and planning, Vastushastra, governed the alignment of
roads, orientation of buildings and arrangement of internal rooms based on astrological and
religious criteria (Volwahsen, 1969). The square was used in the creation of the
vastupurusha mandala, which was the terrestrial representation of the cosmic universe
inhabited by Brahma, the creator. The mandala could be divided into smaller squares, padas.
In planning the town a
vastupurusha mandala which was
most auspicious, and which had as
many padas as there were to be
residential sectors was selected. The
streets ran from north to south and
from east to west. The town wall
enclosed the mandala, and four
gateways were situated at the cardinal
points. The final shape of the town
Figure 3.1 Circle and Swastika
depended on the natural features of the
site. If it could not be a perfect square,
a perfect rectangle was accepted. Certain other shapes were also considered to be auspicious
like the circle, cyclical and swastika (Figure 3.1).
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
27
The residential districts were divided among the four castes. Generally, the
Brahmans worked and lived in the northern district, Kshatriyas in the eastern and
southeastern part, Vaishyas in the southern part and Sudras in the western district. There was
further subdivisions within each district depending on the sub-caste. The Brahmans and
Kshatriyas lived in the parts of the town which were climatically more comfortable sheltered from the hot sun, and the south-west monsoon.
Characteristics from medieval times are Islamic in nature (14th to 17th centuries A.
D.). During this time, the Hindu tradition continued, and Hindu elements of this period are
not distinct from earlier ones. The Islamic elements included the mosque and domestic
architecture which emphasized the purdah through enclosed courtyards, jali (carved screens)
and projecting balconies (Kopardekara, 1986). The residential character throughout this
period was segregated. The urban segregation was based on function and occupation
premises. Areas for selling of specific goods – cloth, jewelry, pottery, metalware, and wood
formed niches in the urban pattern. Residential areas associated with the commercial area
were contiguous or within the commercial area (Hall, 1980). In India where occupation and
caste are synonyms, this has led to segregation and creation of enclaves within the city.
The colonial influence (17th to early 20th century A. D.) was the third phase of
historical urban form, especially seen in the port cities associated with the East India
Company (Mills, 1988). The morphological components include buildings used for trade warehouses, counting houses. This led to the development of commercial centers and zoning
based on Western market principles. On the periphery of these urban centers, military
establishments - the cantonment - were developed (Hall, 1980).
At the time of independence in 1947, India inherited a complex urban fabric.
Diversification of professions due to industrialization in the post-independence era has
resulted in further complexity (Becker, Williamson and Mills, 1992). Residential segregation
is no longer based only on occupation and caste, but also on socioeconomic factors
(Ramachandran, 1989). Large migration of people from the rural area, and insufficient
infrastructure in cities has led to the creation of slums and shantytowns (Misra, 1978). Many
researchers have tried to fit Indian urban growth into a theoretical model. “In the case of
India, many researchers have pointed to the lack of penetration of urban values into the
countryside, and the apparent timelessness and permanence of village life” (Hall, 1980). It
has been shown that rural values have penetrated the urban philosophy due to large-scale
migration.
The characteristics of the social and built form of the city contribute to its pattern. A
generalization of these patterns has been made. These are the theories which pertain to the
built and social form of the city. The three leading theories described below are based on the
built form of the city. As the built form depends on the social characteristics portrayed by its
residents, the same theories are being used to describe the social patterns as well.
3.7 Theories of Urban Social Patterns
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
28
Various spatial theories of the social pattern of cities have been advanced; some
static, others dynamic in nature. The same city may express different models at different
time periods (Scargill, 1979). The three leading Western models are:
i Concentric zone model
i Sector model
i Multiple nuclei model
These models have become frameworks for studying urban social patterns across the world
(Hartshorn, 1992).
3.7.1 Concentric Zone Theory
This theory put forth by Burgess in 1925
related population mobility and societal
organization to the physical expansion of the city
(Burgess, 1929). Burgess was interested in
determining a pattern for the social structure of
the city, and studying how the city grew (Scargill,
1979). Thus, it is a descriptive framework to
CBD
analyze spatial organization of land use in a city
Transition
and its change over time. It was partly based on
Low income
economic factors. The model made many
assumptions such as uniform land surface, free
Middle income
market, accessibility to a single-centered city,
High income
heterogeneous population and a commercialindustrial base (Herbert and Thomas, 1990).
Figure 3.2 Concentric Zone Theory
Burgess’ research on the distributional pattern of
Source: Burgess, 1929
various groups of society led him to conclude that
the city was made up of concentric zones with the central business district (CBD) at the
center (Figure 3.2).
The CBD core had all major commercial, political and social activities. This was
surrounded by a transition zone, which had factories and slums. It also had older residential
districts, which were being taken over by the expanding CBD. The next zone had lower
income housing, and successive zones had higher income residences (Burgess, 1929).
Families moved out into the next zone when their zone was invaded. The basic premise in
this model was that of succession and invasion whereby population groups gradually moved
out as their economic and social status improved. Mobility and migrant influx were though
of as the main cause of the social pattern (Hartshorn, 1992).
This model was based on Burgess’ experience in the American mid-west cities, and
especially in Chicago. In the early 1920s, most American cities in the mid-west absorbed
many immigrant groups from Europe. These immigrants first found cheap housing in the
inner city. With affluence, they moved to better housing districts (Burgess, 1929). The
movement was towards the periphery. Diversification in employment opportunities gave rise
to the growth of mixed land use development. This also forced an outward expansion. The
public transport system had also improved significantly and allowed the middle-class to
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
29
travel from outer zones to the CBD for work. These reasons complemented a concentric
zone development model (Scargill, 1979).
The model is very simple and can be used to predict how urban land markets work. It
was intended to serve as a framework for studying urban growth and change (King and
Golledge, 1978). However, Burgess has been criticized for not having considered
topographical criteria. The original model did not take into account specialized clusters of
industry. It also did not explain the impact of transport networks on these zones (Scargill,
1979). The real world is more complicated than what was represented by Burgess’ very
general model. Hence, empirical studies did not confirm his model one hundred percent
(Herbert and Thomas, 1990).
3.7.2 Sector Theory
Income group 1
Income group 2
Income group 3
Figure 3.3 Sector Theory
Source: Hoyt, 1939
Homer Hoyt put forth a land use theory
after studying over 100 cities in the U. S (Hoyt,
1939). Hoyt primarily studied residential land
use. Hoyt studied the city as an economist
concerned with how the housing market worked.
Rental value was the main criterion for studying
the pattern (King. and Golledge, 1978). He said
that residential sectors of similar rent are
situated in wedges radiating from the center
(Figure 3.3). The wedge pattern represents
residential area growth (Scargill, 1979).
Neighborhoods for each income group are
common. The model also accounts for growth
along transport routes. For example, industries
may cluster around the railway line or lowincome housing along a riverbank. This model
also accommodates growth (Hartshorn, 1992).
Hoyt also stressed the need to consider zoning
laws and slum clearance laws in making models.
3.7.3 Multiple Nuclei Theory
commercial
ethnic group
residential
industrial
Figure 3.4 Multiple Nuclei Theory
Source: Hartshorn, 1992
The multiple nuclei theory was put
forth by Harris and Ullman. This model
proposes that patterns in many cities be
arranged around several centers (Scargill,
1979). This is because concentration of certain
activities may prove to be more beneficial.
Concentric zones or sectors may emerge from
these nuclei. This is not a generalized model.
It is more specific to some cities (King and
Golledge, 1978). It gives strength to cities
with original nucleus in the center, and
subsequent decentralization (Figure 3.4).
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
30
3.8 Case Studies of Urban social Patterns
The study of the urban social pattern of a city primarily focuses on the residential
land use (Herbert and Thomas, 1990). Analysis of individual cities shows that the pattern is
not uniform and is characterized by residential segregation. In Western cities the reasons for
non-uniformity have been identified as socioeconomic status, ethnic status and family status
(Timms, 1971). The non-uniform pattern is consistent over many cities because similar
households exert similar housing choices. However, every city has some constraints. For
example, housing choices may not be made on economic basis, but on cultural ones.
It is assumed that any planned city consists of neighborhood units. The concept of
neighborhood units became popular since the1920s in planned settlements (Perry, 1929). It
serves as the building block to construct the whole town. A neighborhood is the basis for
formally organized residential space. Hence, the neighborhood unit is used as the unit of
analysis in the study of human settlements (Herbert and Thomas, 1990). It is not only a
physical design concept, but also an expression of socioeconomic and cultural values of the
people. The values are also related to family, neighborliness, community and social and
civic responsibilities such as aesthetics, safety, security and identity.
This concept, however, has been under strong criticism (Hartshorn, 1992). Critics say
that neighborhood unit strongly emphasizes physical environment; it does not address the
needs of a social environment. A neighborhood unit is not the only model or universally
appropriate unit of analysis. It is only the most convenient one. Individualistic frameworks,
which analyze the physical environment under consideration, are suitable modifications of
the concept (Timms, 1971).
3.8.1 Western Cities
Many studies of the social and physical urban pattern have been done. The city was
viewed as a part of society, and social change was expected to be reflected in studies which
were repeated over a time period (Herbert and Thomas, 1990). The data source was census
tracts. In the analysis of urban social patterns, three indices were used. These were social
rank, family status and ethnic status. Social rank used the variables, employment, education,
value of home, housing conditions and material possessions; family status used the variables
related to demographics and type of house; ethnic status used religion and social groups. The
use of these three indices for analysis is a social area analysis.
The broad generalization of the social rank produced a sector model. The main
assumption here was that social rank is related to transportation links which influence
residential location in a sectoral manner (Scargill, 1979). This type of urbanization is also
related to the housing market described by Hoyt (1939). Family status in American cities
shows a concentric distribution. As a family’s needs for space increase, they move outwards.
The outward mobility is related to different stages of life - marriage, parenthood, social status
and retirement (Scargill, 1979). Ethnicity causes the social phenomena of segregation. In
the built environment this corresponds to ethnic neighborhoods (Timms, 1971). This is
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
31
predominant in cities where migration is high. Ethnicity, however, does not always emerge
as an independent component (Scargill, 1979).
A study of Baltimore (Knox, 1995) shows that the four important factors in the social
pattern are underclass, socioeconomic status, youth/migrants and black poverty. The
changing pattern of family cycle reflects concentric zones while that of social rank is in
sectors. Studies of Brisbane, Australia (Timms, 1971), Winnipeg, Canada (Herbert and
Thomas, 1990) showed similar results.
3.8.2 Third World Cities
commercial
ethnic group
residential
industrial
Ethnic Status
CBD
Family Status
Transition
Low income
Middle income
High income
Socioeconomic Status
Income group 1
Income group 2
Income group 3
Figure 3.5 Urban Social Patterns
Source: Knox, 1995, Hartshorn, 1992.
Cities in the Third World are frequently dual environments; traditional and modern
design elements juxtaposed in seemingly dichotomous ways, but socially with more complex
relations to one another. Traditional places are typically more dense with narrow streets and
housing spaces around central courtyards. Public open spaces are generally found only
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
32
around religious buildings. The modern place is more spacious. A classic example can be
seen in the design of New Delhi, which is adjacent to, and surrounds old Delhi (Figure 3.6)
(Herbert and Thomas, 1990).
Processes quite different from those in
western cities govern the pattern of Third World
cities. Even single cities, as opposed to
conglomerations, are very complex and have
evolved over a very long time. Thus, social and
economic variables may not be the only factors,
which contribute significantly to the urban pattern
(Kopardekara, 1986). A large number of models
of Third World cities have been made (Lowder,
1986).
Social morphological models constructed
for the Third World cities show that there is a
central concentration of commercial activity and a
number of residential neighborhoods. The model
shows that the indigenous elite were closely
associated with the commercial area. The more
Figure 3.6 Plan of Delhi and New
educated and professional classes followed the
Delhi, 1980.
Western ideas of suburbanization and formed their
Source: Drakakis-Smith
own neighborhoods (Lowder, 1986). The migrants
and poor did not live in the core of the city, but
formed shantytowns in the peri-urban fringes and in unserviced areas (under bridges, along
riverbanks).
But, the morphological pattern of each Third
World city is different mainly because of the
presence of an indigenous city enclosed by a
colonial city, and subsequently surrounded by an
industrial city (Lowder, 1986). The
morphological model of Asian port cities shows a
multiple nucleus (Figure 3.7). The nuclei are
original village, traditional commercial areas and
modern commercial areas. An analysis of
Calcutta showed a pattern based on land use,
family ties, ethnicity and literacy. The social
Figure 3.7 Asian Ports
pattern showed concentric zones for land use.
Source: Lowder, 1986
Literacy and ethnic patterns emerged in a sectoral
form. A study of Colombo (Herbert and de Silva, 1974) found that social status, land use,
substandard living conditions and ethnicity were the broad variables that defined the social
pattern of the city.
The colonial cities in Latin America show a centralized social pattern (Portes, 1975).
The center of the city was the plaza. Around the plaza was the important buildings including
a church. The residences of the richer class formed the first concentric zone around the
plaza. The second and third concentric zones were occupied progressively by poorer people.
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
33
Here, the residences became smaller and public
amenities were reduced. The outer ring
bordered on farmland (Figure 3.8). A consistent
relationship existed between socioeconomic
position of the household and their distance from
the center of the city; the farther away from the
center, the poorer the household (Cornelius,
1975). In the 18th and 19th centuries, many large
cities became crowded. Wealthier families
began to move out of the center and settle in
more isolated locations. The pattern is similar to
the one described by the sector model of North
Figure 3.8 Latin American Cities
America. In Lima, Santiago and Chile
Source: Lowder, 1986
residential colonies moved from the center of the
city to the urban periphery which were selected
for their better geographic, climatic and aesthetic factors. Soon socioeconomic status related
to nearness to the center became related to distance away from the center. The pattern was a
creation of the lifestyle choices of the urban rich (Portes, 1977).
3.8.3 Indian Cities
In cities of India, spatial segregation based on ethnicity, caste, religion and language
rather than demographics and economics can be seen. The social ties are horizontal and
vertical. The horizontal relationships are between people of the same cultural background
while vertical relationships are between caste and class. Many studies have been done to
study Indian urban areas, and especially to construct a structural model. It has been found
that Indian cities defy social modeling. But, in general, the Indian urban social scene
essentially reflects two facets of non-western structure (Hall, 1980):
i Residences have not yet come to serve the symbolic function they do in the
Western world.
i Symbolic functionalism is performed by religion and caste and buttressed by
regional affiliations, languages and customs. The nature of traditional social
status and the interdependence and spatial interpretation of diverse, yet
complementary, status groups help to produce a very obscure patterning of social
groups at the micro-level of analysis.
