The Spatial Structure of Employment and Its Impacts on the Journey To

The spatial structure of employment and its impacts on the journey to work in the Jakarta Metropolitan Area: a Southeast Asian Extended Metropolitan Region (EMR) perspective

Ikhwan Hakim

Submitted to the Graduate Research School of The University of New South Wales in conformity with the requirements for the degree of Doctor of Philosophy

2009

Abstract

This thesis is developed upon inquires on urban spatial structure of Southeast Asian extended metropolitan region (EMR) and its impacts on travel. Literature suggests that Southeast Asian EMRs exhibit more complex spatial configuration than those of developed cities and suffer severely from a wide range of sustainability problems. While efforts in promoting transport sustainability in the developed world have included policy measures involving urban spatial structure and its physical features as a consequence of the understanding on strong link between land use and transport, there has been lack of understandings on the spatial structure in major cities in Southeast Asia.

Exploratory spatial data analysis (ESDA) is adopted for identification of important components of the spatial structure of employment in the Jakarta Metropolitan Area (JMA). The approach has been specifically designed in order to extract clusters as suggested in the Southeast Asian EMR concept. It is found that the spatial structure of employment in the JMA consists of the following major components: the urban core of Jakarta; the single dominant and expanded regional CBD within the urban core of Jakarta; manufacturing corridors that are largely follow toll roads radiating out of the urban core; local government regions that in general have not been developed into substantial sub-centres; desakota areas overlapping the manufacturing corridors and the agricultural areas; and portions of agricultural areas in the outer parts of Bekasi, Bogor and Tangerang regencies. The result shows that spatial structure of JMA conforms to the Southeast Asian EMR concept rather than the monocentric, polycentric or sprawl patterns debated for the case of developed cities.

Commuting impacts of the identified spatial structure of employment and its physical features are investigated using the desireline analysis, home-to-work trip pattern comparisons (ANOVA) by the employment clusters, and ordinary linear regression and logistic regression models. It is found that the spatial structure

ii identified and its physical features have significant associations to variations in the pattern of commuting across the region. The physical features of the employment spatial structure identified include important policy sensitive variables such as job density, job to household ratio, land use diversity and job accessibility. Policy implications of the findings are developed and centred on recommending both the spatial structure of employment and its physical characteristics that promote more sustainable transport in the JMA.

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Contents

Abstract ii List of Figures viii List of Tables xi Acknowledgements xiii Abbreviations xiv

1 Introduction 1 Background to the study 1 Aim, study area and scope of the thesis 4 Research questions 6 Overview and structure of the thesis 8

2 Urban spatial structure and travel impacts: placing the Southeast Asian EMR in context 12 Introduction 12 Globalisation and urbanisation in the Asia-Pacific: background 13 Asia-Pacific globalisation 13 Urbanisation impacts of globalisation 18 Urban spatial structure and travel impacts: an overview on theories and empirical findings 20 Urbanisation and urban spatial structure 20 Methodologies in identifying urban spatial structure 26 Sustainability issues and travel 28 Travel impacts of urban spatial structure 31 The Southeast Asian Extended Metropolitan Region 37 Continuation of urbanisation trends 37 Conception and characteristics of the Southeast Asian EMR 38 Sustainability problems facing the Southeast Asian EMRs 45 Probing the urban spatial structure of the Southeast Asian EMRs 50 Conclusions 57

3 Jakarta Metropolitan Area: urban evolution and planning 59 Introduction 59 Recent figures and trends 60 Administrative boundary 60 Economic activities 61 Gross regional domestic product 61 Foreign investments and trade 63 Labour force 64 Population and housing 67 Land use and transport 70 Land use and land conversion 70 Transport system and travel 73 From Sunda Kelapa to JABODETABEK: an overview of urban evolution and planning 75 Pre-colonial times and company town: up to 1800 75 Colonial capital: 1800s to 1950s 77 Early independence: 1950s to 1970s 79 Globalisation era: 1970s to present 82 JMDP and JABOTABEK Structure Plan 2005 82 JMDPR and the economic crisis 91 The post-crisis and JABODETABEKPUNJUR 97 Conclusions 102

4 Research design 104 Introduction 104 Formulation of research questions 104 Data sources 106 Home interview survey and population census 106 GIS layers 110 Zoning system 110 Transport network 113 Land use related data 114 Methods 117 The spatial structure of employment 117 Identification of clusters 117 Spatial impacts 124

Characteristics of the components of the spatial structure of employment 125 Travel dimensions and links with the spatial structure and its physical features 128 Flow of the empirical analysis 130 Conclusions 132

5 Spatial structure of employment in the JMA: an EMR perspective 133 Introduction 133 Employment profiles within the JMA 134 Identification of employment clusters 137 Co-location of job industries 137 Major employment clusters 140 Factor 1 141 Factor 2 143 Factor 3 144 Factor 4 146 Factor 5 147 Factor 6 149 Probing desakota 151 Job density and planned sub-centres: a LISA approach 153 The overall spatial structure of employment 155 Features and spatial impacts of employment clusters 158 Spatial characteristics of employment clusters 158 Density variables 158 Job diversity 162 Access to transport facilities and job accessibility 165 Spatial impacts 169 Weighted central feature 169 The regional CBD and employment density 171 Urban cores and negative population growth 172 The east-west axis and high population growth 174 Manufacturing corridors and urban-rural mix 178 Conclusions 179

6 Journey to work impacts of the JMA’s spatial structure of employment and its physical features 182

Introduction 182 Journey to work and the degree of spatial interaction 183 Variations in journey to work patterns 193 Distance travelled 193 Time travelled 200 Travel mode 206 Measure of “local trips”: share of trips by distance by travel mode 209 The influence of physical features 212 Travel dimensions, explanatory variables and model estimation 212 Summary of implications of the models estimations 217 Transport sustainability and policy implications 221 Conclusions 226

7 Conclusions 227 Thesis summary and conclusions 227 Limitation of the thesis and further research 231

References 233

Appendix 1 Appendix 2

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List of Figures

1.1 Structure of the thesis 11 2.1 The world’s urban and rural population, 1950-2025 14 2.2 The flying geese pattern of shifts in comparative advantage in Asia Pacific 17 2.3 Foreign direct investment (FDI) net inflows in South-Eastern and Eastern Asia, 1970-2007 18 2.4 Emerging spatial network of development in Pacific Asia 20 2.5 A general framework of urbanisation processes 21 2.6 Bid-rent functions in a largely monocentric city 23 2.7 Land-use – transport feedback cycle 33 2.8 Foreign direct investment (FDI) net inflows in the four Southeast Asian countries, 1970-2007 37 2.9 Spatial configuration of a hypothetical Asian country 40 2.10 Core areas in Asia 41 2.11 Schematic changes in transport networks at different phases of colonisation process 44 2.12 Delineation of (a) Bangkok, (b) Jakarta and (c) Manila EMRs into three zones 53 2.13 Spatial configuration of an Asian mega-urban region (ca. 2000) 57 3.1 Cities and regencies within the Jakarta Metropolitan Area 61 3.2 FDI approval in JMA, 1990-2007 63 3.3 Population growth by sub-region in JMA, 1961-2000 67 3.4 Population density distribution by kelurahan in JMA, 2000 69 3.5 Planned housing, kampung and real estate in JMA, 2000 70 3.6 Land use changes in selected surveyed areas in JMA, 1985-2000 71 3.7 Land use in JMA, 2000 72 3.8 Main transport network in JMA, 2008 74 3.9 Inner city railway network in Batavia and Weltevreden, 1938 79 3.10 Thamrin corridor at the end of 1970s 83 3.11 “Bundled deconcentration” alternatives for JMA 85 3.12 JMDP’s five potential development zones 86 3.13 Reliance on road based transport network in JMA as planned in 1985 89

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3.14 Total office supply in the Jakarta’s CBD, 1982-1992 91 3.15 Land subsidence in Jakarta within 1974/78 to 1989/90 period 93 3.16 JMDPR’s three alternative development paradigms for JMA 95 3.17 Recommended urban centres in JMA in 2010 96 3.18 Trunk transport system in JMA as recommended in JMDPR 97 3.19 Development zoning in JABODETABEKPUNJUR 100 3.20 Transport network for JABODETABEKPUNJUR 101 4.1 Kecamatan, kelurahan and desa zoning system in JMA 111 4.2 Traffic Analysis Zone system 112 4.3 Railway network and toll road network in JMA 114 4.4 Land use survey area conducted under SITRAMP study in 2000 116 4.5 Process of the empirical analysis 131 5.1 The number of jobs in cities and regencies by major type of industry within the JMA, 2002 137 5.2 Scree plot of factor analysis 138 5.3 Significant local Getis-Ord statistic of (a) Factor 1; (b) Factor 2; (c) Factor 3; (d) Factor 4; (e) Factor 5; and (f) Factor 6 141 5.4 Factor 1 142 5.5 Factor 2 144 5.6 Factor 3 145 5.7 Factor 4 147 5.8 Factor 5 148 5.9 Factor 6 150 5.10 Desakota areas 153 5.11 LISA of job density 155 5.12 Spatial structure of employment in the JMA, 2002 157 5.13 Job density by employment clusters 160 5.14 Changes in job density by distance to MONAS 161 5.15 Job diversity index by employment clusters 165 5.16 Job accessibility index by car by employment clusters 169 5.17 Central features of employment clusters 170 5.18 Spatial association between the regional CBD and employment density 172 5.19 Spatial association between the Jakarta urban core and negative population growth 173 5.20 Spatial association between Bogor urban centre and negative population growth 174 5.21 Spatial association between Tambun-Cibitung-Cikarang 175

manufacturing corridor and population growth 5.22 Spatial association between Pasar Kemis-Jatiuwung-Cikupa manufacturing corridor and population growth 176 5.23 Spatial association between Depok Municipality and population growth 177 5.24 Spatial association between Bogor Regency and population growth 177 5.25 Spatial association between Tambun-Cibitung-Cikarang manufacturing corridor and “mixed agriculture-manufacturing household” 178 5.26 Spatial association between Pasar Kemis-Jatiuwung-Cikupa manufacturing corridor and “mixed agriculture-manufacturing household” 179 6.1 Journey to work desirelines to the Jakarta urban core 187 6.2 Journey to work desirelines to manufacturing corridors within the Jakarta urban core 188 6.3 Journey to work desirelines to manufacturing corridors outside the Jakarta urban core 189 6.4 Journey to work desirelines to the regional CBD 190 6.5 Journey to work desirelines to local government regions outside the Jakarta urban core 191 6.6 Journey to work desirelines to selected kecamatans in desakota areas 192 6.7 Journey to work desirelines to Kecamatan Serpong 193 6.8 Travel distance by car 197 6.9 Travel distance by motorcycle 198 6.10 Travel distance by public transport 199 6.11 Travel distance by non-motorised transport 200 6.12 Travel time by car 203 6.13 Travel time by motorcycle 204 6.14 Travel time by public transport 205 6.15 Travel time by non-motorised transport 206 6.16 Shares of transport modes 209 6.17 Share of trips by distance and by transport modes to manufacturing corridors outside the Jakarta urban core 210 6.18 Share of trips by distance and by transport modes to the regional CBD 211 6.19 Share of trips by distance and by transport modes to local government regions of four municipalities outside the Jakarta urban core 211 6.20 Share of trips by distance and by transport modes to desakota areas 212

List of Tables

2.1 Transportation impacts on sustainability 30 2.2 Sustainable transport indicators 30 2.3 Empirical findings of urban structure and urban form impacts on travel 34 2.4 The comparative scale of Southeast Asian EMR economies in 1993 45 2.5 Relative severity of environmental problems in selected Asian subregions 47 2.6 Sustainability problems in Southeast Asian EMRs 48 2.7 Population growth in Bangkok, Jakarta and Manila EMRs, 1990- 2000 54 2.8 The share of employed persons by industry and zone in Jakarta and Bangkok EMRs, 1980 and 1990 55 3.1 Contribution of cities and regencies to gross regional domestic product (GRDP) in JMA in 2005 62 3.2 Foreign direct investments in JMA, 2007 64 3.3 Distribution of labour force by sector of industry within cities and regencies 65 3.4 Share of labour force by sector of industry across cities and municipalities, 2000 66 3.5 Location quotient of labour force by sector of industry, 2000 66 3.6 Population size and density in JMA, 2000 68 3.7 Land use changes by selected categories in surveyed areas in JMA, 1985-2000 72 3.8 Land uses by category and by city and regency in JMA, 2000 73 3.9 Proposed centres in JMA in 2005 88 3.10 JABODETABEKPUNJUR development zoning 98 4.1 The Home Interview Survey sample conducted in 2002 107 4.2 Data collected under the Home Interview Survey 2002 108 4.3 Classification of housing types in Home Interview Survey 2002 108 4.4 Occupation, job industry and land-use facility categories in Home Interview Survey 2002 109 4.5 Transport modes in Home Interview Survey 2002 110 4.6 Land use categories in the JMA land use map, 2000 117

4.7 Classification of land use types 127 4.8 Urban form variables and measurements considered for the empirical analysis 128 5.1 Distribution of the number of jobs by type of industry 135 5.2 Distribution of the number of jobs by city and regency and by major type of industry within the JMA, 2002 136 5.3 Rotated component matrix from factor analysis 139 5.4 Euclidean distance to MONAS and gross job density 159 5.5 Population density, household density and jobs to household ratio 162 5.6 Job diversity index 164 5.7 Access to transport facilities 166 5.8 Job accessibility index by mode of transport 168 6.1 Home-based trips in the JMA, 2002 184 6.2 The number of trips by employment clusters 185 6.3 Travel distance by transport modes by employment clusters 196 6.4 Travel time by transport modes by employment clusters 202 6.5 Travel mode share by employment clusters 208 6.6 Ordinary linear regression model of home-to-work travel distance by car (in kilometers) 214 6.7 Ordinary linear regression model of home-to-work travel distance by motorcycle (in kilometers) 215 6.8 Logistic linear regression model of private vehicle versus public transport mode choice 217 6.9 Summary of impacts of physical features of employment structure on home-to-work travel 220

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Acknowledgements

I am grateful to my supervisor, Dr. Bruno Parolin, whose seminal suggestions have led me to the development of the topic of this thesis, and whose advise, patience and support have helped me overcome difficulties along this research journey.

I would like to thank AusAID for providing the funding support for the research, and my office, the National Development Planning Agency (BAPPENAS) of Indonesia, for providing permit and supports for me to pursue a higher degree in Australia. I am indebted to Professor John Black for his supports at the beginning of the research. I wish to acknowledge my gratitude to people who provided support with regard to the dataset used in this thesis: Mr. Wachi, Mr. Yagi, Mr. Arikawa and Ms. Miyao from the former JICA Study Team for SITRAMP JABODETABEK, Pak Hanung Harimba at the Investment Coordinating Board (BKPM) and Pak Tri Widodo at the Agency for the Assessment and Application of Technology (BPPT) of Indonesia. I would also like to thank Ms. Anna Rees and people at International Student Services, people at the Built Environment Computing Unit, and my colleagues at Faculty of the Built Environment, UNSW, especially Pak Aan, Mamun, Ahsan (who helped much with printing the thesis), Catharina, Mas Tata and Farid.

Finally, I dedicate this thesis to my wife, Siti, and children, Adel and Nabil, for their patience and love that have inspired me towards finishing this thesis.

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Abbreviations

ADB Asian Development Bank

ANOVA Analysis of Variance

ASEAN Association of Southeast Asian Nations

BAPPEDA Badan Perencanaan Pembangunan Daerah (Regional Development Planning Agency)

BAPPENAS Badan Perencanaan Pembangunan Nasional (National Development Planning Agency)

BKPM Badan Koordinasi Penanaman Modal (Investment Coordinating Board)

BKSP Badan Kerjasama Pembangunan (Development Cooperation Board)

BOD Biological Oxygen Demand

BODETABEK Bogor, Depok, Tangerang and Bekasi

BOTABEK Bogor, Tangerang and Bekasi

BPPT Badan Pengkajian dan Penerapan Teknologi (Agency for the Assessment and Application of Technology)

CBD Central Business District

CBS Central Bureau of Statistics

CO Carbon Monoxide

CO2 Carbon Dioxide

COD Chemical Oxygen Demand

DKI Daerah Khusus Ibukota (Special District of Capital)

EMR Extended Metropolitan Region

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ESDA Exploratory Spatial Data Analysis

FDI Foreign Direct Investment

GDP Gross Domestic Product

GIS Geographic Information Systems

GRDP Gross Regional Domestic Product

HIS Home Interview Survey

IBRD International Bank for Reconstruction and Development

JABODETABEK Jakarta, Bogor, Depok, Tangerang and Bekasi

JABODETABEKPUNJUR Jakarta, Bogor, Depok, Tangerang, Bekasi, Puncak and Cianjur

JABOTABEK Jakarta, Bogor, Tangerang and Bekasi

JICA Japan International Cooperation Agency

JMA Jakarta Metropolitan Area

JMDP JABOTABEK Metropolitan Development Plan

JMDPR JABOTABEK Metropolitan Development Plan Review

JORR Jakarta Outer Ring Road

LISA Local Indicators of Spatial Association

LQ Location Quotient

MNCs Multi-national Companies

MONAS Monumen Nasional (National Monument)

MPW Ministry of Public Works

MRT Mass Rapid Transit

MUR Mega-urban Region

NICs Newly Industrialised Countries

NIDL New International Division of Labour

NMT Non-Motorised Transport

PM10 Particulate Matter of 10 micrometers or less in diameter

ROI Republic of Indonesia

Rp. Indonesian Rupiah

SIJORI Singapore-Johor-Riau

SITRAMP The Study on Integrated Transportation Master Plan for JABODETABEK

SOV Single Occupant Vehicle

TAZ Traffic Analysis Zone

TOD Transit Oriented Development

UN United Nations

UNCTAD United Nations Conference on Trade and Development

UNDESA United Nations – Department of Economic and Social Affairs

USD United States Dollar

VMT Vehicle-Miles Travelled

WCED World Commission on Environment and Development

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1 Introduction

Background to the study

During the past few decades, the growth in global urban population has been attributable to rapid urbanisation in the Asian developing countries. By the time the world’s urban population exceeded that of the rural population in the year 2008 (UNDESA, 2008), almost half resided in Asian developing countries. Within the period 1950 to 2007, the rate of urbanisation in Asian developing countries was nearly double the world’s figure. Within that period, while urban population figures had increased by four times, the number of urban dwellers in developing Asian countries had increased by more than seven fold. Among the sub-regions in Asia, the most rapid urbanisation has been experienced by countries in the Eastern and South-East Asian areas (UNDESA, 2008), due mainly to their active participation in the Asia-Pacific and global economic networks (Marcotullio, 2003; Lo and Marcotullio, 2000).

A prominent impact of globalisation and rapid urbanisation on the urbanisation patterns of major cities in Asia is the emergence of mega-urban regions, or what is interchangeably called extended metropolitan regions (EMR) (McGee, 2008; Douglass, 1995, p. 50-51). An EMR is envisaged as an economically integrated vast area, comprising of the completely urbanised urban core (in most cases the capital of the nation or province) where the central business district (CBD) is located, the surrounding peri-urban areas where smaller cities and

1 municipalities, manufacturing sites and new towns are commonly situated, and the least urbanised outer zones, where rural activities are still dominant (McGee and Robinson, 1995b, p. ix-x). It is in the outer zones that desakota, an important feature of EMR, can be found. Desakota is the term popularised by McGee (1991), is dubbed from the Indonesian words desa (village) and kota (town), and is characterised by the co-existence of urban and rural activities. This whole urban pattern extends many kilometres away from the urban core (McGee and Robinson, 1995b, pp. ix-x; McGee, 2008).

While the EMR concept has been prevalent in the discussion on the pattern of urbanisation in the major cities of Asia, in the past two decades, particular attention has been given to EMRs in Southeast Asia (see McGee and Greenberg, 1992; McGee and Robinson, 1995b) not only because the intimate relationship between globalisation and urbanisation has left clearer spatial imprints and evidence of EMR formation, but also for rising concerns on severe sustainability problems in the sub-region, associated with the urbanisation pattern. A report by the Asian Development Bank (1997) indicated that sustainability problems facing countries in South-Eastern Asia were worse than those experienced by China and developing countries in the Eastern Asia sub-region.

Meanwhile, many empirical studies on urbanisation patterns in the world have focused on the identification of employment spatial structure and its physical features as part of their efforts to promote urban and transport sustainability (e.g., Marshall and Banister, 2007; Williams et al., 2000; Black et al., 2002). Following the pervasiveness of employment suburbanisation in many cities of the world, empirical research has been guided by both observations on the emergence of sub-centres outside the traditional central business district (CBD) and findings that the monocentric city model, which assumes a single, centrally located centre of employment in the city, is no longer adequate in explaining the distribution of population and employment across urban areas (Anas et al., 1998). Many of these empirical studies have also been motivated by growing concerns on urban and transport sustainability, so that identification of the spatial structure of employment has often been followed with assessments of its impacts on travel. Travel has been recognised as the major contributor to

2 sustainability problems facing cities in the world (Newman and Kenworthy, 1999; Banister, 2005).

The gap is shown in which empirical investigations of the spatial structure of employment in many developed cities have looked into detailed spatial configuration and physical features, or urban forms dimension, of employment and relate these to travel pattern. Furthermore, the resulting results have been used to guide planning and policy efforts to promote more sustainable transport. There also appears to be some confusion with regard to the relationship between compact city and sustainability when applied to developing Asian cities. Newman and Kenworthy (1999, p. 101), for example, showed the relationship between private transport energy use per capita and city compactness across cities in the world, and highlighted that the developing Asian cities including Jakarta, Manila and Bangkok fall into more sustainable cities. Yet, it is reported in the same study that traffic congestion and air pollution, which are important indicators of transport sustainability, are among the worst in the world (Newman and Kenworthy, 1999, p. 122).

This thesis is developed from a recognition that despite the overwhelming transport sustainability problems facing major cities in Southeast Asia, there exists a serious absence of empirical findings on the urban spatial structure of these cities – particularly in terms of how the emerging spatial structure contributes to transport sustainability problems. This thesis also argues that the seemingly paradoxical evidence with regard to transport sustainability in the major developing Southeast Asian cities stems from two key reasons. First, there is lack of understanding on the spatial configuration of economic activities within the Southeast Asian, and what does exist relies heavily on aggregate measures and spatial scales of analysis and, finally, this research tends to assume that Western style urbanisation patterns are also present in these Southeast Asian cities (Alpkokin et al., 2008; Vichiensan, 2007). The inadequacy of such a view has been discussed by McGee (1995a), Lin (1994), and Kelly (1999). When region-based urbanisation, as suggested in the Southeast Asian EMR concept (McGee and Greenberg, 1992) is applied, it is hard to regard the Jakarta Metropolitan Area, for example, as compact. Second, empirical investigations of

3 urban spatial structure in Southeast Asian cities generally tend to lack the theoretical context on which the distribution of economic activities can be explained. The case has been the opposite for developed cities, where practices on urban structure have focused on testing the theoretical monocentric and polycentric urban patterns, allowing further assessments of the findings on travel impacts and transport sustainability (see Anas et al., 1998 for excellent treatments of the application of these theoretical structures).

The thesis further argues that the Southeast Asian EMR concept is a more relevant and context specific concept for the empirical identification of the spatial structure of employment in the study area, and offers the possibility of refining or modifying the concept to suit the case study. As such, this thesis also argues that empirical findings on the spatial structure of employment can be followed up to investigate its impacts on travel, providing a basis for promoting urban and transport sustainability. The lack of understanding of the above interrelationships has generally contributed to the inability of transport planners and policy makers in the region in formulating relevant planning and policy initiatives.

Aim, study area and scope of the thesis

The aim of the thesis is to explore the nature and characteristics of the spatial structure of employment, and its impact on journey to work patterns, with reference to the Southeast Asian EMR concept. The aim is a direct response to the problems identified above in the sense that the empirical investigations are conducted to test the notion that the Southeast Asian EMR can provide a theoretical basis to explain and to guide identification of the spatial formation of employment in the study area and that the identified spatial structure of employment can be followed up with assessments of its impacts on journey to work and transport sustainability.

Jakarta Metropolitan Area (JMA), interchangeably referred to as JABODETABEK (which is the acronym of Jakarta, Bogor, Depok, Tangerang and Bekasi), is used as the area of study. The study area is very suitable due to the availability of the right type of data at a more disaggregated scale. The Home

Interview Survey data collected in the year 2002, which covers the whole of the JMA, contains information necessary to investigate the spatial distribution of employment at the smallest administrative zone level (i.e., kelurahan or desa) and to identify journey to work patterns required by this study. This is also complemented with information on zoning systems, land-use and transport infrastructure stored in GIS (geographic information system) format, which is necessary for the spatial statistical approach adopted in the study. Another advantage of using the JMA as the study area is that among the three Southeast Asian primate cities of Jakarta, Bangkok and Manila, JMA seems to be the only one which has all the features of an EMR within its administrative boundary (McGee, 1994; Jones et al., 1999). Furthermore, the administrative boundary of the JMA has been largely maintained since its delineation in 1976. This suggests that relevant, consistent and comparable data, and other empirical studies and planning documents are more likely to exist and to all be consistent in their definition of the JMA. Furthermore, the JMA has been considered as the more serious area that is suffering from the complex problems associated with EMR formation, mainly due to the fact that it has the largest area (6,580 square kilometres) and population (around 21 million people in the year 2000) among all the Southeast Asian EMRs (see Jones et al., 2000). In this sense, it is expected that the findings of this study will have practical value for the relevant planning authorities, and make a significant contribution to the processes of the spatial formation of the JMA and its consequent impact on journey to work travel patterns.

However, the focus of this thesis has several limitations that need to be mentioned at the outset. First, the empirical analysis is limited to the employment dimension of urban spatial structure. Urban spatial structure may be explained in terms of employment, population and transport system characteristics. The term “spatial structure” in this study refers to the definition used by Mohan (1994): “the kind, location, and density of activities as they are distributed across space in urban areas.” The study therefore focuses on the types, spatial location and intensity of employment across the JMA. Second, since the travel patterns investigated are regarded as impacts of the spatial structure of

5 employment and its physical attributes, the study focuses solely on the journey to work pattern destined to each type of employment clusters. The pattern of the one way journey to work, i.e., home-to-work trip, is investigated following the identification of the spatial structure of employment in the JMA. Third, the journey to work pattern is analysed from the trip destination point of view (e.g., Cervero, 1989; Cervero and Wu, 1997; Pivo, 1993), as opposed to the trip origin one (e.g., Cervero and Gorham, 1995; Giuliano and Small, 1993). The focus is therefore on the spatial characteristics of the types of jobs found at workplace locations, especially those locations that demonstrate a significant concentration or cluster of types of jobs. On the other hand, a focus on the characteristics of workers and their residential origins is left for a future study.

Research questions

Three sets of research questions have been formulated in this thesis based on the literature review conducted in Chapter 2 and Chapter 3. These research questions are formulated in Chapter 4 as part of the research design process, yet presented in this introductory chapter because these are the enquiries upon which the thesis is developed, and are used to benchmark the fulfilment of the aims of the thesis. In other words, the arguments posed by the thesis that the Southeast Asian EMR can be used as a reference to explain the spatial structure of employment in Southeast Asia and that the identified spatial structure can be used to measure travel impacts and to assess transport sustainability performance (within the context of Southeast Asian EMR) are evaluated based on how the three sets of research questions are addressed. The three sets of research questions are as follows:

Based on a review of the literature it can generally be concluded that urbanisation processes experienced by Southeast Asian cities differs significantly from that experienced by cities in the developed world. In particular, urban spatial structure as recognised in the developed cities falls largely on either a monocentric or polycentric structure. On the other hand, the Southeast Asian EMR concept generally suggests that major cities in Southeast Asia exhibit more complex urban structure than that found in the developed world. The literature

6 on the Southeast Asian EMR concept, however, reveals that such a complex urban structure has not been empirically tested or investigated at any in-depth level as to the nature and characteristics of this structure (Hakim and Parolin, 2008). The first set of research questions is centred on the identification of urban spatial structure in the JMA with reference to the Southeast Asian EMR concept:

1.a What important components of the Southeast Asian EMR constitute the spatial structure of employment in the JMA? 1.b How can we identify those major components of the Southeast Asian EMR in JMA? Which methods are suitable? 1.c Does the overall spatial structure of employment in JMA conform to the Southeast Asian EMR concept? 1.d What are the spatial characteristics of and how they vary among the components of the spatial structure of employment in JMA?

Transport sustainability has been a prominent issue in major developing cities in Southeast Asia. The literature generally shows strong impacts of both the spatial structure, and its physical attributes, on travel patterns. This thesis argues that failure to promoting transport sustainability in the region can be partly attributed to lack of understanding on the nature of urban structure. The second set of research questions follow up the findings from the first set of research questions above with enquires on journey to work impacts of the identified urban spatial structure of employment:

2.a Given the identified urban spatial structure of JMA, what is the overall journey to work pattern? 2.b How the identified journey to work pattern explains the spatial structure of employment identified? 2.c Whether major components of the spatial structure of employment in JMA exhibit substantial variations in terms of journey to work pattern? 2.d What physical features of those components influence journey to work patterns, and how?

The final research question deals with the policy implications of the findings:

3.a How the empirical findings on the spatial structure of employment and journey to work patterns in JMA can be assessed against theories and practices as found from the literature?

3.b How the spatial structure of employment and journey to work patterns in JMA, based on the findings, are associated with the success or failure of planning efforts in JMA? What lessons can be learnt? 3.c What policy recommendations can be proposed to improve urban and transport conditions in JMA?

Overview and structure of the thesis

Figure 1.1 shows the structure of the thesis. Chapter 2 provides the context of the thesis. It begins by highlighting the strong influences of globalisation on urbanisation in the Asia-Pacific region. It is shown that EMR is part of a much wider scale urbanisation pattern resulting from economic integration of the countries in the Asia-Pacific region. Presentation on the concept of EMR is preceded by reviews on theories and practices of urban spatial structure, travel impacts and transport sustainability. Southeast Asian EMR is then discussed in terms of its concept development, characteristics, and associated sustainability problems. Finally, the chapter looks for features of urban spatial structure of the Southeast Asian EMR as suggested from its concept and empirical findings up to date.

Chapter 3 narrows down the review on urbanisation processes to the case study area, the Jakarta Metropolitan Area, while referring to the Southeast Asian EMR context. It begins by presenting recent figures and trends on the three important components of urban structure; namely employment, population and transport in the region. A historical overview of urban form evolution in the JMA is presented, covering the pre-colonial, colonial, early independence and globalisation times, showing how each of these periods contributed to the current urban formation of the JMA. Sustainability problems facing the region and planning initiatives and studies attempting to guide urban growth in JMA are also discussed.

Chapter 4 presents the research design guiding the empirical analysis to be conducted in the study. Based on a literature review presented in the previous two chapters, three sets of research questions are posted with regard to the spatial

8 structure of employment, its impacts on journey to work patterns and policy implications of the findings. Data sources are listed and explained, and methods adopted to address the research questions are presented. Research questions on the spatial structure of employment in JMA are addressed in Chapter 5 and those on journey to work impacts and their policy implications are addressed in Chapter 6. The process of the empirical analysis is shown in the diagram at the end of the chapter.

Chapter 5 begins by presenting employment profiles in JMA based on the Home Interview Survey (HIS) conducted in 2002. Identification of employment clusters is performed using a combination of factor analysis and ESDA, which in this study is argued to be most suitable for the investigation of the spatial structure of employment for the case of the Southeast Asian EMR. The extent of desakota areas and possible employment sub-centres outside the Jakarta urban core are examined using exploratory spatial data analysis (ESDA) methods. There are 18 employment clusters identified in JMA, including the regional CBD, the urban core, manufacturing corridors, local administrative centres, desakota, agricultural areas and a planned sub-centre. Physical features of the clusters are identified and compared using ANOVA. Spatial impacts analysis is performed to provide insights into the spatial associations of identified employment clusters on several dimensions.

Chapter 6 follows up the findings in the previous chapter by investigating the impacts the employment structure in JMA on journey to work patterns. The chapter begins by conducting desireline analysis allowing visual examination of the extent and size of journey to work trips destined to each of the employment clusters and providing insights into the degree of spatial interaction and economic integration, in terms of journey to work, in the JMA. Variations in journey to work patterns are investigated in terms of travel distance, travel time, share of travel modes and the degree of “local trips” associated with each of the clusters. Influences of physical features of the employment clusters are investigated through models of home-to-work travel distance by car, home-to- work travel distance by motorcycle, and private versus public transport mode choice. Inclusion of socioeconomic characteristics and physical features of

9 residential areas of workers allows the measurements of influences of these variables, in addition to the physical features of the employment clusters, on the travel dimensions investigated. The findings are discussed in terms of transport sustainability issues and policy implications.

