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The Urban Social Pattern of Navi ,

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, , Bombay, - 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 of India, Navi Mumbai was specifically built as a planned decentralization of a large metropolitan . 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 of Architecture and , 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. Banerjee-

Guha at the University of Bombay. 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 1.1 Research Problem Statement 1.2 Significance of Thesis 1.3 Organization of the Thesis

2. The Research Setting…………………………………………………………. 3 2.1 Introduction 2.2 The Planning History of Bombay and the Greater Bombay 2.3 The Creation of Navi Mumbai 2.4 The Draft 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………………………………………………………….. 43 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

6. Interpretation / Discussion…………………………………………………… 65 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

7. Conclusion…………………………………………………………………… 74

Bibliography…………………………………………………………………….. 77

Glossary of Terms

Appendix A Appendix B Appendix C Appendix D Appendix E Appendix F Appendix G List of Tables

Table Title page number 2.1 Population Density of Bombay 4 2.2 Immigrant population of Bombay 5 2.3 Land Fragmentation in 1970 6 2.4 Household Income and Capacity to Pay 8 2.5 Population Density in Various Sectors of Bombay 16 2.6 of Navi Mumbai 17

4.1 Constructs and Variables 39 4.2 Survey Sampling 40

5.1 Constructs and Variables 43 5.2 Work Force 44 5.3 Number of Earners 44 5.4 Occupational Classification of Workforce 45 5.5 Household Income 46 5.6 Location of Education Institutions 47 5.7 Level of Education 47 5.8 Male Population 48 5.9 Female Population 49 5.10 Family Size 50 5.11 Type of Housing 51 5.12 Ownership of House 52 5.13 Housing built by CIDCO 52 5.14 Housing built by Private Enterprise 53 5.15 Year of Occupation 53 5.16 Previous Place of Residence 54 5.17 Religion 55 5.18 Language 56 5.19 Spatial Pattern of Variables 57 5.20 Attributes of Principal Components 60 5.21 Attributes of Principal Components 61 List of Figure

Figure Title Page Number 2.1 Expansion of Bombay 2 2.2 Twin City Across the 5 2.3 Development Potential of the Site 7 2.4 Nodes of Navi Mumbai 11 2.5 Institutional Hierarchy in Implementation of Development Plan for 15 Navi Mumbai 2.6 Land Use of Navi Mumbai 18

3.1 Circle and Swastika Plans 26 3.2 Concentric Zone Theory 28 3.3 Sector Theory 29 3.4 Multiple Nuclei Theory 29 3.5 Urban Social Patterns 31 3.6 Plan of and 32 3.7 Asian Ports 32 3.8 Latin American Cities 33 3.9 Pattern of Indian Cities 34 3.10 Theories of Urban Social Patterns and Corresponding Case Studies 36

5.1 Distribution of Single-earner Families 45 5.2 Frequency of Families with Income range Rs. 2651-4450 46 5.3 Frequency of Families with at least one individual with Secondary 48 Education 5.4 Frequency of Male Population in the age group 25-45 49 5.5 Frequency of Households with 4 or 5 members 50 5.6 Frequency of Houses built by CIDCO 51 5.7 Frequency of Housing built by CIDCO 52 5.8 Frequency of Houses built by Private Enterprise 53 5.9 Frequency of Tenure 54 5.10 Frequency of Bombay as Previous Place of Residence 55 5.11 Frequency of 56 5.12 Frequency of 56 5.13 Frequency of Marathi 57 5.14 Frequency of 57 5.15 Components in Rotated Space 59 5.16 Loadings of Principal Components 59 5.17 Dendrogram using Average Linkages between groups 60 5.18 Loadings of Principal Components 62 5.19 Dendrogram using Average Linkages between groups 63

6.1 Cluster of Nodes of Navi Mumbai 65 6.2 Average Linkage between Factor Scores 66 6.3 Average Linkage between Variables 66 6.4 Clustering of Sectors of 67 6.5 Average Linkage between Factor Scores 68 6.6 Average Linkage between Variables 68 6.7 Hypothetical Sector Pattern for Socioeconomic variables 69 6.8 Distribution of Number of Earners 69 6.9 Distribution of Income 69 6.10 Hypothetical Concentric Pattern for Family Status variables 70 6.11 Distribution of Ownership of Apartment 70 6.12 Hypothetical Multiple Nuclei Pattern for Ethnic variables 71 6.13 Distribution of Households speaking Marathi 71 6.14 Distribution of Households which follow 71 6.15 Clustering of Sectors 72 6.16 Score 1 72 6.17 Score 2 72 6.18 Score 3 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 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 / ) 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 . 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 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 .

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 long- range 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. and subsequent of Bombay have created a such that the central business (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 . South Bombay is the center of India’s 1950 banking and service industries. This BOMBAY range of activities led to crowding at an NAVI Arabian MUMBAI unprecedented scale. In Bombay, for Sea 1910 those who could not afford to make the 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

Figure 2.1 Expansion of Bombay Bombay is not a city built on Indian Source: Dwivedi and Mehrotra, 1995. traditional planning ideas. The city of Bombay had its beginnings in a series of fishing 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 . 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).