Research findings point out that while caste is important in rural societies for its very
functioning, in urban environments the meaning of caste becomes more important in terms of
identity rather than function. For example, in rural areas, farming is done only by the Sudra
caste, and religious duties performed by the Brahmins. In the cities where new professions
were created, new definitions had to be made. Soon, industrial and office workers belonged
to all castes. The greater complexity of urban life and the difficulty of maintaining caste
identity through residential segregation alone, has created social organizations for each caste
(Kopardekara, 1986). A second indigenous factor suffusing urban society is that of regional
affiliation. "Particularly in cosmopolitan cities cultural or linguistic diversity and regional
associations develop to extol their culture and language and to participate in their own
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
34
regional festivals if not usually celebrated in the region within which they live now" (Hall,
1980:35). Certain areas are known for their residents speaking a particular language only.
Although the neighborhoods that result are not corporate groups in the sense in which they
are defined, such neighborhoods are the source for the development of the corporate groups.
Weinstein (1974) made an attempt to produce a conceptual model for the social
segregation of an Indian city. He postulated three dimensions as being important
contributors to residential segregation. These three dimensions were
i socioeconomic dimension symbolized by the bazaar
i political dimension represented by an administrative symbol
i prestige dimension derived from the religious function of a temple.
These three dimensions would form
concentric zones (Figure 3.9). Their
influence and interplay causes residential
segregation. The centroid of the system
represents the optimum location for
accessibility to all three functions.
However, real case studies did not prove
this theory. Instead, it was found that
multiple nuclei were present, and that the
temple acted as the most meaningful focus
for the spatial distribution of social
characteristics.
Bazaar
Centroid
Fort
Temple
Brush (1977) studied 24 cities in
India and discerned four types of gradients
of population directly related to their
Figure 3.9 Pattern of Indian Cities
Source: Weinstein, 1974
evolutionary pattern. Pune and Varanasi,
cities that were well developed even before
the colonial period, had retained their residential core (Mehta, 1968). Bombay, Calcutta and
Madras, colonial cities, had western style CBDs. Hyderabad had two nuclei – the old city
and the colonial city. Industrial towns like Jamshedpur were planned around their industrial
core.
Ahmad (1965) did a factor analysis of the socioeconomic characteristics of Indian
cities. He had the following conclusions.
i North Indian cities had low female employment rates, low literacy, low migration
and equal male to female ratio.
i South Indian cities had higher female employment rate, higher literacy, higher
migration and equal male to female ratio.
i Metropolitan cities (Bombay, Madras, Calcutta) has low-density commercial
centers surrounded by high-density residential neighborhoods.
i The modern planned cities (Jamshedpur, Chandigarh) have low population
densities with no concentration of industrial, commercial or administrative areas.
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
35
Such broad conclusions are results of regional analyses. Analysis at the level of a single city
gave patterns that are more complex. A systematic analysis of census data for Bombay was
done (Kosambi, 1986). Census data from 1881, 1901, 1831 and 1961 was used to determine
the evolution and change of the social pattern. The patterns were attributed to Europeanism,
commercialism, religious polarity, transportation and socioeconomic status (Kosambi, 1986).
These examples show that the urban social pattern of Indian cities is very complex
due to the influence of a variety of factors. The presence of many religions, languages,
castes and classes produces a more heterogeneous pattern. The social patterns were also
strongly influenced by the age of the city. The existence of multiple physical urban patterns
caused by the presence of indigenous settlements, British cities and industrial towns within
the boundary of the urban area.
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
C oncentric Z one T h eory
S ector T heory
36
M ultiple N uclei T heory
Incom e g rou p 1
commercial
ethnic group
CBD
In com e g roup 2
Tran sitio n
residential
L ow incom e
M iddle inco m e
In com e g rou p 3
H igh inco m e
D elhi
Latin A m erica
A sian P orts
C oncentric + S ector T heories
C hicago
C alcutta
C oncentric + S ector + M ultiple N uclei T h eories
Figure 2.10 Urban Social Patterns and Relevant Case Studies.
Source: Lowder, 1986, Hartshorn, 1992.
industrial
Malathi Ananthakrishnan
Chapter 3: The Conceptual Framework
37
3.9 Conclusion
The urban social pattern is the complex manifestation of the underlying cultural
values of the population within a particular built environment. In the case of India, the
sociocultural factors are related to caste, class, religion and language. These characteristics
stratify the society into vertical and horizontal systems. Stratification causes social
inequality in terms of wealth, power and status. The historical evolution of cities has
supported this stratification. In the design of Navi Mumbai, an effort was made to prevent
this social stratification and use residential allotments to fulfill this objective.
The growth of cities across the world has been studied. The urban social pattern of
these cities has been generalized. Three leading western theories describing the urban social
pattern of cities dominate the literature on urban social patterns (Hartshorn, 1992). These
are concentric zone theory, sector theory and multiple nuclei theory. These theories have
been combined in a social area analysis to describe the social pattern based on a few social
variables. Social area analysis assumes that a few independent factors can explain the spatial
patterning of a city. In the American cities, the components derived from social area
analysis were termed as socioeconomic status, family status and ethnic status.
The components of the analysis of American cities are not entirely apparent in the
Third World cities. Status in Third World cities is based on family membership or
socioeconomic class. The lifestyle depends on ethnicity and migration. The lifestyle factor
in North American cities relates small nuclear families with higher education achievements
and better employment opportunities. In Third World cities, this is not evident due to the
existence of multi-generational families. The households are generally large with a range of
ages, skills and professions. Migration may also be restricted to the men of the family. The
reasons for migration are also varied – they may be migrating as a result of natural
calamities, or in search of opportunities in the city. Male dominance, migration or ethnic
group represent the ethnic factor.
Traditional Indian cities have grown over a very long period of time. The residential
neighborhoods of such cities are not as well defined as they are in the American cities. In
the case of Navi Mumbai, the residential neighborhoods have been designed using the
neighborhood principle as those designed in America. Land-use is also similar in that it is
predominantly single-use zoning. A market economy strongly influences the lifestyle of the
citizens of Navi Mumbai. In such a case study, it is appropriate to use a social area analysis
to delineate the urban social pattern. However, this social area analysis must take into
consideration the indigenous factors. Here, the researcher’s knowledge of the local
environment is important to contextualize the pattern more appropriately.
Chapter 4: Research Design
Determining the urban social pattern of Navi Mumbai is the primary research
objective of this thesis. The issue of spatial distribution of different kinds of people in Navi
Mumbai is of primary interest. The research investigates the relationship between the spatial
pattern of Navi Mumbai and the different theories of urban social patterns discussed in the
literature review. The analysis looks at the variables at once and at their respective locations
in their distribution. The general issue of social areas will be accomplished through social
area analysis. The theories put forth by Burgess, Hoyt, and Harris and Ullman will be the
theoretical framework for the conceptualization of the social pattern of Navi Mumbai.
4.1 Social Area Analysis
Social area analysis provides a broad framework for analyzing the social patterns of a
city. It was first put forth by Shevky and Williams (1949) in a study of Los Angeles. This
analysis classifies census tract data into three main constructs - socioeconomic status, family
status and ethnic status. The basic premise of social area analysis is that a city cannot be
studied in isolation from the overall society (Shevky and Bell, 1955). The increase in
industrialization creates an occupational status system (Timms, 1971). The family as a unit
becomes weaker. Better transportation systems increase mobility and lead to a greater
sorting of population (Cadwallader, 1985). Under these conditions, immigration of rural
population leads to segregation based on language, religion and ethnic background. These
factors are taken into consideration in social area analysis.
Cities are complex entities that have many different functions performed by many
different people. The pattern of the city may be determined by statistical analysis or by
discerning people’s mental images of the city. A set of variables describing the social
structure of the city can be used in the statistical analysis. These involve population,
economic, and housing characteristics. The aim is to identify key combinations of different
measures that provide an adequate basis on which to differentiate the sub-areas from one
another (King and Golledge, 1978).
Social area analysis shows how family characteristics, economic status and ethnic
background produce a certain spatial pattern in the city. The study involves the
categorization of a city based on social rank, urbanization and segregation. Earlier, there was
considerable criticism about the choice of variables. They were considered to be very narrow
and not universally applicable. However, mapping of social area analysis for a large sample
of cities showed that socioeconomic status, urbanization index, and ethnicity confirmed the
validity of the analysis. These three factors also corresponded to the theoretical models
proposed by Burgess, Hoyt and Harris and Ullman. Thus, the city was analyzed as a
composite made up of three layers. Generally the economic model showed a sectored
pattern, the urbanization component showed a concentric ring pattern, and ethnic segregation
showed a multiple nuclei arrangement. Although these analyses have been more effective for
studying North American cities, studies in Calcutta, Cairo and Helsinki showed some useful
generalization. The social area analysis may be done statistically by a factor analysis. It is a
Malathi Ananathakrishnan
Chapter 4: The Research Design
39
device that seeks interrelationships among the set of input variables (Herbert and Thomas,
1990).
Social area analysis based on western thinking can not be naively applied to the study
of urban social patterns in India. Social structure in India is a result of cultural, religious and
historic development with both horizontal (kinship, religion, language) and vertical
(occupation, education, caste) dimensions (Hall, 1980). Variables that arise from such
cultural determinants need to be used in the factor analysis.
4.2 Hypotheses
As discussed in the literature review, mapping of social patterns in many cities across
the world show that the socioeconomic status, family status and ethnic status correspond
respectively to the sector theory, concentric zone theory and multiple nuclei theory. In this
case study of Navi Mumbai, my null hypothesis, H0, is: no significant difference in key
variables is expected and hence no social patterning will occur. This hypothesis is put forth
on the assumption that the social agenda put forth in the Development Plan has been
successfully implemented. If H0 is false, then the pattern will be explained using the existing
theories.
4.3 Operationalization
Certain variables will be used to operationalize the social area analysis to obtain the
urban social pattern. The variables are tabulated below:
Table 4.1 Constructs and Variables
Construct
Variable
Socioeconomic status Profession
Number of earning members
Income
Education
Family status
Demographics
Women at home
Family size
Dwelling size
Type of house
Year of occupation
Ethnic status
Religion
Language
4.4 Data Collection
The data required for the analysis can be obtained from census tracts of Navi
Mumbai. This database provides aggregated information about each node (township), and
each sector (neighborhood) of the nodes. The sectors (neighborhoods) are identical to census
block tracts. This provides a spatial hierarchical data set. The data available is based on a
Malathi Ananathakrishnan
Chapter 4: The Research Design
40
socioeconomic survey done by CIDCO in December 1995. In this research, the sector is the
unit of analysis. The survey was carried out on a ~22% sample basis for each node.
Table 4.2 Survey Sampling
Node
Total Number of
Dwellings
Vashi
27,283
Nerul
16,056
New Panvel
9,338
Belapur
9,109
Kalamboli
9,007
Airoli
13,378
Kopar-khairane
14,161
Sanpada
2,357
Survey Coverage
% of total
6656
4219
2125
2034
2282
2530
2506
544
24
26
23
22
25
19
18
23
The issues of validity and reliability arise in the use of census data for testing the
hypothesis. For a social area analysis, data covering a large area is required. The only data
source that provides this information, is census data. The census data is not 100% reliable.
An error of 5-8% is expected. All data is standardized. Statistics are weighted for spatial data
because, although variables are related, the units of analysis are not identical.
4.5 Methodology
Four methodologies are used to analyze the data. These are techniques in multivariate
analysis. The first is a descriptive analysis of the data setting out the parameters that need to
be considered to define the meaning of heterogeneity. The second is a principal components
analysis. This is a detailed stage of analysis. Although principal components analysis is no
longer considered the most favorable mode of analysis to delineate patterns, for the purpose
of this thesis it shall be used. The third is cluster analysis of the cases to see which variables
are closely associated. Finally, cartographic mapping, and GIS overlay techniques are used
to determine the social pattern at the regional and sub-regional levels. The variables are
expected to cluster based on the constructs described above.
The descriptive analysis helps understand the finer dimensions of the data, and
compare it to other cities. The principal components analysis draws out the relationship
between the variables. The cluster analysis puts together cases which are similar based on
the relationship between the variables. The GIS and mapping techniques convert all the
statistical information into a graphic representation. These four methods are collectively
used to analyze the data.
4.5.1 Descriptive Analysis
The first stage of analysis describes the data at both the regional and sub-regional
scale. At the regional scale the data is tabulated, and at the sub-regional scale attached as
Appendix C. The single variable from that data set is selected and a histogram of it at the
Malathi Ananathakrishnan
Chapter 4: The Research Design
41
sub-regional scale is drawn. The data is interpreted in terms of its mean and standard
deviation. Comparative figures at the national scale are also given.
In order to interpret this descriptive statistics for homogeneity, it is necessary to
provide a permissible range of variation. A variation greater than thirty percent of the total
population from the mean (15% on either side of mean) is used here to show unequal
distribution. If the standard deviation at the 95% confidence interval is within 15% of the
mean, then the pattern shall be interpreted as homogeneous.
4.5.2 Principal Components Analysis
A principal components analysis reduces a large number of variables to a smaller
number of underlying components. Principal components analysis can be thought of as four
matrices. The first matrix is a simple data matrix. The cases are the rows and the variables
are the columns. The N by M matrix is standardized in terms of standard deviation. The data
matrix is converted into a correlation matrix. This matrix is next converted into a factor
matrix. This matrix contains components that represent a group of interrelated variables.
Principal components are the eigenvalues of the correlation matrix (Davis, 1986). The
elements of the eigenvectors that are used to compute the scores are called principal
component loadings. These loadings indicate the strength of the relationships between
variables and underlying components. Finally, the matrix of component scores is computed.
Each original observation is converted into a principal component score. Patterns can be
delineated from mapping these components.
The first step of principal components analysis is to obtain an initial solution. The
initial solution is based on the orthogonal solution. This solution determines whether a small
number of the components can be used to explain the covariance between a large number of
variables. The eigenvalue criterion (eigenvalue greater than or equal to 1) helps eliminate
components which are not meaningful. Corresponding communalities are also estimated.
Generally variables with communalities less than 0.7 are not significant in the correlation
matrix. "To obtain the initial solution, certain restrictions are imposed. These restrictions are
(1) there are k common components (2) underlying components are orthogonal to each other
(3) the first component accounts for as much variance as possible, the second component
accounts for as much of the residual variance left unexplained by the first factor, and so on"
(Kim and Mueller, 1978). The second step is to rotate the axis to get a simpler solution. The
axis has been rotated orthogonally (assuming the factors are uncorrelated). This is varimax
rotation. Rotating the axis more closely intersects the clusters of variables. The rotation
normally removes the negative loadings, and results in a simpler pattern.
4.5.3 Cluster Analysis
Classification of data places objects in one or more homogenous groups.
Characteristics of the urban social pattern can be revealed by considering the relationship
within groups. Cluster analysis classifies the groups according to the observations into moreor-less homogenous and distinct groups (Davis, 1986). This approach to classification is
very subjective. It has very little theory and depends largely on experience. The
Malathi Ananathakrishnan
Chapter 4: The Research Design
42
classification procedure used here is hierarchical clustering. This method joins similar
observations, then connects the next most similar observations to these. The levels of
similarity are used to construct the dendrogram. A correlation coefficient or distance
coefficient may be used to evaluate similarities. The distance coefficient is not constrained
within the range of +1.0 to -1.0, as is the correlation coefficient, and so produces better
dendrograms. Distance coefficients are linked at low values. The criteria for clustering is
that both observations mutually have the highest correlation with each other. A measure of
similarity between every pair of objects is computed using Euclidean distance. A low
distance would indicate that two objects are similar and a large distance would indicate that
the two objects are dissimilar.