Finally, conclusions and suggestions of further research are presented in Chapter 7.

Chapter 1. Introduction Background; problem statement; importance of the thesis; aim and scope of the study; research questions

Literature Review

Chapter 2. Urban spatial structure and Chapter 3. Jakarta Metropolitan travel impacts: placing the Southeast Area: urban evolution and planning Asian EMR in context Recent figures and trend on Globalisation and urbanisation in the Asia- employment, population and Pacific; theories and practices of urban transport; Historical overview, urban spatial structure and travel impacts; concept form evolution and planning in JMA development of the Southeast Asian EMR

Chapter 4. Research Design Statement of research questions; data; methods; flow of the empirical analysis

Research questions on journey to work impacts Research questions on the spatial of JMA’s employment spatial structure and its structure of employment in JMA physical features; Research questions on their policy implications

Empirical Analysis

Chapter 5. Spatial structure of employment in the JMA: an EMR perspective Identification of employment clusters; physical features of the clusters; spatial impacts

Chapter 6. Journey to work impacts of the JMA’s employment spatial structure and physical features The degree of spatial interaction; journey to work pattern by clusters; the influence of physical features; transport sustainability and policy implications

Chapter 7. Conclusions Summary of the findings; future research

Figure 1.1 Structure of the thesis

Source: Author.

2 Urban spatial structure and travel impacts: placing the Southeast Asian EMR in context

Introduction The purpose of this chapter is to provide context to the thesis in terms of urban spatial structure and travel impacts for the Southeast Asian EMR. The objectives are: to review the relevant background to Southeast Asian urbanisation that has lead to the formation of the extended metropolitan region (EMR); to discuss on the concepts and practices of urbanisation, urban spatial structure, travel impacts and sustainability; and to discuss on the conception and characteristics of the Southeast Asian EMR with a focus on identifying the spatial structure of employment and travel as suggested from the Southeast Asian EMR concept. The chapter starts with highlighting the strong influence of globalisation in the recent history of urbanisation in the region. Specific impacts of globalisation on urbanisation, as found from the literature, are presented and discussed. Before elaborating the concept and characteristics of the Southeast Asian EMR, it is important to review the theories and practices of urban spatial structure and impacts on travel. The review of Southeast Asian EMR theory and empirical studies is then followed by examining the spatial structure of the Southeast Asian EMR while bearing in mind

12 the concept and practices of urban structure and travel, which happen to focus on developed cities.

This chapter, and the next chapter on the evolution and planning context of the study area of Jakarta EMR, provide a basis for the development of research questions on spatial structure and travel impacts of the JMA, as formulated specifically in Chapter 4.

Globalisation and urbanisation in the Asia-Pacific: background

Asia-Pacific globalisation

During the past few decades, the growth of urban population in the world has been significantly contributed to by rapid urbanisation in developing countries. Asian developing countries have been the centre of urbanisation-related issues for their magnitudes in terms of their share of world urban population, the rate of urbanisation and their impacts on urban sustainability (ADB, 1996). By the time the world’s urban population outnumbered their rural counterparts in the year 2008 (Figure 2.1), almost half of them resided in Asian developing countries1. While since the mid-twentieth urban population in the world has increased by more than four times (i.e., from 737 million in 1950 to 3.29 billion people in 2007), the number of urban dwellers in developing Asia has been multiplied by more than seven fold (i.e., from 207 million in 1950 to 1.56 billion in 2007). The rate of urbanisation2 in Asian developing countries within the period is 1.67 per cent, which is nearly double the world’s figure of 0.93 per cent and thirty per cent higher than the rest of the developing countries’ figure of 1.28 per cent (author’s calculation from UNDESA (2008)).

1 Asian developing countries refer to all countries in Asia except Japan (UNDESA, 2008). 2 Rate of urbanisation, as calculated in UNDESA (2008), is the average annual change of the share of urban population given a certain period of time. 13

Figure 2.1 The world’s urban and rural population, 1950-2025 Source: UNDESA (2008).

The rate of urbanisation during the 1950 to 2007 period is, however, not balanced across the Asian sub-regions. Eastern Asia (which includes China, Hong Kong (SAR), Macao (SAR), North Korea, Mongolia and South Korea) and South- Eastern Asia (which includes the ten ASEAN3 countries plus Timor Leste) account for the most rapid urbanisation rate of 2.06 per cent and 1.92 per cent respectively. They are followed by Western Asian countries with 1.46 per cent and South- Central Asian countries with 1.13 per cent. While it has been recognised that urbanisation level is associated with the degree of economic development4, rapid urbanisation experienced by the Eastern and South-Eastern developing Asian countries within the past few decades has been more specifically associated with their integration into the global economic system, or globalisation in short (Marcotullio, 2003; Lo and Marcotullio, 2000).

Rondinelli and Heffron (2007, p. 1) define globalisation as “the movement toward greater interaction, integration, and interdependence among people and

3 The Association of Southeast Asian Nations (ASEAN) members are Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand and Vietnam. 4 A common measure of association between urbanisation and economic development is the plot of the share of urban population against gross domestic product (GDP) across countries in the world. 14 organisations across national borders.” Globalisation is not a new phenomenon. A historical overview of economic globalisation, trading in particular, dates back to the Silk Road era and “Oriental Globalisation” from around 200 BCE to 1800 CE (Coclanis, 2008). There are, however, four characteristics that make globalisation today much more influential than it was in the past (UN-HABITAT, 2001, p. xxx- xxxi): the speed of the process is much greater due to the advanced transportation and communication technologies; the scale is much larger that hardly any place or anyone in the world has not been affected by it; the scope is much broader that its impacts cover a wider range of dimensions such as economic, environment, social, politics and culture; and the complexity is much higher due to greater number of players and their higher degree of interactions in the process.

Today’s economic globalisation phenomenon is particularly intriguing for the case of the Asia-Pacific region, which has been recognised as the emerging centre of the global economy replacing the Atlantic region (Berry et al., 1999, p. 3). As a facet of globalisation, economic globalisation is defined as a “greater integration in the organisation of production, distribution, and consumption of commodities in the world economy” (Chase-Dunn et al., 2000, p. 79). Four types of transactions, namely goods, capital, people and information (McGee, 2007) help integrate countries economically in the globalisation context. In the Asia-Pacific region, the most important integrating factors are the flows of foreign direct investment (FDI), trade and the formation of production system based on countries’ comparative advantage (Yeung and Lo, 1996, p. 20). The later is commonly referred to as the new international division of labour (NIDL), which defines the roles of countries that meet global production system requirements in which more developed countries function in the ‘core’ of the global network and maintain capital and skill intensive activities while less developed countries function in the ‘periphery’ and specialise in labour-intensive activities (Sit, 2001).

The positions of the rapidly urbanising Eastern and South-Eastern Asian developing countries in the global production network of the Asia-Pacific are depicted in the popular “flying geese” model. The model, which was originally developed by the Japanese scholar Kaname Akamatsu in the 1930s (Kojima, 2000), has since been developed and modified into a few versions, one of which sees it as

15 the transfer of the centres of industrialisation based on the principle of comparative advantage (Kwan, 1994, p. 81-82). The plot of the “rise and fall” of the degree of comparative advantage across different industries and different countries against time (Figure 2.2a) resembles an inverted V-shaped curve and is seen as a formation of flying geese (Figure 2.2 (b)). The later shows the shifts of centres of manufacturing sites consecutively from Japan in the 1950s and 1960s to the formerly known ‘newly industrialised nations’ (NICs) of Singapore, Hong Kong (SAR), South Korea and Taiwan in the 1960s and 1970s, the four Southeast Asian nations of Malaysia, Thailand, Indonesia and Philippines in the 1980s and 1990s, and finally China and Vietnam in the 1990s.

Investment has continued as the most important instrument of globalisation, particularly during the past three decades. In the late 1990s, investment already accounted for 90 per cent of global capital flows, overtaking trade flows (which were more dominant in the 1980s) (Hong, 2001, p. 72). Due to their important roles within the Asia-Pacific global production system, countries in the Eastern and South-Eastern Asia have maintained their sustaining increase of FDI inflows (Figure 2.3). The world FDI inflows reached USD 1,833 billion in 2007, which was the highest level in history (UNCTAD, 2008b). Of this, the two regions hosted almost 12 per cent, accounting for more than USD 217 billion.

Figure 2.2 The flying geese pattern of shifts in comparative advantage in Asia Pacific Source: (a) Kwan (1994), p. 82 and (b) Smith (2001), p. 421.

180 160 140 120 100

80 South-Eastern Asia 60 Eastern Asia 40

USD Billion (current (current price) Billion USD 20 0 1970 1975 1980 1985 1990 1995 2000 2005 year

Figure 2.3 Foreign direct investment (FDI) net inflows in South-Eastern and Eastern Asia, 1970- 2007 Source: Processed by author from UNCTAD statistics.

Urbanisation impacts of globalisation

As countries in the Pacific Asia region place themselves in the global production system, their role in the system is practically represented by major cities of the corresponding countries. Tokyo, Seoul and Taipei, for example, function as the centre of management, Hong Kong and Singapore as the borderless entrep ts, while Bangkok, Jakarta and Shanghai become the centers for industrial production (Marcotullio and Lo, 2001, p. 8). These global cities, allowed by advances in transportation and telecommunication technologies and trade liberalisation (Firman et al., 2007), form an integrated network to facilitate the ‘transactional revolution’ (McGee, 2007) of the flows of people, goods, information and capital. There is no doubt that urbanisation in the Eastern and South-Eastern Asia region during the past few decades has been driven mainly by the integration of its nations into the global economic system.

Globalisation impacts on urbanisation patterns in the Asia-Pacific region are imprinted into five characteristics (Douglas, 1995, p. 51-57) as illustrated in Figure

2.4. First, the overall urbanisation pattern is seen as an integrated international network and growth corridor spanning along the coast of the Pacific Basin. From Tokyo to Seoul, Beijing, Shanghai, Hong Kong, Manila, Ho Chi Minh, Bangkok, Kuala Lumpur, Singapore, Jakarta, Bandung and Surabaya, to mention some, this corridor of cities forms an economic sub-system that is part of the larger global economic network. Second, within the corridor there are a few trans-border regions formed as a result of the global economic linkages among cities or regions that are located nearby but belong to different countries. An example is the Singapore- Johor-Riau (SIJORI) growth triangle, cross-bordering Singapore, Malaysia and Indonesia. Third, world cities and international urban hierarchies are defined according to a new international and regional division of labour that meets the global production network requirement. Fourth, countries in the region, particularly the less developed ones, have experienced the increasing polarisation of development on a few core regions. While unbalanced development and urban primacy have been the characteristics of the former colonial countries (Hackenberg, 1980), foreign investments, particularly those of the secondary and tertiary sectors, have favoured the relatively more developed and densely populated cities for infrastructure, labour and market availability. As a result, the existing few large urban areas have been the major hosts of the FDI inflows and have experienced rapid growth of development at the cost of slower economic growth for the rest of the country. These core regions become the centres of their corresponding national economy such that they attract high numbers of migrants from across the nation for job opportunities. And fifth, these core regions have expanded towards their surrounding, in many cases the predominantly rural areas, creating a juxtaposition of urban-rural activities and as a whole has evolved into a distinctive urbanisation pattern called the extended metropolitan region (EMR) or mega-urban regions (MUR).

Figure 2.4 Emerging spatial network of development in Pacific Asia Source: Douglass (1995), p. 54.

Urban spatial structure and travel impacts: an overview on theories and empirical findings

Urbanisation and urban spatial structure

A general framework connecting urbanisation processes, outcomes and impacts and planning responses can be conceptually illustrated in Figure 2.5 as an adaptation from Knox and McCarthy (2005, p.9). As discussed in Knox and McCarthy (2005, pp. 9-16), urbanisation is driven by simultaneously interacting and changes in a wide range of dimensions including economy, demography, politics, culture, social, technology and environment. Outcomes of urbanisation can take various forms, both physical such as urban systems and land use, and non-physical such as

20 urbanism. The adapted framework highlights urban spatial structure as an outcome of urbanisation and its impacts in terms of travel pattern. Planning and policy responses are shown as motivated by sustainability issues associated with both urban spatial structure and travel impacts; this, in turn, influences the urbanisation processes to achieve more desirable outcomes. This thesis will not discuss this general framework of urbanisation processes in further details, as the purpose of presenting the framework is to conceptually place together and connect the important components of the thesis (i.e., urban structure, travel impacts and transport sustainability, each of which will be discussed further).

PROCESSES POLICY RESPONSES, PLANNING DEMOGRAPHIC CHANGE CONCERNS ON SUSTAINABILITY POLITICAL CHANGE OUTCOMES IMPACTS CULTURAL CHANGE

ECONOMIC URBANISATION SPATIAL TRAVEL CHANGE STRUCTURE

SOCIAL CHANGE

TECHNOLOGICAL CHANGE

ENVIRONMENTAL CHANGE

LOCALLY & HISTORICALLY CONTINGENT FACTORS

Figure 2.5 A general framework of urbanisation processes Source: Modified by author based on Knox and McCarthy (2005), p. 9.

The term “urban” refers to an area of higher population density (Mills and Hamilton, 1994, p. 7). Understandings on the reasons for the emergence of cities can be offered from the “central place theory”, developed by Christaller in 1933 (Mills and Hamilton, 1994). The model centred on scale economies as forces driving firms and population to concentrate spatially and transport cost disutility as limit to

21 urban size. In this theory, scale economies are realised through spatial proximity, so these benefits are commonly referred to as agglomeration economies. More precisely, agglomeration economies are defined as “decline in average cost as more production occurs within a specified geographical area” (Anas et al., 1998). Three types of agglomeration economies are elaborated by Mills and Hamilton (1989, pp. 19-20). First are agglomeration economies operating at a firm scale, at which firms realise scale economies by spatially concentrating their production at one location. Second, there are agglomeration economies operating at an urban scale. Here the benefits of agglomerative forces are realised by firms and population at the urban scale. Workers, for example, enjoy the availability of different types of jobs, consumers have options of a wider range of products at lower prices and firms benefit from many buyers and wider market segments. The third type of agglomeration economies refers to “knowledge spill-over.” More intense interactions have been allowed due to the high spatial concentration of people, which in turn generates new and innovative ideas, products and processes.

While central place theory was inter-urban in nature as it aimed to figure out the distribution, size and number of towns across a region, the interest of this study is on the intra-urban scale. Urban spatial structure can be defined as “the kind, location, and density of activities as they are distributed across space in urban areas” (Mohan, 1994). From an urban economics perspective, urban spatial structure is the result of location decisions made by government, firms and households over urban space (McDonald, 1997, p. 30). These spatial location decisions are driven by profit maximising behaviour (for the case of firms) and utility maximising behaviour (for the case of households). These imply competition among firms and households in occupying land. A representative model of such competition is called bid-rent theory, originating from Von Thunen’s model of agricultural land use developed in 1826. Von Thunen’s model introduced bid-rent functions representing “willingness to pay” of competing types of crops in occupying agricultural land around the market town located at the centre of “the isolated state”. The extension of Von Thunen’s model to the intra-urban land use case, called the monocentric city model, was developed by Alonso (1964). In this model, an urban area is illustrated as a circle with all the jobs concentrated at its centre, the Central Business District (CBD), while population occupies the rest of the area. Bid-rent functions are used 22 to represent the competing power of households to locate closer to the CBD. A more general model includes competition among different household income segments and different urban job sectors such as commerce, manufacturing and agriculture, in occupying urban area (Anas et al., 1998). Figure 2.6 illustrates the distribution of various job sectors and households as dictated by their corresponding bid-rent curves, over a largely monocentric city.

Commercial Bid-Rent

Light manufacturing

Residential

Manufacturing & distribution

City Centre Distance

Figure 2.6 Bid-rent functions in a largely monocentric city Source: Knox and McCarthy (2005), p. 135.

Empirical findings supporting the monocentric city model were traced to Clark’s (1951) model of population densities:

where is population density of distance from the central city having population density and is the density gradient that is greater than zero. While the model had been highly statistically significant when applied to cities in the United States during the 19th and early 20th centuries, thus supporting

23 monocentricity, more recent studies found that the density gradient of the model was declining over time (Greene, 1977). These findings implied that cities become less and less monocentric. A conception to explain the alternative spatial structure of urban areas was called “multiple nuclei” (Harris and Ullman, 1945), which depicted cities as having a few centres in addition to the CBD. As opposed to the term monocentric, this conception is also referred to as a polycentric model, which has gained increasing attention during the past decades due to the trend of suburbanisation of employment in many cities in the world, particularly those of North American and European cities. This trend has been driven by changes in communication and transport technology, an increase in automobile ownership, wide use of assembly-line production systems, the switch of freight transport modes from rail to truck and extensive development of highway networks (Anas et. al., 1998).

There are three possible scenarios leading to the formation of a polycentric urban structure (Greene, 1977). The first scenario suggests that manufacturing, which is non-market-area5 type of industry, pioneers the suburbanisation of employment outside the CBD. These non market-area firms tend to locate close to transport nodes, especially those with good access to export-import nodes. Intersections and corridors of trunk transport systems such as highways can be alternative locations. Other types of employment including retail and service industries are later attracted to these nodes to form bigger clusters. The second scenario suggests that formation of subcentres is triggered by household suburbanisation, particularly those of medium and high income segments, which is then followed by market-area firms particularly retail and shopping centres. Location decisions of market-area firms are however sensitive to the economies associated with multi-purpose trips and comparison shopping, so that suburbanisation of this type of firms requires already existing subcentres or pre-organised ones such as planned shopping centres. Later, these emerging clusters attract other types of employment around them including those of services, forming new employment centres outside the CBD. Finally, the third scenario involves the expansion of an urban core that engulfs smaller cities in its

5 In Greene (1977), non market-area firms are defined as firms “whose revenues are determined exogenously to their intrametropolitan location” while market-area firms are those “whose revenues depend critically on their location with respect to their potential customers.” 24 surrounding. These smaller cities then become subcenters within a larger urban area.

A few theoretical works on polycentric urban structure such as those by Hartwick and Hartwick (1974), White (1976) and Ogawa and Fujita (1980) are present in the literature, but it is the empirical approach that has dominated the topic within the past two decades. Most have been on cities in the United States, from which Anas et al. (1998) drew some general conclusions as follows. First, subcentres have been prevalent in both new and old cities. Examples include 32 centers identified in Los Angeles and its surroundings in 1980 (Giuliano and Small, 1991), 15 subcentres found in the surrounding of Chicago city in 1980 and 1990 (McMillen and McDonald, 1998) and 22 centres identified in San Fransisco in 1990 (Cervero and Wu, 1997). Second, the result of centres identification is sensitive to parameters used in methodology and scale of observation. Third, subcentres may form corridors along highways, resembling a “megalopolis” (inter-regional urban corridor connecting Boston and Washington). Fourth, employment centres appear to influence the spatial pattern of employment and population surrounding them. This finding validates polycentricity, as opposed to a monocentric model which assumes that population and employment density distributions are explained solely by distance from a single centre. Fifth, it is found that the traditional CBD has maintained its importance despite the emergence of subcentres surrounding it. McMillen (1996), for instance, found the CBD has maintained its land-value peak over one and half centuries despite the increasing importance of centres outside the CBD. Sixth, it is found that job dispersion is a strong phenomenon, accounting for more than half of total employment in the urban areas studied. Anas et al. (1998) argue that there is no strong evidence yet that spatial structure is changing towards dispersion, as suggested by Gordon and Richardson (1996). And seventh, theoretical models of polycentric urban pattern, as in the case of a monocentric city model, have not been able to satisfactorily explain commuting patterns in urban areas. This prompts the notion that factors other than commuting cost have also played important roles in the residential location decisions of commuters.

Methodologies in identifying urban spatial structure

Identification of employment clusters has been a prominent issue as it is a necessary step before further investigations such as functions of centers, impacts on commuting, etc. can be made (Parolin and Kamara, 2003). The most common method used to identify employment clusters is the one proposed by Giuliano and Small (1991), which involves the application of thresholds of employment density and the level of employment to decide whether certain groups of zones in an urban area can be categorised as a sub-centre. As stated in their pioneering paper, a centre is defined as

“…a continuous set of zones, each with density above some cutoff , that together have at least total employment and for which all the immediately adjacent zones outside the subcenter have density below .”

Giuliano and Small (1991) applied the thresholds of employment density of 10 employees per hectare and total employment of 10,000 to reveal 29 centres, including the CBD, in the city of Los Angeles in 1980. Functions of the centres identified in terms of industrial specialisation and commuting pattern destined to each of the centres were also investigated. This popular method has been adopted to identify employment centres in several cities in the United States such as Cleveland (Bogart and Ferry, 1999), Indianapolis, Portland and St. Louis (Anderson and Bogart, 2001), Los Angeles (Small and Song, 1994; Cervero and Wu 1997), and Chicago (McMillen and McDonald, 1998). Polycentricity of several cities outside the United States has also been examined using Giuliano and Small’s method such as Sydney (Parolin and Kamara, 2003), Dijon (Baumont et al., 2004), Boerdeaux (Gaschet, 2000), Ile de France (Boiteaux-Orain and Guillain, 2002), and Seoul (Jun and Ha, 2002).

Despite its popularity, the method has been criticised for the arbitrariness of the thresholds of employment density and level of employment used to define centres (Anas et al. 1998). Giuliano and Small (1991), for instance, applied two sets of thresholds to define centres, in which the lower cut-offs were used to identify clusters in less urbanised regions surrounding Los Angeles. While on the one hand the method offers flexibility in deciding the cut-offs that are suitable for individual cases, subjectivity may lead to inconsistent results. For example, using a more strict 26 cut-off, Gordon and Richardson (1996) concluded that there were 17 per cent of jobs located in centres in Los Angeles in 1980, while the figure found in Giuliano and Small (1991) was around 40 per cent. A few methods have been proposed to eliminate such arbitrariness. Examples include those proposed by McMillen (2001) and Craig and Ng (2001), which are largely grouped into the non-parametric approach.

Another approach in cluster identification (groups of zones that make up employment centres) is offered within the area of spatial statistics and is referred to as exploratory spatial data analysis (ESDA). As the name of the method implies, ESDA attempts to make use of the spatial nature of the (spatial) data in identifying clusters. The first law of geography proposed by Tobler in 1979 (Anselin, 1989) “everything is related to everything else, but near things are more related than distant things” is inherent to any spatial data. This is referred to as the spatial dependence of observations, which is formally considered in spatial statistical methods as “spatial autocorrelation”. The use of ESDA in cluster identification has gained momentum since the extension of global measures of spatial autocorrelation to the local ones. These include the local Getis-Ord and the local Moran’s (known as Local Indicators of Spatial Association - LISA), developed by Ord and Getis (1995) and Anselin (1995), respectively.

Empirical works that use ESDA in cluster identification include among others those by Carrol et al. (2008) on four states in the Midwest of the United States, Riguelle et al. (2007) on four urban regions in Belgium, Guillain et al. (2006) on Ile-de- France, Sohn et al. (2005) on Seoul and Chicago, Baumont et al. (2004) on Dijon, Ceccato and Persson (2002) on rural areas in Sweden and Scott and Lloyd (1997) on Los Angeles. Some empirical works have also used another application of spatial statistics called “spatial association” or “spatial impacts” that is applicable for probing impacts of urban structure following cluster identification. The Getis- Ord statistics offers an intuitive measure of spatial impacts within the distance of a hypothetical origin, as shown in Getis and Ord (1992). The local Moran’s has also been used to investigate spatial impacts of clusters of employment on population density surrounding them (Bao et al., 1995).

Sustainability issues and travel

Concerns motivating the efforts in identifying urban spatial structure following the pervasiveness of suburbanisation of employment and population in many cities in the world have been centred on urban sustainability. Since the introduction of the general conception of sustainability development as the one “that meets the needs of the present without compromising the ability of future generations to meet their own needs” (WCED, 1987, p. 43), more specific definitions and indicators that are applicable to urban cases have been proposed. In the developed European world, while not without controversies, there have been strong advocates towards the compact city, as opposed to sprawl, as a more sustainable urban structure for its association to more efficient energy consumption (Newman and Kenworthy, 1999, p. 101), less

CO2 emission and less travel demand. Intensifying development within the existing urban areas is also considered advantageous in terms of inner city revitalisation, promotion of non-motorised transport, preservation of agricultural areas and promotion of public transport and transit oriented development (Sorensen et al., 2004, p. 7-8).

Because the term compact is often a fuzzy concept as that of sprawl, studies commonly refer to the characteristics of urban spatial structure that can constitute either a more compact or more sprawling in nature. For example, the definition of sprawl as adopted by Gilham (2002, p. 8) is basically the list of its characteristics as “a form of urbanisation distinguished by leapfrog patterns of development, commercial strips, low density, separated land uses, automobile dominance, and a minimum of public open space.” As pointed out by Ewing (1997), however, such characteristics themselves are a matter of degree that quantifiable indicators are needed to decide whether certain types of urban spatial structure or land use can be categorised as desirable or not. Lindquist (1998) for instance, spelled out sustainability indicators such as mixed-use neighbourhoods and jobs/housing balance, which correspond to sustainability objectives of integrating land uses and providing employment opportunities close to residential areas respectively.

The emerging polycentric urban structure of cities is often seen as a compromise within the context of agglomeration economies, suburbanisation trends and urban compactness versus sprawl and sustainability impacts. Anderson et al. (1996) and

Ewing (1997) considered polycentric urban structure as a variant of the compact city for its potential of maintaining the characteristics of compact cities while channelling the suburbanisation of economic activities into its subcentres, in which the benefits associated with agglomeration economies (Shukla and Waddell, 1991; Giuliano and Small, 1999) can be realised.

Theoretically, the emergence of subcenters can shorten overall commuting time and cost by sending employment opportunities closer to population. Advantages of polycentricity as pointed out by Robinson (1995, p. 88), include alleviating traffic burden in the central city thus reducing congestion, promoting urban and economic growth more efficiently in the surrounding central city, facilitating high density urban land use mix that improves viability of mass transit development, preserving natural resources and green areas by concentrating development in the subcentres, preserving public expenditure through avoiding unnecessary infrastructure development associated with sprawl development, and facilitating participatory planning and development by allowing involvement of local community in the development and growth of the subcenters.

While urban structure has a wide range of implications that may fall into one or more of the economic, social or environmental facets of urban sustainability, empirical investigations have focused largely on travel. There are two possible reasons for this. First, the shifts of urban structure from monocentric to polycentric or dispersed pattern lend themselves to the question on commuting pattern, which has been inherent in earlier urban models. The term “wasteful commuting” or “excess commuting”, for instance, had been a subject of debate as the travel cost minimising behaviour assumed in the monocentric city model did not turn out to be the case in the real world (Hamilton, 1982; Small and Song, 1992; Giuliano and Small, 1993). In this case, the interest of empirical investigations is to test the validity of theoretical models of the corresponding urban structure. Second, and more importantly, there is strong evidence from many urban areas in the world that transport has been a major contributor to degradation of urban sustainability performance (Table 2.1). Litman and Burwell (2006) emphasised the overlapping nature of transport impacts on the wider context of sustainability. Traffic congestion, for example, can be categorised as an economic sustainability problem

29 in terms of its associated increase in fuel consumption and travel time, as an environmental problem for the increased air pollution, and as a social problem for the impacts on human health. The goals and objectives of policy and planning that attempts to alleviate such interactive impacts should therefore be benchmarked by a set of more comprehensive indicators (Table 2.2). Many of these are travel behaviour indicators (such as commute travel time, share of walking, cycling, public transit) or functions of it (such as fuel consumption, emissions, noise, public expenditure on transport). Travel pattern or travel behaviour is strongly influenced by spatial structure and its characteristics, as discussed in the next section.

Table 2.1 Transportation impacts on sustainability Economic Social Environmental Traffic congestion Inequity of impacts Air and water pollution Mobility barriers Mobility disadvantaged Habitat loss Accident damages Human health impacts Hydrologic impacts Facility costs Community interaction DNRR Consumer costs Community liveability DNRR Aesthetics DNRR: Depletion of non-renewable resources. Source: Litman and Burwell (2006).

Table 2.2 Sustainable transport indicators Objectives Indicator Direction Economic Accessibility – commuting Average commute travel time Less is better Accessibility – land use Number of job opportunities and More is better mix commercial services within 30-minute travel distance of residents Accessibility – smart Implementation of policy and planning More is better growth practices that lead to more accessible, clustered, mixed, multi- modal development Transport diversity Mode split: portion of travel made by More is better walking, cycling, rideshare, public transit and telework Affordability Portion of household expenditures Less is better devoted to transport by 20% lowest- income households Facility costs Per capita expenditures on roads, traffic Less is better services and parking facilities Freight efficiency Speed and affordability of freight and More is better commercial transport Planning Degree to which transport institutions More is better reflect least-cost planning and investment practices

Objectives Indicator Direction Social Safety Per capita crash disabilities and fatalities Less is better

Health and fitness Percentage of population that regularly More is better walks and cycles Community liveability Degree to which transport activities More is better increase community liveability (local environmental quality) Equity – fairness Degree to which prices reflect full costs More is better unless a subsidy is specifically justified

Equity – non-drivers. Quality of accessibility and transport More is better services for non-drivers Equity – disabilities Quality of transport facilities and services More is better for people with disabilities (e.g., wheelchair users, people with visual impairments) Non-motorised transport Degree to which impacts on More is better planning non-motorised transport are considered in transportation modelling and planning Citizen involvement Public involvement in transport planning More is better. process Environment Climate change emissions Per capita fossil fuel consumption, and Less is better emissions of CO2 and other climate change emissions Other air pollution. Per capita emissions of ‘conventional’ air Less is better pollutants (CO, VOC, NOx, particulates, etc.) Noise pollution Portion of population exposed to high Less is better levels of traffic noise Water pollution Per capita vehicle fluid losses Less is better Land use impacts Per capita land devoted to transportation Less is better facilities Habitat protection Preservation of wildlife habitat (wetlands, More is better forests, etc.) Resource efficiency Non-renewable resource consumption in Less is better the production and use of vehicles and transport facilities

Source: Litman and Burwell (2006).

Travel impacts of urban spatial structure

The influence of urban spatial structure on travel is conceptually drawn in the “land use – transport feedback cycle” as adapted from Wegener (1996) (Figure 2.7). As shown in the figure, travel demand, which consists of its traditional components of

31 the four-step travel decisions, is influenced by the distribution of activities across the urban area. The later is referred to as urban spatial structure (Mohan, 1994), made up largely by spatial distribution and intensity of employment and population across the urban area. Travel pattern dictates performance of transport links, which often call for services and infrastructure improvements due to increasing travel disutility. Furthermore, the provision of transport infrastructure and services influences accessibility which functions as a feedback to the existing urban spatial structure. Adjustments to the distribution of activities are made as a result which, in turn, change the urban spatial structure and hence travel pattern. While the impacts of urban spatial structure on travel are made clear in the model, another important characteristic of travel is also implied. From the trip makers’ behavioural point of view, travel has been regarded as a derived demand, which does not function as the fulfilment of demand for the trip itself (unless for the case of sightseeing perhaps) but rather as the fulfilment of demand for activities, such as going to work or shopping. This point of view strengthens the notion of looking into urban spatial structure, such that the spatial distribution and physical characteristics of those activities are defined as an effort in influencing travel patterns towards the one that is more desirable in the context of urban sustainability.

TRANSPORT LINK LOADS

TRAVEL DEMAND: TRAVEL DISUTILITY: TRIP DECISION, TIME, DISTANCE, DESTINATION, AND COST MODE AND ROUTE CHOICES

TRANSPORT PROVISION

TRANSPORT DISTRIBUTION ACCESSIBILITY LAND USE OF ACTIVITIES

LOCATION ATTRACTIVENESS ADJUSTMENTS

FIRMS’ LOCATION RESIDENTIAL DECISIONS LOCATION DECISIONS

Figure 2.7 Land-use – transport feedback cycle Source: Adapted by author from Wegener (1996), p. 110.

As the conceptual model above explicitly shows the link between urban spatial structure and impacts on travel, empirical studies have focused on investigating how a particular physical feature influences a particular dimension of travel. Various features of urban structure, commonly referred to in many studies as urban forms6, can be categorised broadly into three groups: density, diversity and design (Cervero and Kockelman, 1997) while the travel impacts commonly investigated are trip rates, travel times and travel distance (each of which is often examined by trip purpose and travel mode) and travel mode choice. The features of urban structure can be analysed from the trip origin and trip destination point of views. While the former has been associated with efforts at influencing travel through the

6 The term urban spatial structure (or urban structure), as used in Anas et al. (1998) refers to general configuration of spatial distribution of activities across urban or region while the terms urban form (Handy, 1996; Badoe & Miler, 2000) or built environment (Cervero and Kockelman, 1997) are often used to represent spatial characteristics of land-use. 33 spatial characteristics of residential areas, the later has been focused on the spatial characteristics of workplaces. Along with the trend of employment suburbanisation and the emergence of polycentric urban structure, many empirical studies have included the spatial characteristics of employment centres as explanatory variables of their travel pattern modelling. A survey of empirical studies investigating the impacts of urban structure on travel is shown and summarised in Table 2.3.