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 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 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 area as well as the of Bombay. However, Greater Bombay came into existence only after the 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 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 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 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 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 and created unequal development, and mixed land use (UNCHS, 1993). Unhealthy and insanitary conditions for 1 million 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 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 New Growth implementation occurred through Centers 'correct' political and bureaucratic channels in 1969. This was in the form Growth Town Centers of the Bombay Municipal Regional Center of Planning Board's recommendation that Bombay a new city be designed within the Bombay Metropolitan region to facilitate the decongestion of Bombay Arabian (Correa, 1997). If the new city was too Sea far away, then this would not be Harbor of possible (BMRPB, 1973). Bombay The site that was finally chosen was across the harbor from Bombay island. It is a narrow piece of land Figure 2.2 Twin City Across the Harbor bounded by the Western mountain Source: CIDCO, 1973. ranges on the north, south and east, and

1 is a prominent architect and urban designer in Bombay. 2 Pravina (late) was a structural engineer. 3 Sirish Patel, engineer and planner, was incharge of the planning and design of Navi Mumbai (1970-75). Malathi Ananthakrishnan Chapter 2: The Research Setting 6 the 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 (), 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 (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 self- sufficient 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

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 MIDC Industrial Estates port at Nhava-Sheva; Creek • the existence of two municipal bridge Taloja corporations at and ; Arabian Sea • the newly commissioned bridge across the creek, and transport Panvel corridors along Thane-Belapur; Nhava-sheva • the Thane- National 4, Panvel-Uran rail and 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 '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 . Le Corbusier had played an important role in the design of in Punjab in the mid- 1950s (Le Corbusier, 1961). Some of the highlights of the design elements of this plan were sector planning, hierarchy of 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 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 ” 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. 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 11

on walking distances to the BOMBAY mass transit stop. The node should be Airoli large enough to provide schools, Ghansoli shopping areas and other facilities. Kopar-Khairane

Vashi The Development Plan of Navi Sanpada Mumbai is an example of the new consciousness for sustainable Jui settlements (CIDCO, 1995). The plan Arabian Belapur 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 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 Source: CIDCO, 1973. recreation and timber. The streams flowing from the Western mountain ranges would irrigate these trees. The plan called for the construction of holding ponds to retain excess 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).

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 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 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 (self- government) 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 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 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 leprosy- affected 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, 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 ) 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 1992 1993 Residential 101.15 133.99 127.08 129.87 128.71 127.61 Commercial 6.51 6.51 6.51 5.75 5.75 5.75 Industrial 43.21 43.14 43.14 43.14 43.14 43.14 Port 12.00 22.7 22.7 22.7 22.7 22.70 Wholesale market 6.08 4.54 4.54 4.6 5.76 6.86 Woodlands / Park 90.26 61.24 68.15 69.35 69.35 69.35 Institutional .76 1.09 1.09 1.09 1.09 1.09 Fishing and allied 6.14 3.44 3.44 3.44 3.44 3.44 Transportation 30.86 30.35 30.35 29.73 29.73 29.73 No development 46.73 36.70 36.70 34.03 34.03 34.03 Total 343.70 343.70 343.70 343.70 343.70 343.70 (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 colleges (CIDCO, 1995). Medical facilities are provided by private medical practitioners. Every node has a hospital run by the 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 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 NEW BOMBAY in other development sectors of BOMBAY 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 Arabian construction of more houses. CIDCO Sea Residential Woodlands provides serviced sites for both Industrial government and private ownership. Port Houses have been constructed for Institutional different sectors of society - Trucking economically weaker section, lower Wholesale income group, middle-income group Fishing and high-income groups. Commuter Wetlands services have become operational since Figure 2.6 Land Use of Navi Mumbai May 1992, and housing occupancy Source: CIDCO, 1995. 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 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 three- dimensional, 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 socio- economic 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, , 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 birth- ascribed 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 (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 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 cut- off 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 – and two minor religions - and . 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 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 . 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 are , Bengali, Tamil, Gujarati, Marathi, Malayalam, , Telugu, 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 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 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 in particular (Hindi is a Indo- Aryan language while the languages of South India belong to the Dravidian group). The South Indian state of 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 / 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 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 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 , 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 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 i 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, CBD 1979). Thus, it is a descriptive framework to 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 Middle income assumptions such as uniform land surface, free High income market, accessibility to a single-centered city, heterogeneous population and a commercial- industrial 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 . 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 Homer Hoyt put forth a land use theory after studying over 100 cities in the U. S (Hoyt, 1939). Hoyt primarily studied residential land Income group 1 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 Income group 2 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 Income group 3 along transport routes. For example, industries may cluster around the railway line or low- income housing along a riverbank. This model Figure 3.3 Sector Theory also accommodates growth (Hartshorn, 1992). Source: Hoyt, 1939 Hoyt also stressed the need to consider zoning laws and slum clearance laws in making models.