4.5.4 Mapping and Overlays
The final stage is the mapping of the descriptive analysis, principal components
analysis and cluster analysis. This mapping helps explain the statistics through a easily
interpretable graphic representation. This stage of analysis integrates the theoretical
framework, and the statistical analysis to determine an interpretation of the pattern.
4.6 Data Analysis
Descriptive analysis of the data was done using Microsoft Excel and SPSS. The
SPSS program was also used to perform a principal component analysis and a cluster
analysis on this data set. The aim of these two kinds of analysis was to determine if the data
set clustered into the three constructs given above. Both the analyses were done at a regional
and sub-regional scale. The regional scale was comparisons between the eight nodes of Navi
Mumbai. Analysis was then done of one particular node of Navi Mumbai, namely Vashi.
Mapping of the principal components determined if any pattern exists in the social
characteristics of Navi Mumbai at the regional and sub-regional scales.
Chapter 5: Presentation of Data
5.1 Introduction
The aim of this research is to study the urban social pattern of the population across a
hierarchical scale. This spatial scale is
• regional scale (nodes),
• sub-regional scale (sectors of a node)
The study areas at the regional level of analysis are those of the nodes of Navi
Mumbai including Vashi, Nerul, Belapur, Kalamboli, Panvel, Kopar-khairane, Airoli and
Sanpada. The methodological reason for selecting these eight nodes out of the total of
thirteen is because data was available for only these eight nodes. Then the data set was
studied at a sub-regional level by analyzing the neighborhoods of Vashi node. Vashi is the
oldest node, and has fully developed residential sectors. As this node had the most complete
data, it was selected out of the eight nodes.
Data for the regional and sub-regional scale was collected from the 1995
socioeconomic survey conducted by CIDCO. As the 1995 survey data was the most recent
data, it was used for analysis. One or two variables from each set was selected for this
study. The criteria used to select the variables were based on the expectations of the
hypothesis. The variables needed to explain the constructs as well as possible; only then
would they bring out the characteristics of the construct. All the variables belonged to closed
sets. Hence, only one or two representative variables from each set was selected. The
analysis is divided into descriptive analysis of variables and detailed analysis at the regional
and sub-regional.
5.2 Descriptive Analysis of Data
The different constructs and variable names described in the methodology section are
tabulated below (Table 5.1) with the actual variable from the data set.
Table 5.1 Constructs and Variables
Construct
Variable Name
Variable from data set
Socioeconomic Profession
highly skilled, unskilled
status
Number of earning members 1 earning member
Income
Rs. 2651-4450
Education
high school
Family status
Demographics
Male pop. age 25-45, female pop. age 25-45
Family size
4 to 5 members
Dwelling size
26-35 sq. m.
Type of housing
CIDCO
Tenure
1980s
Last place of residence
Bombay
Ethnic status
Religion
Hindu, Muslim
Language
Marathi, Malayalam
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
44
All data tables are for the regional scale while the histograms are from the sub-regional scale.
Data tables for the sub-regional scale are given in Appendix C.
5.2.1 Socioeconomic Status
The socioeconomic status is an indicator of social class. A profession brings with it a
certain prestige and social class. An increase in the number of earning members increases
family income and the socioeconomic class. Better education facilitates getting better jobs
and higher income. All these variables are closely correlated, and form the socioeconomic
indicator.
Number of earning members: Out of the total population of 91787 recorded in the survey,
30430 are the working population. 33.15% (a slight increase from 32.8% recorded in the
1987 survey) of the population makes up the workforce of Navi Mumbai. The percent of
males and females is shown in Table 5.2 and the number of earners in Table 5.2.
Table 5.2Work Force
Percent of male
Percent of female
population in work population in work
force
force
Vashi
53
10
Nerul
55
7
Belapur
52
12
Kalamboli
54
6
Panvel
57
8
Kopar-khairane
56
10
Airoli
53
7
Sanpada
58
9
Mean
54.75
8.62
Standard Deviation
2.12
1.99
The average number of earners per household is 1.35, while it is 1.67 in Greater Bombay.
Seventy-five percent of families had one earning member and twenty percent of families had
two earning members (Table 5.3).
Table 5.3 Number of Earners
Single
2
3
4+
Vashi
68
23
6
2
Nerul
78
16
3
1
Belapur
68
22
4
2
Kalamboli
79
15
4
1
Panvel
78
17
3
1
Kopar-khairane
76
17
5
1
Airoli
74
20
4
1
Sanpada
70
19
7
2
Mean
74
19
5
1
Standard deviation
5
3
1
1
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
6000
5000
4000
3000
Frequency
2000
1000
Std. Dev = 7.96
Mean = 66.3
N = 19127.00
0
45.0
50.0
55.0
60.0
65.0
70.0
75.0
80.0
Cases weighted by population
Figure 5.1 Distribution of Single-earner families
45
For the analysis, the variable, single
earning member, was selected. This
is most representative of the entire
population, and has a normal
distribution over eight cases. The
mean is 74 with a very low standard
deviation of 5.
The distribution of the single
earner families is shown in Figure
5.1. The distribution of the singleearner family at the regional level
shows a standard deviation of only 5
(mean=74). This means that the
distribution is homogeneous. At the
sub-regional scale the standard
deviation is 7.96 (mean=66.3). Both
the values are within 15% of the
mean. The pattern is homogeneous.
Profession: Good employment opportunities are offered by the manufacturing industries of
Navi Mumbai. 25% of the workforce is employed there. Government offices including
banks and public sector enterprises employ 21% of the workforce. Small businesses account
for 15% of the employees, while service professions such as shops and hotels employ 7% of
the workforce. Professional workers in teaching and medical institutions are 7% of the
workforce. For this analysis classification based on skills is tabulated (Table 5.3). Highly
skilled professionals hold higher level managerial and supervisory jobs or are professional
business persons, contractors and consultants. In Navi Mumbai this economic class
constitutes 38% of the work force. The standard deviation is 11. Skilled workers are factory
workers, carpenters, construction workers and trainees. They form 17% of the workforce.
Unskilled persons are construction laborers and housemaids. On an average, they are 19% of
the work force and the standard deviation is 11. Kopar-khairane has a low number of highly
skilled workers and a large number of unskilled workers (Table 5.4). The main reason is that
this node is presently under construction and has a large workforce of construction workers.
Table 5.4 Occupational Classification of Workforce
Highly
skilled unskilled
office
selfteacher other
skilled
worker
worker assistant employed
Vashi
45
12
12
15
9
4
3
Nerul
38
23
13
15
4
4
3
Belapur
47
12
8
20
3
6
4
Kalamboli
24
31
20
12
8
3
2
Panvel
43
19
9
16
4
7
2
Kopar-khairane
20
9
41
9
9
0
12
Airoli
34
18
44
12
5
1
4
Sanpada
49
9
20
14
3
3
2
Mean
38
17
19
14
6
4
4
Standard Deviation
11
8
11
3
3
2
3
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
46
The corresponding data was not available at the sub-regional scale.
Income: The income groups are defined by the Government of India’s household income
classification into:
• economically weaker section (EWS) earning less than Rs1250 per month
• lower income group (LIG) earning between Rs. 1251 and Rs. 2650
• middle income group (MIG) earning between Rs. 4451 and Rs 7500 and
• higher income group (HIG) earning more than Rs. 7500 per month.
The proportion of EWS:LIG:MIG:HIG is 2:16:34:48. This shows a proportionately large
middle and higher income groups. Thus, in Navi Mumbai it appears that the four income
groups have to be redefined based on the median and/or mean income of this region rather
than using the national urban averages (Table 5.5). The monthly average household income
is Rs. 4900 and the monthly average per capita income is Rs. 1230.
Table 5.5 Household Income
upto 12511250 2650
Vashi
2
14
Nerul
3
27
Belapur
2
12
Kalamboli
2
26
Panvel
2
24
Kopar-khairane
2
9
Airoli
1
14
Sanpada
1
5
Mean
1.88 16.38
Standard deviation 0.64
8.26
26514450
27
36
27
46
31
32
39
31
33.63
6.46
7000
6000
5000
4000
3000
Frequency
2000
Std. Dev = 10
1000
Mean = 27.9
N = 19127.00
0
5.0
15.0
10.0
25.0
20.0
35.0
30.0
45.0
40.0
50.0
Cases weighted by population
Figure 5.2 Frequency of Families with income
range Rs. 2651-4450
44517500
30
21
35
21
31
36
34
42
31.25
7.29
750110000
15
6
12
3
5
9
8
12
8.75
4.06
1000115000
7
3
5
1
3
7
2
4
4
2.2
15000+
3
1
2
0
2
0
0
1
1.13
1.13
The income range of Rs. 2651-4450 was
selected for the principal components
analysis because the median income of
Rs. 4200 fell within this range. Almost
34% of the population falls within this
category, and the standard deviation is
6.46.
The regional scale shows a
standard deviation of 6.46 (mean=33.45)
and the sub-regional scale, the standard
deviation is 10.98 (mean=27.9) (Figure
5.2). Both cases do not show a
homogeneous distribution of people
based on income as the standard
deviation is greater than 15% of the
mean.
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
47
Education: The survey shows that 27% of the total population is children going to school,
while 4% of the population is going to college. Most students attend school and college
within their node (township). Sanpada is the only node without any education facilities.
Vashi has all the major colleges. Hence, the column titled Vashi shows that some students
from all other nodes also go there to attend school or college (Table 5.6). 76% of the students
walk to their school or college, 12% use public transport, 10% use bicycles and only 2% go
by school bus.
Table 5.6 Location of Education Institutions
Vashi Nerul Belapur Kalamboli Panvel Kopar- Airoli Sanpada Bombay
khairane
Vashi
88
1
1
1
0
0
0
0
9
Nerul
9
77
2
1
1
0
0
0
10
Belapur
10 12
67
1
1
0
0
0
9
Kalamboli
1
0
1
90
4
0
0
0
4
Panvel
2
1
2
8
76
0
0
0
11
Kopar-khairane 17
0
0
0
0
81
0
0
2
Airoli
7
0
0
0
0
0
83
0
10
Sanpada
47
8
1
1
0
0
16
0
27
In the Bombay region literacy rates are seventy-five percent for adult population. 51% of the
children go to schools where the medium of instruction is English, and 35% of the children
go to schools where the medium of instruction is Marathi (12% did not specify their medium
of instruction). The level of education is categorized into illiterate, children, primary school
education, secondary school education, high school education, technical education, Bachelors
and Masters degrees. The value given represents the highest level of education achieved by
at least one member of the family (Table 5.7).
Table 5.7 Level of Education
illiterate
Vashi
Nerul
Belapur
Kalamboli
Panvel
Kopar-khairane
Airoli
Sanpada
Mean
Standard deviation
4
3
5
7
3
4
4
4
4.25
1.28
Children Primary secondary
9
5
8
10
8
6
7
8
7.63
1.60
14
15
18
20
14
13
16
12
15.25
2.66
27
27
30
34
25
27
37
21
28.5
5.07
high
school
22
17
21
16
19
15
18
25
19.13
3.36
technical
1
2
1
2
4
1
1
2
1.75
1.04
BS
MS
22
4
24
5
15
2
9
1
22
4
29
4
13
3
21
4
19.38 3.38
6.52 1.30
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
48
The variable ’secondary school’ was selected under level of education. 28.5% of the
population falls under this category with a standard deviation of 5.07. Secondary school
means an education of up to Grade 10 and the passing of a government examination
(matriculation). This level of education is provided to everyone by the government free of
cost. The national average for this variable is 16.6 (Census of India, 1991)
The standard deviation of this variable at the regional scale is 5.07 (mean=28.5), and
at the sub-regional scale is 7.13 (mean=40.6). The variation is not homogeneous at either
scale (Figure 5.3).
5.2.2 Family Status
Demographics: The nodes of
Navi Mumbai have a female to
5000
male ratio of 848 to 1000
(comparative figures for Bombay
4000
are 819 to 1000). Children up to
the age of 15 constitute 33% of
3000
the total population. The age
group 16 to 24 is 10% of the
2000
population. The working age
group of 25 to 44 is 39% of the
1000
Std. Dev = 7.13
population. About 9% of the
Mean = 40.6
population are in the age group of
N = 19127.00
0
45 to 59, and only 3% of the
15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0
population are in the 60+ range.
Cases weighted by population
The present pattern clearly shows
Figure 5.3 Frequency of Families with at least one
a younger population with a high
individual with Secondary Education
percentage of children.
The demographic
indicators used are male and female population of the age group 25-45. This age group was
selected because it is a subset of the population and it makes most of the decision regarding
social choices (Table 5.8, Table 5.9).
Table 5.8 Male Population
below 3 4-5
6 - 9 10 -15 16 - 21 22 -24 25 -44 45 -59
60+
Vashi
4
3
7
15
12
5
34
14
5
Nerul
7
5
10
12
8
5
41
8
3
Belapur
6
4
8
14
12
5
37
11
4
Kalamboli
8
6
11
13
8
5
43
6
1
Panvel
8
4
8
11
9
5
44
9
3
Kopar-khairane
10
6
10
10
8
5
43
6
1
Airoli
7
5
10
14
11
4
39
8
2
Sanpada
7
4
6
10
10
5
43
10
4
Mean
7
5
9
12
10
5
41
9
3
Standard deviation
2
1
2
2
2
0
3
3
1
Frequency
6000
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
49
The standard deviation of the
population is 3 (mean=41) at the
regional level, and 3.39 (mean=38)
at the sub-regional level (Figure
5.4). The population age structure is
uniformly distributed over the whole
region.
7000
6000
5000
4000
3000
Frequency
2000
Std. Dev = 3.39
1000
Mean = 38.0
N = 19127.00
0
32.0
36.0
34.0
40.0
38.0
44.0
42.0
48.0
46.0
52.0
50.0
Cases weighted by population
Figure 5.4 Frequency of male population in the age
group 25-45
Figure 5.9 Female Population
below 3 4-5
Vashi
5
3
Nerul
7
5
Belapur
5
4
Kalamboli
15
10
Panvel
8
4
Kopar-khairane
9
6
Airoli
6
5
Sanpada
6
4
Mean
8
5
Standard deviation
3
2
6 -9
8
10
8
16
8
10
10
8
10
3
10 -15
14
13
14
20
11
9
15
10
13
3
16 -21
11
9
11
13
10
12
10
13
11
1
22 -24
5
7
6
12
9
10
6
9
8
2
25 -44
39
40
40
6
40
37
39
39
35
12
45 -59
10
6
8
6
6
5
6
9
7
2
60+
4
2
3
2
3
1
2
3
3
1
The female population of the age group 25-45 is also uniformly distributed over the
study area.