Table 2.3 Empirical findings of urban structure and urban form impacts on travel Spatial Characteristics Dimension Measure Impacts on Travel Author(s) I. Density Population or The number of population per Zones with higher Gordon et al. household density hectare population density generate (2004) higher share of transit use The number of households per Zones with higher Cervero & acre household density generate Gorham higher share of commuting (1995) trips by transit Urban versus Strongly urbanised (>2500 People living in areas of Bouwman & rural settings addresses/km2); Urbanised rural setting tend to make Voogd (2005) (1500-2500 addresses/km2); fewer short trips and more Weakly urbanised (1000-1500 long trips. addresses/km2); Rural (500- 1000 addresses/km2); Very rural (<500 addresses/km2) Urban (> 400 persons /km2); Exchange commuting Green & Rural (< 400 persons /km2). pattern between urban and Meyer (1997) rural settings is investigated. Average commuting distance from highest to lowest is: Urban-Urban (35.8 km) Urban Rural (30.5 km) Rural-Rural (19.0 km) Rural-Urban (13.8 km) Employment The number of jobs per acre Zones with higher Frank & Pivo density employment density (1994) generate higher share of non-motorised trips and lower share single occupant vehicle (SOV) trips for both work and shopping trip purposes. Square feet of office space per Clusters of employment Pivo (1993) area of employment cluster with higher job density attract less share of trips made by automobile.

Spatial Characteristics Dimension Measure Impacts on Travel Author(s) Jobs-housing Residential workers per job Zones with higher Giuliano & balance proportion of workers per Small (1993) job generates less commuting time Commercial workers per Zones with higher Gordon et al. commercial land; proportion of workers per (1989) Manufacturing workers per land-use of the same manufacturing land category generates shorter commuting time Employees per on-site housing Subcentre having higher Cervero (1989) unit in suburban centres proportion of employees to housing attracts lower share of non-motorised commuting trips Distance to CBD Zonal centroid to centroid Zone located farer from Crane & Euclidian distance to CBD CBD generates higher share Crepeau of car use and higher car (1998) trip frequency Straight-line distance of the Employment cluster located Pivo (1993) employment cluster centre to farer from the regional CBD the regional CBD attracts higher share of trips made by automobile Location of employment Employees of suburban Cervero & Wu centres with respect to overall centres make higher VMT (1997) metropolitan than those of CBD; Suburban centres attract higher share of commuting by car and lower share of commuting by transit and walk compared to CBD; Suburban centres attract shorter commuting distance and travel time compared to CBD. II. Diversity Degree of land- Land-use mix diversity = Higher land-use mix Rajamani et al. use mix diversity contributes to (2003) where r, higher probability of non- c and o are acres of residential, motorised travel (walk and commercial and other land- bicycle) for non-work trips. use category, respectively, and T = r + c + o Dissimilarity index = Higher degree of land-use Cervero & mix reduces the probability Kockelman of choosing “drive alone” (1997) Where K is the number of by car travel mode for non- actively developed hectares work trip grid cells in tract and Xik is 1 if neighbour grid-cell has

different land use category (Xik is 0 otherwise). Employment entropy index Suburban centres having Cervero (1989)

= where pi is higher degree of job the proportion of employees diversity attracts shorter

Spatial Characteristics Dimension Measure Impacts on Travel Author(s) in job classification i. commuting time. Retail square footage within 3 Suburban centres having Cervero (1989) mile radius of suburban more retail areas around employment centres per on- attract lower share of drive site employee alone by car trips III. Accessibility and transport facilities Job accessibility Zonal accessibility index Higher job accessibility Gordon et al. index is associated to lower (2005) where Ej is the commuting time. number of jobs at zone j; dij is distance between i and j; is impedance coefficient Zones with higher job Cervero & accessibility generate Kockelman shorter vehicle miles (1997) travelled (VMT) of commuting. Local Accessibility at zone i Zones having higher “local” Handy (1993) and “regional” accessibility generate shorter distance of shopping trips. Regional accessibility

where is impedance coefficient Proportion of Zonal share of four-way Zones having higher Cervero & four-way intersections proportion of four-way Kockelman intersections intersections generate lower (1997) non-work trip rate and VMT and lower share of “drive alone” car trips Distance to public Zonal centroid Euclidian Zones located farer from Kitamura et al. transport facilities distance to nearest rail station nearest bus stop generate (1997) Zonal centroid Euclidian higher share of trips made distance to nearest bus stop by car; Zones located farer from nearest rail station generates lower share of trips made by transit. Whether or not employment Employment cluster located Pivo (1993) cluster is located within within walking distance to walking distance (0.25 mile) subway station attracts from a subway station lower share of automobile trips Distance to road Whether or not employment Employment cluster located Pivo (1993) transport cluster is located within 0.5 within 0.5 mile from interchange mile straight-line distance highway interchange from a freeway or expressway attracts higher share of interchange automobile trips

Source: Compiled by author.

The Southeast Asian Extended Metropolitan Region

Continuation of urbanisation trends

Within the period 2003-2007, Southeast Asia had experienced a 4.6 per cent real growth in GDP per capita, which was a significant improvement from the post crisis period (1998-2002) of 1.1 per cent (UNCTAD, 2008a). The Asian financial crisis started in mid-1997 and cast doubt on the continuation of FDI inflows to a few countries, particularly those affected severely by the crisis. A closer look at the four Southeast Asian countries (Figure 2.8), however, reveals the “back on track” trend of FDI inflows, including that of Indonesia, the country affected most by the crisis as it had experienced negative net inflows of FDI in the six years following the crisis. Foreign direct investment inflows to Southeast Asia reached USD 61 billion in 2007, an 18 per cent increase from the 2006 figure.

6 Indonesia Malaysia 4 Thailand 2 The Phillipines 0

-21970 1975 1980 1985 1990 1995 2000 2005 USD Billion (current (current price) Billion USD -4 year

-6

Figure 2.8 Foreign direct investment (FDI) net inflows in the four Southeast Asian countries, 1970-2007 Source: Processed by author from UNCTAD statistics.

The above figures suggest the continuation of rapid urbanisation for the region. UNDESA (2008) predicted that the share of urban population in South-Eastern Asia will reach 55 per cent by the year 2020, with the annual urbanisation rate of 1.5 per cent from 2007 to 2020. What is hidden from the data is the variation

37 among countries in the region, urban primacy and polarisation of development in a few mega-urban regions. Urban primacy had remained high in some of the Southeast Asian nations. Five nations in the region had a primacy index7 more than four, including Thailand (33.8), Lao PRD (5.4), Indonesia (5.0), Philippines (5.0) and Myanmar (4.7) (Hugo, 1999, p. 69). In the mid-1990s, almost two-thirds of urban population in Southeast Asia was concentrated in only the five mega- urban regions: 30 million in the Bangkok mega-urban region, 6 million in the Kuala Lumpur-Klang mega-urban region, 10 million in the Singapore Growth Triangle, 100 million in the Java mega urban corridor, and 30 million in the Manila mega- urban region (McGee, 1995b, p. 5). In the year 2000, Bangkok, Jakarta and Manila mega-urban regions share 17, 10 and 27 per cent of total population in their corresponding countries, respectively (Jones et al., 2004).

Conception and characteristics of the Southeast Asian EMR

While urbanisation theories and empirical studies have been long dominated by what McGee referred to as “Eurocentrism” (McGee, 1995a), the urbanisation process of cities in developing countries differs fundamentally in several respects to that of Western cities (Lin, 1994). First, in developing cities, urban growth has been contributed from a natural increase of population, in contrast to rural-urban migration that is largely driven by economic growth experienced in developed cities. Second, the industrialisation phase, which has characterised the urban transition in Western cities, often is not required in developing cities. The industrialisation phase has often been skipped even in the formation of mega-cities in some developing cities. Third, and related to the previous point, many developing cities are dominated by service employment sectors, in contrast to the sequence of manufacturing – retail – tertiary sector employment as has happened in the developed world. Fourth, the widely accepted agriculture – manufacture – tertiary sector urban transition in developed cities does not apply to developing cities. Instead, the review of Lin (1994) is that developing cities are characterised by urban dualism resulting from the persistence of predominantly agricultural employment to a rapid transition towards service sector dominance. Fifth, social change associated

7 Calculated as ratio of population of the largest city to that of the second largest city. 38 with urbanisation in developing countries has been completely different to that of the Western world. In developing cities, as contrasted to developed ones, Lin (1994) argued that village-like social and political organisations and social interactions have been retained. Such fundamentally different features of urban transition have been argued to result in different urbanisation outcomes to those of the developed Western world.

Assertions of the uniqueness of urbanisation in Asia were pointed out by McGee (1989) by highlighting differences between the urban-rural mix of activities experienced by urban regions in Asia and those in the United States’ megalopolises as indicated by Jean Gottman in 1961. The coined word kotadesasi, a combination of the words kota (town) and desa (village) in the Indonesian language, was used to represent the urban-rural formation in the Asian context which has five main characteristics as outlined by McGee (1989): First, the penetration of urban activities into the predominantly agricultural areas has created a juxtaposition of urban-rural activities in these kotadesasi zones which function as economic linkages between urban centres and kotadesasi areas. Second, the availability of cheap transport modes such as motorcycles, buses and trucks has allowed relatively easy movements of people and goods not only within kotadesasi zones but also to access the urban centres. Third, it is highlighted that such a juxtaposition of urban and rural activities is so intense that completely different land use such as agriculture and cottage industry can co-locate side by side. Fourth, kotadesasi zones are characterised by an increased proportion of female labour engaged in non- agricultural activities due both to the demand from industry and the change of agricultural production from rice to horticultural. Fifth, kotadesasi zones are often regarded as rural in terms of regulations by authorities in spite of their increasing urban activities. The less strict regulations applied to rural areas allow informal sectors and squatter housings to proliferate in these “gray zones”.

Following his hypothesis on kotadesasi, McGee (1991) proposed a more comprehensive model representing the spatial configuration of a typical Asian country (Figure 2.9). The five regions indicated in the model are: (1) major cities, which are the largest cities, or commonly referred to as primate cities in the developing Asian countries; (2) peri-urban areas, which are the hinterland of the

39 major cities, often regarded as areas within daily commuting distance to the major cities; (3) desakota zones, which are the predominantly agricultural areas that are increasingly penetrated by urban activities. These zones are commonly located along transport corridors connecting major cities and characterised by intense urban-rural mix. These resemble and are therefore an update to kotadesasi zones indicated by McGee in 1989; (4) densely populated rural areas, which are the villages of wet-rice agriculture commonly found in Asian countries; and (5) sparsely populated frontier areas, which are remote areas that have not been well developed.

Major cities Peri-urban desakota Densely populated rural Sparsely populated frontier Smaller cities and towns

Communication routes

Figure 2.9 Spatial configuration of a hypothetical Asian country Source: McGee (1991), p. 6.

Because the model was intended for the whole region of Asia where levels of development varied significantly among and within the countries, further classification of mega-urban regions in Asia was proposed by McGee (1991, p.13) based on the characteristics of the “transition economy” or desakota zones (Figure 2.10). Those located in countries or regions that have been highly urbanised and experienced significant decline in agricultural productions are labelled desakota type I. An important characteristic is the dominance of urban activities in the desakota

40 zones, in which agricultural activities are maintained due to government protection policies. Japan and South Korea are examples of countries where desakota type I can be found surrounding their mega-cities. Desakota type II can be found in countries experiencing polarisation of development in which rapid economic growth, hence urbanisation and shift towards non-agricultural activities, is polarised in a few mega-cities. These mega-cities and the surroundings are associated with increased household incomes and improvements in transport infrastructure. Examples include Nanjing-Shanghai-Hangzhou, the Central Plains of Thailand, the Taipei-Kaohsiung corridor, the Calcutta region and Jakarta Metropolitan Area (JABODETABEK). The other is desakota type III, located in regions characterised with slow economic growth, rapid increase of population, labour surplus and low productivity of both urban and agriculture activities. Examples are Jogjakarta region in Indonesia, Kerala in South India, Bangladesh and the Sichuan Basin in China.

Figure 2.10 Core areas in Asia Source: McGee (1991), p.13.

While the transition zones desakota were identified across the Asian countries, the dynamics of change and potential urban impacts demand particular attention on

41 those experiencing particularly rapid urbanisation and industrialisation. McGee and Greenberg (1992) started their focus on mega-urban regions of Southeast Asia, in which desakota type II, and a few type III, are located. Clearer imprints of globalisation were also expected in these mega-urban regions considering their intense integration to the Asia Pacific economic sub-system since the 1960s that peaked in the mid 1990s. McGee and Greenberg (1992) found that it is “region- based”, as opposed to “city-based”, urbanisation that has operated in the extended metropolitan regions of Southeast Asia. As a consequence, urban boundaries should be reconsidered to account for such a type of urbanisation. They suggested the divisions of EMRs into three components: the city core, which is defined by the original boundary of the city; the metropolitan area, which is the built-up areas surrounding the city core, and the extended metropolitan area, which is the transitional desakota zones commonly located along transport corridors radiating out of the metropolitan area, characterised by an intense mix of urban and rural activities. It is, however, important to emphasize that strong economic linkages exist between the three broad regions so that the EMR has to be seen as an economically integrated region. This is stated explicitly in a more comprehensive definition of Southeast Asian EMR proposed by McGee and Robinson (1995b, pp. ix-x):

“Extended metropolitan development tends to produce an amorphous and amoebic-like spatial form, with no set boundaries or geographic extent and long regional peripheries, their radii sometimes stretching 75 to 100 km from the urban core. The entire territory – comprising the central city, the developments within the transportation corridors, the satellite towns and other projects in the peri-urban fringe, and the outer zones – is emerging as a single, economically integrated ‘mega-urban region,’ or ‘extended metropolitan region.’ Within this territory are a large number of individual jurisdictions, both urban and rural, each with its own administrative machinery, laws, and regulations. No single authority is responsible for overall planning or management.”

Three major distinguishing characteristics of Southeast Asian EMR urbanisation were emphasised by Kelly (1999). First, urbanisation in the periphery of Southeast Asian cities has been triggered by the availability of low cost labour in comparatively densely populated rural areas. Here, the western notion of agglomeration economies as an important factor for urbanisation is largely absent.

Second, the cost efficiency associated with the theory of agglomeration economies (Mills and Hamilton, 1989, p. 19) is to some extent offset by the availability of relatively cheap transport modes (two-stroke motorcycles, informal public transport modes), facilitating intensive transactions among urban socioeconomic activities in predominantly rural areas. Third, the colonial background of major Southeast Asian cities as the terminal points for transporting exploited goods from the rural hinterlands, combined with the globalisation economies starting from the 1960s, have paved the way for mega-urban formations. Ports in major Southeast Asian EMRs have sustained their importance across the pre-colonial times to globalisation era. The existence of port, combined with good transport access from the hinterland to the port, has helped Jakarta, Bangkok and Manila EMRs participate in the multi-national companies’ (MNCs) global production system (McGee 1994, p. 83).

Overall, the transport system in Southeast Asian EMRs is characterised as a “hybrid” of local and outside factors (Rimmer, 1977). This view is modelled, within the context of colonialism, into four phases of evolution as shown in Figure 2.11. The first phase, named pre-contact phase, illustrates the “ceremonial city”, located inland, as the regional centre. The river functioned as the transport access to a market city located at the coast. Land transport was illustrated as a “track” connecting the ceremonial city to smaller centres. The second phase, early colonialism, shows the success of Western power in developing their garrison and trading posts near the port, and the development of a permanent base following the occupation of the coastal city. During this era, the city was not expanded very much due to orientation of the colonial power towards coastal trading. The next era, high colonialism, indicated by the change of colonial interest towards agriculture exploitation to meet demands in Europe at the time. Transport infrastructure was developed to transport agriculture to the port. The city was also expanded further towards the inland. Neo-colonialism followed as a result of political independence gained by third world countries. An important characteristic of neo-colonialism is that transport infrastructure in the former colonised cities has not changed much and is still oriented towards servicing the interest of the former “colonisers”, now represented by the multi-national companies.

Figure 2.11 Schematic changes in transport networks at different phases of colonisation process Source: Rimmer (1977).

Another important characteristic of Southeast Asian EMRs is that they have been the centres of economic growth for their corresponding countries. Bangkok and

Manila as the urban cores of EMRs shared 37% and 24%, respectively, of their corresponding national GDPs (ADB, 1997) and the Jakarta EMR contributed 21.8% to Indonesia’s GDP in 1995 (Soegijoko and Kusbiantoro, 2001). Prior to the economic crisis that hit the region in 1997-1998, FDI inflows had dominated the economic growth of the regions. Yendaka had also boosted FDI inflows in the Southeast Asian EMRs as Japanese investments had been heavily concentrated in these national capitals (Fuchs and Pernia, 1989) for infrastructure, market and labour considerations (Syamwil and Tanimura, 2000). Table 2.4 shows the comparative scale of Bangkok, Manila and Jakarta EMRs economies in the year 1993. As can be seen from the table, FDI inflows in those three EMRs were heavier on the regions outside the urban cores (i.e., the capital cities of Bangkok, Manila and Jakarta).

Table 2.4 The comparative scale of Southeast Asian EMR economies in 1993 Population in Gross Domestic the core city Product Share of FDI Extended National Amount National Metropolitan Number Share (USD Share Core City Area City (millions) (percentage) millions) (percentage) (percentage) (percentage) Bangkok 5.55 9.9 53.1 42.6 -- 46.8 Manila 7.93 13.6 17.5 32.2 14.7 56.1 Jakarta 8.26 4.6 20.1 12.7 15.2 45.7

Source: Japan Development Bank (1996), as quoted in Lo and Marcotullio (2001).

Sustainability problems facing the Southeast Asian EMRs

While compact cities have often been advocated for the sake of urban sustainability, the case of developing Asian cities might be seen as a paradox. Many large cities in Asia, including the core cities of Southeast Asian EMRs, are characterised by high population density and more centralised employment (Barter, 1999, p. 195-205), yet their environmental problems are mounting (Marcotullio, 2001a). In the context of urban economics, scholars often see mega-cities in the developing world as having experienced “excessive agglomeration” in that the efficiency of agglomeration economies has been offset by negative externalities associated with over-crowding.

Another view is offered in the concept of “carrying capacity”. According to Nas and Veenma (1996), carrying capacity is a pre-requisite to sustainable development. Quoting the definition by White and Whitney (1992), carrying capacity sets a threshold of “the maximum population that can be supported indefinitely in a given habitat without permanently impairing the productivity of the ecosystem upon which that population is dependent.” While the carrying capacity concept has not been adopted in the investigation of the sustainability performance of Southeast Asian EMRs, sustainability problems indicated may be associated with uncontrolled growth of population in these mega-urban regions that has passed such a threshold.

Along with the economic and development gains from globalisation, Asian countries have experienced serious environmental problems (Marcotullio, 2001b, pp. 464-469). Figures of environmental performance in the mid 1990s (ADB, 1997) revealed that Southeast Asian cities suffered the most among other Asian cities, with ten of seventeen indicators investigated being categorised as “very severe” (Table 2.5). The environmental deterioration “coincided” with the peak period of industrialisation of Southeast Asian countries such as Indonesia, Thailand, Malaysia and the Philippines in the Asia-Pacific globalisation (referring to FDI inflows and the flying geese theory). The shifts of manufacturing sites to developing Southeast Asian countries were not associated solely with their comparative advantage. As argued by Soegijoko (1995), lower environmental and health standards have allowed highly polluted manufacturing to be relocated to mega- urban regions in these countries.

Table 2.5 Relative severity of environmental problems in selected Asian subregions Pollutant China East Asia Southeast Asia South Asia Air pollution Sulphur dioxide XX XXX XX X Particulates XX XX XXX Lead X XXX XX Water pollution Suspended solids XXX XX XX Faecal coliforms XX XXX XX Biological oxygen demand XXX XX (BOD) Nitrates XX XX X XXX Lead X XX XXX X Access to water and sanitation Lack of access to safe water X XXX XXX Lack of access to sanitation XXX XXX XXX Deforestation Deforestation rate XX XXX XX Land degradation Soil erosion XXX XXX XXX Waterlogging and salinisation XX XX XXX Desertification XXX Imperata spread XXX Energy consumption Annual growth rate XX XXX XXX XXX CO2 emissions XXX XX X X X: moderate but rising XX: severe XXX: very severe blank: data not available Source: Asian Development Bank (1997).

A further survey of literature on the sustainability performance of Southeast Asian EMRs confirmed such unfavourable conditions (Table 2.6). Economic aspects of sustainability has been challenged by severe traffic congestion, which has been indicated as a major contributor to economic loss in the form of increased fuel consumption and travel time. Social segregation by income classes has been prevalent along with penetration of urban activities in the peri-urban areas of EMRs. Environmental problems have encompassed many aspects such as conversion of farmland, disturbance of ecological system, water crisis, flooding, air pollution, noise, water pollution, soil pollution, land subsidence and acid rain.

Table 2.6 Sustainability problems in Southeast Asian EMRs

Sustainability Problems Case Author(s) I. Economic Traffic around the CBD suffered severe traffic Bangkok EMR Pendakur (1995) congestion with average peak hour speed ranging from 6 to 10 km/hour and 1 to 2 km/hour at worst. Rapid motorisation had been contributed by increase of the number of motorcycles and cars at annual rate of 18 per cent and 12 per cent, respectively. The core city of Jakarta had suffered traffic congestion Jakarta EMR Steinberg (2007) associated with rapid increase of motor vehicles combined with inadequate roads. Rapid increase in private vehicle use was caused among others by low quality public transport. The number of motor vehicles had increased by 10 times from 1985 to 2002. II. Social The formation of EMR had brought about social Jakarta EMR Leaf (1996) segregation by economic classes particularly in desakota zones in which housing estates and amenities such as golf courses penetrated the predominantly rural areas. Social disparities, formation of slums and crime Manila EMR Ocampo (1995) increase had been associated to the pattern of development which was unfavourable to low income classes: intensive development of shopping malls and condominiums which increased land prices in central city dramatically; sprawl of medium to high income housing developments towards peri-urban areas and by arterial transport due to limited road network; slums and housing for low income groups were neglected. Increasing of social segregation associated with new Jakarta EMR Firman (2004) towns development; social segregation by income groups was perceived at two scales: city or regional scale at which luxurious new towns and amenities contrasted themselves to kampungs, which were still dominant in the urban and per-urban areas of JMA; town scale at which houses were segregated by types of houses associated with income groups of the dwellers. III. Environment Massive prime agricultural conversion into housing and Jakarta EMR Firman and industrial estates; horticulture activities in the southern Dharmapatni (1994) part of region contributed to soil erosion; water recharge zones was disturbed by new towns development; excessive ground water extraction beyond its recharge rate; rivers with high concentration of BOD and COD; uncollected solid waste and increasing non-biodegradable wastes; air pollution with high concentration of lead from vehicle emission; increasing acidity of rainfall. Large scale housing were developed in conservation Bandung EMR Firman (1996) areas at Southern part of the city; farmlands were converted into industrial estates and tourist resorts; rivers were polluted from factories and had experienced rapid sedimentation, such as Citarum river whose depth had reduced from 10 to 4 meters within the past

Sustainability Problems Case Author(s) two decades. Surface waters were indicated with high levels of Jakarta EMR Atkinson (1993) dissolved oxygen and faecal contamination; over- pumping of ground water beyond its recharge rate; solid waste (including non-degradable) pollution on open spaces and riverbanks which contributed to flooding; air pollution both from suspended particles and exhaust gas. Pressure on agriculture activities due to conflicts with Manila EMR Kelly (1999) residential land use including pollution of irrigation by solid waste and household sewage disposal. Most serious problems were identified in the areas of Bangkok EMR Kittiprapas (2001) air quality, traffic, open space, slums, sewerage and ground pollution; traffic was the biggest problem contributing to air pollution, suspended particles and noise; water pollution was associated with domestic waste disposal; other problems include water supply, flooding and land subsidence. A combination of rapid urbanisation, globalisation and Jakarta EMR Douglass (2005) mega-projects taking the forms of mega-shopping malls, new towns, manufacturing and tourist resorts was the major source of water sustainability problems facing mega-urban regions. Impacts included excessive extraction of ground water, land subsidence, salt water intrusion, surface water disturbance, flooding, drought, contamination of rivers from households and industrial wastes, disturbance of sensitive coastal and upland ecological systems and sustained water problems due to lack of public funding and attention from authorities. Urban expansion in the form of housing development Manila EMR Murakami and towards the fringe area not only had converted large Palijon (2005) area of paddy fields into urban use but also disturbed sewage system and caused flooding. The flooding had resulted in serious damages on the remaining agricultural areas. Households and factories contributed to 1.4 million m3 Bangkok EMR Kaothien (1995) waste water annually to canals and rivers, in addition to 1,000 tons of garbage daily and 1.9 million tons of toxic waste annually; land subsidence and flooding had been associated to extraction of ground water due to inadequate provision of potable water. Deforestation in the fringe of EMR reducing forest Manila EMR Conover (1995) cover from 53 per cent in 1940s to 5 per cent in 1990s leading to soil erosion. Industrial wastes causing air and lake pollution.

Source: Compiled by author.

Probing the urban spatial structure of the Southeast Asian EMRs

What represents urban spatial pattern in Southeast Asian developing countries has been a subject of investigation, along with recognition of their distinctive characteristics such as features associated with colonial background (urban primacy and international migrants), national led urbanisation, ethnic and religious diversity, polarisation of development in a few centres, and those associated with globalisation (McGee and Robinson, 1995a, pp. 343-345) in addition to fundamental differences in urban transition as identified by Lin (1994). The outcomes of the urbanisation processes in terms of spatial structure seem to be much more complex than monocentric and polycentric dichotomies recognised in the developed world.

Centralised urban economic activities have been largely maintained in many developing cities, including those in Southeast Asia, due to the unbalanced concentration of transport and other infrastructure between the city centre and its surroundings (Richardson, 1989). A few empirical works also implied that the city centre, particularly within the urban core of EMRs, had maintained its importance. Cervero and Susantono (1999) found that variation of office rents in Jakarta was explained more satisfactorily by their distance to the traditional city centre rather than their access to upper-income households. Despite the importance of distance to CBD in explaining variation of land values in Jakarta (Han and Basuki, 2001), however, land values were in general “highly disorganised” (Dowall and Leaf, 1992), most likely due to the dominance of urban kampung and the juxtaposition of kampung and modern buildings within the city centre.

Decentralisation of economic activities, on the other hand, had been realised through market processes rather than careful urban growth strategies (Richardson 1993). As a consequence, employment decentralisation has not seemed to transform developing cities into a genuine polycentric urban structure. There is very little evidence that substantial subcentres in terms of size and number have emerged (Richardson, 1989; Fourarche and Turner 1992; Robinson, 1995, p. 87). Decentralisation of manufacturing employment towards the peri-urban areas, driven by globalisation, has also failed to trigger the formation of subcentres as it has not been accompanied by decentralisation of higher-order services (Firman,

1996). Instead, the rapidly decentralised manufacturing has formed a sprawl-like corridor along trunk transport routes radiating out of the core cities (Ocampo, 1995; Henderson et al., 1996). The complexity is added to by the juxtaposition of urban and rural activities surrounding the core cities. Using a joint-count method, Murakami (2005) revealed complex land use patterns in the peri-urban areas of Jakarta, Bangkok and Manila, conforming to desakota characteristics.

Urban dualism (Hackenberg, 1980; Lin, 1994) that exists in many developing cities in Southeast Asia also adds to the complexity of urban spatial structure. For employment, urban dualism is contributed to by the presence of informal sectors. According to Hackenberg (1980) informal sectors characterising primate cities in Southeast Asia originated from the decolonisation period, during which industrialisation and rural-urban migration had become important factors for urbanisation. The import-substitution policy adopted by the newly independent Southeast Asian nations at the time had created only a limited number of employment opportunities in small-scale manufacturing. Higher-skilled services jobs were more numerous but they did not suit most of the new migrants who had limited education backgrounds. Informal sectors had emerged to balance the over supply of these unskilled labours. For the case of housing, urban dualism is contributed to by the dominance of kampung within and surrounding the city centres. Formation of kampung in urban areas originated from colonial era, in which formal housing was provided only for the minority of residents (Polle’ and Hofstee, 1986). The neglected native population lived in the surrounding part of the city, and when the city expanded, kampungs were engulfed and became part of the larger urban area. A kampung is characterised with very high population density, lack of proper utilities such as water, sewer and electricity, informal and low quality housing construction, and a juxtaposition of residential and (informal) workplaces (Silver, 2008, p. 61). These two facets of urban dualism, informal housing and informal jobs, are actually inter-dependent. Often people engaged in informal jobs live in urban kampung which offer substantially low cost of living yet good accessibility to centre of economic activities.

Because EMRs extend beyond administrative boundaries, studies on EMRs have to pass the step of delineating regions that constitute major components of this

51 economically integrated unit. Jakarta EMR is the most convenient in the sense that it is simply JABODETABEK (McGee, 1994; Jones, 2002), whose boundaries have been largely maintained since the delineation of 1976. Delineation of other EMRs is probably more arbitrary. Bangkok EMR was defined as Bangkok Metropolitan Region plus changwats of Smaut Prakan, Pathum Thani, Samut Sakhon, Nakhon Pathom, Nonthaburi, Ayuthaya, Saraburi, Chachoengsao, Chonburi and Rayong while Manila EMR was defined as Metro Manila plus the provinces of Pampanga, Bulacan, Rizal, Laguna, Cavite and Batangas (Jones, 2002). The three broad zones of the EMR suggested by McGee and Greenberg (1992), namely the city core, the metropolitan area (also called peri-urban or inner ring) and the extended metropolitan area (also called outer ring) have been largely followed by empirical studies which aim to reveal the spatial characteristics of EMRs. While the city cores are defined by the city administrative boundaries, delineation between the peri- urban and the extended metropolitan area involves different criteria such as planning conventions, a scoring system and a combination of variables like population density and the share of population engaged in the primary sector (Jones, 2002). Figure 2.12 illustrates some of these results.

The delineation of zones allows further investigations on some important characteristics of EMRs and helps contrast the findings by the three zones and by points in time. The “region based” nature of urbanisation in Jakarta, Manila and Bangkok EMRs, as shown in Table 2.5., for instance, is revealed by highlighting the rapid increase of population outside the core cities within a ten year period. The actual rate of urbanisation in these regions is often underestimated due to population growth considerations based on the cities administrative boundaries (Jones, 2004, p. 129). When considering the urban core only, one would mistakenly conclude that urbanisation has been slowing, while the evidence would suggest that the actual growth has been shifted to the peri-urban regions and been operating at regional level.

(a)

(c)

(b)

Figure 2.12 Delineation of (a) Bangkok, (b) Jakarta and (c) Manila EMRs into three zones Source: Jones (2002).

Table 2.7 Population growth in Bangkok, Jakarta and Manila EMRs, 1990-2000 Population (thousands) Average annual Mega-Urban & Country 1990 2000 increase (%) Metro Bangkok 5,882 6,320 0.7 Rest of EMR 2,707 3,760 3.3 Bangkok EMR 8,590 10,080 1.6 Thailand 54,549 60,607 1.1

Metro Jakarta 8,259 8,385 0.2 Rest of EMR 8,876 12,749 3.7 Jakarta EMR 17,135 21,134 2.1 Indonesia 179,379 202,000 1.2

Metro Manila 7,945 9,933 2.3 Rest of EMR 6,481 9,855 4.3 Manila EMR 14,426 19,788 3.2 The Philippines 60,703 72,345 1.8

Source: Jones (2004).

One important implication of the peri-urbanisation pattern on the spatial structure of the region is the proliferation of housing estates, satellite towns and their associated amenities such as shopping malls and golf courses to meet the demand of medium income people moving into the surrounding areas outside the core city (Firman, 2004; Winarso, 1999; Ocampo, 1995; Dharmapatni and Firman, 1995). Developers also have seen it more profitable to develop housing estates in peri- urban areas due to a higher difference of land price before and after development (Winarso, 2000). Access to the core city, in which most economic activities have been maintained, is highly valued so that these housing estates tend to locate by arterial roads radiating out of the core city. Firman (2004), however, pointed out how developer-initiative development in Jakarta EMR had been speculative in nature; in addition to the mushrooming of housing estates, many parcels of land bought by developers had been left idle, contributing to sprawl in the peri-urban areas.