3.7.3 Multiple Nuclei Theory 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, commercial 1979). This is because concentration of certain ethnic group activities may prove to be more beneficial. residential Concentric zones or sectors may emerge from industrial 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). Figure 3.4 Multiple Nuclei Theory Source: Hartshorn, 1992 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 (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 Source: Lowder, 1986 pattern showed concentric zones for land use. 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 , Santiago and Chile Source: Lowder, 1986 residential 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 Bazaar 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 Centroid 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.

Fort Temple Brush (1977) studied 24 cities in India and discerned four types of gradients Figure 3.9 Pattern of Indian Cities of population directly related to their Source: Weinstein, 1974 evolutionary pattern. Pune and , 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. had two nuclei – the old city and the colonial city. Industrial towns like 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 . Malathi Ananthakrishnan Chapter 3: The Conceptual Framework 36

Concentric Zone Theory Sector Theory Multiple Nuclei Theory

Income group 1 commercial ethnic group CBD Income group 2 residential Transition industrial

Low income Middle income

High income Income group 3

Delhi Latin America Asian Ports

Concentric + Sector Theories

Chicago Calcutta Concentric + Sector + Multiple Nuclei Theories

Figure 2.10 Urban Social Patterns and Relevant Case Studies. Source: Lowder, 1986, Hartshorn, 1992. 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 . 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, 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 Survey Coverage % of total Dwellings Vashi 27,283 6656 24 Nerul 16,056 4219 26 9,338 2125 23 Belapur 9,109 2034 22 Kalamboli 9,007 2282 25 Airoli 13,378 2530 19 Kopar-khairane 14,161 2506 18 Sanpada 2,357 544 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" ( 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 more- or-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 45

For the analysis, the variable, single 6000 earning member, was selected. This is most representative of the entire 5000 population, and has a normal distribution over eight cases. The 4000 mean is 74 with a very low standard deviation of 5. 3000 The distribution of the single earner families is shown in Figure 2000 5.1. The distribution of the single- earner family at the regional level 1000 Std. = 7.96 shows a standard deviation of only 5 Mean = 66.3 (mean=74). This means that the N = 19127.00 Frequency 0 distribution is homogeneous. At the 45.0 50.0 55.0 60.0 65.0 70.0 75.0 80.0 sub-regional scale the standard Cases weighted by population deviation is 7.96 (mean=66.3). Both Figure 5.1 Distribution of Single-earner families 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 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 self- teacher 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 1251- 2651- 4451- 7501- 10001- 15000+ 1250 2650 4450 7500 10000 15000 Vashi 2 14 27 30 15 7 3 Nerul 3 27 36 21 6 3 1 Belapur 2 12 27 35 12 5 2 Kalamboli 2 26 46 21 3 1 0 Panvel 2 24 31 31 5 3 2 Kopar-khairane 2 9 32 36 9 7 0 Airoli1143934820 Sanpada 1 5 31 42 12 4 1 Mean 1.88 16.38 33.63 31.25 8.75 4 1.13 Standard deviation 0.64 8.26 6.46 7.29 4.06 2.2 1.13

The income range of Rs. 2651-4450 was 7000 selected for the principal components

6000 analysis because the median income of Rs. 4200 fell within this range. Almost 5000 34% of the population falls within this category, and the standard deviation is 4000 6.46. 3000 The regional scale shows a standard deviation of 6.46 (mean=33.45) 2000 and the sub-regional scale, the standard Std. Dev = 10 1000 deviation is 10.98 (mean=27.9) (Figure Mean = 27.9 5.2). Both cases do not show a N = 19127.00

Frequency 0 5.0 15.0 25.0 35.0 45.0 homogeneous distribution of people 10.0 20.0 30.0 40.0 50.0 based on income as the standard

Cases weighted by population deviation is greater than 15% of the mean. Figure 5.2 Frequency of Families with income range Rs. 2651-4450 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 Vashi8811100009 Nerul97721100010 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 Airoli70000083010 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 Children Primary secondary high technical BS MS school Vashi 4 9 14 27 22 1 22 4 Nerul 3 5 15 27 17 2 24 5 Belapur 5 8 18 30 21 1 15 2 Kalamboli 7 10 20 34 16 2 9 1 Panvel 3 8 14 25 19 4 22 4 Kopar-khairane 4 6 13 27 15 1 29 4 Airoli 4 7 16 37 18 1 13 3 Sanpada 4 8 12 21 25 2 21 4 Mean 4.25 7.63 15.25 28.5 19.13 1.75 19.38 3.38 Standard deviation 1.28 1.60 2.66 5.07 3.36 1.04 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 6000 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

Frequency 0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 45 to 59, and only 3% of the 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 4371512534145 Nerul 751012854183 Belapur 6 4 8 14 12 5 37 11 4 Kalamboli861113854361 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 7461010543104 Mean 759121054193 Standard deviation212220331 Malathi Ananthakrishnan Chapter 5: Presentation of Data 49

The standard deviation of the 7000 population is 3 (mean=41) at the

6000 regional level, and 3.39 (mean=38) at the sub-regional level (Figure 5000 5.4). The population age structure is uniformly distributed over the whole 4000 region.