Family size: The average family size is 4.01 for all the nodes (Table 5.10). A descriptive
analysis of the data over the last 20 years shows that household size has been constantly
increasing. The reason for this is not only marriage and children, but also the need to
accommodate older parents. In Vashi, average family size has increased from 3.73 in 1987
to 4.21 in 1985. The comparative family size for Bombay is 4.76 and the national average is
5.52.
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
Table 5.10 Family Size
Single
2,3
4,5
6,7
8,9,10
Vashi
Nerul
Belapur
Kalamboli
Panvel
Kopar-khairane
Airoli
Sanpada
Mean
Standard deviation
26
34
31
31
41
41
27
39
33.8
6.0
57
54
53
52
45
45
56
45
50.9
5.1
14
10
13
14
8
10
15
12
12
2.4
2
0
1
0
1
1
1
1
0.9
0.6
1
2
1
3
5
3
1
3
2.4
1.4
50
Average
family size
4.21
3.87
4.03
3.99
3.67
3.81
4.22
3.85
The families with a size of 4 or 5
members was chosen as 50% of the
population belongs to this
category. The variable has a
standard deviation of 5.1.
6000
5000
4000
3000
Frequency
2000
1000
Std. Dev = 5.85
Mean = 56.0
N = 19127.00
0
42.5
47.5
45.0
52.5
50.0
57.5
55.0
62.5
60.0
67.5
65.0
The variation of the data is
minimal. At the regional scale the
standard deviation is 5.1
(mean=50.9), and 5.85 (mean=56)
at the sub-regional scale (Figure
5.5).
Cases weighted by population
Figure 5.5 Frequency of households with 4 or 5
members
Type of Housing: Initially CIDCO built ninety percent of the housing stock. CIDCO began
all construction in Navi Mumbai. Later, private builders and cooperative housing began
developing residential sectors. Since Vashi is the oldest node, the data shows more
diversification of the housing stock. All other nodes show a dominance of CIDCO housing
(Table 5.11).
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
Table 5.11 Type of Housing
CIDCO
Vashi
64
Nerul
95
Belapur
91
Kalamboli
99
Panvel
80
Kopar-khairane
98
Airoli
100
Sanpada
88
Mean
89.38
Standard Deviation
12.24
Pvt. House
2
0
0
0
5
0
0
1
1.00
1.77
7000
6000
5000
4000
3000
Frequency
2000
Std. Dev = 35.62
1000
Mean = 66.4
N = 19127.00
0
0.0
20.0
10.0
40.0
30.0
60.0
50.0
80.0
70.0
100.0
90.0
Cases weighted by POP
Figure 5.6 Frequency of Houses built by CIDCO
Pvt. Co-op Commercial
29
2
5
0
9
0
0
1
15
0
2
0
0
0
11
0
8.88
0.38
9.76
0.74
51
Other
1
0
0
0
0
0
0
0
0.13
0.35
For this variable, only houses
built by CIDCO was selected. Houses
built by CIDCO are 90% of the
houses available. The standard
deviation is 12.24.
The standard deviation at the
regional scale is 12.24 (mean=89.38)
while at the sub-regional scale it is
35.62 (mean=66.4) (Figure 5.6). This
is a very significant result. CIDCO’s
aim to promote heterogeneity was to
be implemented by having a strong
hold over the housing market. At
Vashi, the oldest node, the strong
control is no longer evident. The
large deviation shows that private
construction has taken place. This
may be one of the main reasons for
the greater variability in the pattern at
the sub-regional scale rather than at the regional scale.
Table 5.12 shows present ownership of the house. CIDCO is still the major owner.
Most government offices that provide housing for their employees obtain long term lease
from CIDCO. Some houses are mortgage through CIDCO. The categories, private
ownership, resale and rental fall under private ownership.
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
Table 5.12 Ownership of House
Mortgage CIDCO
Vashi
11
23
Nerul
21
36
Belapur
8
40
Kalamboli
25
25
Panvel
7
33
Kopar-khairane
0
34
Airoli
0
51
Sanpada
15
32
Mean
10.88
34.25
Standard Deviation
9.09
8.75
Private
17
3
4
1
9
1
0
7
5.25
5.68
52
Resale
21
16
0
0
0
14
0
18
8.63
9.43
Rental
23
36
37
43
36
49
42
26
36.5
8.64
Dwelling size: The average size of dwelling units constructed by CIDCO is less than that
built by private builders (Table 5.13, Table 5.14). While CIDCO is building houses for the
EWS/LIG/MIG, the private builders are predominantly building for the HIG.
Table 5.13 Housing built by CIDCO
<15 16-25
26-35
36-50
51-75
76-100 101-150 150+
Vashi
11
30
22
14
15
3
2
0
Nerul
7
57
18
8
7
2
1
0
Belapur
0
26
10
33
20
11
0
0
Kalamboli
24
37
24
5
7
2
0
0
Panvel
10
33
16
18
22
1
0
0
Kopar-khairane
0
20
10
42
18
9
1
0
Airoli
0
30
28
17
18
6
0
0
Sanpada
0
61
18
12
9
0
0
0
Mean
6.50 36.75
18.25
18.63
14.5
4.25
0.50
0
Standard deviation 8.52 14.64
6.36
12.65
6.02
3.99
0.76
0
10000
The standard deviation of the data was
21.25 while the mean was 14.2 (Figure
5.7).
8000
6000
Frequency
4000
2000
Std. Dev = 21.85
Mean = 14.2
N = 19127.00
0
0.0
20.0
10.0
40.0
30.0
60.0
50.0
80.0
70.0
90.0
Cases weighted by population
Figure 5.7 Frequency of Housing Built by CIDCO
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
Table 5.14 Housing built by Private Enterprise
<15 16-25
26-35
36-50
Vashi
4
2
2
14
Nerul
0
6
6
6
Belapur
0
1
2
2
Kalamboli
0
8
0
0
Panvel
0
0
1
1
Kopar-khairane
0
91
5
5
Airoli
0
0
0
0
Sanpada
13
60
2
2
Mean
37.50 16.63
18.50
14.38
Standard Deviation 10.76 7.69
10.86
3.16
53
51-75
14
8
33
5
18
42
17
12
5.50
2.67
76-100
24
23
9
0
24
1
0
5
3.75
2.12
101-150
8
5
5
0
8
0
0
2
3.75
3.41
150+
2
0
1
0
2
0
0
1
14.0
6.0
The frequency distribution of houses
built by private enterprise shows a
12000
standard deviation of 18.67 and mean
10000
16.2 (Figure 5.8).
Dwelling size was selected
8000
based on type of house. For both
CIDCO-built houses and privately
6000
built houses, the dwelling sizes
4000
selected was 26-35 sq. m.
corresponding to middle income
Fre
2000
Std. Dev = 18.67
que
groups.
Mean = 16.2
ncy
N = 19127.00
0
Tenure: The growth of Navi Mumbai
0.0
10.0 20.0 30.0 40.0 50.0 60.0
can be divided into three stages: early,
Cases weighted by
population
slow phase in the 1970s, middle phase
in 1980s and accelerated phase in the
1990s. There is a great variation in the
Figure 5.8 Frequency of Houses built by Private
number of houses occupied between
Enterprise
nodes (Table 5.15). Only Vashi and
Belapur had a household population in the 1980s. Families began to reside in Nerul,
Kalamboli, Panvel and Airoli in the latter 1980s and in Kopar-khairane and Sanpada only in
the 1990s.
Table 5.15 Year of Occupation
before 1980 1981-85 1986-90 1991-92 1993
1994
1995
Vashi
11
28
24
8
9
14
5
Nerul
0
6
29
10
6
35
14
Belapur
4
23
24
11
13
18
7
Kalamboli
0
5
31
10
11
37
6
Panvel
0
11
14
15
14
34
12
Kopar-khairane
0
0
0
20
18
34
28
Airoli
0
0
47
14
12
16
11
Sanpada
0
0
0
0
8
48
44
Mean
1.88
9.13
21.13
11.00
11.38
29.50
15.88
Standard Deviation
3.94
10.88
15.99
5.83
3.78
12.09
13.50
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
54
The three time periods of 1970s, 1980s and 1990s account for the entire span of
growth of the city. Only the middle phase was selected as a representative variable.
However, this table only indicates the year of occupation of the present accommodation. It is
thus, not entirely accurate as families may have shifted after their first place of residence.
The standard deviation at the
regional scale is 20.25 (mean=30.25)
and 18.25 (mean=52.8) (Figure 5.9).
There is a very large variability,
which can be attributed to the pace of
construction.
7000
6000
5000
4000
3000
Previous Place of Residence: The
two variables describing previous
Std. Dev = 18.25
place of residence are Bombay and
1000
Mean = 52.8
Navi Mumbai (Table 5.16). These
N = 19127.00
0
describe migration from Bombay and
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0
movement within Navi Mumbai.
Cases weighted by population
Migration from Bombay is usually
Figure 5.9 Frequency of Tenure
the first stage of relocation where the
choice of house is not very important. This is because any house in Navi Mumbai would be
better than the existing living conditions in Bombay. Movement within Navi Mumbai shows
desire to move to a house of the homeowner’s choice.
Frequency
2000
Table 5.16 Previous Place of Residence
Island City Western Eastern
suburbs suburbs
Vashi
18.06
6.19
26.94
Nerul
13.58
5.55
23.56
Belapur
10.83
5.65
10.23
Kalamboli
5.79
2.94
11.39
Panvel
3.62
2.26
5.27
Kopar
14.2
2.63
17.16
Airoli
8.05
4.51
20.43
Sanpada
17.1
5.15
24.63
Mean
11.4
4.36
17.45
Standard
5.25
1.54
7.79
deviation
Thane
3.5
2.58
4.82
3.46
3.11
2.63
9.29
4.23
4.20
2.19
Navi
Mumbai
35.89
47.53
32.34
66.3
68.28
55.23
49.78
39.34
49.34
13.39
Within
state
3.44
2.58
13.32
5.26
6.82
4.51
3.75
2.57
5.28
3.54
Outside Outside
state
India
4.45
0.53
2.94
0.07
19.42
0.2
2.54
0.04
6.4
0.05
1.36
0.04
2.85
0
6.25
0.18
5.78
0.14
5.8
0.17
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
6000
5000
4000
3000
Frequency
2000
1000
Std. Dev = 9.56
Mean = 53.0
N = 19127.00
0
35.0 40.0 45.0 50.0 55.0 60.0 65.0 70.0 75.0
Cases weighted by population
Figure 5.10 Frequency of Bombay as Previous
Place of Residence
55
The variables, island city, western
suburbs, eastern suburbs and Thane
have been summed up to obtain the
variable, Bombay. This variable shows
the families whose most immediate
place of origin is Bombay.
The standard deviation of the
families whose previous place of
residence was Bombay is 9.42
(mean=26.01) at the regional scale and
9.54 (mean=53) at the sub-regional
scale. There is a large variation
because there has been migration from
the rural areas, from Bombay and
within Navi Mumbai.
5.2.3 Ethnic Status
This construct is very important because it is the construct that creates segregation in India.
Ethnic enclaves are formed mainly by religious and linguistic groups.
Religion: This variable is very important for this analysis because India has a number of
well-defined religions. The means of the religion variable correspond with the national
averages. This variable shows diversification of the population based on a cultural variable
(Table 5.17).
Table 5.17 Religion
Hindu
Vashi
84
Nerul
88
Belapur
79
Kalamboli
84
Panvel
94
Kopar-khairane
89
Airoli
88
Sanpada
80
Mean
85.75
Standard deviation 4.98
Christian
6
3
6
4
2
2
3
9
4.38
2.45
Islam
6
5
4
5
2
6
3
7
4.75
1.67
Jain
1
0
0
0
0
0
0
0
0.13
0.35
Sikh
2
3
7
6
1
1
1
3
3.00
2.33
Buddhist
1
0
2
1
0
2
5
1
1.50
1.60
Others
0
1
0
0
1
0
0
0
0.25
0.46
The variables Hindu and Muslim were selected for analysis. The Hindu population is the
majority and is homogenous. The mean is 85.75% and the standard deviation is only 4.98.
However, it is more important to analyze the minority religions to see if they are forming
ethnic enclaves. The Muslim population is 4.75% of the total and has a standard deviation of
1.67. An analysis of the other minority populations also show very large standard deviations.
Chapter 5: Presentation of Data
5000
10000
4000
8000
3000
6000
2000
4000
1000
Std. Dev = 4.
Mean = 82.4
0
N = 19127.00
Frequency
Frequency
Malathi Ananthakrishnan
56
2000
Std. Dev = 3.91
Mean = 6.9
0
N = 19127.00
Figure 5.11 Frequency of Hindus
Figure 5.12 Frequency of Muslims
The Hindu population is spread uniformly over the study are with standard deviation
4.98 (mean=85.75). The Muslim population and other minority religions show a nonuniform distribution over the study area.
Language:
The variable language is very important in the Indian context because civil violence
due to language has taken place across India. Hindi is the dominant language of the country.
Marathi is the local language, Gujarati is the language of the adjoining state, Punjabi is a
northern language, Bengali an eastern one and Tamil, Malayalam and Kannada southern ones
Table 5.18 Language
Marathi
Vashi
42.41
Nerul
45.75
Belapur
40.76
Kalamboli 55.87
Panvel
66.78
Kopar
67.93
Airoli
42.46
Sanpada
63.79
Mean
53.22
Std. dev
11.73
Hindi Gujarati Malayalam Punjabi Tamil Kannada Bengali Other
13.81 7.23
7.41
4.48 5.14
2.82
3.17
13.53
16.50 2.99
10.19
5.50 3.34
3.56
3.01
9.16
16.47 3.98
8.31
9.64 2.90
2.36
4.08
11.5
14.29 2.19
8.11
6.66 2.32
3.20
0.83
6.53
9.65
2.92
5.04
1.74 2.35
3.29
2.96
5.27
16.72 1.44
2.99
1.12 1.68
1.72
0.80
5.6
12.50 3.13
13.60
5.33 5.33
2.57
3.49
11.59
12.37 2.69
5.77
1.34 3.08
3.72
1.11
6.13
14.04 3.32
7.68
4.48 3.27
2.91
2.43
8.66
2.50
1.74
3.26
2.97 1.32
0.67
1.31
3.22
The two languages selected are Marathi and Malayalam. Marathi is the local language.
54% of the population speaks this language. Malayalam is the language of the state 1000
miles away, and there is a large population of Malayalam-speaking people in the greater
Bombay region. This forms a major minority language. This has been used to study if there
are any ethnic neighborhoods formed due to linguistic considerations.