The suburbanisation of manufacturing and housing estates discussed above has been responsible in converting agricultural areas in the outer ring of EMRs (Kelly, 1999; Firman, 1996; Murakami and Palijon, 1995; Dharmapatni and Firman, 1994). Jones et al. (2000) highlighted the decreasing share of population engaged in agriculture and forestry and the increasing share of those working in urban sectors,

54 particularly manufacturing, within the period of 1980 and 1990 in the inner and outer rings of Jakarta and Bangkok EMRs (Table 2.8). Clearly desakota formations have been triggered by penetration of these urban activities into the predominantly rural areas, particularly in the outer rings. However, there have been limited empirical studies that have attempted to examine the nature and influence of urban activities on desakota formations and to identify explicitly the desakota areas, let alone the overall spatial structure of EMRs that includes such distinctive patterns. This is one of the key issues of this thesis.

Table 2.8 The share of employed persons by industry and zone in Jakarta and Bangkok EMRs, 1980 and 1990 1980 1990 Inner Outer Inner Outer EMR and Industry Core Ring Ring Core Ring Ring Jakarta EMR Agriculture, 1.9 18.4 30.9 1.1 5.5 23.5 forestry Mining and 0.7 1.3 1.3 0.6 1.2 1.6 quarrying Manufacturing 14.9 14.6 15.8 20.6 26.6 22.3 Utility 0.6 0.4 0.2 0.6 0.9 0.3 Construction 6.7 6.7 4.6 6.4 7.5 5.9 Trade 24.6 25.5 22.1 26.7 20.8 24.1 Transport 8.3 5.7 4.4 7.4 7.6 7.2 Finance 3.4 1.7 0.9 7.3 3.2 1.7 Services 38.8 25.8 19.9 29.2 26.6 15.7 Total 100% 100% 100% 100% 100% 100% N (thousands) 1,928 207 1,280 2,932 1,088 1,735

Bangkok EMR Agriculture, 5.5 45.1 63.9 2.6 27.4 50.0 forestry Mining and 0.1 0.7 0.5 0.1 0.3 0.4 quarrying Manufacturing 22.8 21.6 9.6 26.3 29.4 13.2 Utility 1.2 0.9 0.3 1.0 1.1 0.4 Construction 5.7 3.7 2.8 7.0 4.1 4.7 Trade 23.8 11.0 9.8 27.2 14.4 12.8 Transport 7.5 3.9 2.6 6.9 4.3 2.6 Finance 3.6 0.8 0.4 4.1 1.3 0.5 Services 29.7 12.4 10.2 24.8 17.7 15.4 Total 100% 100% 100% 100% 100% 100% N (thousands) 2,014 961 1,283 2,967 1,468 1,703

Source: Jones et al. (2000).

The limitation of the empirical works mentioned above has also been due to a lack of data. Investigations on the spatial structure of employment, i.e., spatial distribution and intensity of employment by sectors, in particular, has been 55 prevented because successive censuses do not collect information on the location of workplaces. Jones and Mamas (1996) and Jones et al. (2000), in their attempt at revealing changes of economic activities in Jakarta, Bangkok and Manila EMRs using census data in 1980 and 1990, were forced to limit their findings on those related to residential location such as changes of the share of labour by sector of industries, as shown in Table 2.8 above. This is in contrast to the growing empirical investigations on the spatial structure of employment in developed cities that have been motivated by concerns in urban sustainability, particularly that associated to travel. Hakim and Parolin (2008) argued that the Southeast Asian EMRs’ spatial structure had not been investigated empirically in proper detail, which was alarming considering the mounting urban sustainability problems that are often associated with the type, intensity and spatial distribution of economic activities across the region.

Empirical investigations on EMRs have prompted the latest hypothetical conceptualisation of the spatial configuration of EMRs in Asia (McGee, 2008). The model (Figure 2.13) shows the persistence of EMR as an outcome of urbanisation processes in Asia, in which an EMR covers a vast area of up to 200 kilometres radii from the core city. High order service employment such as finance, and high income residential areas (including apartments and condominiums), are still highly centralised in the core city, while industrial estates, satellite towns and housing estates (and their amenities) have been suburbanised in a largely disorganised manner. Low income housing is scattered both in the peri-urban area and the outer ring. Smaller unregulated industries, together with housing (and amenities) create a juxtaposition of urban and rural activities in the predominantly agriculture outer ring, and as a whole form the distinctive desakota zone. Flows of migrants and industries encompassing the three broad zones have also helped result in such a spatial configuration.

Desakota Zone

Peri-Urban Zone

Core City HI residential Airport Financial 50 km 50 km 150 km 150 km

Port

Residential Estates Agriculture

Industrial Estates Small Unregulated Industry Low Income Housing Migration Flows Satellite Towns Industry Flows

Figure 2.13 Spatial configuration of an Asian mega-urban region (ca. 2000) Source: McGee(2008).

Conclusions Urbanisation in Southeast Asia has been influenced by integration into the Asia Pacific global economic system. Rapid urbanisation has been experienced but it has been polarised in a few major cities. Combined with their colonial background and characteristics as developing countries, these major cities have experienced region- based urbanisation and evolved into a distinctive formation called extended metropolitan region (EMR). EMR spatial form is characterised with irregular, amorphous growth centred on its urban core, engulfing the surroundings including smaller cities and predominantly rural areas, forming intermingled urban and rural land-uses in peri-urban areas and beyond. The spatial configuration of Southeast Asian EMRs exhibits complexities, but there has been a lack of understanding with

57 respect to concepts and practices of urban spatial structure that have been recognised in the literature. The literature suggests that the spatial configuration of Southeast Asian EMRs falls beyond monocentric, polycentric or sprawl patterns of urban structure as recognised in the developed world. This is a notion that will be examined in this thesis.

Southeast Asian EMRs have been challenged by many sustainability problems ranging from the transformation of agricultural areas into urban uses, disturbance of the ecological system, water crisis, flooding, water, air and land pollution, land subsidence, acid rain and traffic congestion and noise. Literature shows that efforts in alleviating urban sustainability problems, travel in particular, have involved attempts at influencing urban spatial structure and its physical features in order to promote more sustainable transport. However, the urban spatial structure of Southeast Asian EMRs has not been empirically identified in any detail. Questions still remain on the type, spatial distribution and intensity of economic activities in these vast regions. It is hypothesised in this study that the methodologies to identify urban spatial structure and to measure the impacts of its characteristics on travel, as have been applied in developed country contexts can be utilised for the case of Southeast Asian EMRs.

3 Jakarta Metropolitan Area: urban evolution and planning

Introduction Chapter 2 discusses the theories and concepts on urbanisation in general and the Southeast Asian cases in particular. This chapter focuses on the Jakarta Metropolitan Area (JMA), which is the study area for the empirical analysis in this thesis. This chapter begins with a brief introduction on recent figures and past trends in terms of economic activities, population size and distribution, land use and transport in the JMA. Evolution of urban structure in the JMA is discussed from the perspective that the regional urbanisation of JMA has been largely realised through urban expansion of Jakarta and suburbanisation of its activities into the surrounding Bogor, Depok, Tangerang and Bekasi (BODETABEK) areas. A historical overview on urban structure evolution and planning is discussed within the wider timeframe that includes the origin of Jakarta in the pre-colonial era, its role as a colonial trading post up to the 19th century, its function as the colonial capital up to the first half of the 20th century, the early independence, and the independence and globalisation era that leads to regional urbanisation. While Chapter 2 helps to develop the research questions in the regional context of EMR,

59 this chapter provides a basis for the development of more specific research questions pertaining to the case study.

Recent figures and trends

Administrative boundary

The JMA refers to the Indonesian capital city of Jakarta and its surrounding neighbours: Bogor, Depok, Tangerang and Bekasi. The capital city of Jakarta is regarded as a special province for its status as the national capital. Bogor, Depok and Bekasi are parts of the Province of West Java, while Tangerang is part of the Province of Banten. Also referred to as JABODETABEK, which is the acronym of Jakarta, Bogor, Depok, Tangerang and Bekasi, JMA occupies an area of 6,580 square kilometres. The latest population census recorded 21.1 million people living in JMA (ROI CBS 2001a, 2001b, 2001c), accounting for around 10 per cent of the nation’s total population. Of this, Jakarta shares 8.3 million while Bogor, Depok, Tangerang and Bekasi (known also as BODETABEK) shares the other 12.8 million people.

There are seven comparable local governments in addition to the city of Jakarta (Figure 3.1). Three municipalities (kota) border Jakarta; Tangerang in the west, Depok in the south and Bekasi in the east. The other municipality is Bogor, located further south, about 45 kilometres from Jakarta. Three regencies (kabupaten) make up the outer parts of the JMA: Tangerang in the west, Bogor in the South and Bekasi in the east. Each of the cities, municipalities and regencies is made up of smaller administrative zones called kecamatan, and each kecamatan is made up of the smallest administrative zones called kelurahan or desa. The term kelurahan is used for those within cities and municipalities, while the term desa is used for those within regencies. There are 1,485 kelurahan and desa in the JMA1.

1 The figure excludes Kecamatan Thousand Islands, which consists of islands located at the Java Sea. Kecamatan Thousand Islands is part of the North Jakarta city. 60

Figure 3.1 Cities and regencies within the Jakarta Metropolitan Area

Source: Processed by author from SITRAMP GIS.

Economic activities

Gross regional domestic product

The region has experienced dramatic fluctuations in economic growth. Jakarta had enjoyed 9.1 per cent of gross regional domestic product (GRDP) growth from 1995 to 1996 before it shrunk to minus 17.6 per cent in 1997-1998 and minus 2.7 per cent in 1998-1999 following the financial and economic crisis that commenced in mid- 1997. During the past few years, however, JMA has shown a steady improvement in its economy. GRDP growth of Jakarta, West Java and Banten reached 3.9 per

61 cent, 4.7 per cent and 3.2 per cent respectively during 2000-2001 and improved further to 6.4 per cent, 6.4 per cent and 6.0 per cent respectively during 2006-2007.

JMA has traditionally maintained its large contribution to the Indonesian economy. A snapshot in 2005 showed that the region’s GRDP accounted for Rp. 425.2 trillion, which was more than 15 per cent of the national GDP. Jakarta shared more than 65 per cent of the combined GRDP, while the rest was contributed accordingly by Bekasi Regency (9.3 per cent), Bogor Regency (8.4 per cent), Tangerang Municipality (6.1 per cent), Bekasi Municipality (4.5 per cent), Tangerang Regency (3.8 per cent), Bogor Municipality (1.2 per cent) and Depok Municipality (1.1 per cent). As shown in Table 3.1, Jakarta’s economy relies on the tertiary job sectors (finance and trading in particular), while the three regencies of Bogor, Tangerang and Bekasi were more specialised in the secondary sector (i.e., manufacturing). The municipalities of Depok, Tangerang and Bekasi relied almost equally on the secondary and tertiary sectors while the Municipality of Bogor was more tertiary sector oriented. Within the regencies of Tangerang, Bogor and Bekasi, where a large portion of agricultural land within JMA is located, the primary sector contributed to 9.5 per cent, 6.5 per cent and 3.3 per cent respectively to their corresponding GRDPs. Within the JMA as a whole, however, the primary sector only accounts for less than 2 per cent of the combined GRDP.

Table 3.1 Contribution of cities and regencies to gross regional domestic product (GRDP) in JMA in 2005 GRDP Share within district Share within (Rp. (percentage) JMA Trillion) Primary Secondary Tertiary Total (Percentage) Jakarta 278.52 0.4 17.3 82.3 100 65.5 Municipalities Bogor 4.98 0.4 24.1 75.5 100 1.2 Depok 4.76 3.9 41.1 55 100 1.1 Tangerang 26.10 0.3 51.2 48.5 100 6.1 Bekasi 19.23 0.9 46.7 52.4 100 4.5

Regencies Bogor 35.89 6.5 61 32.5 100 8.4 Tangerang 16.19 9.5 55.5 35 100 3.8 Bekasi 39.56 3.3 80.4 16.3 100 9.3 JMA 425.23 1.6 32.1 66.3 100 100

Source: Compiled by author from various city and regency “in Figures”.

Foreign investments and trade

JMA has been an important node in the global production system of the Asia Pacific and the world since the 1970s as foreign direct investment (FDI) inflows have played a major role in the region’s economy. FDI approval in JMA increased from USD 0.6 billion in 1985 to USD 2.9 billion in 1990. Before the economic crisis, FDI approval to JMA had been increasing, with peaks in 1995 and 1996 at USD 12.1 billion and 14.0 billion respectively. The amount dropped to USD 4.2 billion in 1998 and USD 1.6 billion in 1999 but has been increasing again since that time (Figure 3.2). The amount reached USD 7.4 billion in 2007, with USD 5.5 billion of this amount for tertiary sector in Jakarta (Table 3.2). The secondary sector shared USD 1.4 billion with almost half of it hosted by Bekasi Regency. The importance of JMA in Indonesia’s economy is also shown from trade figures. The nation’s total values of export and import in 2005, including oil and gas, were USD 85.7 billion and USD 57.7 billion respectively. In the same year, Jakarta’s export and import values were USD 26.9 billion and USD 23.9 billion, accounting for 31.4 per cent and 41.4 per cent of the nation’s figures respectively.

16 14 12 10 primary 8 secondary 6 tertiary 4 USD billion (current (current billion price) USD total 2 0 1990 1995 2000 2005 Year

Figure 3.2 FDI approval in JMA, 1990-2007

Source: Processed by author from BKPM raw data of FDI approval by sector of industry and by city and regency in JMA, 1990–2008.

Table 3.2 Foreign direct investments in JMA, 2007

FDI Approval (USD thousand and percentage) Primary Secondary Tertiary Total Jakarta 239,252 95.3 332,571 23.9 5,481,227 87.1 6,053,050 81.6 Municipalities Bogor 750 0.3 36,200 2.6 63,918 1.0 100,868 1.4 Depok 0 0.0 9,399 0.7 15,150 0.2 24,549 0.3 Tangerang 0 0.0 50,941 3.7 40,428 0.6 91,368 1.2 Bekasi 0 0.0 70,455 5.1 3319 0.1 73,774 1.0 Regencies Bogor 6,823 2.7 81,695 5.9 56,459 0.9 144,977 2.0 Tangerang 2,914 1.2 133,601 9.6 527,060 8.4 144,977 2.0 Bekasi 1,200 0.5 677,453 48.7 102,527 1.6 781,180 10.5 JMA 250,939 100 1,392,315 100 6,290,088 100 7,414,743 100

Source: Processed by author from BKPM raw data of FDI by sector of industry and by city and regency in JMA, 1990–2008.

Labour force

There were 8.7 million workers2 in JMA in the year 2000 (ROI CBS, 2001a; 2001b; 2001c), of which 34.1 per cent were engaged in the services industry, followed by 19.1 per cent in manufacturing, 17.6 per cent in trade and 6.2 per cent in the agriculture/fishery industry (Table 3.3). There are variations among the cities and regencies. The majority of workers living in Jakarta were engaged in services (44.2 per cent) and trade (21.2 per cent). The municipalities of Bogor and Depok also showed a similar pattern, while the municipalities of Tangerang and Bekasi having larger shares of workers in manufacturing. Quite different figures are revealed in the three regencies. The largest share of workers living in the Regency of Bogor were also engaged in services, but the figures were balanced for agriculture/fishery (17.6 per cent), manufacturing (15.6 per cent) and trade (15.8 per cent). In the Regency of Bekasi, on the other hand, manufacturing (24.5 per cent) accounted for the largest share, followed with agriculture/fishery (21.3 per cent). Among the three regencies, Tangerang has the lowest share of workers in agriculture (5.1 per cent).

2 The Indonesian Census 2000 recorded those employed as population aged 15 years or over who worked within the previous week of census date. 64

Table 3.3 Distribution of labour force by sector of industry within cities and regencies

Number of Share within district (percentage) workers by residence Agriculture, (persons) fishery Manufacturing Trade Services Others Total Jakarta 3,715,685 1.3 16.4 21.2 44.2 16.7 100 Municipalities Bogor 281,798 3.3 13.0 17.5 33.6 32.5 100 Depok 443,417 3.8 14.2 13.9 43.0 25.1 100 Tangerang 657,259 2.0 31.1 12.6 23.8 30.6 100 Bekasi 710,741 2.4 19.5 14.9 36.7 26.5 100 Regencies Bogor 1,344,094 17.6 15.6 15.8 20.1 30.9 100 Tangerang 827,740 5.1 26.9 14.1 27.7 26.2 100 Bekasi 716,063 21.3 24.5 16.5 16.5 21.2 100 JMA 8,696,797 6.2 19.1 17.6 34.1 23.0 100

Source: ROI CBS (2001a); ROI CBS (2001b); ROI CBS (2001c).

Census data in Indonesia does not provide information about the spatial distribution of jobs because it does not collect information on the workplaces of respondents. Instead, it can provide information on the spatial distribution of workers by types of industry. Table 3.4 shows the share of workers across cities and municipalities in JMA by sectors of industry. Eighty per cent of the agriculture/fishery workers in JMA live in the three regencies; Bogor and Bekasi in particular. Manufacturing workers are distributed more equally among Jakarta (36.7 per cent), the four municipalities (26.6 per cent) and the three regencies (36.7 per cent). More than half of workers in trade and services live in Jakarta. While these figures show the absolute number of workers living in each district by sector of industry, they do not explicitly reveal the degree of a district’s specialisation of the labour force. One common measure of the degree of specialisation is the Location Quotient (LQ), calculated in this case as the proportion of workers engaged in a particular industry divided by the share of workers who live in that region. An LQ larger than 1 indicates that the area has a higher than proportionate number of workers in a particular industry. Table 3.5 shows the result of the LQ calculation. Jakarta is more specialised in the trade and services labour force, Bogor Municipality in trade and services, Depok in services, Tangerang Municipality in manufacturing, Bekasi Municipality in manufacturing and services, Bogor Regency

65 in agriculture/fishery, Tangerang Regency in manufacturing and Bekasi Regency in agriculture/fishery and manufacturing.

Table 3.4 Share of labour force by sector of industry across cities and municipalities, 2000

Share within JMA (percentage) Agriculture/ fishery Manufacturing Trade Services Others Jakarta 9.0 36.7 51.3 55.4 29.6 Municipalities Bogor 1.8 2.2 3.2 3.2 8.4 Depok 3.1 3.8 4.0 6.4 11.0 Tangerang 2.4 12.3 5.4 5.3 16.2 Bekasi 3.1 8.3 6.9 8.8 17.3 Regencies Bogor 44.1 12.7 13.8 9.1 39.2 Tangerang 7.9 13.4 7.6 7.8 19.4 Bekasi 28.5 10.6 7.7 4.0 16.5 JMA 100 100 100 100 100 N (persons) 536,244 1,660,120 1,533,841 2,962,185 2,004,407

Source: ROI CBS (2001a); ROI CBS (2001b); ROI CBS (2001c).

Table 3.5 Location quotient of labour force by sector of industry, 2000

Agriculture/ fishery Manufacturing Trade Services Jakarta 0.21 0.86 1.20 1.30 Municipalities Bogor 0.56 0.68 0.99 0.99 Depok 0.61 0.75 0.78 1.26 Tangerang 0.32 1.63 0.71 0.70 Bekasi 0.38 1.02 0.84 1.08 Regencies Bogor 2.85 0.82 0.89 0.59 Tangerang 0.83 1.41 0.80 0.82 Bekasi 3.46 1.29 0.94 0.49

Source: Calculated by author based on data from ROI CBS (2001a); ROI CBS (2001b); ROI CBS (2001c).

Population and housing

JMA has experienced rapid population growth during the past decades. JMA population has increased from 3.1 million in 1961 to 8.3 million in 1971, 11.9 million in 1980, 16.9 million in 1990 and 21.1 million in 2000. The later figure accounts for 10.2 per cent of Indonesia’s population. The rate of growth by districts, however has shifted substantially, so that the growth of Jakarta has slowed down while the rapid growth of BODETABEK has been maintained (Figure 3.3). Within the period between 1990-2000, the average annual population growth of Jakarta was only 0.2 per cent as compared to 3.7 per cent in BODETABEK. Of the 21.1 million people in the year 2000, 8.3 million lived in Jakarta and the other 12.8 million lived in BODETABEK. Table 3.6 presents the distribution of population and gross population density by cities and municipalities in JMA.

9 8 7 Jakarta 6 Bogor and Depok 5 Tangerang Bekasi population (million) 4 3 2 1 0 year 1960 1970 1980 1990 2000

Figure 3.3 Population growth by sub-region in JMA, 1961-2000

Source: Processed by author from ROI BAPPENAS-JICA (2004a).

Table 3.6 Population size and density in JMA, 2000

Area Population Population Density (km2) (persons) (persons/km2) Jakarta 650 8,347,083 12,842 Municipalities Bogor 119 750,819 6,309 Depok 200 1,143,403 5,717 Tangerang 158 1,325,854 8,391 Bekasi 210 1,663,802 7,923 Regencies Bogor 2,868 3,508,826 1,223 Tangerang 1,113 2,781,428 2,499 Bekasi 1,274 1,668,494 1,310 JMA 6,593 21,189,709 3,214

Source: Census 2000 as stored in SITRAMP database.

The availability of census data by the smallest administrative zone (i.e., kelurahan or desa) allows a more detailed examination of population density distribution across JMA. As shown in Figure 3.4, high density clusters of population are clearly seen within Jakarta and a smaller one further south in Bogor. The “corridors” of high population density were also formed connecting Jakarta to Bogor, Bekasi and Tangerang. As discussed later, the pattern of corridors is associated with both the pattern of the transport network and planning initiatives that have channelled rapid population growth to the east-west axis of the region.

Figure 3.4 Population density distribution by kelurahan in JMA, 2000

Source: ROI BAPPENAS-JICA (2004b).

JMA has historically been dominated by kampung but along with rapid economic growth within the past decades, formal housing has also been developed on a large scale (Winarso and Firman, 2002; Firman, 2007). The large scale housing projects called “new towns” have mainly occupied the east (Bekasi) - west (Tangerang) axis of JMA amid the dominant kampungs and some planned housing areas (Figure 3.5). Firman (2007) listed those occupying 1,000 hectare or more for the year 2003, including Bumi Serpong Damai (6,000 hectare), Kota Tiga Raksa (3,000 hectare), Kota Jaya (1,745 hectare), Bintaro Jaya (1,700 hectare), Citra Jaya (1,000 hectare), and Gading Serpong (1,000 hectare) in Tangerang; Cikarang Baru (5,400 hectare), Lippo Cikarang (3,000 hectare), Delta Mas (3,000 hectare) and Kota Legenda (2,000 hectare) in Bekasi; and Royal Sentul (2,700 hectare), Maharani Citra Pertiwi (1,680 hectare) and Resor Danau Lido (1,000 hectare) in Bogor. This list occupies a total of more than 33,000 hectares of land in JMA. Another concern is the gap between location permits and actual development of large scale housing projects. Soegijoko and Kusbiantoro (2001) reported that 121,631 hectares of land permit were given to developers between 1990 and 1995. However, by 1995, only 16,609

69 hectares of land had been built. Leapfrog development in the BODETABEK region was expected due to these vast allocated but undeveloped lands.

Figure 3.5 Planned housing, kampung and real estate in JMA, 2000

Source: Processed by author from SITRAMP GIS.

Land use and transport

Land use and land conversion

Apart from the mushrooming of large scale housing projects, land use in the JMA has also been characterised with industrial estates development, as a direct consequence of intensifying globalisation of the region in the past decades. Detailed land use surveys conducted in Jakarta and its adjacent areas in the year 1985 and 2000 revealed significant increases in planned housing and industrial areas at the cost of agriculture lands within that period (Figure 3.6 and Table 3.7). Figure 3.7

70 shows land use by 14 categories across the JMA based on partially updated land use map conducted by SITRAMP in the year 2000. The map shows that agriculture and open space land use still took the largest portion of the JMA, accounting for 51.1 per cent, followed by residential (27.9 per cent), industry and warehouse (2.4 per cent) and commercial and business (0.8 per cent) (Table 3.8).

Figure 3.6 Land use changes in selected surveyed areas in JMA, 1985-2000

Source: ROI BAPPENAS-JICA (2004c).

Table 3.7 Land use changes by selected categories in surveyed areas in JMA, 1985-2000

1985 2000 Change Land Use Description (hectare) (hectare) (percentage) Residential㧔planned housing㧕 10,816 20,900 93.2 Residential㧔kampung 37,865 43,167 14.0 Industry 4,621 7,346 58.9 Agriculture 44,074 23,501 -46.7 Source: ROI BAPPENAS-JICA (2004c).

Figure 3.7 Land use in JMA, 2000

Source: Processed by author from SITRAMP GIS.

Table 3.8 Land uses by category and by city and regency in JMA, 2000

Share within each district (percentage) Industry & Commercial Agriculture & Residential warehouse & business open space Others Total Jakarta 58.5 7.1 6.7 17.3 10.4 100 Municipalities Bogor 60.4 3.4 1.0 32.9 2.2 100 Depok 62.7 1.6 0.4 27.4 7.9 100 Tangerang 50.7 10.3 0.8 33.5 4.8 100 Bekasi 74.2 3.8 0.6 20.6 0.9 100 Regencies Bogor 15.4 0.5 0.0 54.1 29.9 100 Tangerang 29.8 2.5 0.3 60.3 7.2 100 Bekasi 20.2 2.8 0.1 65.7 11.2 100 JMA 27.9 2.4 0.8 51.1 17.9 100

Source: Processed by author from SITRAMP GIS.

Transport system and travel

Transport infrastructure in the JMA has been dominated by a road network that forms two circumferential arterial or toll roads around Jakarta and four radial toll roads radiating out of Jakarta towards the east, west, south and southwest directions (Figure 3.8). The rail network forms a semi-loop in the centre of Jakarta with four radial lines located along the same corridors as the toll roads. The existing rail network is the one built during the colonial era and has not been expanded since. Outside those arterial roads and toll roads, a higher density road network is found within the city of Jakarta. The road network in the municipalities and regencies has been limited by comparison, which partly explains the corridor pattern of residential and other economic activities along the trunk transport network. Public transport has been dominated by road-based modes including the newer bus rapid transit introduced in 2004 in Jakarta, large bus, medium bus and minibus. Apart from bus rapid transit, both road-based and rail-based public transport systems in the JMA has been characterised with low quality of service (Steinberg, 2007; ROI BAPPENAS-JICA, 1999). This makes public transport in

73 general less competitive to private transport modes including automobile and motorcycle; this is a trend that has increased sharply in the past few years.

Figure 3.8 Main transport network in JMA, 2008

Source: Processed by author from SITRAMP GIS.

From Sunda Kelapa to JABODETABEK: an overview of urban evolution and planning

Pre-colonial times and company town: up to 1800

It may not be easy to trace the contribution of the pre-colonial era in the spatial structure of JMA due to limited literature and the fact that the Dutch colonisation began with the demolition of the previous town, Jayakarta, and the development of a completely different design of the colonial city Batavia3. As argued by Abeyasekere (1987, p. 3), however, the evolution of Jakarta cannot be completely separated from its pre-colonial times. The role of Jakarta as a regional and international trading post dates back at least to the era of the local kingdom Pajajaran in the early 16th century which used Sunda Kelapa as its port4. The port, which had around ten thousand population (Surjomihardjo 1977, p. 9) was already functioning as an export point to Malacca, another busy export-import point in the region. Regional connections, moreover, already existed in this era with the kingdom, located at the now the city of Bogor, about 50 kilometres south of the port, as the regional centre. River transport, through Ciliwung river, was used by the kingdom to access its Sunda Kelapa port. Another regional connection, mainly for trading purposes, was made through streets connecting Pajajaran to Karawang at the east and to Serang (Banten) at the west.

An important political change occurred in 1511, when Malacca, the most important trading port in the Southeast Asian region at the time, was captured by the Portuguese. The Portuguese wanted to widen their political and economic influence in the region by establishing a fort and warehouse in Sunda Kelapa. Further efforts by the Portuguese to establish their influence in Sunda Kelapa was however denied when another local Kingdom, Demak, led by Prince Fatahillah, defeated the Portuguese navy and took over Sunda Kelapa from the Pajajaran Kingdom in 1527. The name of the port, and also the city around it, was changed to Jayakarta, or Jacatra, as Westerners called it. An important change in spatial structure following this event was the establishment of political power closer to the port. The

3 McGee (1967, p. 49) refers to Batavia colonisation as “transplants of the European town of the time”. 4 The port and the name are retained until today, but its function as the export-import point has been replaced by Tanjung Priok since the later phase of the colonial era. 75 conqueror, prince Fatahillah, decided to stay and build his own “kingdom’ by the port. The town of Jayakarta was developed by the Ciliwung river, probably just one or two kilometres south of the port.

Under Prince Fatahillah, or the “King of Jacatra”, as the Dutch called him, Jayakarta established close trading contacts with East India Companies of both the Dutch (known as VOC) and the British, and were given rights to build defence buildings and warehouses outside the town. The relationships became inharmonious as competition was heightened between the British, the Jayakarta people and the Dutch, and this led to war in 1618. This situation benefitted the Dutch in that the Governor-General J.P. Coen, with the help from another Dutch military post in Maluku, led the destruction and the conquest of town of Jayakarta, after which it was renamed to Batavia. This event started the era of Dutch colonisation in the city and ended the era of Jayakarta, which lasted less than a century.

The first important decision made by the Dutch was to establish Batavia town close to the port, about the same location as Jayakarta. The decision confirmed the orientation towards coastal trading at the time. This early Batavia was developed with Dutch city characteristics of straight streets and canals. The town remained protected by a wall up to the late 17th century and did not see much expansion at this time. There were slightly more than 27,000 inhabitants within the wall in 1673, with the Dutch accounting for just about two thousand while more than half of the figure were slaves. Other ethic groups included the Mardjikers, Chinese, Moors, Javanese, Malayans, Balinese and Eurasians (Abeyasekere 1985, p. 19-20). The Chinese, whom were given economic privileges, had a couple of establishments inside the town. The Chinese however was forced to live outside the wall following a riot in 1740. The special location was created for them at the south of the town, which was later developed as the China Town of Jakarta known as Glodok.

The expansion towards the south direction was begun when the Chinese requested the digging of Molenvliet canal in 1648 to support their economic activities. The digging of the canal, which is the current canal in the middle of Majapahit and Gajah Mada roads, was very influential in city growth as it led the development of the city southward. This canal, in addition to the increase in economy and security,

76 resulted in higher income inhabitants moving outside the city between the late 17th and the early 18th century to escape from the increasingly crowded Batavia. The growth of the city towards the south established a new residential district called Weltevreden (the current Kelurahan Gambir, Senen, Pasar Baru and Gunung Sahari). Along with the decline of the company (VOC), and the city’s stronger link to the hinterland, the city functioned more as the colonial capital rather than as a trading post. Weltevreden and its surroundings were seen as a good alternative location for the centre of the colonial capital.

Colonial capital: 1800s to 1950s

The 19th century marked very influential changes in the spatial structure of the city as Daendles, appointed Governor General in 1807, moved the centre of the colonial power further south to Weltevreden. Waterloopin (the current Lapangan Banteng) was decided as the place of the new palace. The palace was completed in 1826. The square Koningsplein (the current Merdeka Square, of which the National Monument or MONAS is located), built in 1818, was located at about the centre of Weltevreden. Figure 3.9 shows the location of the old Batavia and Weltevreden in the city plan made in 1858.

Along with the expansion of the city and increased economic activities, transport development was a necessity. Two important transport developments followed. First was the railway system. The first railroad section, Batavia – Weltevreden (about 6 kilometres length), was developed in 1871 connecting the old and the new centres. This was followed shortly by the development of Weltevreden – Meester Cornelis (the current Jatinegara) in 1872 (6 kilometres) and Meester Cornelis – Buitenzorg (44 kilometres), connecting Batavia and the present Bogor, in 1873. Second, the increase in sea trading as a result of the Suez canal opening in 1869 demanded faster service that could not be provided by the old Batavia port. As a consequence, a new port was developed about 8 kilometres east of the old port in 1883. Soon afterward the port was connected to the old centre by Batavia – Tanjung Priok railway (8 kilometres), developed in 1885. This established the first direct connection from agricultural hinterland in the south to the port.