3000

2000

Std. Dev = 3.39 1000 Mean = 38.0 N = 19127.00

Frequency 0 32.0 36.0 40.0 44.0 48.0 52.0 34.0 38.0 42.0 46.0 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 6 -9 10 -15 16 -21 22 -24 25 -44 45 -59 60+ Vashi 5381411539104 Nerul 751013974062 Belapur 5 4 8 14 11 6 40 8 3 Kalamboli1510162013126 6 2 Panvel 8 4 8 11 10 9 40 6 3 Kopar-khairane9610912103751 Airoli 6 5 10 15 10 6 39 6 2 Sanpada 648101393993 Mean 8 5 10 13 11 8 35 7 3 Standard deviation3233121221

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 50

Table 5.10 Family Size Single 2,3 4,5 6,7 8,9,10 Average family size Vashi 1 26 57 14 2 4.21 Nerul 2 34 54 10 0 3.87 Belapur 1 31 53 13 1 4.03 Kalamboli 3 31 52 14 0 3.99 Panvel 5 41 45 8 1 3.67 Kopar-khairane 3 41 45 10 1 3.81 Airoli 1 27 56 15 1 4.22 Sanpada 3 39 45 12 1 3.85 Mean 2.4 33.8 50.9 12 0.9 Standard deviation 1.4 6.0 5.1 2.4 0.6

6000 The families with a size of 4 or 5 members was chosen as 50% of the

5000 population belongs to this category. The variable has a

4000 standard deviation of 5.1.

3000

2000 The variation of the data is minimal. At the regional scale the

1000 Std. Dev = 5.85 standard deviation is 5.1 Mean = 56.0 (mean=50.9), and 5.85 (mean=56) 0 N = 19127.00 Frequency at the sub-regional scale (Figure 42.5 47.5 52.5 57.5 62.5 67.5 45.0 50.0 55.0 60.0 65.0 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 51

Table 5.11 Type of Housing CIDCO Pvt. House Pvt. Co-op Commercial Other Vashi 64 2 29 2 1 Nerul 95 0 5 0 0 Belapur 91 0 9 0 0 Kalamboli 99 0 0 1 0 Panvel 80 5 15 0 0 Kopar-khairane 98 0 2 0 0 Airoli 100 0 0 0 0 Sanpada 88 1 11 0 0 Mean 89.38 1.00 8.88 0.38 0.13 Standard Deviation 12.24 1.77 9.76 0.74 0.35

For this variable, only houses 7000 built by CIDCO was selected. Houses 6000 built by CIDCO are 90% of the houses available. The standard 5000 deviation is 12.24.

4000 The standard deviation at the regional scale is 12.24 (mean=89.38) 3000 while at the sub-regional scale it is 35.62 (mean=66.4) (Figure 5.6). This 2000 is a very significant result. CIDCO’s Std. Dev = 35.62 1000 aim to promote heterogeneity was to Mean = 66.4 be implemented by having a strong 0 N = 19127.00 Frequency 0.0 20.0 40.0 60.0 80.0 100.0 hold over the housing market. At 10.0 30.0 50.0 70.0 90.0 Vashi, the oldest node, the strong

Cases weighted by POP control is no longer evident. The large deviation shows that private Figure 5.6 Frequency of Houses built by CIDCO 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 52

Table 5.12 Ownership of House Mortgage CIDCO Private Resale Rental Vashi 1123172123 Nerul 21 36 3 16 36 Belapur 8 40 4 0 37 Kalamboli 25 25 1 0 43 Panvel 7 33 9 0 36 Kopar-khairane 0 34 1 14 49 Airoli 0510042 Sanpada 15 32 7 18 26 Mean 10.88 34.25 5.25 8.63 36.5 Standard Deviation 9.09 8.75 5.68 9.43 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 11302214153 2 0 Nerul7571887210 Belapur 0 26 10 33 20 11 0 0 Kalamboli24372457200 Panvel10331618221 0 0 Kopar-khairane 0 20 10 42 18 9 1 0 Airoli 0302817186 0 0 Sanpada06118129000 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 8000 21.25 while the mean was 14.2 (Figure 5.7). 6000

4000

2000 Std. Dev = 21.85 Mean = 14.2 N = 19127.00

Frequency 0 0.0 20.0 40.0 60.0 80.0 10.0 30.0 50.0 70.0 90.0

Cases weighted by population

Figure 5.7 Frequency of Housing Built by CIDCO Malathi Ananthakrishnan Chapter 5: Presentation of Data 53

Table 5.14 Housing built by Private Enterprise <15 16-25 26-35 36-50 51-75 76-100 101-150 150+ Vashi 4 2 2 14 14 24 8 2 Nerul066682350 Belapur012 23395 1 Kalamboli08005000 Panvel001 1182482 Kopar-khairane 0 91 5 5 42 1 0 0 Airoli000017000 Sanpada 13 60 2 2 12 5 2 1 Mean 37.50 16.63 18.50 14.38 5.50 3.75 3.75 14.0 Standard Deviation 10.76 7.69 10.86 3.16 2.67 2.12 3.41 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

6000 CIDCO-built houses and privately built houses, the dwelling sizes 4000 selected was 26-35 sq. m.

Fre corresponding to middle income 2000 que Std. Dev = 18.67 ncy Mean = 16.2 groups. 0 N = 19127.00 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 Figure 5.8 Frequency of Houses built by Private 1990s. There is a great variation in the Enterprise number of houses occupied between 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 291063514 Belapur 4 23 24 11 13 18 7 Kalamboli 0 5 311011376 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 7000 regional scale is 20.25 (mean=30.25) 6000 and 18.25 (mean=52.8) (Figure 5.9). There is a very large variability, 5000 which can be attributed to the pace of

4000 construction.