Chapter 5: Presentation of Data
6000
6000
5000
5000
4000
4000
3000
3000
2000
2000
1000
Std. Dev = 15.73
Mean = 46.6
N = 19127.00
0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
Frequency
Frequency
Malathi Ananthakrishnan
57
1000
Std. Dev = 3.77
Mean = 6.9
N = 19127.00
0
2.5
7.5
5.0
12.5
10.0
17.5
15.0
22.5
20.0
25.0
Cases weighted by population
Cases weighted by population
Figure 5.13 Frequency of Marathi
Figure 5.14 Frequency of Malayalam
The standard deviation of Marathi is 11.73 (mean=53.22) at the regional scale and
15.73 (mean=46.6). The distribution of families with Marathi as their native language is not
very uniform (Figure 5.13). This is probably the result of the many other linguistic groups,
which have formed their own enclaves. The standard deviation of Malayalam is 3.26
(mean=7.68) at the regional scale and 3.77 (mean=7.6) at the sub-regional scale (Figure
5.14). The standard deviation is very large showing some areas have more Malayalamspeaking persons than others leading to the conclusion that ethnic enclaves do exist.
The descriptive analysis suggests that the urban social pattern is not defined by
homogeneous socioeconomic classes. There is a non-uniform pattern in socioeconomic
variables as well as in the ethnic variables. This pattern is more apparent at the sub-regional
scale rather than at the regional scale (Table 5.19).
Table 5.19 Spatial Pattern of Variables
Variable
Regional scale
Number of earning members
Uniform
Income
Non-uniform
Education
Non-uniform
Demographics
Uniform
Family size
Uniform
Type of housing
Non-uniform
Tenure
Non-uniform
Last place of residence
Non-uniform
Hindu
Uniform
Muslim
Non-uniform
Marathi
Non-uniform
Malayalam
Non-uniform
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
58
5.3 Regional Scale - Nodes
5.3.1 Principal Components Analysis (PCA)
The analysis at the regional scale uses the eight nodes (townships) as the cases for the
study. The use of PCA as a method of analysis was limited by the small number of cases.
The number of variables used in the analysis could not be more than the number of cases.
The constructs described on page 1 suggest the need for 12 variables. However, as PCA
limited the number of variables to 8, the variables selected were number of earning members,
income, secondary school education, family size, tenure, migration, religion and language.
The variables were weighted by the total population of each node. A PCA was run, and three
components were obtained. The rotated component matrix is used here for interpretation and
discussion (Appendix D).
The communalities of all the variables are very high, and in a range of 0.824 and
0.985. The total of the communality is 7.18, explaining 90% of the variance. Hence, the
assumption can be made that all the variables are significant and are useful for the study. The
outputs obtained from the SPSS program are used to determine which variables, or principle
components, are needed for the complete explanation of the difference in the data. The
principal components obtained from the rotated component matrix are used as they are more
simple to interpret. The components with eigenvalues greater than 1 will be used to explain
the variance. Component 1 with an eigenvalue of 3.468 explains 43.347% of the variation.
Component 2 with an eigenvalue of 1.902 explains 23.771% variation and Component 3 with
an eigenvalue of 1.818 explains a variation of 22.728%. Cumulatively these three
components explain 89.845% of the variation. Thus, nearly 90% of the variance of the 8
nodes lies within a 3-dimensional space.
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
59
Figure 5.15 Components in Rotated Space
education
1.0
income
earner
.5
Component 2
family size
religion
tenure
0.0
language
migration
-.5
1.0
.5
0.0
-.5
Component 1
-.5
1.0
.5
0.0
Component 3
Analysis weighted by population of each node
C o m p o n e n ts
1.5
TENURE
RELIGION
INCOME
MIGRATN
-1
FAM.SIZE
-0 . 5
EDUCATN
0
LANGUAGE
0.5
EARNER
loading
1
va r i a b l e s
Figure 5.16 Loadings of Principal Components
The eight original variables are combined linearly to define principal components. The
loadings produced by the principal components analysis for the variables is used to create bar
charts to better visualize the magnitude of the loading. These loadings help explain the
contributions of the variables to each principal component. It does not directly express
which, if any, components contribute more or less to the overall data association
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
The three components are (Table 5.20):
Table 5.20 Attributes of Principal Components
Principal Components
Variables
Component 1
Family size
Previous place of residence
Tenure
Component 2
Education
Income
Component 3
Number of earners
Language
Religion
60
Name
Family status
Socioeconomic status
Ethnic status with high
number of earners.
5.3.2 Cluster Analysis
A cluster analysis was done using the scores obtained from the principal components
analysis. Analysis of the raw data was not carried out because the SPSS program did not
weight the raw data while running a cluster analysis. Cluster analysis of the scores from
PCA ensured that the data was standardized in the same manner for both types of analysis.
As the number of cases was only 8, only two clusters were formed. The first cluster (Cluster
1) had the nodes Belapur and Kalamboli while the second cluster (Cluster 2) had the rest of
the nodes, Nerul, Vashi, Sanpada, Panvel, Kopar-khairane, Airoli (Appendix E).
5.3.3 Discussion
The principal components analysis produced three components with eigenvalues
above 1. The three components correspond to family status, socioeconomic status and
ethnic status. This analysis does not show any differentiation based on variables of ethnicity.
As the analysis was constrained by the reduced number of variables, this PCA does not
directly correspond to the descriptive analysis. The cluster analysis shows that the two of the
Rescaled Distance Cluster Combine
Cluster 2
0
5
10
15
20
25
+---------+---------+---------+---------+---------+
Node
Panvel
Kopar
Sanpada
Nerul
Airoli
Vashi
Belapur
Kalamboli
-+-----------------+
Cluster 1
-+
+-------------------------+
-------------------+
+---+
-------------+-------+
I
I
-------------+
+-----------------------+
I
---------------------+
I
---------------------------------------+---------+
---------------------------------------+
Figure 5.17 Dendrogram using Average Linkage (Between Groups)
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
61
nodes are different from the other six. The main reason for this is the high variability in the
language data set for Belapur, and the high percentage of families in the selected income
range for Kalamboli.
5.4 Sub-regional Scale - Sectors of Vashi
5.4.1 Principal Components Analysis (PCA)
The analysis at the sub-regional scale uses the 23 sectors (neighborhoods) of Vashi as
the cases for the study. From the data, 13 variables were selected for the analysis. These
were: families with one earning member, household income range of Rs. 2651-4450, high
school education, male and female population of the age group 25-45, families with 4 or 5
members, houses built by CIDCO, tenure of house in the 1980s, migration from Bombay,
Hindus and Muslims, linguistic groups speaking Marathi and Malayalam. The variables were
weighted by the total population of each node. A PCA was run, and three components were
obtained. The rotated component matrix is used here for interpretation and discussion
(Appendix F).
The PCA shows the communality of the 11 variables to be 8.01, explaining 73% of
the variance. The extracted sums of squared loadings of the first three components is
cumulatively 72.917%. Component 1 with an eigenvalue of 2.75 explains 25.001% of the
variation. Component 2 with an eigenvalue of 2.690 explains 24.453% variation and
Component 3 with an eigenvalue of 2.581 explains a variation of 23.463%. More
components could have been used, but interpretation would have been more difficult. The
attributes of the principal components are (Table 5.21)
Table 5.21 Attributes of Principal Components
Principal Components
Variables
Component 1
Education
Income
Ownership of house
Previous place of residence
Muslim
Component 2
Marathi
Component 3
Number of earners
Malayalam
Demographics
Hindu
Name
Socioeconomic status and
Muslim enclave
Ethnic status
Ethnic status with high
number of earners.
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
62
WOMEN
RELGION2
RELGION1
OWNRSHIP
MIGRATN
MEN
LANGUAG2
LANGUAG1
INCOME
EDUCATN
1
0.8
0.6
0.4
0.2
0
-0 . 2
-0 . 4
-0 . 6
-0 . 8
-1
EARNER
loadings
C o m po ne nts
va r i a bl e s
Figure 5.18 Loadings of Principal Components
The bar chart explains the loadings of each variable on the component. These
loadings help explain the contributions of the variables to each principal component. These
define which values contribute more or less significance to that particular component.
5.4.2 Cluster Analysis
A cluster analysis of the scores obtained from PCA was done. Three clusters were
formed using the 23 cases. The first cluster (Cluster 1) had had only sector 5. The second
cluster (Cluster 2) had sectors 12, 14, 16A, 17, 28 and 29, and the third cluster (Cluster 3)
had all the rest of the 16 sectors (Appendix G).
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
63
Figure 5.19 Dendrogram using Average Linkage (Between Groups)
Rescaled Distance Cluster Combine
0
5
10
15
20
+---------+---------+---------+---------+----Sector
2
6
1
4
16
20
9
10
15
26
21
3
7
9A
8
10A
14
29
12
17
16A
28
5
-+
-+---+
-+
+-+
-----+ +-----+
---+---+
I
---+
+---+
-+-+
I
I
-+ +---+
I
+-----------+
---+
+-----+
I
I
Cluster 3
-------+
I
I
-----------------+
+---------+
---+---+
I
I
---+
+---------+
I
I
-------+
+-----------+
I
---------+-------+
+---------+
---------+
I
I
-------+---------------+
I
I
-------+
I
I
I
-+---+
+---------------+
I
Cluster
-+
+-------------+
I
I 2
-----+
+---+
I
-------------------+
I
Cluster
-------------------------------------------------+ 1
5.4.3 Discussion
The principal components analysis produced three equally important components
with eigenvalues in the range of 2.75 to 2.58. Each of the three components have an ethnic
variable in them. The first component is one which has a high socioeconomic component
dominated by a Muslim population. The second component has only the population speaking
Marathi. As the Marathi population is 53% of the total population, it represents a majority
of the population. This can be translated into a middle-class population. The third
component is the economically active age group dominated by the Hindu population. Again,
as Hindus are 83% of the population, this component also describes the general population.
All the components are equally important and separated only by ethnic variables. It appears
that there is a segregation based on the ethnic component.
The cluster analysis shows a segregation in Cluster 1 caused by high number of
earners with a high percentage of households speaking Marathi and a low percentage of
Malathi Ananthakrishnan
Chapter 5: Presentation of Data
64
households speaking Malayalam. Cluster 2 shows a dominance of households speaking
Marathi.
5.6 Conclusion
The analysis of the data shows that the urban social pattern appears to be non-uniform
at the regional scale, and distinctly driven by an ethnic component at the sub-regional scale.
The descriptive analysis of individual variables also shows this non-uniform pattern. PCA
and cluster analysis brings forth the variability of the data and shows which variables and
which cases cluster together. At the sub-regional scale as there is a smaller percentage of
CIDCO-built houses, individual households have exercised their choice, and a strong ethnic
component is seen. In summary, although the government policy was to prevent the
formation of ethnic enclaves, the outcome of the implementation strategy shows otherwise.
Chapter 6: Interpretation and Discussion
A preliminary interpretation of the data analysis in the previous chapter shows the
details of the social urban pattern are best seen in the sub-regional scale. However, a brief
interpretation of the regional scale is described here before proceeding to the detailed
interpretation at the sub-regional scale.
6.1 Regional Scale
Figure 6.1 shows the spatial
distribution of the clusters. Cluster 1
has two nodes close to each other and
BOMBAY
possibly influenced by one another. All
Airoli
Kopar-Khairane
Kalamboli
Vashi
Sanpada
Nerul
Arabian
Sea
Belapur
Panvel
Cluster 1
Cluster 2
Figure 6.1 Cluster of Nodes of Navi Mumbai
the other nodes are in the second cluster.
Malathi Ananthakrishnan
Chapter 6: Interpretation and Discussion
66
Figure 6.2 shows that different
3
factor scores influence the two
2
Airoli
1
clusters. Cluster 1 is linked to
Factor score 1
0
score 1 and cluster 2 to score 2
-1
Factor score 2
while score 3 exerts almost equal
-2
Factor score 3
influence on both cluster.
-3
N=
67116
67116
67116
14543
14543
1
14543
2
Figure 6.2 Average Linkage between Factor Scores
Analysis weighted by population
Further, Figure 6.3 shows the strength of variables, which are contributing to the clustering.
Cluster 1 is influenced by family size, previous place of residence and tenure while cluster 2
is affected by income, education and language. The variables, number of earners and
religion, have an equal influence on the two clusters.
100
Panvel
EARNER
80
EDUCATN
60
FAM.SIZE
40
INCOME
LANGUAGE
Kopar-khaira
20
MIGRATN
0
Sanpada
RELIGION
Kopar-khaira
TENURE
-20
1
2
Figure 6.3 Average Linkage between Variables
Analysis weighted by population
Malathi Ananthakrishnan
Chapter 6: Interpretation and Discussion
67
6.2 Sub-regional Scale
At the sub-regional scale, there were twenty-three sectors. More variables could also
be used to study these cases. The grouping of the sectors into three clusters is shown in
Figure 6.4.
28
12
26
29
10
14
15
9
10A
16
20
9A
8
5
7
6
4
3
16A
2
21
17
1
Figure 6.4 Clustering of the Sectors of Vashi
Cluster 3 (red) has sectors 1, 6, 9, 10, 15, 16, 20, 21, Cluster 2 (green) has sectors 2,
3, 4, 8, 9A, 10A, 12, 14, 16A, 17, 28 and 29, and 26, and Cluster 1 (yellow) has only sector
5.
Malathi Ananthakrishnan
Chapter 6: Interpretation and Discussion
68
Figure 6.5 shows that the
4
three clusters are influenced by
2
different factor scores. Cluster 1 is
0
Factor Score 1
-2
influenced by all three scores,
8
Factor Score 2
cluster 2 more strongly by score 2
-4
Factor Score 3
and cluster 3 by score 3.
-6
N = 164971649716497
1892 1892 1892
738 738 738
2
3
1
Figure 6.5 Average Linkage between Groups
Analysis weighted by population
120
EARNER
100
EDUCATION
INCOME
80
MARATHI
MALAYALAM
60
MEN
40
MIGRATION
OWNRSHIP
20
HINDU
0
MUSLIM
WOMEN
-20
1
2
3
Figure 6.6 Average Linkage between Variables
Figure 6.6 shows the average linkage between the variables. Cluster 2 is the most
significant. Ownership, income and the language Marathi dominate it. This is a
socioeconomic construct, but dominated by an ethnic variable. Cluster 1 is also
differentiated by Malayalam, another ethnic variable. Cluster 3 is an outlier.
Malathi Ananthakrishnan
Chapter 6: Interpretation and Discussion
69
6.2.1 Socioeconomic Status and Sector Theory
As discussed in the literature review, the study of many cities across the world shows
that the socioeconomic construct displays a sector pattern. Figure 6.7 shows a scenario that
could be expected from the mapping of any of the socioeconomic variables. The two
variables selected were income and number
of earners. In both maps the median range is
represented by the color purple. The colors
red and orange are immediately above, and
immediately below the median value while
yellow and green represent the outliers or
extremes.
Figure 6.7 Hypothetical Sector Pattern
for Socioeconomic Variables
Figure 6.8 Distribution of Number of Earners Figure 6.9 Distribution of Income
The pattern that emerges on mapping of the number of earners and income variables
does not show any particular pattern (Figure 6.8, Figure 6.9).