The regional railway development at the time was mainly intended to transport exploited commodities from the hinterland to Batavia, as colonial interests had shifted towards agricultural production from the 1900s. Railway connections to agriculture hinterlands in the east, west and southwest were then developed: Batavia – Bekasi (27 kilometres) in 1887, Batavia – Doeri – Tangerang (23 kilometres) in 1899 and Doeri – Rangkas Bitung (76 kilometres) also in 1899. The era of forced cultivation followed shortly after the development of the radial railway network from the hinterlands of Batavia to the port. Further short railway connections around Batavia and Weltevreden were developed which included Kemayoran – Ancol (4 kilometres) in 1904, Tanah Abang – Krawang Ijin (6 kilometres) in 1904, and Tanah Abang - Manggarai (6 kilometres) in 1922. As a result, a railway loop has been established encircling Old Batavia – Weltevreden and commercial centres including Meester Cornelis (Jatinegara), Tanah Abang and Pasar Senen. Fig 3.9 shows the railway loop encircling the built-up areas in Batavia and Weltevreden in 1938.

The emergence of Koningsplein (the current Lapangan Merdeka) and its surroundings as the new urban centre was intensified during the 1920s when many large houses around it were replaced with company and government offices. The rapid growth of the city, however, was halted due to World War II and the Japanese occupation that commenced in 1942. Despite the political uncertainties after the end of the war (the Dutch wanted to regain its colonisation over the country while Indonesian national independence was proclaimed in 1945), city expansion resumed with the plan for a new “satellite city” further south of the now Jakarta (around 8 kilometres from the Merdeka Square). The plan was mainly intended for residential development to accommodate 100,000 people, but a few sections were also planned for commercial and industrial activities. Its close proximity to the city centre, however, had prevented it from being an ideally self- contained “satellite city.” As mentioned in Surjomihardjo (1977, p. 63), Kebayoran emerged mainly as a suburb for commuters working around the city centres. The plan also drew upon the concept of decentralising the rapid population growth towards the surroundings of Jakarta, more specifically to Tangerang at the west, Bogor at the south and Bekasi at the east.

Railway

Figure 3.9 Inner city railway network in Batavia and Weltevreden, 1938

Source: Surjomihardjo (1977), p. 54.

Early independence: 1950s to 1970s

The political turmoil between the Dutch and Indonesia ended as the Republic of Indonesia gains its supremacy in 1951 and accordingly decided upon Jakarta as its national capital. Consequently, urban growth was unprecedented following independence: the economic condition at the time was difficult and people from all

79 over the nation were looking for ways to improve their lives after centuries of economic oppression. Unfortunately, there were few job opportunities available following the end of World War II. Jakarta and a few other large cities such as Surabaya or Bandung became the target of migrants including those of peasants from the predominantly agricultural regions from the rest of Indonesia (see for example Jellinek, 1985). A study conducted in the early 1950s estimated an average influx of 100,000 migrants to the city per year within the next few years. Within the period 1948 to 1970, the population of Jakarta increased by more than three fold, i.e., from 1.2 million to 4.2 million. Not only was the number of jobs not adequate to meet demand, but housing provision and other infrastructures were not able to cope with the increasing number of migrants (Kelly and McGee, 2003). These explained two important characteristics of Jakarta, and that of other Southeast Asian developing cities at the time: the informal sector and urban kampungs.

Amid the rapidly increasing population, the period between 1959 and 1965 saw Jakarta adopt what was referred to as the “lighthouse policy” which focused on the development of landmarks and luxurious buildings that conformed to the political orientation called Guided Democracy under President Sukarno. This was characterised by development of the National Monument at the Merdeka Square, Sarinah Department Store by Thamrin road, the luxurious Hotel Indonesia at the meeting point of Thamrin and Sudirman roads, the Asian Games sport complex Senayan by the Sudirman road and the clover-leaf bridge at the intersection of Sudirman road and the Jakarta bypass road. This policy led to expansion of urban centres further to the south, forming a corridor along Thamrin-Sudirman roads towards Kebayoran. These modern buildings contrasted themselves to the kampungs that gave ways for modernisation but retained their existence just outside the corridor and elsewhere in the city.

Urban kampungs probably originated from as early as the first phase of the colonial era, when the demolition of Batavia’s wall in the late 17th century allowed the city to expand and engulf traditional housing of native peasants previously located outside the wall. As set out by Silver (2008, p. 61), the view of the Dutch was that kampungs were the clusters of low-quality, semi-permanent like housing that were made mostly of wood or bamboo and characterised by very high density population

80 and lack of utilities (including water, sewerage and electricity). There were not many changes to these kampungs because infrastructure and services provided in the city, including formal housing and utilities, were mainly intended for the European minorities. The growth of kampungs was outside the regulation of the Dutch up to the early 1900s. Only from the 1920s did the colonial government begin to consider kampungs as part of the city’s problems. Some programs to improve the conditions of kampungs were prepared accordingly, although there was not enough time for effective implementation due to the World War.

Following independence, these kampungs became the place to accommodate poor migrants. Kampungs were also segregated along the lines of ethnicity, as each ethnic group tended to support migrants from the same ethnic background; although kampungs became more and more multi-ethnic through time, along with the assimilation process (Surjomihardjo 1977, p. 73). In addition to kampungs, there were also homeless people who built squatter housing all over the city. These illegal occupants of land would later prove difficult to remove and they tended to develop into or integrate with existing kampungs. Kampungs already dominated Jakarta by the mid 1970s and the areas occupied by kampungs have increased because of inadequate supply of formal housings and the unaffordablility of housing to many migrants.

Informal sectors of the economies have naturally grown because formal sectors have not been able to provide jobs to all the migrants. “Hawkers” or petty traders were commonplace in Jakarta since the time of independence. The pedicab called becak that had appeared in the 1930s (Abeyasekere, 1985) became more popular as a transport mode and thus offered informal job opportunities for unskilled migrants as becak drivers. The number of becak drivers in Jakarta was estimated at 300,000 in 1971 (Castles, 1989, p. 237). The informal sector has been in fact existed side by side with the formal sector, just like modern buildings have been located side by side with inner-city kampungs.

Globalisation era: 1970s to present

JMDP and JABOTABEK Structure Plan 2005

A plan legalised in 1965, called the Jakarta Master Plan 1965-1985, guided for the development of Jakarta towards a radial direction in order to overcome population growth pressure. An outline of the plan included Tangerang, Bekasi and Bogor municipalities, Serpong and Depok. Although the term JABOTABEK (which later changed to JABODETABEK to include Depok as the new municipality) only came up in 1976, the idea of Jakarta’s expansion towards BODETABEK already initiated in this plan. The orientation towards radial development outside Jakarta was considered feasible due to the availability of a railway network connecting Jakarta and its surroundings. In terms of urban centres development within Jakarta, the plan seemed to maintain the already existing trend. The designated zones in inner city Jakarta strengthened the existing commercial corridors from Kota, Gajah Mada and Hayam Wuruk (the Molenvliet), Senen, Kramat up to Jatinegara (Mester Cornelis). Government offices were designated at around the Merdeka Square and business corridors were given along the Thamrin – Sudirman corridor which were already centres of development under lighthouse policy (Figure 3.10).

Figure 3.10 Thamrin corridor at the end of 1970s

Source: Abeyasekere (1985), between pages 142 and 143.

A new leadership took over national power in the late 1960s. The new regime prospered as the period from the mid 1970s to mid 1980s was regarded as the golden era of Jakarta and of the nation due to the oil boom. Economic growth in the 1970s was at an average annual rate of 8.1 percent (Hugo, 1996). The 1970s era was also recognised as a “construction boom” era (Abeyasekere, 1985), referring to corporate highrise buildings and shopping malls developed along Sudirman road, elite housing estates developed around the city centre such as those at Slipi and Kuningan and industrial estates in Pulo Gadung and along Raya Bogor road. The oil boom driven rapid growth ended in the mid 1980s and economic growth slowed down.

Meanwhile, a regional perspective of Jakarta and its surroundings came into consideration as it was already indicated that the future growth of Jakarta would encompass its administrative boundary. At this point the term “JABOTABEK” 83 appeared as the acronym of Jakarta and its neighbours Bogor, Tangerang and Bekasi. The limit of the region was decided by the watersheds of the Cikarang river to the east, the Cisadane river to the west and the mountainous area of Bogor to the south (Silver 2008, p. 117). The study to formulate the development planning of JABOTABEK, referred to as JABOTABEK Metropolitan Development Plan (JMDP), was initiated in 1973 by the Indonesian Ministry of Public Works with assistance of the Dutch planning team before taken over by the International Bank for Reconstruction and Development (IBRD). Presidential Instruction number 13, 1976, was issued as the legal basis for the conduct and the recommendations of the study. As set out in the Presidential Instruction, the aim of the plan was mainly to lessen the population pressure experienced by Jakarta. Although there was mention about the balance of development and urbanisation between Jakarta and BOTABEK, the Presidential Instruction clearly emphasised the distribution of population and housing as the target of the plan while identifying the capacity of the region for development.

The initial phase of JMDP, as summarised by Giebels (1986, p. 111-115), identified two alternatives for the development of JMA: concentric and linear systems, both of which relied heavily on the railway network connecting “bundled deconcentrations” of self-contained growth centres across JMA (Figure 3.11). The bundled deconcentration concept was derived from the Dutch’s Randstad city model as an alternative to ribbon development and the increasingly dominating “wild kampungs” in JMA. The JMDP report of 1973 recommended the linear system alternative for three reasons: first, the linear system is in accordance with the existing development pattern and trend in the region; second, the linear system required less infrastructure development and was therefore more cost efficient. For example, it made better use of the already existing railway network than the concentric model; and third, the linear system better preserved green areas between centres. In terms of administrative arrangement, the report proposed three alternatives: the establishment of a coordinating body between Jakarta and West Java Province; the establishment of a JABOTABEK Planning and Development Authority (which was the one recommended by the report); and the expansion of administrative boundaries of Jakarta into JMA.

Figure 3.11 “Bundled deconcentration” alternatives for JMA

Source: Giebels (1986).

JMDP study took about five years to complete before the final report was issued in December 1981. The final report recommended the structure plan for JABOTABEK for the year 2003. As summarised by Stolte (1995) there were five main strategies of JMDP: (1) an integrated growth and investment strategy for the region; (2) promotion of east-west development to avoid groundwater contamination of the water catchment areas in the south; (3) minimization of developments in the environmentally sensitive wetlands at the northern coasts; (4) priorities for utility and infrastructure services including roads, water, sanitation, kampung improvement, and flood protection; and (5) a mechanism to arrange the informal developments into residential and industrial areas. Urban centres and functions were recommended based on the suitability of land use for development (Figure 3.12). The five categories based on suitability for development were Zone I (Avoid Urban Development), Zone II (Agricultural Intensification, Limit Urban

Development), Zone III (Major Urban Development, Agricultural Intensification), Zone IV (Limit Urban Development, Agricultural Intensification) and Zone V (Upland Forest Plantations, Recreation and Conservation, Avoid Agricultural Intensification). Zone III was identified as the most suitable area for urban expansion so that the study prioritised future development of JMA along the east- west axis in this zone. As conceptually drawn from the beginning of the study, railway was prioritised for the trunk transport in JMA, but arterial and toll roads network were also outlined.

Figure 3.12 JMDP’s five potential development zones

Source: ROI MPW(1981) as reproduced in ROI Jakarta (1985).

Unfortunately, most of the recommendations of JMDP have not been implemented. Firstly, the weakest alternative was chosen for the administrative arrangement: a coordinating body called BKSP (Badan Kerjasama Pembangunan)

JABOTABEK was established as part of the Presidential Instruction number 13 in 1976 to facilitate development cooperation between Jakarta and West Java Provinces. Secondly, the recommended railway transport did not seem to conform to the non-oil export strategy begun in the mid 1980s. Tight competition among Southeast Asian countries to attract foreign investment had forced competing countries to provide fast yet cost efficient transport infrastructure that met the requirements for export-based industries. Toll roads were simply an easy option to serve freight movements from the industrial estates to the port. The Ministry of Public Works issued a planning strategy for JMA called Structure Planning for Metropolitan JABOTABEK 1985-2005 in July 1985, just four years after the completion of JMDP study. The Structure Plan referred mostly to the five potential development zones and centres identified in JMDP. It however updated the centres, their functions and their population targets (Table 3.9). For example, the target population for Serpong in 2005 was updated from 135,000 people to 800,000, to accommodate the Bumi Serpong Damai large scale housing project. A substantial change was made in terms of the trunk transport system. As seen in Figure 3.13, what was planned in 1985 in terms of a trunk road network is generally what JMA has today. The railway network that was prioritised in JMDP was not shown in the JABOTABEK Structure Plan 2005. Toll roads, on the other hand, were accordingly developed. The toll road to the south (Jakarta –Bogor) was opened in the early 1980s, followed by Jakarta-Tangerang to the west in the mid 1980s and Jakarta- Bekasi to the east in the early 1990s (Susantono, 1998).

Table 3.9 Proposed centres in JMA in 2005 Population target Centre in 2005 Function of centre Government, manufacturing, regional trading, Jakarta 12,000,000 services, transport services Bogor Region Bogor Municipality 1,000,000 Government, education, manufacturing, regional trading, services, research Depok Municipality 370,000 Government, education, cluster of commuters, services, trading Cibinong 200,000 Government, services, trading Leuwiliang 150,000 Mining, services Parung 100,000 Regional trading, services, animal husbandry Cileungsi 200,000 Large and medium scale industries Jasinga 75,000 Residential, services Jonggol 75,000 Services, animal husbandry Parung Panjang 75,000 Construction material industry, services Kecamatan centres 630,000 Services, local trading, agriculture Desa centres 1,965,000 Agriculture, household-scale industry

Tangerang Region Tangerang Municipality 850,000 Government, education, manufacturing, regional trading, transport services Serpong 800,000 Research, education, residential Balaraja 100,000 Regional trading, medium and small scale industry, construction material industry, services Cikupa 100,000 Large and medium scale industry, services Pasar Kemis 60,000 Medium and small scale industry Ciputat 250,000 Residential, services, local trading, education Pondok Aren 150,000 Residential Curug 100,000 Local trading, services, medium scale industry Kecamatan centres 360,000 Services, local trading, agriculture Desa centres 1,060,000 Agriculture, household-scale industry

Bekasi Region Bekasi Municipality 800,000 Government, regional trading, services, education Cikarang 400,000 Regional trading, large, medium and small scale industry, services Pondok Gede 150,000 Residential Jatiasih 100,000 Residential Bantar Gebang 100,000 Services, local trading, small scale industry Tambun 100,000 Services, local trading, industry Kecamatan centres 340,000 Services, local trading, agriculture Desa centres 1,010,000 Agriculture, household-scale industry

Source: ROI MPW (1985).

Figure 3.13 Reliance on road based transport network in JMA as planned in 1985

Source: ROI MPW (1985).

The rather comprehensive JMDP study had therefore merely been regarded as a set of recommendations since the outcome was never legalised. The JABOTABEK Spatial Structure 2005 issued by the Ministry of Public Works in 1985 referred to the five potential development zones and updated the centres foreseen in JMDP, but did not adopt the recommended railway transport options. Another spatial structure planning that referred to JMDP was the subsequent Jakarta Master Plan 2005 issued in 1985. The plan also referred to JMDP’s five potential zone development and therefore fostered city growth towards an east-west direction while limiting developments towards the aquifer recharge zones at the south (ROI BAPPEDA DKI, 1985). The plan also guided the location of heavy and medium scale industries at the West Development Zone at Rawabuaya and the East Development Zone at Pulogadung, which was expected to trigger city expansion

89 towards the east-west direction. According to Stolte (1995), JMDP has also been referred by donor countries with regard to location of projects funded by loan or grant in JMA. The Province of West Java did not adopt JMDP for its structure plan, which partly explains the failure of the emergence of substantial employment centres outside Jakarta, as recommended in JMDP.

Desentralisation of manufacturing had been realised along with the increasing trend of foreign direct investments in the secondary sector, hosted mainly by BODETABEK. The decision of developing Jakarta-Merak and Jakarta-Cikampek toll roads on the west and east sides of Jakarta had proven helpful in facilitating the emergence of large scale manufacturing centres in Cikupa, Tangerang Regency (west) and Cikarang, Bekasi Regency (east) as recommended both in JMDP and JABOTABEK Spatial Structure 2005. Cumulative approved foreign and domestic investment in the secondary sector hosted by BODETABEK during the 1990-1997 period reached USD 17.2 billion, of which USD 9.9 billion and USD 3.2 billion were hosted by Bekasi Regency and Tangerang Regency, respectively.

Besides manufacturing, investments in JMA had also focused on services, and the finance and property sectors (Firman, 1998). In the property sector, large scale housing development had penetrated the periurban areas of JMA from the early 1980s. Within the period 1983-1992, almost 61,000 hectares of land had been approved for housing development in BODETABEK. Of that figure, more than 54,000 hectares were located in Tangerang Regency, showing the strong influence of the Jakarta-Merak toll road, opened in the mid 1980s, in the location decisions of these housing projects.

The new towns however were criticized for having to failed to create jobs to balance those clustered in Jakarta. Housing in the new towns was intended for medium to higher income residents whose jobs were more likely to be located in the Jakarta CBD, namely the Thamrin-Sudirman and the emerging Golden Triangle at Kuningan. Combined with the failure of not implementing the public transport system recommended by JMDP, the growth of dormitories for medium to higher income households who would commute by private cars, would actually worsen traffic conditions along the radial transport corridors radiating out of Jakarta. The rapid growth of demand for office space at the CBD of Jakarta (i.e., the Thamrin-

Sudirman corridor) revealed the strengthening of the Jakarta CBD for the location of higher order services. Meanwhile, the other designated and newly emerging “subcenters” did not emerge as a contender to the Jakarta CBD as the place for office jobs. Manufacturing, on the other hand, was given priority as the leading sector for the growth in BODETABEK as part of the export-oriented economic policy at the time. In 1989, a further 6500 hectares of land in Bogor, Tangerang and Bekasi was intended for industrial estate development. While manufacturing investments and large scale housing projects have been suburbanised to BODETABEK, finance and services investments have been largely maintained in Jakarta. The role of the Thamrin-Sudirman corridor as the regional central business district (CBD) of JMA was reflected in the increasing pattern of office floor supply from the early 1980s to 1990s (Figure 3.14).

2,000 1,800 1,600 1,400 1,200 1,000 800 600 400 200 Total supply (thousand sq. metres) 0 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 year

Figure 3.14 Total office supply in the Jakarta’s CBD, 1982-1992

Source: ROI MPW (1993).

JMDPR and the economic crisis

It was clear that the JMDP study had not been effective in guiding the growth of JMA, especially with regard to employment decentralisation within the “bundled deconcentration” strategy. It was also realised that the five potential development

91 zones had generally failed in guiding urban growth, as large scale residential development and manufacturing had penetrated zones intended as agricultural purposes and had converted significant portions of paddy fields into urban areas. Another failure in the implementation of JMDP was in transport network development, as the priority had been given to toll and arterial roads instead of the original recommendations of mass public transport. Pressure was also apparent from environmental degradation associated with non-compliance of urban growth to the various plans and studies.

The Indonesian Government therefore felt necessary to review the JMDP and formulate a set of new recommendations for the future growth of JMA. A study called JMDPR (JABOTABEK Metropolitan Development Plan Review), was conducted to fulfil these objectives. JMDPR, completed in 1993, identified the constraints of the region in terms of water supply and infrastructure. Problems were identified with regard to maintaining adequate supply of groundwater. First, groundwater level had been declining, indicating that the discharge rate was higher than the recharge rate. Second, the salinity level of groundwater had been increasing, due to sea water intrusion. Third, substantial land subsidence had been identified especially in areas where groundwater was excessively extracted. The figures were particularly concerning in the northern part of Jakarta, with some areas experiencing an average of up to 5 centimetres land subsidence per year (Figure 3.15). Fourth, a shallow aquifer had been polluted by domestic and industrial wastes. Fifth, although in JMA the total amount of groundwater that was safe was greater than demand, some areas were over-used due to the unregulated nature of groundwater extraction in the region. In addition, JMDPR highlighted issues with regard to surface water supply for irrigation, environmental performance of Jakarta bay, privatisation of water supply, continuation of agricultural land conversion to urban land uses, flooding, solid waste management and sanitation.

Figure 3.15 Land subsidence in Jakarta within 1974/78 to 1989/90 period Source: ROI MPW (1993).

In formulating future growth for JMA, JMDPR came up with three options, namely “self-sustaining new towns (paradigm AAA)”, “finger city (paradigm BBB)” and “linear city (paradigm CCC)” (Fig. 3.16). The decision was made based on a scoring system that takes into account a few factors such as conformity to environmental zoning, the risk of urban sprawl, requirement for new infrastructure, preservation of agriculture and greenbelts, traffic burden on Jakarta and difficulties in enforcement. Eventually, the JMDPR study recommended what it referred to as “paradigm CCC” which guided the development of JMA towards linear development along the trunk transport corridor radiating out of Jakarta. The recommendation showed that the trend scenario had been too difficult to alter so the study instead looked at the best that can be achieved from the trend pattern of development. Another major consideration was of course the sunk costs of the

93 already developed infrastructure, including toll roads that were just developed during the 1980s and 1990s and the required cost of developing new infrastructure if one of the other three strategies was adopted. Also, it was thought that “paradigm CCC” still conformed to the environmental point of view, including concerns on damaging water recharge areas at the southern part of JMA and the sensitive coastal zones to the north.

There were five features of the recommended “paradigm CCC”: first, the urban centres identified and recommended by the study were proposed to form a polycentric urban structure in JMA (Figure 3.17); second, the idea of developing modern mass rapid transit (MRT) that had emerged in 1980s was recommended as part of the future transport system of JMA; third, green wedges were to be preserved, but realising the pressure of green space against urban penetration, “high value added” green space (golf courses among others) was recommended; fourth, higher land use diversity was promoted for its association to more favourable commuting patterns and less pressure on the environment, less infrastructure requirement, more socially inclusive to the informal sector and kampung, and more self-sustaining growth; and fifth, an urban management and strategy were introduced to promote more comparable development resources between Jakarta and BOTABEK.

Figure 3.16 JMDPR’s three alternative development paradigms for JMA

Source: ROI MPW (1993).

Figure 3.17 Recommended urban centres in JMA in 2010

Source: ROI MPW (1993).

While JMDPR’s recommendation was made in the argument of preservation of agriculture and greenbelts, the option to expand east-west toll road corridors revealed a strong motivation to maintain the nation’s and JMA’s heavy reliance on manufacturing and an export-oriented economy. The mass transit development recommended in JMDP was revived into the concept of modern mass rapid transit (MRT) in addition to improving and upgrading the existing railway network. The overall urban structure for JMA was described as a linear city supported by a combination of extensive toll road systems and mass public transport consisting of the heavy and light railway network (Figure 3.18). The JMDPR recommendations

96 were, however, practically ineffective given the economic crisis that hit the nation severely in mid-1997.

Figure 3.18 Trunk transport system in JMA as recommended in JMDPR

Source: ROI MPW (1993).

The post-crisis and JABODETABEKPUNJUR

Environmental degradation, frequent flooding and land sliding in JMA has placed enormous pressure on local governments in JMA and relevant central government agencies with regard to urban growth patterns in the region, in particular non- compliance of land use to planning and regulations. A presidential regulation was issued in 2008 with the extension of the region towards Puncak and Cianjur, where land conversion to villas, resorts and hotels has been blamed for the frequent flooding problems in Jakarta. As such, the region is called JABODETABEKPUNJUR to include the two additional zones. The Presidential Regulation number 54 in 2008 was used as the legal basis for enforcement of the JABODETABEKPUNJUR development zoning. While the development restriction zones were somehow similar to those in JMDP, this recent approach is

97 more aggressive in that it was directly legalised as a regulation to be adopted by the spatial structure plan produced by local governments in the region.

The regulation was preceded by governmental decision number 26 2008 which decided JABODETABEKPUNJUR as a “National Strategic Region”, defined as a region with prioritised spatial structure due to its importance on national supremacy, economic, social, culture and/or environment, and is regarded as a world heritage. There are three major zonal categories based on the development restriction: Zone B (B stands for Budidaya, meaning developed), Zone N (N stands for Non-budidaya, meaning non-developed, or protected) and Zone P (P stands for Penyangga, meaning buffer). Each zone was divided further into more detailed categories (Table 3.10).

Table 3.10 JABODETABEKPUNJUR development zoning

Code Development guides, land uses or functions

N (non-developed) N-1 Not to be developed; existing developed land use in the area to be removed; protection forest; research; forest along riverbanks, lakesides, sea coasts and steep hill; water conservation forest; mangrove. N-2 Not to be developed; eco-tourism; cultural, floral and fauna preservation and conservation; research. B (developed) B-1 High density (urban) residential; trade and services; non-polluted, market oriented light industry. B-2 Medium density (rural) residential; agriculture and farming; labour oriented industry. B-3 Low density residential (low floor area ratio); agriculture and farming. B-4 Low density residential; wet- or dry-farming agriculture; cultivation, fishery, agricultural-industry based animal husbandry; production forest. B-4/HP B-4 zones decided as production forest areas under regulations. B-5 Irrigated agriculture. B-6 Low density residential with 50 per cent maximum floor area ratio; low environmental carrying capacity, production forest; land uses subject to permit by the National Spatial Structure Coordinating Agency. B-7 Low density residential with 40 per cent maximum floor area ratio; low environmental carrying capacity, production forest; land uses subject to permit by the National Spatial Structure Coordinating Agency. B-7/HP B-7 zones decided as production forest areas under regulations; production

forest under regulations. P (buffer) P-1 As to preserve functions of N-1 zones. P-2 As to preserve functions of N-1 and P-5 zones. P-3 As to preserve functions of B-1 zones. P-4 As to preserve functions of B-2 and B-4 zones. P-5 As to preserve functions of N-1 and B-1 zones.

Source: Presidential Regulation No. 54 (2008).

The designated development zones are shown in Figure 3.19. The existing urbanised areas, including the Jakarta city and the surrounding municipalities are designated as B-1. Urban centres (marked as yellow circles) in the figure are assigned to zones of B-1 and mostly located along the existing and proposed toll road network in the region. Transport network development recommended in JABODETABEKPUNJUR still relies heavily on toll road, as shown in Figure 3.20 (the proposed new toll road network is marked as dashed lines). The proposed new toll road network forms a second ring outside the existing Jakarta Outer Ring Road (JORR) toll network and extensions at the eastern and western parts of the region, where manufacturing estates are located, which can be explained given the “back on track” trend of foreign investment inflow to the region, as shown in the previous chapter.

Figure 3.19 Development zoning in JABODETABEKPUNJUR

Source: Attachment to Presidential Regulation No. 54 (2008).

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Figure 3.20 Transport network for JABODETABEKPUNJUR

Source: Attachment to Presidential Regulation No. 54 (2008).

A transport master plan study conducted by BAPPENAS-JICA (2004a) has highlighted unsustainable transport conditions in the region. It is found that between 1985 and 2002, commuting trips from BODETABEK to Jakarta has increased by 10 times, which caused daily severe traffic congestion along the radial transport network and within Jakarta, associated with waste in fuel consumption and time. The study also found that road side PM10 in the region exceeded the international standard by four times, and both road side and background noise levels LAeq also exceeded the international standards. High rates of road traffic accidents are also shown, with fatality rate on toll roads, calculated as the number

101 of fatality per million vehicle kilometre, is nearly six times as high that in developed countries.

Conclusions This chapter confirms the importance of the region within the country in terms of the economy and population size. During the past few years, it has been shown that rapid population growth in the JMA has been shifted towards its peri-urban region. Such a trend has been encouraged by planning initiatives which aim to reduce population pressure in the Jakarta city. Urban expansion in the peri-urban areas has also been facilitated by the trunk transport connecting Jakarta to its surroundings. Urban expansion pattern, however, has been uncontrolled that the rapid population growth in the peri-urban areas has been manifested in the forms of sprawl-like manufacturing and large-scale housing estates, located mainly along the east-west toll road corridor. As shown from the land-use survey conducted by the SITRAMP study (ROI BAPPENAS-JICA, 2004a), rapid urban expansion towards the surroundings of Jakarta has been associated with massive land conversion from agricultural land uses to urban uses.

Overview of urban evolution and planning in the JMA provides insights into the growth of the region from as early as pre-colonial era up to the current globalisation time. The colonial background of the region has been shown to have a very strong influence on its spatial structure. Imbalance development that has favoured the city of Jakarta dates back to coastal trading orientation of the colonial power. When the interests were shifted towards agriculture exploitation, trunk transport network, in the form of railway, was established in the form of radial lines connecting agricultural areas to the export-import point. Infrastructure development had been limited and concentrated as it was mainly intended to serve the minority of people. Rapid population growth was unprecedented following the independence that planning initiatives since then have focused on channelling out the population growth outside the urban cores. Centralised nature of political orientation in the early independence, however, tended to strengthen the attractiveness of the city of Jakarta, through concentrating the development in its central business district.

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A few planning efforts have been made to channel the urban growth of the JMA. While the earlier planning initiative recommended public transport development and urban growth of JMA in the form of “bundled deconcentration”, somewhat similar to polycentric urban structure, later planning and implementations in the JMA have favoured toll road as its trunk transport that seem to support the role of the region as a production centre within the global economic system, as implied within the Southeast Asian EMR concept. While planning initiatives are often motivated by serious environmental problems facing the region during the past decades, such as flooding and drought, transport sustainability issues do not seem to be properly considered and assessed.

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4 Research design

Introduction

This chapter presents the research design for the thesis that includes formulation of research questions based on the literature reviews conducted in the previous two chapters, and the methods employed to address the research questions. Data and methods used for the empirical analysis are discussed and the overall flow of the empirical analysis is presented as a framework showing the relationships between the research questions, data, methods and results. The research design provides a detailed and rationale guides for the empirical analysis conducted in the next two chapters.

Formulation of research questions

Chapter 2 has shown that urbanisation in developing Southeast Asian countries differs significantly from that of the developed world. In many developed urban regions, urbanisation outcomes, in terms of spatial structure, has been broadly categorised into monocentric, polycentric or sprawl patterns. It has been shown that the case is more complex for Southeast Asian developing cities, in which the urbanisation processes have led to the formation of the extended metropolitan region (EMR). The development of the Southeast Asian EMR concept has been followed by empirical investigations on its characteristics that have been largely identified by three broad zones, namely the urban core, peri-urban (inner ring) and 104 extended metropolitan area (outer ring). What has been missing is empirical investigation of the urban spatial structure of EMR that is comparable in the level of detail with those conducted on developed cities. Given Jakarta Metropolitan Areas (JMA) as the case study, the first set of research questions is centred on the identification of the spatial structure of employment in the JMA with reference to the concept of Southeast Asian EMR:

Serious sustainability problems have been identified in Southeast Asian developing cities. Empirical studies on developed cities frequently assess travel impacts of urban spatial structure as efforts in promoting more sustainable urban transport. As discussed in Chapter 2, both urban spatial structure and its physical features, or urban forms, influence travel patterns and hence transport sustainability performance. Journey to work patterns, as measured by commuting distance and travel mode choice have been associated with the distribution and intensity of employment and residential labour force, and their spatial characteristics. The second set of research questions enquires about the nature of these influences for the case of the Southeast Asian EMR:

Finally, both the inquiries on urban spatial structure and impacts on travel in JMA are expected to contribute to improving urban sustainability performance through

105 policy recommendations. The final research question enquires about policy implications of the findings:

3.a How the empirical findings on the spatial structure of employment and journey to work patterns in JMA can be assessed against theories and practices as found from the literature? 3.b How the spatial structure of employment and journey to work patterns in JMA, based on the findings, are associated with the success or failure of planning efforts in JMA? What lessons can be learnt? 3.c What policy recommendations can be proposed to improve urban and transport conditions in JMA?

Data sources

Home interview survey and population census

The Home Interview Survey (HIS) conducted in 2002 (see Appendix 2 for the HIS survey forms) under the Study on Integrated Transport Master Plan for JABODETABEK (SITRAMP) Phase -2 (ROI BAPPENAS- JICA, 2004a) was a standard person-trip survey intended to collect information on travel decisions made at the level of household members for the purpose of the conventional four- step travel demand modelling process (Ortuzar and Willumsen, 1994). The survey collected information on socioeconomic profiles and travel decisions made by more than 166,600 households, which met the standard 3 per cent sampling rate for such a purpose. The “random with equal interval” sampling method, involving random selection of candidates for samples followed with the final selection of respondents based on equal interval number, was adopted in the survey (ROI BAPPENAS- JICA, 2004d). The distribution of the number of samples by cities and regencies is shown in Table 4.1.

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Table 4.1 The Home Interview Survey sample conducted in 2002

Number of Households Number of Samples Jakarta 2,253,709 67,606 Municipalities Bogor 187,259 5,615 Depok 293,378 8,803 Tangerang 364,085 10,924 Bekasi 444,203 13,322 Regencies Bogor 856,394 25,687 Tangerang 696,450 20,905 Bekasi 456,015 13,774 JMA 5,554,493 166,636

Source: ROI BAPPENAS-JICA (2004d).