3000 Previous Place of Residence: The 2000 two variables describing previous 1000 Std. Dev = 18.25 place of residence are Bombay and Mean = 52.8 Navi Mumbai (Table 5.16). These 0 N = 19127.00 Frequency 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 describe migration from Bombay and

Cases weighted by population movement within Navi Mumbai. 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.

Table 5.16 Previous Place of Residence Island City Western Eastern Thane Navi Within Outside Outside suburbs suburbs Mumbai state state India Vashi 18.06 6.19 26.94 3.5 35.89 3.44 4.45 0.53 Nerul 13.58 5.55 23.56 2.58 47.53 2.58 2.94 0.07 Belapur 10.83 5.65 10.23 4.82 32.34 13.32 19.42 0.2 Kalamboli 5.79 2.94 11.39 3.46 66.3 5.26 2.54 0.04 Panvel 3.62 2.26 5.27 3.11 68.28 6.82 6.4 0.05 Kopar 14.2 2.63 17.16 2.63 55.23 4.51 1.36 0.04 Airoli 8.05 4.51 20.43 9.29 49.78 3.75 2.85 0 Sanpada 17.1 5.15 24.63 4.23 39.34 2.57 6.25 0.18 Mean 11.4 4.36 17.45 4.20 49.34 5.28 5.78 0.14 Standard 5.25 1.54 7.79 2.19 13.39 3.54 5.8 0.17 deviation Malathi Ananthakrishnan Chapter 5: Presentation of Data 55

6000 The variables, island city, , and Thane

5000 have been summed up to obtain the variable, Bombay. This variable shows 4000 the families whose most immediate place of origin is Bombay. 3000 The standard deviation of the families whose previous place of 2000 residence was Bombay is 9.42

1000 Std. Dev = 9.56 (mean=26.01) at the regional scale and Mean = 53.0 9.54 (mean=53) at the sub-regional 0 N = 19127.00 Frequency 35.0 40.0 45.0 50.0 55.0 60.0 65.0 70.0 75.0 scale. There is a large variation because there has been migration from Cases weighted by population the rural areas, from Bombay and Figure 5.10 Frequency of Bombay as Previous within Navi Mumbai. Place of Residence 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 Christian Islam Jain Sikh Buddhist Others Vashi846 6 1 210 Nerul883 5 0 301 Belapur796 4 0 720 Kalamboli 84 4 5 0 6 1 0 Panvel 94 2 2 0 1 0 1 Kopar-khairane 89 2 6 0 1 2 0 Airoli883 3 0 150 Sanpada 80 9 7 0 3 1 0 Mean 85.75 4.38 4.75 0.13 3.00 1.50 0.25 Standard deviation 4.98 2.45 1.67 0.35 2.33 1.60 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. Malathi Ananthakrishnan Chapter 5: Presentation of Data 56

5000 10000

4000 8000

3000 6000

2000 4000

1000 2000 Std. Dev = 4. Std. Dev = 3.91 Mean = 82.4 Mean = 6.9

0 N = 19127.00 requency 0 N = 19127.00 Frequency F

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 non- uniform 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 Hindi Gujarati Malayalam Punjabi Tamil Kannada Bengali Other Vashi 42.41 13.81 7.23 7.41 4.48 5.14 2.82 3.17 13.53 Nerul 45.75 16.50 2.99 10.19 5.50 3.34 3.56 3.01 9.16 Belapur 40.76 16.47 3.98 8.31 9.64 2.90 2.36 4.08 11.5 Kalamboli 55.87 14.29 2.19 8.11 6.66 2.32 3.20 0.83 6.53 Panvel 66.78 9.65 2.92 5.04 1.74 2.35 3.29 2.96 5.27 Kopar 67.93 16.72 1.44 2.99 1.12 1.68 1.72 0.80 5.6 Airoli 42.46 12.50 3.13 13.60 5.33 5.33 2.57 3.49 11.59 Sanpada 63.79 12.37 2.69 5.77 1.34 3.08 3.72 1.11 6.13 Mean 53.22 14.04 3.32 7.68 4.48 3.27 2.91 2.43 8.66 Std. dev 11.73 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. Malathi Ananthakrishnan Chapter 5: Presentation of Data 57