Malathi Ananthakrishnan
Chapter 6: Interpretation and Discussion
70
6.2.2 Family Status and Concentric Zone Theory
The study of many cities across the world shows that the family status construct
displays a concentric pattern. Figure 6.10 shows a possible scenario in Vashi for a variable
representing the family status. The variable selected to describe the family status is
ownership of apartment. In the descriptive analysis, this variable showed a great degree of
variability. The purple color represents the
range within which the mean falls. The colors
red and orange are immediately above, and
immediately below the median value while
yellow and green represent the outliers or
extremes.
Figure 6.10 Hypothetical Concentric Zone
Pattern for Family Status Variables
The number of sectors which falls within the mean range is very small. Sectors which have
slightly more or slightly less percentage of
apartments built by CIDCO are represented by
red and orange. It is important to note that five
sectors are colored green while one sector is
yellow (Figure 6.11). This shows a high
degree of variability in the data.
Figure 6.11 Distribution of Ownership of Apartment
Malathi Ananthakrishnan
Chapter 6: Interpretation and Discussion
71
6.2.3 Ethnic Status and Multiple Nuclei Theory
Multiple Nuclei theory supports the spatial pattern of the ethnic factor. A possible solution is
mapped for any ethnic variable in Figure 6.12. A language variable and a religion variable
were selected from the data set for mapping. The mapping of language and religion variables
shows a segregation of both of them. Yellow
and green colors, which represent the
extremes in the data set, are present in both
the variables (Figure 6.13, Figure 6.14). This
is especially true of the variable Muslim,
which shows a largely non-uniform
distribution.
Figure 6.12 Hypothetical Multiple Nuclei Pattern for
Ethnic Variables
Figure 6.13 Distribution of Households
speaking Marathi
Figure 6.14 Distribution of Households
which follow Islam
Malathi Ananthakrishnan
Chapter 6: Interpretation and Discussion
72
6.3 Summary
The set of figures below shows the mapping of the cluster analysis as well as the individual
factor scores.
Figure 6.15 Clustering of Sectors
Figure 6.16 Score 1
Figure 6.17 Score 2
Figure 6.18 Score 3
Although the four maps above (Figure 6.15, Figure 6.16, Figure 6.17, Figure 6.18)
show that there is a different colored sector within a group of one color, the multiple nuclei
pattern is not very obvious. However, looking at the descriptive analysis, principal
components analysis, cluster analysis and the mapping collectively, the multiple nuclei
pattern can be inferred. The descriptive analysis brought out the fact that the spatial pattern is
Malathi Ananthakrishnan
Chapter 6: Interpretation and Discussion
73
not uniform or heterogeneous. The principal components analysis shows that the cause of
this spatial pattern is ethnicity. The clustering indicates that
some sectors are dissimilar from others. The mapping of individual variables and factor
scores verifies that within a fairly homogeneous group of sectors there exists a dissimilar
sector.
In conclusion, as the pattern is not uniform, the policy has not been successful.
However, a pattern did emerge at this present stage. This is the multiple nuclei pattern of an
ethnically driven spatial organization. The aggregation of household data at the sector scale
has limited this research from drawing out the finer details of the spatial pattern.
6.4 Potential Utility of the Research
This research is a starting point for further studies in spatial patterns in Navi Mumbai.
As Navi Mumbai has been constructed over the last 25 years, the pattern is strongly
influenced by factors as year of occupation of the house and reasons for moving. The policy
of the government to promote social heterogeneity influenced the type of residential
construction in Navi Mumbai.
Future research could involve:
•
Delineating the pattern at intervals of time to study the change in pattern,
•
scaling down the study to stories of individual households to reach a more
detailed level of interpretation,
•
putting forth a new theory to generalize social pattern in planned cities in India,
•
examining the policy instruments and policy goals.
Chapter 7: Conclusion
The purpose of this thesis is to delineate the urban social pattern of Navi Mumbai,
India. This particular case study was chosen for two reasons: Navi Mumbai is the first
planned city that is not a capital city or industrial township, and the government had a
specific social and political agenda. One of the social objectives in the planning of Navi
Mumbai was to use the government machinery to diversify the spatial distribution of the
population based on socioeconomic criteria. Ethnic enclaves have always characterized
traditional settlements in India. The government had a very practical interest in avoiding
ethnic confrontation. It was also influenced by the concept of the city as a melting pot
(Engel, 1991), and formulated a policy to support it. The thesis addresses this social
objective.
Bombay is the financial and economic capital of India. Navi Mumbai is separated
from the metropolis of Bombay only by the Thane Creek. Every effort was taken by the
government to make Navi Mumbai an independent city and not a suburb or satellite city to
Bombay. However, Navi Mumbai is still dependent on Bombay for much of its activity.
The important objectives of Navi Mumbai were: attract some of the immigrant population,
support an aggressive industrialization policy, raise the standard of living and reduce social
inequalities, and provide an infrastructure which would promote ethnic heterogeneity.
The draft development plan of Navi Mumbai had very strong functional and social
objectives. Planning policies in Navi Mumbai were strongly influenced by the teachings of
Mahatma Gandhi. It was hoped that a majority of the residential construction could be
achieved though a policy of swavalambhan (self-reliance) and swatantrya (mutual selfhelp). The government also decided to take up most of the initial building construction.
Housing would be allotted according to the preference of size of dwelling provided by
applicants. Households would normally place this preference based on how much they can
pay. The government hoped that this would distribute people based on socioeconomics and
break barriers based on religion and language.
Traditional Indian cities have always had a strong ethnic component in their urban
social pattern. The segregation is attributed to the ethnic variables, caste, religion and
language. The Hindu laws and treatises specified residential locations for different castes.
This was the first cause of separation in residential neighborhoods. Religious tensions have
always existed in India. The Muslims came to India as invaders. The culture of this race
of people is very different from the Hindus. Areas dominated by Muslims are common in
most cities in India. The religious divide was used in the partition of united India into India
and Pakistan. The other feature that is unique to India is the existence of many languages.
Political and administrative boundaries in independent India were decided on linguistic
lines. Partition and the first years of independence were, thus, strongly influenced by ethnic
variables.
The review of secondary source material shows that urban social patterns have been
studied across the world. Three leading theories put forth were concentric zone theory,
sector theory and multiple nuclei theory. These theories explain the urban social pattern
Malathi Ananthakrishnan
Chapter 7: Conclusion
75
and its change over time. The concentric zone theory relates the pattern of cities to
population mobility. Succession and invasion based on social and economic status is the
basic assumption of this theory. Mobility and immigration are the key variables of this
theory. The second theory, sector theory, is an analysis primarily of economic variables.
Wedge patterns representing income groups are the outcome of the theory. The multiple
nuclei theory proposes that patterns could be arranged around several centers.
Analysis was done to map the urban social pattern of many cities across the world.
The methodology used was that of social area analysis. Social area analysis broadly
classifies variables into three constructs. These are socioeconomic construct, family status
and ethnic status. Heterogeneity of the population is detected if these three constructs
emerge from the analysis. That would indicate that enclaves have not been caused by
individual variables. In the case of Navi Mumbai this is important because of the policy to
prevent segregation based on ethnic variables.
The constructs of the social area analysis have been found to correspond to the three
theories. Generally the socioeconomic model showed a sectored pattern, the family
component showed a concentric ring pattern, and ethnic segregation showed a multiple
nuclei arrangement. The variables selected under each construct were drawn out of
experience of the researchers. In Navi Mumbai, special emphasis has to be given to the
ethnic components. Two religion variables and two language variables have been selected
representing the ethnic construct. The other variables selected were number of earning
members, income and education under the socioeconomic construct, and demographics,
family size and type of house under family status.
The hypothesis put forth in this study is: no significant difference in key variables is
expected and hence no social segregation will occur. This hypothesis is put forth on the
assumption that the social agenda put forth in the Development Plan has been successfully
implemented. If H0 is false, then the pattern will be explained using the existing theories.
Analysis of data was done at two scales. These scales were the regional scale of the nodes
(townships), and the sub-regional scale of the sectors (neighborhoods) of Vashi node. At
the regional scale the analysis was done between the eight nodes to study their similarity.
Twenty-three sectors of Vashi were then analyzed. Since, the scale was smaller, the
analysis allowed a more detailed interpretation.
The software package SPSS was used to do the analysis. Four methods were used to
analyze the data. The methodologies were techniques of multivariate analysis. The first
methodology is a descriptive analysis. The data at both scales is tabulated, and histogram
drawn of the variable selected from each data set. A variation in the data greater than 15%
on each side of the mean is considered as unequal distribution. The second methodology is
principal components analysis (PCA). The PCA reduces the dimensionality of the data into
a more interpretable form. The variables selected are reduced into a smaller number of
constructs. Using the secondary source material as reference, grouping of variables is
expected to be under the three constructs, socioeconomic, family status and ethnic status.
Next, a cluster analysis was done of the cases of the data set. The similarity between the
Malathi Ananthakrishnan
Chapter 7: Conclusion
76
nodes and sectors is determined from this. The final stage was mapping of the clusters,
thereby, graphically representing the analysis.
The interpretation of the descriptive analysis shows that the distribution of most of
the variables is not uniform. This is especially true of the ethnic variables. The
distribution of these variables shows segregation. However, the socioeconomic variables
also show separation. The principal components analysis shows that the variables are not
grouping under the three constructs. All three new constructs are dominated by an ethnic
variable. This indicates that the urban social pattern is strongly influenced by ethnicity.
The interpretation of the analysis also involves comparing the descriptive analysis,
and clustering to the urban social patterns detailed in the secondary source material. As the
socioeconomic variables are expected to take a sectored pattern, family status variables
concentric zones and the ethnicity variables a multiple nuclei arrangement, they were
mapped under expected and observed conditions. None of the variables selected display a
uniform distribution. The extreme value range in the mapping is important because it
represents the dissimilarity in the distribution. The overall pattern of Navi Mumbai is one
of multiple nuclei. The center is an ethnic enclave surrounded by socioeconomic variables.
The hypothesis was proved false. The pattern could, however, be explained using
the theories of urban social patterns. The urban social pattern is best explained as one of
multiple nuclei. The policy has not facilitated the distribution of the population based on
socioeconomic criteria. This can be attributed to two reasons:
1. Distribution was originally controlled through allotment of government-built
houses based only on purchasing power (and indirectly socioeconomic status).
Control is maximum when the government owns all the houses. In Vashi only
64% of the houses were built and allotted by the government.
2. Even in the houses built by the government resale has taken place.
Redistribution shows that people have aligned themselves based on ethnic
variables.
The research brings to the fore many questions than answers.
• Was this an experiment in enhancing quality of life or is it a method for the
government to exert social control?
• The concept of the melting pot has to be re-examined. A moral analysis of
segregation has to be done in the context of the Indian culture. How important is
it to promote integration when self-sorting has been the natural process?
• Can the Modernist synthesis seeking homogeneity in heterogeneity be used as a
template for the Indian culture?
• This leads to the question: is the objective valid? Does it have to be redefined or
is the implementation strategy to be modified?
At this stage it appears that a detailed analysis of the policy instrument and policy goals must
be undertaken. The objective, allotment procedure, physical design and the institutional
framework need to be examined closely to realize their full impact and to understand the
results in their context. In conclusion, although the policy is noble in its aims and aspiration,
it has not succeeded at this stage. The spatial distribution of households is still characterized
by traditional Indian values of ethnic segregation.
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Glossary of Terms
Term
Cuadra
Jali
Masjid
Padas
Panchayati
Pucca
Purdahs
Purushasukta
Rashtrabasha
Swadeshi
Swatantrya
Swavalambhan
Vastupurusha mandala
Vastushastra
Meaning
Spanish measurement
Carved screens
Mosque
Subdivisions of the cosmic universe
Self-government
Durable
Enclosure
Hindu treatise
Language of the State
Fullest utilization of local resources
Self-motivation and self-help
Self-reliance
Terrestrial representation of the cosmic universe
Science of architecture and planning
Appendix A
March 1958
July 1958
February 1959
July 1964
March 1965
March 1966
January 1967
July 1967
January 1970
February 1970
March 1970
March 1971
August 1973
October 1973
Study group on Greater Bombay set up under the chairmanship of
Mr. S. G. Barve.
Bombay Municipal Corporation decided to prepare a development
plan for Greater Bombay.
The study group on Greater Bombay recommended a rail-cum-road
bridge across the Thane creek.
Development plan for greater Bombay was submitted to the State
Government.
A Committee under Dr. D. R. Gadgil was appointed to formulate
broad principles of regional planning for Bombay and Poona.
The Gadgil Committee recommended regional planning legislation
and regional planning boards.
Maharashtra Regional and Town Planning Act 1966 was passed.
Bombay Metropolitan and Regional Planning Board was constituted.
The Board published the Draft Plan with recommendations to set up a
twin city.
State government notified privately owned land in Navi Mumbai for
acquisition.
CIDCO was formed.
CIDCO was designated as New Town Development Authority for
Navi Mumbai.
The Bombay Metropolitan regional Plan was approved by the State
government.
CIDCO published its Draft Development Plan.
Appendix B
The 7Vs (les sept voies)
The 7V Rule was studied in 1950 at the UNESCO’s request (Le Corbusier, 1961). One
discovered that with 7 types of roads, the man of the mechanical civilization could:
cross continents: V1
arrive in town: V1
go to essential public services: V2
cross at full speed, without interruption, the territory of the town: V3
dispose of immediate accesses to daily needs: V4
reach the door of his dwelling: V5 and V6
send youths to the green areas of each sector, where schools and sports grounds are
located: V7.
Appendix C
Number of earning
members
Sector
1
2
3
no.
1 64.04 26.81
7.26
2 57.67 28.57
8.99
3 68.60 24.42
5.81
4 70.26 23.08
5.64
5 46.79 40.37 11.01
6 65.59 27.13
6.48
7 62.16 30.41
6.08
8 52.07 30.58
9.92
9 73.58 20.64
3.77
10 74.19 19.89
5.16
10A 50.00 35.71 11.43
12 65.22 26.09
8.70
14 77.52 18.28
3.57
15 72.31 20.06
5.09
16 65.69 29.29
4.60
16A 71.29 22.49
3.83
17 61.96 27.17
8.23
20 69.23 21.15
5.77
21 63.57 23.43
9.51
26 77.68 16.31
3.86
28 52.38 38.10
0.00
29 82.25 11.26
4.76
9A 74.28 21.38
3.62
mean 66.01 25.33
6.22
std dev
9.40
6.89
2.79
4
1.89
4.76
1.16
1.03
1.83
0.81
1.35
7.44
2.00
0.76
2.86
0.00
0.63
2.54
0.42
2.39
2.64
3.85
3.48
2.15
9.52
1.73
0.72
2.43
2.25
Household
Income
Sector upto
1251- 2651- 4451- 7500- 10001- 15000+
no.