The Home Interview Survey collected and organised information from sample respondents into three groups, namely a household profile, household members’ profile and travel decisions of the household members (Table 4.2). Household profile includes residential address at kelurahan or desa level, types of house (Table 4.3), family size, vehicle ownership by types of vehicle, household monthly income and expenditure and the previous address. Household members’ profile include age, gender, education level, social activity categorised as full-time employee, part-time employee, student by course levels and unemployed, type of occupation, job industry, land-use type of workplace (see Table 4.4 for occupation, job industry and land-use categories used in the survey), workplace or school address at kelurahan or desa level, monthly income, availability of private vehicle by vehicle types and transport expenses. Travel decisions made by household members’ include the number of trips made on the date of survey, origin and destination addresses of trips at kelurahan or desa level, land-use types of trip origin and destination, trip purpose categorised into work, school, shopping, business, private, to home and others, trip departure and arrival times, mode of travel (Table 4.5), and travel cost.

The Home Interview Survey provides many kinds of information including that on population characteristics. However, this study also refers to population census for more reliable data on population in the JMA. Figures on population by kelurahan or

107 desa is available for the 1990 and 2000 censuses, allowing empirical analysis involving changes in the number of residents at kelurahan and desa levels within that period.

Table 4.2 Data collected under the Home Interview Survey 2002 Travel decisions of household Household profile Household members’ profile members Address Age The number of trips made on the date of survey Type of house Gender Trip origin and destination addresses Family size Education level Land use types of origin/destination Vehicle ownership Employment/student Trip purpose Monthly income Occupation Departure and arrival time Monthly expenditure Job industry Mode of travel Previous address Address of workplace or school Travel cost; parking fee Land use type of workplace Monthly income Availability of private vehicle Transport expenses Source: ROI BAPPENAS-JICA (2004d).

Table 4.3 Classification of housing types in the Home Interview Survey 2002 Type of house Characteristics Permanent A One or two story house for the high class, constructed with good materials, possessing garages for more than two cars and large garden. Permanent B One or two story house of 100-200 m2 for the upper middle class, constructed with good materials, possessing garages for one or two cars and comparatively large garden. Permanent C One story house of 45-100 m2 for the middle class with a garage for one car or no garage and small garden. Permanent D Low-cost house of 21-36 m2 for the lower middle class only with small garden. Semi-permanent House partly made by wood with very small garden and low-cost fence. Temporary House mostly made by wood without garden. Source: ROI BAPPENAS-JICA (2004d).

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Table 4.4 Occupation, job industry and land-use facility categories in the Home Interview Survey 2002 Occupation categories Job Industries Land-use types 1. Professor, manager, 1. Agriculture, forestry, 1. Residence or dormitory director, etc. fishery 2. Government or public 2. Industry owner, retail 2. Mining office owner, etc. 3. Manufacturing 3. Business office, company 3. Engineer, doctor, 4. Construction 4. Educational facility accountant, pilot, etc. 5. Transportation and 5. Religious facility 4. Teacher, lecturer communication 6. Medical facility 5. Administration staff 6. Banking, insurance 7. Accommodation, 6. Technician 7. Wholesale and retail entertainment facility 7. Waitress traders 8. Restaurant 8. Vendor, salesman, etc. 8. Electricity, gas and 9. Retail, traditional market, 9. Handyman (carpenter, water supply supermarket jewellery, etc.) 9. Central government 10. Supermarket 10. Construction, station, services 11. Shopping mall or shopping port, warehouse 10. Local government plaza 11. Labourer services 12. Grocery market 12. Public transport driver 11. Rental 13. Factory 13. Private driver 12. Service industry 14. Warehouse, storage facility 14. Housekeeper, office boy, 13. Military and police 15. Transport and gardener, etc. 14. Others communication facility 15. Farmer, fisherman, etc. 16. Supply and disposal facility 16. Security guard 17. Recreational or sport 17. Others facility 18. Park, natural environmental area, etc. 19. Agricultural, forestry and fishery areas 20. Construction site 21. Others

Source: ROI BAPPENAS-JICA (2004d).

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Table 4.5 Transport modes in the Home Interview Survey 2002 Broad category Detailed transport modes

Non-motorised Walking to final destination Walking for transfer Bicycle Becak (three-wheel bicycle taxi)

Private mode Motorcycle Automobile (car, wagon) Pickup truck Truck Taxi Ojek (motorcycle taxi)

Public mode Train (express) Train (economy) Air-conditioned large bus Non-air-conditioned large bus Medium bus Minibus (Angkot or mikrolet) Omprengan (unlicensed public transport) Company bus, school bus, tour bus Others Source: Adapted from ROI BAPPENAS-JICA (2004d).

GIS layers

Zoning system

There are a few zoning systems for the JMA that are stored in geographic information system (GIS) format, including those at city and regency, kecamatan, kelurahan and desa, and traffic analysis zone (TAZ) levels. In order to benefit most from the data available and to minimise bias due to zonal aggregation, it has been decided to use the smallest zoning system available, i.e., the one at kelurahan and desa level, for the empirical analysis. This zoning system, consisting of 1,485 zones of kelurahan and desa in JMA in the year 2000 (Figure 4.1), is much finer than that at TAZ level (Figure 4.2), which consists of 343 zones.

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Figure 4.1 Kecamatan, kelurahan and desa zoning system in JMA Source: Processed by author from SITRAMP GIS database.

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Figure 4.2 Traffic Analysis Zone system Source: Processed by author from SITRAMP GIS database.

Since Home Interview Survey data is collected from a sample of the population, the use of the data in empirical analysis has to be adjusted by factors in order to have figures comparable to the real world. Each of the observations in the three datasets of the Home Interview Survey (i.e., household profile, household members’ profile and trip) has its corresponding multiplying factor termed in the database as “expansion factor.”

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Transport network

Transport GIS layers developed under the SITRAMP study (ROI BAPPENAS- JICA, 2004c) include those of the road, railway and bus network in JMA. Road network data as provided by the National Land Agency includes all classes of roads including local, secondary, main and toll roads. The railway network includes the routes and stations (Figure 4.3). The bus network consists of small, medium and large buses’ routes (bus rapid transit is not included as it was not in operation at the time the data was collected). The use of transport data in the empirical analysis includes zonal centroid distance to particular points including railway stations, toll road ramps and major highway intersections, and another measure called accessibility. TAZ is the zoning system used by the SITRAMP study to perform transport demand modelling and is, therefore, also used as the basis for developing travel impedances (i.e., travel time and travel cost) between TAZs by transport modes. Travel impedances are used to calculate zonal accessibility by transport modes.

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Figure 4.3 Railway and toll road network in JMA Source: Processed by author from SITRAMP GIS database.

Land use related data

Relevant GIS layers that are considered for the empirical analysis in this study include those of the latest land use, industrial area and real estate layers. The latest land use map available for JMA was developed in the SITRAMP study (ROI BAPPENAS-JICA 2004c) in the year 2000. This land use map consists of 19 land use categories (see Table 4.6). The basic input for land use map development was a digital map at the 1:25,000 scale produced in the year 2000 by BAKOSURTANAL, which is the Indonesian agency responsible for land survey and mapping, based on

114 aerial photos taken between 1994 and 1997. SITRAMP then conducted two-phase surveys to update the map for the year 2000, covering area of 2,377 square kilometres. The survey was, however, only conducted in the more urbanised areas of JMA (Figure 4.4). The accuracy of the land use map is therefore not uniform across JMA. The more urbanised area was updated based on the surveys conducted in the year 2000 while the rural areas was based mainly on the BAKOSURTANAL digital map based on aerial photos taken in the mid-1990s. Although land use characteristics may serve as important explanatory variables to empirical analysis on the impacts of physical features of urban structure on travel (as modelled in Chapter 6), the use of land use map developed in the SITRAMP study is precautious due to this issue of accuracy (i.e., land use update conducted in the year 2000 did not cover the peri-urban areas of JMA). As shown in Chapter 3, JMA had experienced rapid land use changes up to the mid-1990s so that models developed based on this land use map will not properly explain the influence of land use characteristics in peri-urban areas on travel pattern. This is the main reason of not employing land use characteristics from this land use map in the empirical analysis in this study. The intensity of economic activities by land use facility (i.e., the number of jobs by land use facility in kelurahan or desa) derived from the SITRAMP Home Interview Survey (see Table 4.4) is used instead to represent zonal land use characteristics and to identify desakota areas and zonal land use diversity.

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Figure 4.4 Land use survey area conducted under SITRAMP study in 2000. Source: ROI BAPPENAS-JICA (2004c).

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Table 4.6 Land use categories in the JMA land use map, 2000

Land Use Code Description

21 Planned Housing Area 22 High Density Urban Kampung 23 Low Density Urban Kampung 24 Industry & Warehouse 25 Commercial & Business 26 Education & Public Facility 27 Government Facility 28 Park & Cemetery 29 Agriculture & Open Space 30 Swamp, River & Pond 31 Transportation Facility 32 Bush & Forest 33 Mangrove 35 Recreation Facility 9999 Unknown

Source: ROI BAPPENAS-JICA (2004c).

Methods

The spatial structure of employment

Identification of clusters

The first set of research question examines the spatial structure of JMA by referring to Southeast Asian EMR theory. The survey of literature in Chapter 2 has shown that there are three main approaches that have been employed to reveal clusters of economic activities in urban areas and regions. The cut-off method, suggested by Giuliano and Small (1991), offers the advantages of relatively simple calculation and flexibility in deciding cut-off values that suit the nature of areas investigated. It is, however, criticised for its arbitrariness in deciding cut-off values that leads to inconsistency of the findings. Two approaches have been proposed to remedy the

117 problem: the non-parametric method and exploratory spatial data analysis (ESDA). While both can be considered non-parametric in that they do not require prior decisions on thresholds of parameters in identifying clusters of employment, the latter has been increasingly popular for its consideration on the spatial dependence (or spatial autocorrelation) inherent in any spatial data (Anselin, 1989). This study employs ESDA for the identification of clusters of activities in JMA. Advantages of employing ESDA over the cut-off method are: ESDA avoids arbitrariness in deciding cut-off values hence offers more consistent results; ESDA systematically takes into account both spatial heterogeneity (in the forms of non-uniform cluster patterns over space) and spatial autocorrelation of the observations; and ESDA allows identification of statistical significance of the results. The only drawback of ESDA over the cut-off method is that ESDA seems to require more computational efforts than the cut-off method (although the availability of spatial statistical packages like GeoDa and ArcGIS allows automatic calculation procedures of the statistics). Both local Getis-Ord and LISA statistics of ESDA are used in this study and briefly described as follows.

The local Getis-Ord takes the form

(4.1) where is the value of the observation at , is a weight matrix element defining neighbourhood relationship between and which depends on the distance between them, and is the number of observations. The expected value of is defined as

(4.2) where

(4.3) and the variance of is defined as

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(4.4) where

(4.5) and

(4.6)

Both the expected value and the variance of can be obtained from randomisation process. Assuming is normally distributed, the Z-score of can be defined as

(4.7)

High or shows the existence of clusters of high values so that the statistic, as argued by Paez et al (2004), is most applicable in identifying urban employment centres.

The other statistic, LISA, is derived from the global Moran’s which takes the form:

(4.8)

Where is the mean value of variable . The weight matrix or spatial lag element defines the neighbourhood relationship between and . As the global Moran’s calculates the deviation from the mean value for each pair of observations, the presence of clusters, either high or low values, returns a high (i.e., close to one) Moran’s . On the other hand, the tendency of the spatial distribution of values towards being dispersed returns a low (close to minus one) Moran’s . The expected value is zero, indicating a random distribution. The local Moran’s is a decomposition of its global version as the sum of the local Moran’s over all is proportional to the global Moran’s . The local Moran’s is defined as

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(4.9)

The expected local value is

(4.10)

Assuming is normally distributed, the Z-score of can be defined as

(4.11)

The use of and to assess the level of significance of and LISA should however be corrected due to multiple comparison problems (Anselin, 1995). Two methods are suggested to approximate the significance levels: Bonferroni type test which correct the significance level for number of observations into

(4.12) and Sidak type test into

(4.13)

The use of Bonferroni or Sidak type tests are however precautious due to their extreme conservativeness especially for a large number of observations. These tests sometimes are omitted in ESDA because they may result in overlooking some less prominent clusters (i.e., false negative of the hypothesis) (Fotheringham et al., 2002, p. 183).

The four possible patterns of local indicators of spatial association (LISA) are High- High (i.e., when both and the neighbours are generally above the mean so that is positive); Low-Low (when both and the neighbours are generally below the mean so that is also positive); High-Low (when is above the mean and the neighbours are generally below the mean so that is negative) and Low-High (when is below the mean and the neighbours are generally above the mean so that is negative). While the local Getis-Ord statistics are beneficial for their

120 intuitive spatial association, LISA is particularly useful to detect not only clusters of high or low values but also local outliers when an observation with low (or high) value is surrounded by observations with high (or low) values. The signs of alone, however, do not tell us which category a pattern of LISA falls into so that scatterplot tools are needed to define it.

The development of ESDA has been complemented with advances in geographic information system (GIS) platforms. LISA calculations and scatter plot tools, for instance, are included in the GeoDa software package (Anselin et al., 2006) and both global and local Moran’s I and Getis-Ord statistics are included in ESRI’s ArcGIS package (Mitchell, 2005). While ESDA is non-parametric in nature, differences in defining relationships among observations, as defined in the spatial weight matrix, may lead to different results. As discussed in Mitchell (2005, pp. 135-145) these spatial relationships are generally defined based on either adjacency or Euclidean distance. Dummy values are often used for the adjacency method, in which values of 1 are assigned to neighbours and 0 otherwise. For the Euclidean distance method, neighbours are defined based on the distance between observations. ArcGIS and GeoDa are both used in this study. Neighbours of features (i.e., kelurahan or desa zone) are defined based on row-standardised, first- order adjacency (Mitchell 2005, p. 143-144).

A few variables have been used in ESDA to identify clusters of employment, including employment density (Scott and Lloyd, 1997; Baumont et al., 2004; Riguelle et al., 2007), employment levels (Baumont et al., 2004), employment levels of particular industry (Ceccato and Persson, 2002; Carrol et al., 2008), and employment to population ratio (Guillain et al., 2006). Identification of employment clusters is often followed with investigation of the internal structure of centres in terms the types of industry located in the centres. Common methods used include the location quotient (e.g., Bogart and Ferry, 1999; Anderson and Bogart, 2001; Parolin and Kamara, 2003; Guillain et al., 2006) and factor analysis (Parolin and Kamara, 2003; Cervero, 1989; Gordon et al., 2005). While the former indicates the degree of specialisation of centres with respect to particular types of industry, the later offers insights into the industrial composition of the centres. Factor scores resulting from the factor analysis are also often used as new composite variables.

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Examples include the works by Cervero and Kockelman (1997) who applied factor analysis on several urban form variables to reveal two factors and used the factor scores as dependent variables in linear regression models and Gordon et al. (2005) who applied factor analysis on several job industries to reveal four factors whose scores then served as dependent variables in cluster analysis.

While almost all empirical studies are conducted in the context of Western cities or regions with reference to the dichotomy of monocentric and polycentric urban structure, it is argued that a different approach should be employed in order to reveal important components of urban structure as suggested by the Southeast Asian EMR concept. More specifically, in addition to the central business district, manufacturing corridor and other possible subcentres, the methods employed should be able to identify clusters of agricultural employment and desakota regions in the JMA. An alternative approach is proposed in this study. Instead of using employment density or the level of employment directly to identify “hot-spots” of employment, co-location of job industries across the region is first identified by applying factor analysis to the Z-scores of the zonal percentage of total employment in the JMA for each of the selected job industries. The number of factors to retain is evaluated based on eigenvalues or the scree plot of eigenvalue against factor (Field 2005, p. 632-634) and the co-location tendencies of job industries as revealed from the analysis. A decision on the number of factors to retain is also determined from the purpose of the analysis, i.e., to identify components of urban spatial structure as suggested by the Southeast Asian EMR concept.

The resulting factor scores are then used as inputs in ESDA to identify clusters of employment associated with the factors. As suggested in Mitchell (2005, p. 175) cluster identification in ESDA should include the target observation in equations 4.1 and equation 4.9 above, in which case local Getis-Ord and local Moran I statistics are denoted with asterisk scripts as and , respectively. The local Getis-Ord statistic is used as the tool to perform the cluster identification in this study. Overall, this modified approach is advantageous in identifying clusters of employment where the clusters are not necessarily associated with high employment density or zonal employment levels. Higher employment density is most likely found in the urban core, but the other important Southeast Asian EMR

122 components such as manufacturing corridors in the peri-urban areas and agricultural zones in the outer ring may not be properly detected using the common approach. The modified method proposed would identify clusters based on job industry co-location tendencies represented by factors. Some of the resulting clusters, however, can be overlapped because some zones may return high values under more than one factor.

The literature has indicated the failures of many developing cities in promoting the emergence of subcentres outside the CBD (Robinson 1995, p. 88-89). It is, however, intended in this study to probe possible isolated subcentres that may have emerged in their early stage and possibly have not developed into sets of contiguous zones. The other method, LISA, offers the measure to detect such possible isolated subcentres through its “high-low” quadrant, indicating “hot-spot” zone(s) surrounded by “cold-spot” ones. In this case, employment density is proposed as the variable to employ in LISA for it is a common variable used in many empirical analyses to identify subcentres. Finally, the land-use mix index developed by Bhat and Gossen (2004) is used to probe desakota areas. Because the Home Visit Survey data includes information on land-use type of the workplace, it is possible to identify kelurahan or desa zones that have a high degree of urban-rural mix. The land-use diversity index developed by Bhat and Gossen is adopted as a “manufacturing-agriculture worker index” in this study as its formulation seems more simple and more intuitive than the employment entropy index developed by Cervero (1989). Another method called the dissimilarity index (Cervero and Kockelman, 1997), on the other hand, is more applicable to a grid cell zoning system. It is proposed in this study to define desakota areas as those where a higher mix of workers in manufacturing and agricultural jobs reside (see reasons for adopting this definition in Chapter 5).

(4.14)

where is the number of workers in manufacturing industry living in zone i, is the number of workers in agricultural industry living in zone i and equals plus

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. The index is then used in the Getis-Ord hot-spot analysis to identify clusters of desakota in JMA.

Spatial impacts

Local spatial statistics have been used to the measure distance impacts of a particular zone on its surroundings in terms of various dimensions. Getis and Ord (1995) used the local statistic to measure the distance at which the AIDS disease in California is significantly associated to San Francisco. In this case, San Francisco as the target feature is not included in the calculation of . By ignoring Bonferroni type tests, it is found that the disease is clustered at a distance of forty miles from San Francisco at 95 per cent level of confidence. Bao et al. (1995) investigated the “impact rings” of functional economic areas in South Carolina and parts of Carolina and Georgia on population growth surrounding the functional economic areas by using local Moran and local Geary statistics. One finding is that Charlotte, which is one functional economic area investigated, is associated with population growth of the rural areas surrounding it by up to 70 miles at 95 per cent level of confidence. Tran and Yasuoka (2001) adopted factor analysis and the “impact rings” to investigate the distance influence of Chiang Mai city and Lamphun Municipality in Thailand on their surroundings in terms of two composite variables. The composite variables include “factor 1” which is positively correlated to the percentage of urbanised and residential areas, road density, property taxes and proportion of trading population, and “factor 2” which is positively correlated to total number of industrial employees, number of employees in large-scale factories, number of factories, total capital investments and percentage of industrial land use. It is found that “factor 1” is clustered at the distance of 11 kilometres from Chiang Mai city and “factor 2” is clustered at the distance of 7 kilometres from Lamphun Municipality, both at 95 per cent level of confidence.

More insights into the spatial structure of JMA can be gained through the understandings of spatial associations between the components of the spatial structure and several dimensions using a similar approach to that above. The local Getis-Ord is used to probe clusters of employment density and population growth from urban centres identified in the employment clusters identification. The 124 distance impacts of manufacturing centres and other centres on the formation of desakota are investigated in terms of the percentage of “mixed manufacturing- agricultural” households, defined as those having family members working in manufacturing and agricultural industry (identified as one characteristic of desakota in McGee (1991, p. 17)). The economic impacts of manufacturing corridors on their surroundings may also be probed by investigating the distance influence of such employment clusters on the share of high income people in their surroundings. A similar method is applied to investigate the spatial association between formal housing clusters and satellite towns to their associated amenities such as shopping malls.

Characteristics of the components of the spatial structure of employment

The components of the spatial structure identified are further investigated in terms of their internal structure by job industry and by their physical characteristics. Although factor analysis is conducted in the first step of cluster identification, and generally reveals co-location tendencies among industries within clusters of employment, it is important to investigate in more detail types of industries that have developed in the clusters of employment identified. This is particularly intriguing in the case of desakota, as the urban-rural diversity index has not yet identified specific types of urban job industries which co-locate with agricultural activities. Also, the possible isolated subcentres are identified using the “high-low” LISA quadrant of employment density and therefore have not been revealed in terms of their internal structure of job industries.

In this study, investigation on the physical attributes or urban form variables of the components of spatial structure in JMA is intended not only to shed light into variations of such clusters in the context of Southeast Asian EMR but also to conduct further investigations on their influences on travel. Variables can be broadly categorised into density, diversity and access to transport facilities. Those categorised as “density” include population density, household density, employment density, job accessibility by mode of transport, jobs-housing balance, and distance to CBD. Job accessibility is calculated using the formulation originally proposed by Hansen (1959):

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(4.15) where is the number of jobs at kelurahan or desa j; is distance between i and j; and is the impedance coefficient. The impedance coefficient value equals to 1 is adopted in this study because it allows more intuitive comparisons of job accessibility among clusters and among different modes of transport.

The land-use mix formulation proposed by Bhat and Gossen (2004) is employed to measure the degree of job industry diversity at kelurahan or desa for each of the clusters of employment. The index is used to measure the balance among private office jobs, “amenities” jobs and the other types of jobs (classification of each of these is shown in Table 4.7). The formulation is given as:

(4.16) where pof, ame and oth are the number of jobs located in private office, “amenities” and “other” land-use facilities, respectively, and T = pof + ame + oth. Variables associated with access to transport facilities include road density, distance to nearest railway station and distance to nearest toll road ramp. The physical attributes of spatial structure used in this study and their measurements are presented in Table 4.8.

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Table 4.7 Classification of land use types Private Office Amenities Others 1. Residence or dormitory 2. Government or public office 3. Business office, company 4. Educational facility 5. Religious facility 6. Medical facility 7. Accommodation, entertainment facility 8. Restaurant 9. Retail, traditional market, supermarket 10. Supermarket 11. Shopping mall or shopping plaza 12. Grocery market 13. Factory 14. Warehouse, storage facility 15. Transport and communication facility 16. Supply and disposal facility 17. Recreational or sport facility 18. Park, natural environmental area, etc. 19. Agricultural, forestry and fishery areas 20. Construction site 21. Others Source: Re-classified by author from SITRAMP HIS.

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Table 4.8 Urban form variables and measurements considered for the empirical analysis Urban form variable Measurement

Density

Population density The number of population per square kilometres of kelurahan or desa.

Household density The number of households per square kilometres of kelurahan or desa.

Employment density The total number of jobs per square kilometres of kelurahan or desa.

Job accessibility by transport Accessibility index of kelurahan or desa i where modes Ej is the number of jobs at kelurahan or desa j; dij is distance between i and j; is impedance coefficient.

Jobs-housing balance The number of jobs per household in kelurahan or desa.

Distance to the regional centre Kilometre straight-line distance between kelurahan or desa centroid or the centre of employment clusters and MONAS.

Diversity

Land use diversity index Job industry diversity = where where pof, ame and oth are

the number of jobs located in private office, “amenities” and “other” land-use facilities, respectively, and T = pof + ame + oth.

Access to transport facilities Road density Total road length in kilometres per area of kelurahan or desa.

Within a particular distance to Whether zonal centroid is within 3 km (for workplace) or 5 nearest railway station km (for residential) straight-line distance from the nearest railway station Distance to nearest toll ramp Kilometre straight-line distance between kelurahan or desa centroid and the nearest toll ramp Source: Compiled by author.

Travel dimensions and links with the spatial structure and its physical features

The degree of spatial interaction given the identified urban spatial structure is visualised in this study as desirelines (see for example Cervero 1996; Cervero 1989,

128 p. 182-183). Furthermore, this study investigates commuting patterns from trip destination point of view as impact of urban spatial structure and its physical attributes. Travel dimensions used in many empirical studies are also used in this analysis. These include commuting distance by the private vehicles and private versus public transport mode choice. Each component of the urban spatial structure identified in JMA and the physical attributes of urban spatial structure, as presented in Table 4.8, are investigated in terms of their impacts on those travel dimensions. The methods used to measure the impacts of the physical attributes of the spatial structure on travel are ordinary regression analysis for the commuting distance models and logistic linear regression for the travel mode choice model.

There are two main approaches in investigating the links between spatial structure and its physical attributes and travel patterns, “group-comparison” analysis and “regression-type” analysis (Shin, 2002). The group-comparison method compares one or more travel dimensions which characterise each of the groups identified in urban spatial structure. For example, the average commuting distance is compared among the CBD and the urban subcentres. Often statistical methods such as Analysis of Variance (ANOVA) is used to test whether the difference of the mean of the travel dimension investigated between the groups is statistically significant. The regression-type method, on the other hand, investigates the influence of each of urban form variable on a particular travel dimension through a regression model. Group-comparison analysis has been used, for example, by Cervero (1989) to compare commuting characteristics in terms of travel speed, transport mode share and arrival and departure times among different types of workplaces. A regression- type analysis is adopted for example by Giuliano and Small (1993) to explain variations in commuting time associated with the resident workers to jobs ratio, and by Cervero and Kockelman (1997) to investigate the influence of a few urban form variables on vehicle miles travelled and the probability of choosing non-private vehicles.

This study adopts group-comparison method to identify and compare differences in terms of commuting distance and travel mode choice by the components of spatial structure identified in the JMA. Furthermore, the influence of the physical attributes of urban structure on those travel dimensions are investigated through

129 ordinary and logistic linear regression models. Socioeconomic characteristics of workers such as age, gender and income level are included as control variables in the models.

Flow of the empirical analysis

The overall flow of the empirical analysis described above is presented in Figure 4.5. The first set of research questions on the spatial structure of JMA is addressed using ESDA and spatial impact methods. Data needed to conduct the empirical analysis includes the Home Interview Survey 2002 and the zoning system in GIS format. The identified spatial structure serves as an input to address the second set of research questions on travel impacts of the spatial structure and its physical attributes. ANOVA and linear regression methods are employed to reveal the pattern of travel associated with urban spatial structure and urban forms of JMA. Insights from the literature and the results of the empirical analysis are synthesised to formulate policy implications of the findings.

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Research Questions 1 a. What important components of the Southeast Asian EMR constitute the spatial structure of employment in the JMA? b. How can we identify those major components of the Southeast Asian EMR in JMA? Which methods are suitable? c. Does the overall spatial structure of employment in JMA conform to the Southeast Asian EMR concept? d. What are the spatial characteristics of and how they vary among the components of the spatial structure of employment in JMA?

Research Questions 2 a. Given the identified urban spatial structure of JMA, what is the overall journey to work pattern? b. How the identified journey to work pattern Data: Methods: explains the spatial structure of employment HIS 2002 ESDA identified? Population census Spatial impacts c. Whether major components of the spatial GIS layers ANOVA structure of employment in JMA exhibit substantial variations in terms of journey to work patterns? d. What physical features of those components influence journey to work patterns, and how?

The spatial structure of employment Methods: ANOVA Data: in the JMA with reference to the HIS 2002 Southeast Asian EMR concept Linear regression Logistic regression GIS layers

Journey to work impacts of the spatial structure of employment

Research Questions 3 a. How the empirical findings on the spatial structure of employment and journey to work patterns in JMA can be Methods: assessed against theories and practices as found from the Synthesis of literature literature? review and empirical b. How the spatial structure of employment and journey to findings work patterns in JMA, based on the findings, are associated with the success or failure of planning efforts in JMA? What lessons can be learnt? c. What policy recommendations can be proposed to improve urban and transport conditions in JMA? Policy implications of the empirical findings

Figure 4.5 Process of the empirical analysis Source: Author.

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Conclusions

Three sets of research question have been formulated based on the literature review conducted in the previous two chapters. The first set of research questions enquires about the spatial structure of JMA with reference to the Southeast Asian EMR concept. More specifically, the first set of research questions explores the components of employment spatial structure in JMA. The second set of research questions enquires about the impacts of the identified spatial structure on travel pattern. The final research questions search for policy implications of the findings.

These three sets of research questions are subsequently addressed within the framework of empirical analysis. While the methods for the empirical analysis are borrowed from studies in the developed world, modifications have been proposed in order to make them suitable for the case of JMA and the context of Southeast Asian EMR. Identification of the spatial structure of JMA and its characteristics is conducted using ESDA and spatial impact methods, while travel implications of the spatial structure are investigated using desireline, ANOVA and linear regression approaches. The empirical findings are synthesised with the literature review to identify policy implications that can be recommended for the improvement of urban and transport conditions in JMA.

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5 Spatial structure of employment in the JMA: an EMR perspective

Introduction

This chapter addresses the first set of research questions regarding the spatial structure of employment in the JMA. Empirical analysis begins by presenting the profiles of employment by city and municipality in the JMA in 2002, as revealed from the Home Interview Survey data. Next, the identification of employment clusters begins with selection of job industries to be included in the factor analysis. Factor analysis is used to reveal co-location tendencies of job industries across the region and the resulting factor scores are used in local Getis-Ord hot-spot analysis to identify clusters of employment by identified factors. Delineation of clusters found is performed based on visual examination of the result of ESDA. Desakota and “isolated” centres are identified using local Getis-Ord statistics and LISA, respectively.

Next, spatial characteristics of the resulting spatial structure of employment are investigated and contrasted among its components. While the focus of the thesis is on the spatial structure of employment, the spatial associations of employment structure are examined on several dimensions to shed more light on the spatial

133 impacts of employment distribution in the JMA within the Southeast Asian EMR context. The results are evaluated and discussed with regard to how they have addressed the research questions set out in Chapter 4. Possible policy implications are also probed along with the discussion of the empirical results. The resulting spatial structure of employment and important spatial characteristics identified will be used in the next chapter to address research questions on their impacts on journey to work within the JMA.

EmploymentprofileswithintheJMA

The Home Interview Survey data shows that there are 7.6 million workers living in the JMA in 2002. This figure is less than that of population census of 2000 which recorded 8.7 million workers within the JMA. Furthermore, only around 6 million jobs can be located spatially within the JMA due to some respondents not reporting their workplace address, and those who worked outside the JMA. The distribution of jobs within the JMA by 14 job industries is presented in Table 5.1. The three major industries in terms of the number of job opportunities they provided were the service industry (25.4 per cent), manufacturing (23.1 per cent) and wholesale and retail (17.6 per cent). Agriculture, forestry and fishery accounted for 7 per cent of the total jobs within the JMA.

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Table 5.1 Distribution of the number of jobs by type of industry

Number of Share in JMA Jobs (percentage) Primary Agriculture, forestry, fishery 422,249 7.0 Mining 58,496 1.0 Secondary Manufacturing 1,397,265 23.1 Tertiary Construction 210,519 3.5 Transportation and 393,496 6.5 communication Banking, insurance 171,607 2.8 Wholesale and retail traders 1,064,955 17.6 Electricity, gas and water supply 56,060 0.9 Central government services 197,533 3.3 Local government services 241,921 4.0 Rental 49,939 0.8 Service industry 1,533,471 25.4 Military and police 112,402 1.9 Others 129,019 2.1 Total JMA 6,038,932 100

Source: Processed by author from SITRAMP HIS database.

The number of jobs located in the cities and regencies in the JMA, and their distribution by major sector of industry, is presented in Table 5.2. As shown in the table, and also in pie-chart form in Figure 5.1, Jakarta city accounted for more than half of the total jobs in the JMA and is highly specialised in the tertiary sector. The four municipalities of Bogor, Depok, Tangerang and Bekasi were also highly dependent on tertiary sector jobs, but the secondary sector accounted a larger share than in Jakarta. The Municipality of Tangerang exhibited high dependence on the secondary sector, accounting for 44.2 per cent of jobs. With the exception of Depok, the primary sector was also more important in these municipalities than in Jakarta. The three regencies of Bogor, Tangerang and Bekasi hosted a much higher share of the primary job sector than in the cities, accounting for more than 20 per cent of total jobs in their respective region. Secondary sector jobs were also important in the regencies. The Regency of Bekasi relied the most on the secondary

135 sector, accounting for 42.2 per cent of its total jobs, which is higher than its 34.7 per cent of tertiary sector jobs. The high proportion of both primary and secondary sector jobs in these three regencies would indicate the formation of desakota, but little is known about this structure until the spatial co-location of urban and rural activities is examined in the empirical analysis.