6000 6000

5000 5000

4000 4000

3000 3000

2000 2000

1000 Std. Dev = 3.77 1000 Std. Dev = 15.73 Mean = 6.9 Mean = 46.6 0 N = 19127.00 0 N = 19127.00 Frequency Frequency 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 2.5 7.5 12.5 17.5 22.5 5.0 10.0 15.0 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 Malayalam- speaking 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 income 1.0

earner

.5 family size religion Component 2 tenure 0.0 language migration

-.5

1.0 1.0 .5 .5 0.0 0.0 -.5 -.5 Component 1 Component 3

Analysis weighted by population of each node

Components

1.5

1

0.5

loading 0

-0.5 TENURE EARNER INCOME RELIGION FAM.SIZE MIGRATN -1 EDUCATN LANGUAGE variables

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 60

The three components are (Table 5.20): Table 5.20 Attributes of Principal Components Principal Components Variables Name Component 1 Family size Family status Previous place of residence Tenure Component 2 Education Socioeconomic status Income Component 3 Number of earners Ethnic status with high Language number of earners. Religion

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 -+ +------+ Cluster 1 Sanpada ------+ +---+ Nerul ------+------+ I I Airoli ------+ +------+ I Vashi ------+ I Belapur ------+------+ Kalamboli ------+

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 Name Component 1 Education Socioeconomic status and Income Muslim enclave Ownership of house Previous place of residence Muslim Component 2 Marathi Ethnic status Component 3 Number of earners Ethnic status with high Malayalam number of earners. Demographics Hindu Malathi Ananthakrishnan Chapter 5: Presentation of Data 62

Components

1 0.8 0.6 0.4 0.2 0

loadings -0.2

-0.4 MEN -0.6 INCOME WOMEN EARNER MIGRATN EDUCATN

-0.8 RELGION1 RELGION2 LANGUAG1 LANGUAG2 -1 OWNRSHIP var iabl 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 ---+---+ I 20 ---+ +---+ 9 -+-+ I I 10 -+ +---+ I +------+ 15 ---+ +-----+ I I Cluster 3 26 ------+ I I 21 ------+ +------+ 3 ---+---+ I I 7 ---+ +------+ I I 9A ------+ +------+ I 8 ------+------+ +------+ 10A ------+ I I 14 ------+------+ I I 29 ------+ I I I 12 -+---+ +------+ I 17 -+ +------+ I Cluster I 2 16A -----+ +---+ I 28 ------+ I 5 ------+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 Airoli possibly influenced by one another. All

Kopar-Khairane the other nodes are in the second cluster.

Vashi Kalamboli Sanpada

Nerul

Arabian Belapur Sea Panvel

Cluster 1

Cluster 2

Figure 6.1 Cluster of Nodes of Navi Mumbai Malathi Ananthakrishnan Chapter 6: Interpretation and Discussion 66

Figure 6.2 shows that different 3

2 factor scores influence the two 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 -3 influence on both cluster. N = 67116 6711667116 14543 14543 14543 1 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

80 EARNER

EDUCATN 60 FAM.SIZE

40 INCOME

LANGUAGE 20 Kopar-khaira MIGRATN 0 Sanpada Kopar-khaira RELIGION

-20 TENURE 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 16A

8 5 4 3 2 21 17 7 6 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

2 three clusters are influenced by

0 different factor scores. Cluster 1 is Factor Score 1

-2 8 influenced by all three scores, Factor Score 2

-4 cluster 2 more strongly by score 2 Factor Score 3 -6 and cluster 3 by score 3. N = 1649716497164971892 1892 1892 738 738 738 1 2 3

Figure 6.5 Average Linkage between Groups Analysis weighted by population

120 EARNER

100 EDUCATION

INCOME 80 MARATHI

60 MALAYALAM

MEN 40 MIGRATION

20 OWNRSHIP

HINDU 0 MUSLIM

-20 WOMEN 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 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 Figure 6.14 Distribution of Households speaking Marathi 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 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 of Bombay only by the . Every effort was taken by the government to make Navi Mumbai an and not a or 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 self- help). 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. Bibliography

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83 Glossary of Terms

Term Meaning Cuadra Spanish measurement Jali Carved screens Masjid Mosque Padas Subdivisions of the cosmic universe Panchayati Self-government Pucca Durable Purdahs Enclosure Purushasukta Hindu treatise Rashtrabasha Language of the State Swadeshi Fullest utilization of local resources Swatantrya Self-motivation and self-help Swavalambhan Self-reliance Vastupurusha mandala Terrestrial representation of the cosmic universe Vastushastra Science of architecture and planning Appendix A