Rs.125 2650 4450 7500 10000 15000
0
1
3.16 24.68 34.49 22.15
9.49
4.11
1.90
2
1.62
7.57 25.41 37.84 18.38
7.57
1.62
3
1.18
6.47 28.82 36.47 15.29
8.82
2.94
4
0.53
1.07 18.72 44.39 20.86 10.70
3.74
5
2.75 26.61 16.51 24.77 22.02
4.59
2.75
6
2.52 11.76 23.11 24.79 18.07 13.45
6.30
7 11.56
7.48 39.46 21.77 14.97
0.00
4.76
8
0.00
2.59
9.48 27.59 18.10 23.28 18.97
9
2.46 17.36 36.39 31.02
7.28
4.26
1.23
10
1.36 26.74 28.10 27.33 11.63
4.07
0.78
10A
0.00
0.00 16.92 30.77 26.15 15.38 10.77
12 13.04
4.35
8.70 17.39 34.78 17.39
4.35
14
0.64
6.14 25.00 35.17 22.67
7.20
3.18
15
0.90 11.73 47.97 28.27
7.22
3.76
0.15
16
1.26 12.55 30.96 33.47 12.97
7.53
1.26
16A
0.49
3.90 14.63 40.49 23.41 12.20
4.88
17
0.48
0.80
5.94 37.08 32.10 15.41
8.19
20
6.45 38.71 34.19 16.13
3.87
0.65
0.00
21
2.09 39.07 35.81 16.51
5.58
0.47
0.47
26
0.86 21.89 45.92 21.46
8.15
1.72
0.00
28
0.00
0.00 10.00 35.00 25.00 20.00 10.00
29
0.00
0.00 24.89 37.99 23.14 10.92
3.06
9A
1.09
2.18 10.55 42.18 25.09 13.45
5.45
mean
2.37 11.90 24.87 30.00 17.66
9.00
4.21
stddev
3.45 12.16 12.15
8.40
8.48
6.52
4.42
Highest Level of
Education
Sector illiterat childre primar second high
vo-tech BS
MS
PhD
no.
e
n
y
ary
school
1
3.79
3.50 11.81 42.27 10.71
3.43 22.16
2.19
0.15
2
3.67
4.01 11.01 45.07
9.06
2.18 21.44
2.64
0.92
3
2.59
3.27 12.38 37.28 10.61
2.18 27.21
3.81
0.68
4
1.23
3.08 13.44 37.98
7.64
2.47 28.24
4.44
1.48
5
3.81
5.01 15.03 44.89
6.81
5.41 14.43
4.01
0.60
6
2.54
2.88
9.85 34.51
7.96
3.54 33.63
4.65
0.44
7
1.73
2.35 10.52 31.71
7.06
4.71 34.85
5.65
1.41
8
4.38
4.03
9.98 29.60
9.11
2.98 31.87
5.25
2.80
9
3.03
4.91 17.24 44.38
8.12
1.80 17.55
2.64
0.31
10
3.46
5.34 16.94 43.06
8.80
2.49 16.32
3.24
0.35
10A
1.81
4.71
9.42 23.55
9.06
2.90 32.97
9.78
5.80
12
0.00
5.75
4.60 17.24
8.05
2.30 59.77
2.30
0.00
14
2.19
4.81 14.11 34.61
9.68
1.75 27.94
4.05
0.87
15
4.23
3.82 15.26 48.95
9.37
2.50 14.07
1.45
0.34
16
3.38
3.28 11.09 46.58
9.35
2.89 20.83
2.12
0.48
16A
2.02
4.15 13.69 31.87
9.43
2.69 26.94
7.30
1.91
17
1.65
4.24 10.59 29.47
8.97
1.39 35.32
5.74
2.63
20 19.07
8.90 22.74 40.68
5.08
0.42
2.97
0.14
0.00
21 13.91
5.81 19.03 50.09
5.40
0.64
4.48
0.64
0.00
26
3.28
7.95 13.13 51.14
7.58
2.90 12.63
1.14
0.25
28
0.00
5.68 12.50 20.45
7.95
2.27 47.73
3.41
0.00
29
1.15
3.45 11.03 34.25 11.15
1.72 30.92
5.06
1.26
9A
1.64
4.09 11.82 30.18
7.73
2.91 32.45
6.18
3.00
mean
3.68
4.57 12.92 36.95
8.47
2.54 25.94
3.82
1.12
stddev
4.29
1.55
3.72
9.40
1.52
1.11 12.92
2.26
1.37
Male
Population
Sector below 4,5
6 to 9 10 to 16 to 22 to 25 to 45 to 60+
no.
3
15
21
24
44
59
1
4.10
2.54
4.38 11.44 15.25
6.36 32.49 19.63
3.81
2
4.12
2.39
5.42 14.75 12.15
7.16 31.02 17.14
5.86
3
4.56
3.29
6.58 16.20 12.91
5.82 29.11 16.96
4.56
4
3.29
2.59
7.76 12.47 13.65
5.18 30.82 16.00
8.24
5
2.05
2.87
9.84 19.67 12.70
3.28 31.56 14.34
3.69
6
2.80
1.40
4.60 12.60 13.60
6.00 27.60 23.00
8.40
7
2.23
2.87
3.82 10.51 15.61
4.78 29.94 21.66
8.60
8
3.46
2.42
6.57 10.38 12.11
6.57 27.34 20.42 10.73
9
5.46
4.12
8.48 17.01 10.12
3.97 37.25 10.91
2.68
10
5.80
3.73
9.45 14.84 12.02
3.40 34.83 12.02
3.90
10A
2.22
4.44
2.96
8.89 12.59
5.93 28.89 21.48 12.59
12
6.12
2.04
4.08 12.24 12.24
6.12 36.73 14.29
6.12
14
5.39
4.56
8.20 11.93 10.27
3.32 37.76 13.80
4.77
15
3.29
3.04
7.88 17.32 13.78
3.97 35.69 11.67
3.35
16
3.26
3.07
6.91 14.20 14.59
4.80 30.71 18.04
4.41
16A
3.25
3.68
7.36 16.67 12.34
3.03 31.82 16.02
5.84
17
3.93
2.97
5.86 10.99 11.14
4.83 34.74 17.37
8.17
20
8.77
5.26
9.52 15.04 10.78
6.77 34.84
8.02
1.00
21
4.54
3.45
8.40 20.42 11.60
4.79 33.19 11.26
2.35
26
6.87
4.66
6.87
8.65
7.98
8.65 46.12
7.32
2.88
28
5.66
1.89 11.32
9.43
9.43
7.55 35.85 16.98
1.89
29
4.51
2.87
7.38 17.62
8.81
2.46 42.21
9.43
4.71
9A
4.35
3.30
5.74 16.52
9.74
4.35 29.57 20.87
5.57
mean
4.35
3.19
6.93 13.90 11.97
5.18 33.48 15.59
5.40
stddev
1.62
0.95
2.13
3.41
1.99
1.60
4.59
4.52
2.90
Female
Population
Sector below 4,5
6 to 9 10 to 16 to 22 to 25 to 45 to 60+
no.
3
15
21
24
44
59
1
3.57
1.86
7.61 10.87 17.55
7.30 33.39 13.98
3.88
2
4.93
3.20
5.42 12.32 12.07
6.16 37.68 12.07
6.16
3
4.57
3.14
6.29 12.86 12.29
6.86 40.29
9.43
4.29
4
3.17
4.22
7.39 13.72 10.29
4.22 40.11 11.08
5.80
5
4.35
1.98
9.88 16.21 14.23
4.74 36.76
9.09
2.77
6
3.50
1.64
6.31 12.85
9.35
5.84 34.58 19.86
6.07
7
2.82
2.19
5.96 11.60 11.60
7.21 32.29 17.87
8.46
8
4.32
1.80
5.40 11.15 13.31
5.76 33.45 17.27
7.55
9
5.84
3.82
9.49 16.01
9.66
4.27 41.52
6.40
2.98
10
5.31
3.36
9.97 14.91 10.44
5.13 38.96
7.92
4.01
10A
6.43
4.29
5.71
6.43 12.14
7.14 32.14 20.00
5.71
12
6.06
0.00
3.03
6.06 15.15
6.06 45.45 15.15
3.03
14
4.35
3.88
6.93 11.63
9.64
6.58 42.07 10.34
4.58
15
4.35
2.90
9.32 15.51 10.77
3.74 41.94
8.33
3.13
16
4.09
1.56
5.46 18.13 14.62
3.90 38.01 10.14
4.09
16A
4.72
2.59
8.25 16.04 10.38
3.54 39.39 11.32
3.77
17
4.35
3.13
5.72 10.07 12.28
5.72 38.22 14.49
6.03
20
8.44
4.55 12.66 17.86
7.79
6.17 37.99
3.57
0.97
21
5.80
2.95 10.18 22.81 11.41
4.79 34.83
5.09
2.14
26
7.06
3.82
5.00 13.82 10.29 12.35 38.53
7.06
2.06
28
2.78
2.78 11.11 11.11
2.78
5.56 47.22 13.89
2.78
29
2.39
3.71
5.57 13.00
5.57
3.18 52.25
9.55
4.77
9A
3.87
2.71
8.32 12.96 10.83
5.22 39.65 12.96
3.48
mean
4.66
2.87
7.43 13.39 11.06
5.71 38.99 11.60
4.28
stddev
1.46
1.08
2.35
3.68
3.09
1.89
4.83
4.48
1.83
Family Size
Sector single 2 to 3 4 to 5 6 to 7 8 to 10
no.
1
2.18 26.17 54.83 14.95
1.87
2
1.57 19.37 54.97 19.90
4.19
3
1.71 24.57 57.71 14.86
1.14
4
0.51 30.46 54.82 12.18
2.03
5
0.00 15.45 62.73 20.00
1.82
6
3.23 36.29 50.40
9.68
0.40
7
0.66 22.52 62.91 11.92
1.99
8
0.00 26.23 46.72 15.57 11.48
9
1.55 23.51 59.38 14.68
0.88
10
0.19 20.49 64.14 14.23
0.95
10A
1.43 34.29 54.29
7.14
2.86
12
0.00 58.33 41.67
0.00
0.00
14
1.88 36.82 51.26
8.58
1.46
15
1.04 20.30 61.63 15.11
1.93
16
0.83 22.41 63.07 12.45
1.24
16A
0.47 20.85 66.82
9.95
1.90
17
1.23 28.92 55.23 12.46
2.15
20
0.64 19.87 52.56 25.64
1.28
21
0.92 12.67 47.93 32.49
5.99
26
2.56 50.43 44.02
2.99
0.00
28
0.00 19.05 66.67 14.29
0.00
29
1.30 44.16 44.16
9.09
1.30
9A
1.79 33.21 54.64 10.36
0.00
mean
1.07 28.19 55.35 13.34
2.05
stddev
0.86 11.50
7.53
6.98
2.53
Type of
Housing
Sector CIDCO Pvt.
Pvt co- Pvt
Other
no.
House op
comme
society rcial
1 100.00
0.00
0.00
0.00
0.00
2 48.40
1.60 19.68
2.13 28.19
3 61.90
0.00 38.10
0.00
0.00
4 45.18
0.00 53.81
0.00
1.02
5 22.73
0.91 76.36
0.00
0.00
6 92.21
6.97
0.82
0.00
0.00
7 89.12
3.40
6.80
0.68
0.00
8 35.25 62.30
0.00
0.00
2.46
9 98.77
0.00
0.56
0.67
0.00
10 83.01
1.74 13.13
2.12
0.00
10A
2.86
0.00 97.14
0.00
0.00
12 17.39
4.35 39.13 39.13
0.00
14 53.07
0.21 46.72
0.00
0.00
15 82.52
0.15 17.33
0.00
0.00
16 83.82
0.00 16.18
0.00
0.00
16A
7.62
0.00 92.38
0.00
0.00
17
0.62
0.15 85.96 12.96
0.31
20 100.00
0.00
0.00
0.00
0.00
21 99.77
0.00
0.00
0.00
0.23
26 100.00
0.00
0.00
0.00
0.00
28
0.00 23.81 76.19
0.00
0.00
29 42.86
0.87 20.78 12.12 23.38
9A
2.51
0.00 92.47
0.00
5.02
mean 53.16
4.84 36.07
3.17
2.75
stddev 37.61 13.83 35.58
8.82
7.58
Tenure
Sector before1 81-85 86-90 91-92
no.
980
1 43.99 12.34 13.92 11.71
2 39.57 12.83 26.74
6.95
3 11.49 32.18 31.61 12.07
4
5.10 40.82 24.49 12.76
5 49.53 20.56 16.82
5.61
6 51.82 12.15 18.62
4.86
7 42.38 15.23 22.52
7.95
8 24.59 38.52 12.30
6.56
9
0.22 52.11 15.19
8.87
10
0.38 37.09 29.06 13.38
10A
0.00 48.57 18.57 14.29
12
0.00
0.00
0.00
4.17
14
0.42
0.64 27.60
7.64
15
8.46 36.35 28.93 10.09
16 27.80 39.42 12.86
4.56
16A
0.00 20.38 56.87
7.11
17
0.31 16.82 48.13 11.68
20
0.00 35.26 28.85
4.49
21 13.02 44.65 17.91
6.28
26
0.00
0.00
0.00
0.00
28
0.00
0.00
0.00
0.00
29
0.87
0.87
1.73
4.33
9A
0.00 31.43 32.50 12.86
mean 12.54 24.36 21.42
7.57
stddev 18.02 17.39 14.56
4.08
93
94
95
3.16
5.35
5.17
6.63
3.74
4.45
6.62
6.56
6.54
6.50
12.86
33.33
18.05
6.82
5.81
6.16
7.94
12.82
4.65
31.88
0.00
47.19
5.71
11.13
11.60
13.61
6.95
5.75
7.14
1.87
6.88
4.64
10.66
14.19
9.94
4.29
41.67
36.73
6.82
8.30
6.16
9.03
10.26
7.44
48.03
75.00
31.17
12.50
16.61
18.40
1.27
1.60
1.72
3.06
1.87
1.21
0.66
0.82
2.88
3.63
1.43
20.83
8.92
2.52
1.24
3.32
6.07
8.33
6.05
20.09
25.00
13.85
5.00
6.37
7.15
Previous Place of
Residence
Sector Island Wn
En
Thane Vashi
no.
city
suburbs suburbs
1
2
3
4
5
6
7
8
9
10
10A
12
14
15
16
16A
17
20
21
26
28
29
9A
mean
stddev
21.45
21.05
19.30
14.58
14.95
12.35
21.19
27.05
18.48
15.71
20.29
20.83
18.03
15.35
15.48
17.62
29.61
12.26
19.63
15.81
4.76
11.26
12.19
17.17
5.36
4.42
8.42
3.51
7.81
0.00
6.58
9.93
8.20
8.51
6.90
7.25
12.50
6.71
5.37
1.67
7.14
6.98
3.87
3.23
3.85
33.33
4.33
8.24
7.47
6.43
17.03
24.74
36.26
23.96
18.69
35.80
37.09
27.05
30.57
28.74
31.88
12.50
27.25
31.30
34.73
25.24
23.10
16.13
26.10
17.95
9.52
22.94
28.67
25.92
7.54
8.83
2.63
5.26
2.60
2.80
4.94
5.30
0.82
3.25
4.79
0.00
0.00
2.52
2.83
3.77
6.67
3.88
1.29
0.69
5.98
0.00
1.30
2.15
2.88
2.01
Navi Inside Out of Intl.