Table 5.2 Distribution of the number of jobs by city and regency and by major type of industry within the JMA, 2002

Share (percentage) Number of Jobs Primary Secondary Tertiary Total Jakarta 3,208,871 2.6 14.9 82.5 100 Municipalities Bogor 212,242 5.6 19.3 75.1 100 Depok 178,155 4.5 23.3 72.2 100 Tangerang 351,168 1.3 44.2 54.5 100 Bekasi 255,298 3.4 27.0 69.6 100 Regencies Bogor 775,345 22.3 27.8 49.9 100 Tangerang 566,549 13.7 33.4 52.8 100 Bekasi 491,303 23.0 42.2 34.7 100 JMA 6,038,932 8.0 23.1 68.9 100

Source: Processed by author from SITRAMP HIS database.

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Figure 5.1 The number of jobs in cities and regencies by major type of industry within the JMA, 2002 Source: Processed by author.

Identificationofemploymentclusters

Co-location of job industries

The approach adopted to identify clusters of employment within the JMA in this empirical study begins with the identification of the co-location of employment by industry type across the region. There are 14 job industry categories used in the Home Interview Survey, of which 8 are selected for factor analysis. Those excluded from factor analysis include mining, construction, rental, police and military and others, accounting for 6.4 per cent of the total number of jobs that can be located spatially within the JMA. In order to capture both the intensity and the spatial distribution of agriculture, forestry and fishery jobs, which is an important

137 component of the spatial structure of the Southeast Asian EMR, this type of industry is “filtered” to only include those located in the agricultural, forestry and fishery areas and therefore excludes government or private office jobs classified into this type of industry but located in urban areas. Kelurahan or desa level of detail is chosen so that there are 1,485 observations for the factor analysis. Variables used are the zonal (i.e., kelurahan or desa level) percentage of total employment in the JMA for each of the eight job industry categories (see Table 5.3).

There is no agreed upon criterion on deciding the number of factors to retain (Field 2005, p. 632-634). The scree plot of eigenvalues against factors (Figure 5.2) would suggest one to four factors to retain for high eigenvalues. Another approach would suggest one or five factors to retain, for these are the points at which the scree plot starts to flatten, meaning that the additional factors add only slightly to the explanation of variance within the data (Rogerson, 2001, p. 195). Six factors have been retained because the study needs more variation in spatial distribution and co- location of employment sectors thereby allowing further analysis within the context of the Southeast Asian EMR. These six factors explain 95.4 per cent of variance, or imply 4.6 per cent loss of information in the dataset.

2 Eigenvalue

1 2 3 4 5 6 7 8 Component Number

Figure 5.2 Scree plot of factor analysis Source: Processed by author.

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Table 5.3 presents the rotated component matrix for the six factors. In order to highlight co-location tendencies of jobs across industries, only component loadings with absolute values of equal to or higher than 0.2 are shown. The results provide some understanding of the industrial composition of the JMA. The results show that factor 1 is positively associated with employment in wholesale and retail, services, transport and communications, finance and local government industries. Factor 2 is highly positively associated with employment in central government and transport and communications and, to a lesser extent, local government, services and finance. Factor 2 may represent higher job diversity centres that emerge around transport facilities. In general these first two factors seem to represent areas with higher job diversity for the number of industries represented in the factors. Factor 3, on the other hand, represents lower job diversity industrial areas as it is positively associated with manufacturing and transport and communications. Factor 4 is positively associated with finance (banking and insurance), services, and central government, and therefore may represent the central business district in which financial services are commonly concentrated. Factor 5 is positively associated with agriculture, forestry and fishery and therefore represents areas where agriculture and fishery employment are dominant. Factor 6 is positively associated with local and central government services and represents areas where city and regency local government offices are situated.

Table 5.3 Rotated component matrix from factor analysis

Factor 1 2 3 4 5 6 Wholesale and retail 0.911 Services 0.781 0.317 0.380 Central government 0.836 0.328 0.310 Transport and communications 0.555 0.703 0.274 Manufacturing 0.978 Banking and insurance 0.343 0.291 0.879 Agriculture, forestry, fishery 0.991 Local government 0.305 0.355 0.864 Note: Component loadings with absolute values less than 0.2 are not shown. Source: Processed by author.

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Major employment clusters

The six factor scores resulting from the application of factor analysis enter separately into the Getis-Ord hot-spot analysis to identify clusters corresponding to each factor. A spatial contiguity (first order) weight matrix is used for the hot- spot analysis. For the 1,485 zones, the Bonferroni-type test method sets Z (Gi*) to 4.16 for the 95% level of confidence. It has been argued, however, that a Bonferroni-type test is too conservative, especially when the analysis involves a large number of zones (as in this case). In addition, for exploratory research, a Bonferroni-type test may not be used strictly as it may result in overlooking some less prominent clusters (i.e., false negative of the hypothesis) (Fotheringham et al. 2002, p. 183). Hot-spot analysis results for both Z(Gi*) 1.96 and 4.16 (the former is adopted in the cluster definition) for the six factors are presented in Figure 5.3.

(a) (b)

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(e) (f) Figure 5.3 Significant local Getis-Ord statistic of (a) Factor 1; (b) Factor 2; (c) Factor 3; (d) Factor 4; (e) Factor 5; and (f) Factor 6 Source: Processed by author.

Factor1

More detailed maps of the six factors are shown in Figures 5.3 to 5.8. Zones having high Z (Gi*) on Factor 1 (Figure 5.4) cover almost all parts of the Jakarta city. Although this study does not apply a Bonferroni-type test in cluster identification, zones returning significant Z(Gi*) values under the test may represent centres of commercial activity within Jakarta as Factor 1 is highly significantly associated

141 with wholesale and retail employment. Those confirmed by the result include, for example, the commercial centres in Taman Sari and Sawah Besar (which are the location of the China Town of Jakarta including Glodok and Mangga Dua commercial centres), Kebayoran Baru (the area where the commercial centre Blok M is located) and Kelapa Gading. Those returning Z(Gi*) of higher than 1.96 may represent secondary commercial centres such as Kramat Jati and Pasar Minggu. Overall, the result confirms the dominance of Jakarta as the location of the tertiary job sector in which most parts of the city offer job opportunities in this sector. Much smaller areas of high Z(Gi*) scores on Factor 1 are also found in Bogor Municipality.

Figure 5.4 Factor 1 Source: Processed by author

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Factor2

Factor 2 represents areas of co-location of both central government and transport and communications employment. As shown in Figure 5.5, areas of significantly high Z(Gi*) scores on Factor 2 include the Sukarno-Hatta international airport, the Tanjung Priok port, and Gambir where the important rail station hub is located. Hot-spots in Factor 2 in general reveal another dimension of employment centres within the urban core of Jakarta in the form of four large clusters. Although this study does not go further into more detailed investigation on these four centres, a hypothesis is that the important transport hubs in the city have triggered the formation of centres that include some zones surrounding them. To a lesser extent, Factor 2 is also associated with services and banking and insurance jobs that may be attracted to locate around these important transport hubs and altogether form centres of higher job diversity.

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Figure 5.5 Factor 2 Source: Processed by author.

Factor3

Hot-spot analysis of Factor 3 (Figure 5.6) reveals the important components of the Southeast Asian EMR, showing corridors of manufacturing jobs along the major transport networks connecting to the urban core of Jakarta. Two clusters, including Tanjung Priok-Cilincing-Cakung and Penjaringan-Cengkareng-Kalideres, are located in the urban core of Jakarta while the others, including Tambun-Cibitung- Cikarang, Pasar Kemis-Jatiuwung-Cikupa, Cileungsi-Kalapanunggal-Citeureup and Ciracas-Cimanggis-Cibinong, are located mostly along toll road corridors radiating out of the urban core of Jakarta. The location pattern of the manufacturing corridors in general confirms the tendency of this type of industry to locate close to

144 export-import points or along major highway corridors that have fast access to export-import points. The fact that such manufacturing jobs form corridors is however more relevant to the Southeast Asian EMR concept than what is proposed in location theory in the Western context. What is envisaged in the Southeast Asian EMR is that foreign and domestic investment led industry and employment locate global production centres along the major highways in order to exploit the benefit of good transport connection and access to labour in the predominantly rural areas. Furthermore, the extent of manufacturing corridors towards the agricultural areas have led to desakota formation as identified further in this study.

Figure 5.6 Factor 3 Source: Processed by author.

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Factor4

Results of the analysis of Factor 4 (Figure 5.7) represents areas where higher order services (banking and insurance) and central government and other services co- locate. The areas returning Z(Gi*) > 4.16 represent the well accepted expanded regional CBD of the JMA, which starts from Gambir (the former Koningsplein) and contiguously down southward along the Thamrin-Sudirman arterial road that includes Tanah Abang and Menteng (the former Weltevreden) up to Kebayoran Baru and Setiabudi, which is the newer CBD of Jakarta known as the Kuningan Golden Triangle. The areas returning Z(Gi*) > 1.96 including portions of Taman Sari, Senen, Mampang Prapatan and Pancoran are areas of spill-over of higher order services that can be considered parts of the regional CBD. The result confirms this expected aspect of an EMR which hypothesises highly centralised higher order services in the heart of the region (McGee, 2008). Moreover, the result is also contrary to the polycentric urban pattern experienced by many cities in the developed world where the role of the CBD has been decentralised into smaller sub- centres at various distances away from the CBD. For the case of the JMA, on the other hand, the importance of the CBD has been maintained and its area is expanded to accommodate the increasing demand of space for higher order services, as shown in Chapter 3.

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Figure 5.7 Factor 4 Source: Processed by author.

Factor5

Hot-spot analysis of Factor 5 (Figure 5.8) captures the clusters of areas where a high number of agricultural jobs are located. Significant portions of agricultural areas in the JMA are detected in Bekasi Regency and Bogor Regency, while smaller areas are found in Tangerang Regency. Those located in Bekasi Regency include the protected irrigated paddy fields in the northern side (Muara Gembong, Sukawangi and Sukatani) and the eastern side (Cikarang) of the region. Agricultural areas in Bogor Regency include Jonggol, Sukamakmur, Cariu, Ciawi, Sukajaya and Cigudeg while those in Tangerang Regency are found in Kronjo. While further

147 breakdown of types of agricultural jobs is prevented due to the “one-digit” job industry classification used in the SITRAMP survey, the agricultural areas in JMA outside the protected paddy fields in northern part of Bekasi were most likely dominated by horticultural crops, as indicated in McGee (1989). This result can be regarded as a recent update on the state of the spatial coverage of agriculture areas, considering the aggressive land conversion of agricultural areas into urban uses that has taken place for long time in the JMA (as discussed in Chapter 3). Moreover, within the Southeast Asian EMR context, the spatial location of agricultural areas provides clues on the possible spatial extent of desakota areas. A more specific definition of desakota areas, however, needs to be formulated in this study to allow further investigation on the spatial characteristics and impacts of desakota and agricultural areas on the journey to work (the later will be discussed in the next chapter).

Figure 5.8 Factor 5 Source: Processed by author.

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Factor6

Areas where local government and central government services jobs are located return high Z(Gi*) scores on Factor 6 (Figure 5.9). Within the urban core of Jakarta, these include the areas where the governor’s office (Gambir) and the five administrative city offices of Central (Gambir), North (Tanjung Priok), West (Kembangan), South (Kebayoran Baru) and East (Cakung) of Jakarta are located. The local government areas of both the Municipality and Regency of Bekasi are located in Bekasi Timur, as at the time the data was collected the local government of Bekasi Regency was not relocated to the current new location at Cikarang. Local government areas of Bogor Municipality, Depok Municipality, Tangerang Municipality, Bogor Regency and Tangerang Regency are identified in Bogor Tengah, Pancoran Mas, Tangerang, Cibinong and Tigaraksa, respectively. There have been efforts to promote the municipalities surrounding Jakarta as a “counter- magnet” to Jakarta as the centre of economic activity in the JMA. Clusters of local government jobs identified here can be used to test the effectiveness of such a policy, which in this study can be measured through the impacts these areas make on the pattern of spatial interaction - in terms of journey to work - in the JMA.

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Figure 5.9 Factor 6 Source: Processed by author.

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Probing desakota

There are a few possible options considered to define desakota areas in this study. A more straightforward approach might define desakota as the areas where co-location of manufacturing and agricultural jobs are more intensive (in terms of the absolute number of jobs and the balance of the share of jobs in those two industries). Such a measure is however probably more applicable in an EMR where the intermingling of manufacturing and agricultural jobs is highly intensive such as that descriptively shown in McGee (2008). In the EMR model proposed by McGee (2008), “small unregulated industries” are dispersedly located within agricultural areas. It however does not seem to be the case in the JMA. Instead, desakota seems to be formed as a result of interactions between the manufacturing corridors - which are also shown in McGee (2008) - and agricultural areas as identified from the hot-spot analysis of Factor 3 and Factor 5 above. It is proposed in this study to define desakota areas as those where a higher mix of workers in manufacturing and agricultural jobs reside.

Although this measurement may be seen a proxy to desakota, it can be viewed as an extension to an important characteristic of desakota set out by McGee (1991, p. 17) in which a family member of one household works in agriculture while another, from the same household, works in manufacturing. Moreover, the proposed definition in this study is more generic as it captures not only the “grey areas” where manufacturing and agriculture job industries co-locate, but also their “spill- over” where a higher balance of workers in both industries reside. For example, agricultural areas in Sukatani, the northern part of Bekasi Regency, may not be considered desakota, in a literal definition, as manufacturing sites are hardly found in the area. However, as can be seen in the next chapter, many manufacturing workers live in this area and make daily commuting trips to the Tambun-Cibitung- Cikarang manufacturing corridors, creating urban-rural interaction in the form of the exchange of commuting trips and most likely other trips as well (shopping trips, school trips, etc.).

A “manufacturing-agriculture worker index”, taking the form similar to the urban diversity index proposed by Bhat and Gossen (2004) (see equation 4.14 in Chapter 4), is used in this study. The index, calculated for each of the 1485 kelurahan and desa in the JMA, is used in the Getis-Ord hot-spot analysis to identify clusters of

151 areas where a higher mix of agricultural and manufacturing workers reside. The method, as in the case of identifying employment clusters, is adopted to avoid arbitrary cut-offs of the index value and at the same time take into account spatial dependence (or spatial autocorrelation) inherent in any spatial data (Anselin, 1989). The result of the Getis-Ord hot-spot analysis (Figure 5.10) of the index shows that desakota areas form bands or corridors that lay approximately in between or overlap areas of manufacturing and agriculture employment clusters (as previously identified). This form of desakota looks somewhat similar to the model in McGee (2008) where desakota areas are likely to form bands or rings taking place in the outer part of an EMR. The extent of desakota areas identified in this study is, however, more limited to that suggested in McGee’s model (2008), as this study does not identify the dispersed “small unregulated industries” within agricultural employment, shown in McGee’s Model (2008). Because the identification of desakota areas in this study is based on an index returning high values for zones where a higher mix of agricultural and manufacturing workers reside, the identified desakota areas confirm the Southeast Asian EMR concept (McGee, 1991) on the tendencies of manufacturing estates to locate near agricultural areas in order to benefit from the availability of a cheap labour force. Areas identified as desakota using this approach include among others Sukawangi, Sukatani, Cikarang, Cibarusah in Bekasi Regency; Cileungsi, Kalapanunggal, Jonggol, Tenjo in Bogor Regency; and Cisoka, Balaraja in Tangerang Regency (see Figure 5.10).

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Figure 5.10 Desakota areas Source: Processed by author.

Job density and planned sub-centres: a LISA approach

LISA, the counterpart local autocorrelation method developed by Anselin (1995) categorising the results of hot-spot (and cold-spot) analysis into the four quadrants, offers the possibility of detecting “isolated” centres in urban areas which may fall into its “high-low” category. In this study, LISA analysis of job density at each of the 1,485 kelurahan and desa in the JMA is calculated using GeoDa (Anselin et al., 2006) to identify the “high-high” (clusters of high job density areas), “low-low” (clusters of low job density areas), “low-high” (low job density area(s) surrounded by high job density areas) and “high-low” (high job density area(s) surrounded by 153 low job density areas). At 0.05 level of significance (without applying Bonferroni- type test), the result (Figure 5.11) reveals only two clusters of high job density in the JMA, i.e., the Jakarta urban core and the much smaller Bogor urban centre. Areas outside the employment clusters identified in the study are generally classified “low- low”, while manufacturing corridors and local government areas outside the Jakarta urban core, with the exception of the urban centre of Bogor Municipality (which is classified “high-high”) and Tangerang Regency (which is classified “low- low”), are not significant at either of the four quadrants. Furthermore, it is found that there are no substantial “high-low” clusters (i.e., sets of contiguous) of zones found, which indicates that substantial sub-centres have not emerged in the JMA. A few single zones of kelurahan or desa categorised as “high-low”1 are found outside the already identified employment clusters such as Sepatan, Pakulonan, Serpong, Parung, Leuwiliang, Dramaga and Cisarua. Two of these kelurahans, i.e., Pakulonan and Serpong, are within the planned sub-centre of Kecamatan Serpong (Noe, 1991) but the result shows that Kecamatan Serpong has not yet developed into a set of contiguous high job density zones. While the emergence of sub-centres is not compelling, it has been decided to include Kecamatan Serpong as a special case in this study in order to conduct more detailed investigation on the spatial characteristics and journey to work patterns of this planned sub-centre.

1 The result does not pass sensitivity analysis under different number of permutation (see Anselin, 2003). 154

Figure 5.11 LISA of job density Source: Processed by author.

The overall spatial structure of employment

The overall spatial structure of employment identified in this study is shown in Figure 5.12. The employment clusters identified and the given cluster codes are: Jakarta urban core (cluster 1-a), urban centre of Bogor Municipality (cluster 1-b), four centres around the transport hubs within the Jakarta urban core (cluster 2), manufacturing corridors of Tanjung Priok-Cilincing-Cakung (cluster 3-a), Penjaringan-Cengkareng-Kalideres (cluster 3-b), Tambun-Cibitung-Cikarang (cluster 3-c), Pasarkemis-Jatiuwung-Cikupa (cluster 3-d), Cileungsi-Kalapanunggal- Citeureup (cluster 3-e) and Ciracas-Cimanggis-Cibinong (cluster 3-f), the regional

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CBD (cluster 4), agricultural areas (cluster 5), local government regions of Jakarta (cluster 6-a), Bogor Municipality (cluster 6-b), Depok Municipality (cluster 6-c), Tangerang Municipality (cluster 6-d), Bekasi Municipality and Regency (cluster 6- e), Bogor Regency (cluster 6-f) and Tangerang Regency (cluster 6-g), desakota areas (cluster 7) and the planned sub-centre Kecamatan Serpong (cluster 8).

The overall spatial structure of employment that has been identified in the JMA seems to conform to the Southeast Asian EMR concept. The strong single expanded area for higher order services (the regional CBD) is found at the heart of dominant Jakarta urban core. Apart from the urban centre of Bogor Municipality, no substantial high job density sub-centres have emerged outside the Jakarta urban core. Local government regions outside the Jakarta urban core, in general, have not triggered the formation of higher job density and job diversity sub-centres. The planned sub-centre of Serpong that has been “in the book” (Robinson, 1995) since the early 1980s has also failed to emerge as a set of contiguous high job density zones. Manufacturing jobs form corridors along the toll roads radiating out of the Jakarta urban core. These manufacturing corridors have penetrated the predominantly agricultural rings in the outer side of the JMA, forming a band of desakota similar to the model shown in McGee (2008).

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Figure 5.12 Spatial structure of employment in the JMA, 2002 Source: Processed by author.

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Featuresandspatialimpactsofemploymentclusters

Spatial characteristics of employment clusters

Densityvariables

Table 5.4 presents the basic characteristics of the identified employment clusters. One-way ANOVA is performed to compare average characteristics of zones belonging to each of the clusters. Euclidean distance to the centroid of Kelurahan Gambir, in which the MONAS landmark is located, is used to represent approximate location of clusters from the urban core of Jakarta. Manufacturing clusters outside the urban core are located, on average, around 30 kilometres from MONAS. Municipalities surrounding Jakarta are located from 20 to 25 kilometres, while Bogor Municipality is located more than 45 kilometres from MONAS. Desakota and agricultural areas are located 38.3 and 43.2 kilometres, respectively, from MONAS. The regional CBD (cluster 4) has the highest job density, followed by the urban centre in Bogor Municipality (cluster 1-b), five local administrations in Jakarta (cluster 6-a), four centres in Jakarta (cluster 2), the urban core of Jakarta (cluster 1-a) and local government of Bogor Municipality (cluster 6-b). These are followed (in general) by the manufacturing corridors, the other local government regions, the planned sub-centre of Serpong, the desakota, and finally, agricultural areas. Figure 5.13 shows the bar-chart which compares job density of the clusters (cluster 2 and cluster 6-a, which are within the Jakarta urban core, and cluster 1-b, which is within Bogor Municipality, are not shown in the chart). Job density is a variable of interest for urban policy and decision making as higher job density is regarded as more favourable to public transport ridership (Pivo, 1993) and therefore to promote more sustainable transport.

Gross job density is also an important variable that is often used to measure the degree of monocentricity of urban spatial structure with respect to the decline of job density as the distance from the CBD increases (Jun and Ha, 2002; Baumont et al., 2004). The plot of job density against distance from MONAS (Figure 5.14) shows an almost monotonically decreasing pattern of job density as the distance increases from MONAS (with the striking exception of Bogor Municipality), thereby

158 confirming a lack of higher job density sub-centres outside the dominant regional CBD and the Jakarta urban core of the JMA.

Table 5.4 Euclidean distance to MONAS and gross job density Average statistics of zones within cluster Euclidean Gross job Number distance to density Total area of jobs MONAS (persons/ Code Cluster name (sq. km) (persons) (km) sq.km) Factor 1: Higher job diversity urban core 1a Jakarta urban core 440.3 2,469,930 7.5 8,381 1b Bogor city centre 3.0 33,784 47.8 14,680 Factor 2: Higher job diversity centres 2 Four centres in Jakarta 148.9 938,210 8.5 10,041 Factor 3: Manufacturing corridors 3a Tj. Priok-Cilincing-Cakung 170.6 586,681 11.2 3,972 3b Penjaringan-Cengkareng-Kalideres 110.5 348,631 12.8 3,641 3c Tambun-Cibitung-Cikarang 209.0 306,130 32.5 1,844 3d Pasar Kemis-Jatiuwung-Cikupa 90.0 183,513 31.4 2,694 3e Cileungsi-Kalapanunggal-Citeureup 130.6 166,260 31.9 1,900 3f Ciracas-Cimanggis-Cibinong 70.8 141,659 29.3 2,046 Factor 4: Higher order services 4 Regional CBD 50.6 864,727 4.3 17,651 Factor 5: Agriculture, forestry, fishery 5 Agricultural areas 2,161.0 411,839 43.2 294 Factor 6: Local government 6a Five kotamadyas in Jakarta 110.7 682,013 8.9 9,762 6b Bogor Municipality 14.4 79,166 46.5 7,616 6c Depok Municipality 5.0 11,947 24.4 2,839 6d Tangerang Municipality 25.1 85,332 21.6 4,229 6e Bekasi Municipality and Regency 35.2 78,354 21.1 2,475 6f Bogor Regency 43.6 66,480 33.2 1,575 6g Tangerang Regency 30.8 11,830 40.6 409 Higher urban-rural mix 7 Desakota 1,488.8 436,012 38.3 370 LISA “high-low” quadrant: planned sub-centre 8 Serpong sub-centre 49.8 47,677 21.2 1,085 F-Statistic 202.61 37.63 (significance level) (0.01) (0.01) Source: Processed by author.

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20 18 16 14 12

jobdensity 10

(1,000jobs/sq.km) 8 6 4 2 0 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8 clustercode

Notes: 1a: Jakarta urban core 3e: Cileungsi-Kalapanunggal- 6d: Tangerang Municipality 3a: Tj. Priok-Cilincing- Cakung Citeureup 6e: Bekasi Municipality and 3b: Penjaringan-Cengkareng- 3f: Ciracas-Cimanggis-Cibinong Regency Kalideres 4: Regional CBD 6f: Bogor Regency 3c: Tambun-Cibitung-Cikarang 5: Agricultural areas 6g: Tangerang Regency 3d: Pasar Kemis-Jatiuwung- 6b: Bogor Municipality 7: Desakota Cikupa 6c: Depok Municipality 8: Kecamatan Serpong

Figure 5.13 Job density by employment clusters Source: Processed by author.

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20 4 18 16 14 12 2 10 6a 6b 8 1a jobdensity(1,000jobs/sq.km) 6 6d 4 3d 6c 3e 3b 2 6e 3c 6g 7 8 3f 5 0 0 5 10 15 20 25 30 35 40 45 50 distancefromMonas(km)

Figure 5.14 Changes in job density by distance to MONAS Source: Processed by author.

Table 5.5 presents the spatial characteristics of the employment clusters in terms of population density, household density and jobs to household ratio. The later is often measured under the job-housing balance policy that aims to promote a higher balance between the number of jobs and the number of workers in order to reduce commuting distance and promote more sustainable transport (Cervero, 1989; Giuliano and Small, 1993; Levine, 1998). The figures show that the regional CBD has the highest ratio of 24.3 jobs to household ratio while desakota and agricultural areas have the lowest. Although more balanced job to household ratios may promote less commuting distance, this indicator is not the sole explanation of commuting patterns. Jobs-housing balance notions have been criticised in some studies (Levinson, 1998; O’Kelly and Lee, 2005; Giuliano and Small, 1993) as they tend to overlook the spatial mismatch problem between the type of jobs and the type of workers that reside around the jobs. When the types of jobs do not match the types of workers that reside nearby, cross commuting (Schwanen et al., 2001) instead of the expected local commuting may be realised.

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Table 5.5 Population density, household density and jobs to household ratio Average statistics of zones within cluster Gross Gross population household density density Jobs to (persons/ (households/ household Code Cluster name sq. km) sq.km) ratio Factor 1: Higher job diversity urban core 1a Jakarta urban core 18,494 4,939 5.2 1b Bogor city centre 19,929 4,819 3.5 Factor 2: Higher job diversity centres 2 Four centres in Jakarta 13,898 3,664 11.7 Factor 3: Manufacturing corridors 3a Tj. Priok-Cilincing-Cakung 13,504 3,600 1.3 3b Penjaringan-Cengkareng-Kalideres 10,669 2,995 1.5 3c Tambun-Cibitung-Cikarang 4,051 1,135 1.9 3d Pasar Kemis-Jatiuwung-Cikupa 6,403 2,052 1.5 3e Cileungsi-Kalapanunggal-Citeureup 3,748 1,030 1.6 3f Ciracas-Cimanggis-Cibinong 7,291 1,931 1.2 Factor 4: Higher order services 4 Regional CBD 11,522 3,262 24.3 Factor 5: Agriculture, forestry and fishery 5 Agricultural areas 1,299 324 0.9 Factor 6: Local government 6a Five kotamadyas in Jakarta 14,689 3,887 6.9 6b Bogor Municipality 14,107 3,414 2.8 6c Depok Municipality 14,151 3,269 0.9 6d Tangerang Municipality 7,004 1,795 2.7 6e Bekasi Municipality 13,551 3,451 0.9 6f Bogor Regency 4,967 1,269 1.3 6g Tangerang Regency 1,374 328 1.1 Higher urban-rural mix 7 Desakota 1,512 380 0.9 LISA “high-low” quadrant 8 Serpong sub-centre 3,170 836 1.2 F-Statistic 57.27 56.52 6.20 (significance level) (0.01) (0.01) (0.01) Source: Processed by author.

Jobdiversity

Another important spatial characteristic or urban form that is used as an indicator of urban and transport sustainability is the degree of diversity of jobs by type of industry. Higher employment diversity is found to be associated with shorter commuting times (Cervero, 1989) and a lower share of private vehicle use (Cervero and Kockelman, 1997; Cervero 1989). In this study, the degree of job diversity is measured using the formulation proposed by Bhat and Gossen (2004) with regard 162 to the balance among private office jobs, amenities jobs and the other types of jobs (see equation 4.16 in Chapter 4). The result (Table 5.6 and Figure 5.15) shows that the regional CBD and the Jakarta urban core have the highest balance among private office jobs, amenities jobs and other jobs while desakota and agricultural areas return the lowest index. Undesirable results are generally shown for the manufacturing corridors, local government areas and the planned sub-centre of Serpong for their smaller index values to the Jakarta urban core. The result reveals the failure of these supposedly counter-magnets to Jakarta in promoting a higher balance of private office jobs and amenities jobs that could reduce the burden of commuting trips to the regional CBD and the Jakarta urban core. As seen in Table 5.6, while some local government regions have been able to co-locate employment with a high percentage of amenities jobs, the share of private office jobs in each of the clusters outside the urban core of Jakarta is well below 15 per cent (with the exception of Serpong sub-centre), or less than half of that in the Jakarta urban core.

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Table 5.6 Job diversity index Average statistics of zones within cluster Share of private Share of Employment office jobs amenities jobs diversity Code Cluster name (percentage) (percentage) index Factor 1: Higher job diversity urban core 1a Jakarta urban core 29.8 33.1 0.82 1b Bogor city centre 9.3 60.9 0.62 Factor 2: Higher job diversity centres 2 Four centres in Jakarta 31.8 23.7 0.72 Factor 3: Manufacturing corridors 3a Tj. Priok-Cilincing-Cakung 25.5 23.3 0.68 3b Penjaringan-Cengkareng-Kalideres 15.7 20.6 0.53 3c Tambun-Cibitung-Cikarang 11.1 16.1 0.40 3d Pasar Kemis-Jatiuwung-Cikupa 8.2 10.3 0.28 3e Cileungsi-Kalapanunggal-Citeureup 5.6 13.9 0.29 3f Ciracas-Cimanggis-Cibinong 15.8 18.2 0.49 Factor 4: Higher order services 4 Regional CBD 43.2 27.5 0.84 Factor 5: Agriculture, forestry and fishery 5 Agricultural areas 3.3 13.9 0.24 Factor 6: Local government 6a Five kotamadyas in Jakarta 25.0 32.6 0.74 6b Bogor Municipality 12.6 52.3 0.66 6c Depok Municipality 11.3 56.0 0.67 6d Tangerang Municipality 14.2 31.4 0.62 6e Bekasi Municipality 13.9 40.0 0.67 6f Bogor Regency 11.2 26.9 0.49 6g Tangerang Regency 6.4 15.0 0.32 Higher urban-rural mix 7 Desakota 3.9 15.4 0.27 LISA “high-low” quadrant 8 Serpong sub-centre 16.3 24.9 0.59 F-Statistic 105.81 26.70 116.46 (significance level) (0.01) (0.01) (0.01) Source: Processed by author.

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0.9 0.8 0.7 0.6 0.5

jobdiversityindex 0.4 0.3 0.2 0.1 0 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8 clustercode

Figure 5.15 Job diversity index by employment clusters Source: Processed by author.

Accesstotransportfacilitiesandjobaccessibility

Provision of transport infrastructure and services and distance to transport facilities are important spatial characteristics that influence travel patterns. Table 5.7 compares the average statistics of zones within the clusters in terms of zonal road density, distance to the nearest toll ramp and distance to the nearest railway station. Zonal road density is probably a more important variable for the case of developing cities where the imbalance of development between urban and peri-urban areas is higher than that of the developed cities. As seen in Table 5.7, road density is substantially higher in the Jakarta urban core and the local government regions of municipalities than that in manufacturing corridors, local government regions of regencies, desakota and agricultural areas. While manufacturing corridors have good access to toll ramps, desakota areas - which result from their interaction with the agricultural areas - have in general limited access to transport facilities. In addition to low road density, desakota areas are located far from both toll ramps and railway 165 stations. Impacts of such limited transport infrastructure in desakota areas on journey to work patterns will be investigated in the next chapter.