March 1958 Study group on Greater Bombay set up under the chairmanship of Mr. S. G. Barve. July 1958 Bombay Municipal Corporation decided to prepare a development plan for Greater Bombay. February 1959 The study group on Greater Bombay recommended a rail-cum-road bridge across the Thane creek. July 1964 Development plan for greater Bombay was submitted to the State Government. 1965 A Committee under Dr. D. R. Gadgil was appointed to formulate broad principles of regional planning for Bombay and Poona. March 1966 The Gadgil Committee recommended regional planning legislation and regional planning boards. January 1967 Maharashtra Regional and Town Planning Act 1966 was passed. July 1967 Bombay Metropolitan and Regional Planning Board was constituted. January 1970 The Board published the Draft Plan with recommendations to set up a twin city. February 1970 State government notified privately owned land in Navi Mumbai for acquisition. March 1970 CIDCO was formed. March 1971 CIDCO was designated as New Town Development Authority for Navi Mumbai. August 1973 The Bombay Metropolitan regional Plan was approved by the State government. October 1973 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 1234 no. 1 64.04 26.81 7.26 1.89 2 57.67 28.57 8.99 4.76 3 68.60 24.42 5.81 1.16 4 70.26 23.08 5.64 1.03 5 46.79 40.37 11.01 1.83 6 65.59 27.13 6.48 0.81 7 62.16 30.41 6.08 1.35 8 52.07 30.58 9.92 7.44 9 73.58 20.64 3.77 2.00 10 74.19 19.89 5.16 0.76 10A 50.00 35.71 11.43 2.86 12 65.22 26.09 8.70 0.00 14 77.52 18.28 3.57 0.63 15 72.31 20.06 5.09 2.54 16 65.69 29.29 4.60 0.42 16A 71.29 22.49 3.83 2.39 17 61.96 27.17 8.23 2.64 20 69.23 21.15 5.77 3.85 21 63.57 23.43 9.51 3.48 26 77.68 16.31 3.86 2.15 28 52.38 38.10 0.00 9.52 29 82.25 11.26 4.76 1.73 9A 74.28 21.38 3.62 0.72 mean 66.01 25.33 6.22 2.43 std dev 9.40 6.89 2.79 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 93 94 95 no. 980 1 43.99 12.34 13.92 11.71 3.16 13.61 1.27 2 39.57 12.83 26.74 6.95 5.35 6.95 1.60 3 11.49 32.18 31.61 12.07 5.17 5.75 1.72 4 5.10 40.82 24.49 12.76 6.63 7.14 3.06 5 49.53 20.56 16.82 5.61 3.74 1.87 1.87 6 51.82 12.15 18.62 4.86 4.45 6.88 1.21 7 42.38 15.23 22.52 7.95 6.62 4.64 0.66 8 24.59 38.52 12.30 6.56 6.56 10.66 0.82 9 0.22 52.11 15.19 8.87 6.54 14.19 2.88 10 0.38 37.09 29.06 13.38 6.50 9.94 3.63 10A 0.00 48.57 18.57 14.29 12.86 4.29 1.43 12 0.00 0.00 0.00 4.17 33.33 41.67 20.83 14 0.42 0.64 27.60 7.64 18.05 36.73 8.92 15 8.46 36.35 28.93 10.09 6.82 6.82 2.52 16 27.80 39.42 12.86 4.56 5.81 8.30 1.24 16A 0.00 20.38 56.87 7.11 6.16 6.16 3.32 17 0.31 16.82 48.13 11.68 7.94 9.03 6.07 20 0.00 35.26 28.85 4.49 12.82 10.26 8.33 21 13.02 44.65 17.91 6.28 4.65 7.44 6.05 26 0.00 0.00 0.00 0.00 31.88 48.03 20.09 28 0.00 0.00 0.00 0.00 0.00 75.00 25.00 29 0.87 0.87 1.73 4.33 47.19 31.17 13.85 9A 0.00 31.43 32.50 12.86 5.71 12.50 5.00 mean 12.54 24.36 21.42 7.57 11.13 16.61 6.37 stddev 18.02 17.39 14.56 4.08 11.60 18.40 7.15 Previous Place of Residence Sector Island Wn En Thane Vashi Navi Inside Out of Intl. no. city suburbs suburbs Mumba state state i 1 21.45 4.42 17.03 8.83 24.29 8.83 8.20 6.62 0.32 2 21.05 8.42 24.74 2.63 27.37 10.53 2.63 2.63 0.00 3 19.30 3.51 36.26 5.26 23.39 2.92 4.09 5.26 0.00 4 14.58 7.81 23.96 2.60 38.54 5.73 4.69 2.08 0.00 5 14.95 0.00 18.69 2.80 26.17 25.23 12.15 0.00 0.00 6 12.35 6.58 35.80 4.94 20.99 7.00 4.12 7.00 1.23 7 21.19 9.93 37.09 5.30 18.54 3.31 0.00 4.64 0.00 8 27.05 8.20 27.05 0.82 19.67 2.46 1.64 7.38 5.74 9 18.48 8.51 30.57 3.25 28.44 5.38 2.91 2.46 0.00 10 15.71 6.90 28.74 4.79 30.84 2.68 3.26 7.09 0.00 10A 20.29 7.25 31.88 0.00 24.64 2.90 2.90 10.14 0.00 12 20.83 12.50 12.50 0.00 33.33 8.33 4.17 8.33 0.00 14 18.03 6.71 27.25 2.52 31.24 5.45 2.31 5.03 1.47 15 15.35 5.37 31.30 2.83 36.07 5.07 3.58 0.45 0.00 16 15.48 1.67 34.73 3.77 28.03 6.69 4.60 4.60 0.42 16A 17.62 7.14 25.24 6.67 26.67 8.10 4.76 3.33 0.48 17 29.61 6.98 23.10 3.88 20.93 3.57 1.40 8.53 2.02 20 12.26 3.87 16.13 1.29 47.10 16.77 2.58 0.00 0.00 21 19.63 3.23 26.10 0.69 21.02 22.17 5.54 1.62 0.00 26 15.81 3.85 17.95 5.98 39.32 8.55 3.42 4.27 0.85 28 4.76 33.33 9.52 0.00 47.62 4.76 0.00 0.00 0.00 29 11.26 4.33 22.94 1.30 45.02 9.52 1.30 4.33 0.00 9A 12.19 8.24 28.67 2.15 32.26 3.58 2.51 10.39 0.00 mean 17.17 7.47 25.92 2.88 30.33 7.76 3.39 4.53 0.55 stddev 5.36 6.43 7.54 2.01 8.82 6.15 2.45 3.24 1.29 Language Sector Marath Hindi 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 Mean Std. Deviation Analysis N EARNER 73.2091 4.6076 81659 EDUCATN 8.7800 3.8538 81659 FAM.SIZE 53.0814 4.4115 81659 INCOME 32.6705 5.9974 81659 LANGUAGE 49.1087 9.8863 81659 MIGRATN 28.8271 8.6486 81659 RELIGION 86.0403 3.7870 81659 TENURE 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 Total % of Variance Cumulative % 1 3.468 43.347 43.347 2 1.902 23.771 67.118 3 1.818 22.728 89.845