Mumba state
state
i
24.29
8.83
8.20
6.62
0.32
27.37 10.53
2.63
2.63
0.00
23.39
2.92
4.09
5.26
0.00
38.54
5.73
4.69
2.08
0.00
26.17 25.23 12.15
0.00
0.00
20.99
7.00
4.12
7.00
1.23
18.54
3.31
0.00
4.64
0.00
19.67
2.46
1.64
7.38
5.74
28.44
5.38
2.91
2.46
0.00
30.84
2.68
3.26
7.09
0.00
24.64
2.90
2.90 10.14
0.00
33.33
8.33
4.17
8.33
0.00
31.24
5.45
2.31
5.03
1.47
36.07
5.07
3.58
0.45
0.00
28.03
6.69
4.60
4.60
0.42
26.67
8.10
4.76
3.33
0.48
20.93
3.57
1.40
8.53
2.02
47.10 16.77
2.58
0.00
0.00
21.02 22.17
5.54
1.62
0.00
39.32
8.55
3.42
4.27
0.85
47.62
4.76
0.00
0.00
0.00
45.02
9.52
1.30
4.33
0.00
32.26
3.58
2.51 10.39
0.00
30.33
7.76
3.39
4.53
0.55
8.82
6.15
2.45
3.24
1.29
Language
Sector Marath Hindi Gujarat Malaya Punjabi Tamil Kannad Bengali Other
no.
i
hi
lam
a
1 51.09
8.72
5.30
9.97
3.43
4.36
4.05
2.18 10.90
2 44.50 10.47
2.62
7.33
6.81
4.71
1.57
1.57 20.42
3 32.57 17.14
1.71
8.00 12.00
6.29
2.29
3.43 16.57
4 46.70
8.12
2.03
9.64
3.55
8.63
2.03
2.03 17.26
5 77.27
0.91
2.73
2.73
0.00
0.00
1.82
3.64 10.91
6 33.87 17.34
4.44
5.24
7.66
8.47
2.82
4.03 16.13
7 37.09 15.23
9.27
5.96
5.96
6.62
1.32
1.99 16.56
8 22.13 20.49
9.84
4.92 14.75
4.10
4.10
2.46 17.21
9 50.66 10.26
3.53
9.60
2.10
5.19
3.53
2.76 12.36
10 44.21 21.44
2.09
6.45
2.85
5.31
3.98
4.17
9.49
10A 24.29 15.71
1.43 24.29
2.86
4.29
2.86
8.57 15.71
12
8.33 29.17 16.67
4.17 16.67
0.00
0.00
4.17 20.83
14 19.87 12.55 32.43
7.95
4.81
5.23
2.51
3.97 10.67
15 57.33 13.19
2.37
4.89
4.89
3.85
1.93
1.93
9.63
16 54.36 11.20
4.98
6.22
3.73
2.90
3.32
2.49 10.79
16A 51.66 10.90
3.32
7.58
2.37
4.74
4.27
2.84 12.32
17 21.38 14.15 16.77
8.00
7.54
7.54
1.54
4.15 18.92
20 60.26 13.46
5.77
3.85
0.00
1.92
3.21
0.00 11.54
21 61.75 14.98
3.92
1.61
1.15
2.30
1.38
0.00 12.90
26 48.72 12.39
4.70 10.26
2.99
3.85
5.13
4.27
7.69
28 28.57 14.29
0.00
9.52 19.05
9.52
0.00
4.76 14.29
29 25.54 27.27
7.36
6.49
4.76
5.63
1.73
3.90 17.32
9A 20.34 12.76
3.79 12.76
4.83
7.93
4.83
8.28 24.48
mean 39.61 14.70
6.44
7.61
5.97
4.96
2.55
3.43 14.73
stddev 17.36
6.08
7.30
4.56
5.22
2.59
1.40
2.08
4.29
Religion
Sector Hindu Christi Islam
Jain Sikh
Buddhi Other
no.
an
st
1 79.18 11.04
5.68 0.32
2.52
0.95
0.32
2 80.42
5.82
7.41 0.53
3.70
0.53
1.59
3 75.29
9.20
8.62 0.57
4.60
1.15
0.57
4 84.26
6.09
6.60 0.51
0.51
2.03
0.00
5 81.65
9.17
2.75 0.00
0.00
6.42
0.00
6 83.81
9.72
4.05 0.00
2.02
0.40
0.00
7 76.82 10.60
8.61 1.32
1.99
0.00
0.66
8 72.13
8.20
10.66 3.28
4.10
0.00
1.64
9 84.85
7.19
5.20 0.22
0.55
1.88
0.11
10 80.76
8.76
5.33 0.00
1.52
3.62
0.00
10A 72.86 22.86
1.43 1.43
1.43
0.00
0.00
12 79.17 12.50
4.17 0.00
4.17
0.00
0.00
14 88.26
5.45
2.73 1.68
1.68
0.21
0.00
15 83.53
4.01
6.82 0.45
2.82
2.37
0.00
16 88.80
3.32
6.64 0.00
1.24
0.00
0.00
16A 91.47
4.74
1.42 1.42
0.47
0.47
0.00
17 85.03
5.56
3.70 2.78
1.85
0.46
0.62
20 86.54
0.64
5.13 0.00
0.00
7.69
0.00
21 81.67
1.16
15.31 0.23
0.46
1.16
0.00
26 86.75
5.13
5.98 0.43
1.71
0.00
0.00
28 100.00
0.00
0.00 0.00
0.00
0.00
0.00
29 86.34
6.61
4.85 0.88
0.88
0.44
0.00
9A 73.93
8.21
15.00 0.36
1.79
0.36
0.36
mean 82.92
7.04
6.02 0.73
1.70
1.33
0.25
stddev
6.55
4.78
3.91 0.92
1.40
2.09
0.49
Appendix D
Factor Analysis
Descriptive Statistics
EARNER
EDUCATN
FAM.SIZE
INCOME
LANGUAGE
MIGRATN
RELIGION
TENURE
Mean Std. Deviation Analysis N
73.2091
4.6076
81659
8.7800 3.8538
81659
53.0814
4.4115
81659
32.6705
5.9974
81659
49.1087
9.8863
81659
28.8271
8.6486
81659
86.0403
3.7870
81659
37.9885
16.2670
81659
Communalities
Initial Extraction
EARNER
1.000 .926
EDUCATN
1.000 .875
FAM.SIZE
1.000 .985
INCOME
1.000 .928
LANGUAGE
1.000 .939
MIGRATN
1.000 .824
RELIGION
1.000 .879
TENURE
1.000 .832
Extraction Method: Principal Component Analysis.
Total Variance Explained
Initial Eigenvalues
Total
% of Variance Cumulative %
1
4.446
55.571
55.571
2
1.946
24.320
79.890
3
.796
.955
89.845
4
.429
5.356
95.202
5
.293
3.660
98.862
6
8.039E-02
1.005
99.867
7
1.064E-02
.133
100.000
8
5.851E-17
.314E-16
100.000
Extraction Method: Principal Component Analysis.
Rotation Sums of Squared Loadings
1
2
3
Total
3.468
1.902
1.818
% of Variance
Cumulative %
43.347
43.347
23.771
67.118
22.728
89.845
Component Matrix
Component
1
2
EARNER
.804
.470
EDUCATN
-.136
.882
FAM.SIZE
-.926
.293
INCOME
.430
.862
LANGUAGE
.937
-.230
MIGRATN
-.785
-7.468E-03
RELIGION
.685
8.925E-02
TENURE
-.880
.236
Extraction Method: Principal Component Analysis.
3
.244
-.278
.202
-1.317E-02
-8.383E-02
.455
.634
4.454E-02
Rotated Component Matrix
Component
1
2
EARNER
-.446
.484
EDUCATN
.201
.900
FAM.SIZE
.951
.107
INCOME
-.156
.881
LANGUAGE
-.888
-7.766E-02
MIGRATN
.878
-.230
RELIGION
-.255
4.796E-04
TENURE
.822
.101
Extraction Method: Principal Component Analysis.
3
.702
-.155
-.264
.358
.379
-2.898E-02
.902
-.381
Appendix E
Cluster
Case Processing Summary
Cases
Valid
N
8
a
b
Missing
Total
Percent
N
Percent
N
Percent
100.0
0
.0
8
100.0
Squared Euclidean Distance used
Average Linkage (Between Groups)
Average Linkage (Between Groups)
Agglomeration Schedule
Stage Cluster 1
1
5
2
2
3
5
4
1
5
3
6
1
7
1
Cluster 2
6
7
8
2
4
5
3
Cluster Membership
1:Vashi
2:Nerul
3:Belapur
4:Kalamboli
5:Panvel
6:Kopar-khaira
7:Airoli
8:Sanpada
1
1
2
2
1
1
1
1
Coefficients
.581
2.946
4.174
4.617
7.919
9.299
10.108
Appendix F
Factor Analysis
Descriptive Statistics
Mean
EARNER
66.3183
EDUCATN 40.5760
INCOME
27.9421
LANGUAG1 46.5535
LANGUAG2 6.9114
MEN
38.0484
MIGRATN 52.9759
OWNRSHIP 66.4424
RELGION1 82.3839
RELGION2 6.8628
WOMEN
33.0375
Std. Deviation
7.9628
7.1339
10.9768
15.7324
3.7719
3.3934
9.5580
35.6247
4.7307
3.9142
3.5835
Analysis N
19127
19127
19127
19127
19127
19127
19127
19127
19127
19127
19127
Communalities
Initial Extraction
EARNER
1.000 .856
EDUCATN 1.000 .836
INCOME
1.000 .855
LANGUAG1 1.000 .889
LANGUAG2 1.000 .527
MEN
1.000 .675
MIGRATN 1.000 .571
OWNRSHIP 1.000 .801
RELGION1 1.000 .722
RELGION2 1.000 .568
WOMEN
1.000 .721
Extraction Method: Principal Component Analysis.
Total Variance Explained
Initial Eigenvalues
Rotation Sums of Squared Loadings
Component Total % of Variance Cumulative % Total
1
3.843 34.937
34.937
2.750
2
2.438 22.161
57.098
2.690
3
1.740 15.819
72.917
2.581
4
.938 8.523
81.441
5
.688 6.257
87.698
6
.466 4.238
91.935
7
.359 3.265
95.200
8
.290 2.638
97.838
9
9.854E-02
.896
98.734
10
9.136E-02
.831
99.564
11
4.794E-02
.436
100.000
Extraction Method: Principal Component Analysis.
Component Matrix
Component
1
2
3
EARNER
.246 .748
.487
EDUCATN .880 -.234
8.427E-02
INCOME
.803 8.915E-04
.458
LANGUAG1 .773 -.373
-.391
LANGUAG2 -.612 .310
.239
MEN
0.096 .816
-5.093E-03
MIGRATN -.500 4.042E-02
.565
OWNRSHIP .777 -.131
.424
RELGION1 .473 .522
-.475
RELGION2 .127 -.448
.592
WOMEN
.538 .657
-2.071E-02
Extraction Method: Principal Component Analysis.
% of Variance Cumulative %
25.001
25.001
24.453
49.455
23.463
72.917
Rotated Component Matrix
Component
1
2
3
EARNER
.351 -.366
.774
EDUCATN .709 .575
5.704E-02
INCOME
.869 .201
.246
LANGUAG1 .333 .877
-9.610E-02
LANGUAG2 -.316 -.647
9.141E-02
MEN
-.110 -.130
.804
MIGRATN 0.046 -.742
-.136
OWNRSHIP .855 .240
.113
RELGION1 -.120 .524
.658
RELGION2 .596 -.231
-.399
WOMEN
.214 .210
.795
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a
Rotation converged in 6 iterations.
Appendix G
Cluster
Agglomeration Schedule
Cluster Combined
Stage Cluster 1
Cluster 2
Coefficients
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
.114
.574
.584
.653
.751
.837
1.032
1.411
1.515
1.840
1.904
2.108
2.151
2.487
3.726
4.558
4.799
5.309
6.449
8.052
11.142
13.918
2
9
1
12
9
15
3
12
1
13
9
3
1
8
1
1
3
12
12
1
1
1
6
10
2
17
14
18
7
16
4
22
20
23
15
11
9
19
8
21
13
3
12
5
Stage Cluster First Appears
Cluster 1
Cluster 2
Next
Stage
0
0
3
0
0
5
0
1
9
0
0
8
2
0
11
0
0
13
0
0
12
4
0
18
3
0
13
0
0
19
5
0
15
7
0
17
9
6
15
0
0
17
13
11
16
15
0
20
12
14
20
8
0
19
18
10
21
16
17
21
20
19
22
21
0
0
Cluster Membership
Case 3 Clusters
1:1
1
2:2
1
3:3
1
4:4
1
5:5
2
6:6
1
7:7
1
8:8
1
9:9
1
10:10
1
11:10A
1
12:12
3
13:14
3
14:15
1
15:16
1
16:16A
3
17:17
3
18:20
1
19:21
1
20:26
1
21:28
3
22:29
3
23:9A
1
Malathi Ananthakrishnan
Date of Birth: 30 June 1973
Education:
Master of Urban and Regional Planning May 1998
Virginia Polytechnic Institute and State University, Blacksburg, VA
Bachelor of Architecture
May 1996
University of Pune, Pune, India
Experience
Graduate Research Assistant to Dr. J. O. Browder, Professor,
Department of Urban Affairs and Planning, Aug. 1997 – May 1998
Graduate Research Assistant to Dr. P. L. Knox, Associate Dean for Academic Affairs,
College of Architecture and Urban Studies, Virginia Tech. Aug. 1996 - May 1997
Worked as an Architect with Suyojan Architects, Pune, India May - July 1996
.
Worked as an intern with Narendra Dengle Architects, Pune, India. Dec. 1994 - Mar. 1995
Worked with the Indian National Trust for Art and Cultural Heritage May 1993 - May 1994
Worked as an intern at Historic Boulder, Boulder, CO, USA. April - July 1992
Honors and Affiliations
• Invited to Phi Kappa Phi National Honor Society, October 1997.
• Awarded Virginia Citizens Planning Associate Fellowship - Outstanding First Year Graduate
Student, May 1997.
• Student member American Planning Association.
• Registered Architect under Council of Architecture, New Delhi, India.
• Rank holder of the University of Pune.
• Won first prize (three member team) for Formica Interior design competition, 1995.
• Won first prize (three member team) in a design competition - Reclaiming a derelict river, 1994.