Component Matrix Component 12 3 EARNER .804 .470 .244 EDUCATN -.136 .882 -.278 FAM.SIZE -.926 .293 .202 INCOME .430 .862 -1.317E-02 LANGUAGE .937 -.230 -8.383E-02 MIGRATN -.785 -7.468E-03 .455 RELIGION .685 8.925E-02 .634 TENURE -.880 .236 4.454E-02 Extraction Method: Principal Component Analysis.

Rotated Component Matrix Component 12 3 EARNER -.446 .484 .702 EDUCATN .201 .900 -.155 FAM.SIZE .951 .107 -.264 INCOME -.156 .881 .358 LANGUAGE -.888 -7.766E-02 .379 MIGRATN .878 -.230 -2.898E-02 RELIGION -.255 4.796E-04 .902 TENURE .822 .101 -.381 Extraction Method: Principal Component Analysis. Appendix E

Cluster

Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent 8 100.0 0 .0 8 100.0 a Squared Euclidean Distance used b Average Linkage (Between Groups)

Average Linkage (Between Groups)

Agglomeration Schedule

Stage Cluster 1 Cluster 2 Coefficients 1 5 6 .581 2 2 7 2.946 3 5 8 4.174 4 1 2 4.617 5 3 4 7.919 6 1 5 9.299 7 1 3 10.108

Cluster Membership

1:Vashi 1 2:Nerul 1 3:Belapur 2 4:Kalamboli 2 5:Panvel 1 6:Kopar-khaira 1 7:Airoli 1 8:Sanpada 1 Appendix F

Factor Analysis

Descriptive Statistics Mean Std. Deviation Analysis N EARNER 66.3183 7.9628 19127 EDUCATN 40.5760 7.1339 19127 INCOME 27.9421 10.9768 19127 LANGUAG1 46.5535 15.7324 19127 LANGUAG2 6.9114 3.7719 19127 MEN 38.0484 3.3934 19127 MIGRATN 52.9759 9.5580 19127 OWNRSHIP 66.4424 35.6247 19127 RELGION1 82.3839 4.7307 19127 RELGION2 6.8628 3.9142 19127 WOMEN 33.0375 3.5835 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 % of Variance Cumulative % 1 3.843 34.937 34.937 2.750 25.001 25.001 2 2.438 22.161 57.098 2.690 24.453 49.455 3 1.740 15.819 72.917 2.581 23.463 72.917 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 12 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. Rotated Component Matrix Component 12 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 First Appears Stage Cluster 1 Cluster 2 Coefficients Cluster 1 Cluster 2 Next Stage 1 2 6 .114 0 0 3 2 9 10 .574 0 0 5 3 1 2 .584 0 1 9 4 12 17 .653 0 0 8 5 9 14 .751 2 0 11 6 15 18 .837 0 0 13 7 3 7 1.032 0 0 12 8 12 16 1.411 4 0 18 9 1 4 1.515 3 0 13 10 13 22 1.840 0 0 19 11 9 20 1.904 5 0 15 12 3 23 2.108 7 0 17 13 1 15 2.151 9 6 15 14 8 11 2.487 0 0 17 15 1 9 3.726 13 11 16 16 1 19 4.558 15 0 20 17 3 8 4.799 12 14 20 18 12 21 5.309 8 0 19 19 12 13 6.449 18 10 21 20 1 3 8.052 16 17 21 21 1 12 11.142 20 19 22 22 1 5 13.918 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.