Table 5.7 Access to transport facilities Average statistics of zones within cluster Road density Distance to Distance to (km/ nearest toll nearest railway Code Cluster name sq. km) ramp (km) station (km) Factor 1: Higher job diversity urban core 1a Jakarta urban core 13.7 2.0 1.9 1b Bogor city centre 10.8 2.5 2.0 Factor 2: Higher job diversity centres 2 Four centres in Jakarta 11.9 2.3 2.1 Factor 3: Manufacturing corridors 3a Tj. Priok-Cilincing-Cakung 12.6 2.9 3.5 3b Penjaringan-Cengkareng-Kalideres 7.1 3.6 2.6 3c Tambun-Cibitung-Cikarang 4.7 3.9 4.3 3d Pasar Kemis-Jatiuwung-Cikupa 6.2 2.8 9.5 3e Cileungsi-Kalapanunggal-Citeureup 5.5 5.4 13.0 3f Ciracas-Cimanggis-Cibinong 7.1 3.0 4.8 Factor 4: Higher order services 4 Regional CBD 14.4 2.2 1.4 Factor 5: Agriculture, forestry and fishery 5 Agricultural areas 2.5 16.2 16.3 Factor 6: Local government 6a Five kotamadyas in Jakarta 13.1 2.1 1.8 6b Bogor Municipality 11.1 2.8 1.5 6c Depok Municipality 12.1 9.1 1.1 6d Tangerang Municipality 10.4 4.4 2.0 6e Bekasi Municipality 13.5 2.1 1.8 6f Bogor Regency 5.7 4.4 4.5 6g Tangerang Regency 3.8 6.2 7.7 Higher urban-rural mix 7 Desakota 3.0 10.3 13.2 LISA “high-low” quadrant 8 Serpong sub-centre 10.1 3.2 3.9 F-Statistic 115.65 73.03 72.15 (significance level) (0.01) (0.01) (0.01) Source: Processed by author.

Accessibility has been regarded as the variable that links land-use and transport (Wegener, 1996; Hanson, 2005). The job accessibility index is measured as the number of jobs that can be reached from a particular point or area divided by travel impedance. This study adopts Hansen’s (1959) formulation of job accessibility (see equation 4.15 in Chapter 4), with the impedance coefficient equal to one. The

166 impedance coefficient value is chosen because it allows more intuitive comparisons of job accessibility among clusters and among different modes of transport. The impedance used is the average travel time between traffic analysis zones (TAZ) by transport mode. The calculation is performed using the integrated traffic assignment package (Arikawa, 2006) which was used in the SITRAMP study (ROI BAPPENAS-JICA, 2004a).

Table 5.8 presents job accessibility by car, motorcycle and public transport and by employment cluster. Job accessibility is often used as an explanatory variable for origin-based travel behaviour (i.e., higher job accessibility is associated with a higher number of job opportunities or less travel barriers). Higher job accessibility from the trip origin point of view has been found to be associated with shorter commuting time (Levinson, 1998). From the trip destination point of view, on the other hand, job accessibility might be used as a proxy for the degree of agglomeration economies at the workplace. As seen in Table 5.8 and Figure 5.16, the job accessibility index is highest for the regional CBD, meaning that there is a high number of jobs that can be accessed with less travel impedance from the regional CBD than from the other employment clusters. Much higher job accessibility of the regional CBD and the Jakarta urban core, as compared to the other employment clusters, therefore suggests how difficult it is to promote substantial sub-centres outside the Jakarta urban core, as firms are attracted to the urban core to take advantages of the higher agglomeration economies. The fact that job accessibility by car is higher than job accessibility by public transport for most of the employment clusters in the JMA shows that car is the fastest transport mode in connecting jobs. This implies higher dependence on car in facilitating the agglomeration economies. Furthermore, higher job accessibility at trip destinations is not necessarily associated with less commuting time or commuting distance. On the contrary, higher job accessibility at the trip destination or at workplaces implies bigger job centres that are often associated with longer commuting time and commuting distance (Aguilera and Mignot, 2004).

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Table 5.8 Job accessibility index by mode of transport Average statistics of zones within cluster Job Job Job accessibility accessibility accessibility by public Code Cluster name by car by motorcycle transport Factor 1: Higher job diversity urban core 1a Jakarta urban core 253,215 237,572 127,433 1b Bogor city centre 105,574 88,287 67,417 Factor 2: Higher job diversity centres 2 Four centres in Jakarta 251,130 234,807 155,638 Factor 3: Manufacturing corridors 3a Tj. Priok-Cilincing-Cakung 197,950 187,284 161,506 3b Penjaringan-Cengkareng-Kalideres 158,281 141,641 99,214 3c Tambun-Cibitung-Cikarang 106,111 78,980 46,738 3d Pasar Kemis-Jatiuwung-Cikupa 126,887 89,908 53,726 3e Cileungsi-Kalapanunggal-Citeureup 120,227 86,903 46,849 3f Ciracas-Cimanggis-Cibinong 129,059 105,191 66,210 Factor 4: Higher order services 4 Regional CBD 326,120 314,545 136,299 Factor 5: Agriculture, forestry and fishery 5 Agricultural areas 79,650 68,281 36,867 Factor 6: Local government 6a Five kotamadyas in Jakarta 240,151 224,862 122,133 6b Bogor Municipality 110,763 93,548 118,874 6c Depok Municipality 100,753 99,137 69,960 6d Tangerang Municipality 140,758 118,740 78,649 6e Bekasi Municipality 121,949 99,964 70,590 6f Bogor Regency 115,908 97,970 50,055 6g Tangerang Regency 99,837 70,823 36,699 Higher urban-rural mix 7 Desakota 89,458 70,116 36,865 LISA high-low quadrant 8 Serpong sub-centre 131,453 108,704 58,678 F-Statistic 119.54 119.44 21.01 (significance level) (0.000) (0.000) (0.000) Source: Processed by author.

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350

300

250

200

bycar(thousands) 150 jobaccessibilityindex 100

0 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8 clustercode

Figure 5.16 Job accessibility index by car by employment clusters Source: Processed by author.

Spatial impacts

Weightedcentralfeature

Some further analyses that include empirical investigations of spatial impacts and spatial interaction associated with the spatial structure of employment in the JMA require the identification of the centres of employment clusters. For a single zone where population or jobs are assumed uniformly distributed across the zone, it is common to use the zonal centroid as the centre of the zone. In this study, each identified employment cluster consists of more than one zone so that it is first required to decide on the one zone whose centroid is used as the centre of the cluster. In GIS terminology, such a zone is referred to as the central feature, calculated as the zone having the shortest total distance to the other zones (Mitchell, 2005, p. 35). It is possible to decide the central feature as the zone whose centroid is the most centrally located among the set of centroids within the cluster.

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A more accurate approach is to apply a weighted central feature method in which each centroid within the cluster is assigned a weight value such as the number of jobs or population corresponding to its zone. In this study, the weighted central feature approach is adopted in which each centroid is assigned the total number of jobs located in its corresponding kelurahan or desa. The identification process is conducted using ArcGIS 9.2 and the results are shown in Figure 5.17.

Figure 5.17 Central features of employment clusters Source: Processed by author.

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TheregionalCBDandemploymentdensity

The result of LISA on employment density has confirmed the dominance of the Jakarta urban core as almost solely the cluster of high employment density in the JMA. The employment clusters that have been identified have also shown the regional CBD at the heart of the Jakarta urban core as the cluster with the highest job density. While the surrounding municipalities of Tangerang, Bekasi and Depok and the manufacturing corridors outside the Jakarta urban core have not been classified as “high-high” in terms of employment density, it is useful to measure the extent of association (Ord and Getis, 1995; Bao et al., 1995; Hung and Yasuoka, 2001) of the regional CBD and the employment density of its surroundings. This study interprets the spatial association results in a similar way to Ord and Getis

(1995), who used the plot of against the radius of the rings centred on the feature from which the distance impacts was investigated. Significant spatial association (at 0.05 level of significance) is defined as the distance at which the radius of the ring is associated with value of 4.16 (with the application of Bonferroni-type test) or 1.96 (without the application of Bonferroni-type test). Measured from Setiabudi, as the central feature of the regional CBD, it is shown from Figure 5.18 that the distance impact of the regional CBD on employment density is between 51 kilometres ( higher than 4.16) to 58 kilometres ( higher than 1.96). The result suggests that zones of high employment density are clustered far beyond the Jakarta urban core limit as may be otherwise implied if looking only at the LISA result. This result also confirms the strength of the regional CBD in explaining the distribution of job density in the JMA, one that strongly resembles a monocentric urban structure. Combined with the results of LISA of job density, which generally does not regard employment clusters outside the Jakarta urban core as zones of higher job density, it can be hypothesised that employment clusters outside the Jakarta urban core form a sprawl-like pattern of between 51 to 58 kilometres from the regional CBD.

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25 23.66 Z(Gi) 20.04 20 16.78 15 14.11 11.68 10 9.72 7.48 6.65 4.45 5 2.19

0 0 102030405060 kilometres

Figure 5.18 Spatial association between the regional CBD and employment density Source: Processed by author.

Urbancoresandnegativepopulationgrowth

Previous studies indicated that while the inner ring and the outer ring of the JMA have experienced rapid increases in population, the population growth in the urban core of Jakarta has been negative (Jones, 2004). Spatial association analysis allows the investigation of the degree of association (measured as distance impacts) between the urban core of Jakarta or other urban centres in the JMA and the negative population growth in their surroundings. The data used is the change in the population at kelurahan or desa level between 1990 and 2000, based on the population census conducted in those years. Visual examination of the Getis-Ord statistic at 4 kilometres from the centroid of kelurahan and desa in the JMA suggests that significant negative population growth in the region has been associated with the centres of the Jakarta urban core (cluster 1-a) and the urban centre of Bogor Municipality (cluster 1-b). The central features of these two clusters as shown in Figure 5.17 are chosen as the centres from which the distance impacts of these employment clusters on negative population growth in their surroundings are measured. As suggested from the spatial association analysis (Figure 5.19 and Figure 5.20), clusters of kelurahan and desa experiencing negative population growth within the period of 1990-2000 are found within the distance of 20 kilometres

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( > 4.16) to 22 kilometres ( > 1.96) from the centre of the Jakarta urban core and within the distance of 14 kilometres from the centre of the Municipality of Bogor. The results confirm previous findings (e.g., Jones, 2004), but with the addition that Bogor urban centre is another point whose surroundings are experiencing negative population growth. The results show that the Jakarta urban core has experienced an increasing jobs to household ratio (as jobs have increased and the population has decreased) within the period between 1990-2000. This partly explains the increasing traffic congestion problems along the radial roads towards the Jakarta urban core in the past few years. While this suggests the importance of developing sub-centres outside the Jakarta urban core, as recommended in past studies (e.g., ROI MPW, 1993; ROI BAPPENAS-JICA, 2004a), another strategy is to maintain or decrease the jobs to household ratio within the Jakarta urban core and the regional CBD.

14 12.84 13.26 Z(Gi) 11.96 12 11.41

10 7.99 8

6 5.05

4 2.73 2

0 0 5 10 15 20 25 kilometres

Figure 5.19 Spatial association between the Jakarta urban core and negative population growth Source: Processed by author.

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4 Z(Gi) 3.19 2.75 3 2.60 2.14 2

0 0 2 4 6 8 10 12 14 16 kilometres

Figure 5.20 Spatial association between Bogor urban centre and negative population growth Source: Processed by author.

Theeastwestaxisandhighpopulationgrowth

The earlier planning of the JMA encouraged urban expansion and population growth of the region towards the east-west direction (ROI-MPW, 1981) as it was recognised that the region along the east-west axis was more suitable for urban expansion than the more environmentally sensitive southern and northern parts of the JMA. Employment clusters identified earlier have shown that urban expansion towards the east and west directions of the JMA have been dominated by manufacturing corridors, which are the Tambun-Cibitung-Cikarang corridor (cluster 3-c) at the east and Pasar Kemis-Jatiuwung-Cikupa corridor (cluster 3-d) at the west. In this study, the extent of high population growth along the east-west axis of the JMA is investigated through spatial association analysis measured from the central features of these two employment clusters. The results (Figure 5.21 and Figure 5.22) reveal that kelurahan and desa that experienced rapid population growth during the period between 1990-2000 are clustered within 26 kilometres

( > 4.16) to 30 kilometres ( > 1.96) from the centre of cluster 3-c in the east and within 28 kilometres ( > 4.16) to 30 kilometres ( > 1.96) from

174 the centre of cluster 3-d in the west of the JMA. Combined with the figure on the spread of large scale housing development (as shown in Chapter 3), the figures confirm how large is the extent of population and housing expansion in the eastern and western sides of the JMA. The concern is that rapid population growth along the east-west axis has helped form a sprawl-like corridor of housing development along the east-west toll network (just like the manufacturing corridors), as shown in Chapter 3.

7 Z(Gi) 6.12 5.94 6 5.25 5.27 5 3.97 4

3 2.53 2.22 2

0 0 5 10 15 20 25 30 35 kilometres

Figure 5.21 Spatial association between Tambun-Cibitung-Cikarang manufacturing corridor and population growth Source: Processed by author.

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9 Z(Gi) 7.68 8 7.48 7 6.65 6 5.48 5 4 3.05 3 2 1.53 1 0 0 5 10 15 20 25 30 35 kilometres

Figure 5.22 Spatial association between Pasar Kemis-Jatiuwung-Cikupa manufacturing corridor and population growth Source: Processed by author.

The extent of slower population growth experienced by areas in the south of the JMA is tested from the central features of Depok Municipality and Bogor Regency, representing areas between the urban core of Jakarta and Bogor Municipality (which are already identified as being associated with negative population growth). The empirical results (Figure 5.23 and Figure 5.24) confirm the lesser extent of population growth in the southern part of the JMA, in which Depok Municipality and Bogor Regency are associated with high population growth within the distance of 20 kilometres and 14 kilometres from their corresponding centres, respectively (both cases are not significant if the Bonferroni-type test is applied). The results confirm the effects of the policy (ROI MPW, 1981) of discouraging urban growth in the southern part of the JMA.

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5 Z(Gi) 3.82 4 3.46 3.55 2.90 3 2.35

2 1.56

0 0 5 10 15 20 25 kilometres

Figure 5.23 Spatial association between Depok Municipality and population growth Source: Processed by author.

4 Z(Gi) 2.97 2.85 3

1.99 2 1.72

0 0246810121416 kilometres

Figure 5.24 Spatial association between Bogor Regency and population growth Source: Processed by author.

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Manufacturingcorridorsandurbanruralmix

While the extent of desakota in the JMA has been proposed using the “manufacturing-agriculture worker index”, spatial association analysis may provide more insights into the extent of the influence of manufacturing corridors on the urban-rural mix characterising desakota. One characteristic of desakota set out by McGee (1991, p. 17) is the phenomenon where one family member of a particular household works as a farmer while another family member from the same household works in manufacturing industry, due to spatial co-location of urban and rural job opportunities in desakota areas. The spatial association analysis is applied to measure the extent to which clusters of zones have a higher percentage of such “mixed agriculture-manufacturing households” from the centres of manufacturing corridors in the east and west of the JMA. The Tambun-Cibitung-Cikarang manufacturing corridor is shown as having 22 kilometres ( > 4.16) to 26 kilometres ( > 1.96) distance impacts of clusters of “mixed agriculture- manufacturing households” from its centre (Figure 5.25). Likewise, the Pasar Kemis-Jatiuwung-Cikupa manufacturing corridor’s influence is 28 kilometres

( > 4.16) to 32 kilometres (Z(Gi) > 1.96) from its centre (Figure 5.26).

6 5.37 Z(Gi) 5 4.62 4.31

4 3.38 3.54

3 2.53

0 0 5 10 15 20 25 30 kilometres

Figure 5.25 Spatial association between Tambun-Cibitung-Cikarang manufacturing corridor and “mixed agriculture-manufacturing household” Source: Processed by author.

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6 5.33 5.5 Z(Gi) 5 4.33 4 3.78 3.26 3 2.17 2

0 0 5 10 15 20 25 30 35 kilometres

Figure 5.26 Spatial association between Pasar Kemis-Jatiuwung-Cikupa manufacturing corridor and “mixed agriculture-manufacturing household” Source: Processed by author.

Conclusions

Exploratory spatial data analysis (ESDA) is adopted for identification of important components of the spatial structure of employment in the JMA. The approach has been specifically designed in order to extract clusters as suggested in the Southeast Asian EMR concept. It is found that the spatial structure of employment in the JMA consists of the following major components:

- the urban core of Jakarta; - the single dominant and expanded regional CBD within the urban core of Jakarta; - manufacturing corridors that are largely follow toll roads radiating out of the urban core; - local government regions that in general have not been developed into substantial sub-centres; - desakota areas overlapping the manufacturing corridors and the agricultural areas; and - portions of agricultural areas in the outer parts of Bekasi, Bogor and Tangerang regencies.

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A key finding is that the spatial structure of employment in the JMA shows some similarities to the model of McGee (2008). In particular, the Jakarta urban core and the single CBD within the urban core as the place of higher order services looks very similar to the model. Manufacturing corridors identified in this study also confirm those postulated in McGee (2008), while dispersed small industries within agricultural areas are not detected in the JMA. Desakota areas are found in the form of bands that lay approximately in between or overlap areas of manufacturing and agriculture employment clusters, and consequently do not form complete rings as shown by McGee (2008).

The JMA may be considered monocentric by looking at the graph of the plot of job density against distance to its regional CBD, but from the identified components of the spatial structure of employment, it is concluded that the JMA conforms largely to the Southeast Asian EMR model of spatial structure (as proposed in McGee, 1991; McGee and Robinson, 1995; McGee, 2008).

Investigations on the spatial characteristics of the major employment clusters reveals significant differences among the clusters in terms of job density, jobs to household ratio, the degree of job diversity, distance to transport facilities and accessibility to jobs. The latter is seen as a proxy to the degree of agglomeration economies, which shows the superiority of the regional CBD and the urban core of Jakarta over the other clusters. Combined with their substantially much higher job density, job diversity and jobs to household ratio, the dominance of the Jakarta urban core and the regional CBD in the JMA seems very difficult to challenge. These in part explain the failure of the emergence of sub-centres outside the CBD, while, of course, such a failure cannot be separated from weak and inefficient implementation of policies that aim to promote sub-centres and reduce the heavy reliance of the region on Jakarta as the place for jobs. Spatial association analysis also reveals significant association between the regional CBD and employment density surrounding it at a distance of up to nearly 60 kilometres.

Jakarta and Bogor urban centres have been found associated with negative population growth surrounding them, while the distance impacts of the east-west axis on population growth are significant to around 30 kilometres. On the other hand, in the southern parts of the JMA where population growth and urban

180 expansion have been discouraged, population growth is significant at a shorter distance of 20 kilometres and 14 kilometres from the local government regions of Depok Municipality and Bogor Regency, respectively. The results of spatial association between manufacturing and one aspect of urban-rural mix complement the extent of desakota identified. It is found that Tambun-Cibitung-Cikarang and Pasar Kemis-Jatiuwung-Cikupa manufacturing corridors are significantly associated with the higher zonal percentage of “mixed agriculture-manufacturing households” surrounding them at the distance of up to 26 kilometres and 32 kilometres, respectively.

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6 Journey to work impacts of the JMA’s spatial structure of employment and its physical features

Introduction

This chapter extends the enquiries on the spatial structure of employment in the JMA by investigating the degree of spatial interaction in the region in terms of the exchange of journey to work trips and the types of impacts the spatial structure of employment and its physical features has on journey to work patterns. The focus of this chapter is on addressing the second and third research questions with regard to journey to work impacts of the spatial structure of employment in the JMA and policy implications for urban form and transport within the Southeast Asian EMR context. The chapter begins by investigating the degree of spatial interaction in terms of journey to work across the JMA. Then, the journey to work dimensions of travel distance, travel time and mode choice are compared across the employment clusters identified in Chapter 5. Investigations of the impacts of the spatial structure of employment on travel are continued with the application of travel models that aim to explore how the physical features of urban structure affect travel decisions.

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The results are discussed in the context of transport sustainability and aim to put forward possible policies that can promote better transport conditions for the region.

Journey to work and the degree of spatial interaction

As discussed in Chapter 2, empirical studies investigating the impacts of urban spatial structure and urban form on travel may fall into either origin or destination points of view. The former aims at revealing the influence of the spatial characteristics of residential areas on the travel behaviour of their residents, while the later investigates the spatial characteristics of workplaces and how they influence the work trip pattern of their workers. This study falls into the second category as the logical consequence of the focus on the spatial structure of employment.

The home interview survey used in this study (ROI BAPPENAS-JICA 2004c) classified trips into eight categories (Table 6.1). This study investigates the home-to- work trip pattern (i.e., one-way journey to work trip) as an impact of the spatial structure of employment in the JMA. Home-to-work trips accounted for 15 per cent of the total trips made in the JMA in 2002. There were more than 4.3 million home- to-work trips destined to the employment clusters identified in Chapter 5. This figure accounts for 78 per cent of all home-to-work trips made daily in the JMA in 2002. In other words, around 22 per cent of home-to-work trips were destined outside the employment clusters identified in Chapter 5.

Table 6.2 shows that 40.5 per cent of home-to-work trips in the JMA were hosted by the Jakarta urban core (cluster 1-a). The regional CBD (cluster 4) located at the heart of the urban core attracted 13.9 per cent of home-to-work trips in the JMA (which were part of those destined to the Jakarta urban core as employment clusters in this study are not mutually exclusive). Outside the urban core of Jakarta, each of the manufacturing corridors (clusters 3-c to 3-f) accounted for 5 per cent or less of home-to-work trips while each of the local government regions (clusters 6-b to 6-g) accounted for less than 1.5 per cent of home-to-work trips in the JMA. Around 7 per cent of home-to-work trips were made in agricultural areas (cluster 5). Areas regarded as desakota (cluster 7) in this study attracted 7.4 per cent of trips. The

183 planned sub-centre of Serpong (cluster 8) only attracted a small proportion (0.8 per cent) of home-to-work trips in the region.

Table 6.1 Home-based trips in the JMA, 2002 Trip purpose The number of trips Share (percentage) Home-to-work 5,608,011 15.0 Home-to-school 5,301,247 14.2 Home-to-others 6,518,380 17.5 Work-to-home 5,083,695 13.6 School-to-home 5,030,892 13.5 Others-to-home 6,463,955 17.3 Non-home-based business 911,437 2.4 Non-home-based others 2,412,124 6.5 Total 37,329,741 100.0

Source: Processed by author from SITRAMP HIS.

More insights into the degree of spatial economic integration in the JMA, as suggested in the definition of the Southeast Asian EMR (McGee and Robinson 1995, p. x), can be assessed from an empirical investigation of the extent of journey to work trips made by workers in the region. In this study, the degree of spatial interaction in the JMA is investigated through the pattern of desirelines of home-to- work trips destined to the employment clusters. The desirelines are drawn connecting centroids of kecamatan as the origins and the centroids of the central features of employment clusters (as identified in Chapter 5) as the destinations. The pattern of desirelines that is identified (Figures 6.1 to 6.7) confirms the spatially integrated nature of the JMA in which spatial inter-dependence between workers and jobs extends across the region. The pattern also suggests a hierarchy of spatial interactions in which the Jakarta urban core and the regional CBD are at the top of the hierarchy (in terms of the spatial extent and the number of trips drawn), followed consecutively by the manufacturing corridors, the local government regions, desakota and the agricultural areas. This confirms the strength of the six factors, representing co-location tendencies of industries, as revealed from the factor analysis conducted in Chapter 5.

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Table 6.2 The number of trips by employment clusters One-way journey to work trips destined to cluster The share among The share in The number of clusters the JMA Code Cluster name trips (percentage) (percentage) Factor 1: Higher job diversity urban core 1a Jakarta urban core 2,260,536 52.1 40.5 1b Bogor city centre 29,969 0.7 0.5 Factor 2: Higher job diversity centres 2 Four centres in Jakarta 872,524 20.1 15.6 Factor 3: Manufacturing corridors 3a Tj. Priok-Cilincing-Cakung 548,503 12.6 9.8 3b Penjaringan-Cengkareng-Kalideres 329,609 7.6 5.9 3c Tambun-Cibitung-Cikarang 288,323 6.6 5.2 3d Pasar Kemis-Jatiuwung-Cikupa 170,919 3.9 3.1 Cileungsi-Kalapanunggal- 3e 156,129 3.6 2.8 Citeureup 3f Ciracas-Cimanggis-Cibinong 133,562 3.1 2.4 Factor 4: Higher order services 4 Regional CBD 778,480 17.9 13.9 Factor 5: Agriculture, forestry, fishery 5 Agricultural areas 399,193 9.2 7.1 Factor 6: Local government 6a Five kotamadyas in Jakarta 630,265 14.5 11.3 6b Bogor Municipality 71,464 1.6 1.3 6c Depok Municipality 11,358 0.3 0.2 6d Tangerang Municipality 78,056 1.8 1.4 6e Bekasi Municipality 75,946 1.8 1.4 6f Bogor Regency 61,073 1.4 1.1 6g Tangerang Regency 11,067 0.3 0.2 Higher urban-rural mix 7 Desakota 410,444 9.5 7.4 LISA “high-low” quadrant 8 Serpong sub-centre 44,238 1.0 0.8 Total to all clusters 4,339,511 Total within the JMA 5,583,345

Source: Processed by author.

The desirelines of journey to work destined to the urban core (Figure 6.1) confirm the spatial structure of employment identified in Chapter 5, in which the JMA exhibits a very dominant urban core, resembling a monocentric urban structure (in terms of the variation of job density over distance from its regional CBD). The pattern shows that the Jakarta urban core attracts commuting from virtually every corner of the JMA, including from areas identified as desakota. However, the

185 pattern clearly shows the influence of the trunk transport corridors radiating out of the Jakarta urban core. The desirelines of journey to work trips destined to the urban core are more concentrated along the east-west axis and from southern and south-western directions, where the toll road corridors are located. It is also revealed that the vast majority of trips originated from within the urban core (shown by the heavier desirelines).

Figure 6.2 highlights smaller catchment areas for journey to work trips associated with the two manufacturing corridors inside the urban core as compared to the Jakarta urban core. While these two clusters are also shown to attract commuting from desakota areas, a higher dependence on the labour force from desakota areas is shown in manufacturing corridors outside the urban core (Figure 6.3), due to their closer locations to agricultural areas in the outer parts of the JMA. A cross- commuting pattern is also shown for the Tambun-Cibitung-Cikarang manufacturing corridor, which attracts substantial commuting trips from the Jakarta urban core.

The pattern of desirelines of commuting to the regional CBD (Figure 6.4) shows a smaller version of that to the Jakarta urban core for the catchment areas. An important difference, however, is shown in terms of the portion of trips attracted from within the Jakarta urban core. Compared to the Jakarta urban core, the regional CBD attracts less localised trips, reflecting the trend of suburbanisation of workers of this higher order services industry.

The notion of failure in promoting local government regions outside the Jakarta urban core into substantial sub-centres is reflected from the pattern of desirelines of commuting trips destined to these local administrative centres (Figure 6.5). While the smaller catchment areas shown is desirable, as it demonstrates less commuting distance, the magnitude of the desirelines indicates that these clusters do not offer a substantial number of jobs that otherwise serve as counter-magnets to the Jakarta urban core. The case is probably more undesirable for the planned sub-centre of Serpong (Figure 6.7), in which a small number of trips (i.e., 0.8 per cent of total home-to-work trips in the JMA) is drawn from relatively large catchment areas. Kecamatan Serpong also attracts journey to work trips from desakota areas and cross- commuting from the Jakarta urban core.

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Figure 6.1 Journey to work desirelines to the Jakarta urban core Source: Processed by author.

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Figure 6.2 Journey to work desirelines to manufacturing corridors within the Jakarta urban core Source: Processed by author.

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Figure 6.3 Journey to work desirelines to manufacturing corridors outside the Jakarta urban core Source: Processed by author.

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Figure 6.4 Journey to work desirelines to the regional CBD Source: Processed by author.

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Figure 6.5 Journey to work desirelines to local government regions outside the Jakarta urban core Source: Processed by author.

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Figure 6.6 Journey to work desirelines to selected kecamatans in desakota areas Source: Processed by author.

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Figure 6.7 Journey to work desirelines to Kecamatan Serpong Source: Processed by author.

Variations in journey to work patterns

Distance travelled

Travel distance is an important travel dimension that is used to measure transport sustainability performance (Newman and Kenworthy, 1999, p. 104-107) due to its

193 direct implications for energy use and emissions. Average travel distance destined to the clusters by transport mode is shown in Table 6.3 and Figures 6.8 to 6.11. For all the clusters, home-to-work trips by car and public transport cover the longest distances of 14.1 and 13.2 kilometres, respectively, followed by those for motorcycle (9.1 kilometres) and for non-motorised modes (2.8 kilometres). Variations in average travel distance by car among the clusters, reveals patterns that are not expected nor desirable. For example, the Tambun-Cibitung-Cikarang manufacturing corridor (cluster 3-c), the local government region of Tangerang Regency (cluster 6-g) and the desakota areas (cluster 7) attracted longer car travel distance than the regional CBD and the Jakarta urban core. A possible explanation is that a substantial share of workers at these clusters opts to live further from their workplaces and take advantage of the comparatively higher travel speeds for the outbound traffic direction, by driving to work. The local government regions of Bogor, Depok and Bekasi municipalities, on the other hand, attracted the lowest average car travel distances of less than 10 kilometres. This figure is alarming as the average commuting distance by car is comparable to that experienced by the infamous auto-dependent American cities (see Newman and Kenworthy, 1999, p. 87).

Average travel distance by motorcycle is lower than that of the car for each of the clusters. While the pattern shows the preference of workers towards this cheaper cost travel mode (ROI BAPPENAS-JICA, 1999) for shorter travel distance, another way of view suggests that higher income workers (i.e., car users) tend to live further from their workplaces than lower income workers (i.e., motorcycle users) do. The regional CBD accounts for the highest travel distance by motorcycle of 12.8 kilometres, among the clusters, followed by the Jakarta urban core (including the centres and the local government regions within it). Average travel distance of motorcycle for the other clusters varies between around 5 kilometres to 8 kilometres. Agricultural areas and desakota areas attract motorcycle work trips at an average distance of 5.9 kilometres and 6.5 kilometres, respectively. These are not the lowest among the clusters.

Average travel distance by public transport modes (including bus and train services) varies significantly among the clusters, with the regional CBD attracting the longest

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(15.9 kilometres), followed by centres within the Jakarta urban core (between 13.5 to 15.2 kilometres), the Tambun-Cikarang-Cibitung manufacturing corridor (14.5 kilometres) and the desakota areas (13.3 kilometres). Among those attracting shortest public transport travel distances are the local government regions of Bogor Municipality (7.1 kilometres), Depok Municipality (8.8 kilometres) and Bekasi Municipality (8.8 kilometres), and the Ciracas-Cimanggis-Cibinong manufacturing corridor (8.3 kilometres). Home-to-work travel by non-motorised transport (including walking, bicycling and taking becak) covers the average distance of 2.8 kilometres among all the clusters. Longest averages of non-motorised home-to-work travel distance are shown in the local government region of Tangerang Regency (4.0 kilometres) and the agricultural areas (3.8 kilometres) but they are not much different to those in the regional CBD (3.5 kilometres) and some of the manufacturing corridors.

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Table 6.3 Travel distance by transport modes by employment clusters Average travel distance destined in cluster (kilometres) By non- By By public motorised Code Cluster name By car motorcycle transport transport Factor 1: Higher job diversity urban core 1a Jakarta urban core 13.5 9.9 13.5 2.1 1b Bogor city centre 10.3 8.1 8.0 3.0 Factor 2: Higher job diversity centres 2 Four centres in Jakarta 15.3 11.0 15.2 2.7 Factor 3: Manufacturing corridors 3a Tj. Priok-Cilincing-Cakung 12.1 8.8 10.9 2.0 3b Penjaringan-Cengkareng-Kalideres 12.3 6.6 9.8 2.3 3c Tambun-Cibitung-Cikarang 20.7 7.2 14.5 3.4 3d Pasar Kemis-Jatiuwung-Cikupa 12.0 6.1 12.5 3.3 3e Cileungsi-Kalapanunggal-Citeureup 13.1 6.0 10.0 3.6 3f Ciracas-Cimanggis-Cibinong 12.5 4.8 8.3 2.1 Factor 4: Higher order services 4 Regional CBD 15.0 12.8 15.9 3.5 Factor 5: Agriculture, forestry, fishery 5 Agricultural areas 11.2 5.9 10.5 3.8 Factor 6: Local government 6a Five kotamadyas in Jakarta 14.3 9.9 13.8 2.1 6b Bogor Municipality 9.5 7.1 7.1 2.7 6c Depok Municipality 9.8 6.1 8.8 1.6 6d Tangerang Municipality 12.3 8.1 10.9 3.4 6e Bekasi Municipality 8.5 6.3 8.8 2.4 6f Bogor Regency 14.1 5.9 9.8 2.9 6g Tangerang Regency 20.9 7.4 12.4 4.0 Higher urban-rural mix 7 Desakota 21.9 6.5 13.3 3.4 LISA “high-low” quadrant 8 Serpong sub-centre 11.9 7.5 12.6 3.0 All clusters 14.1 9.1 13.2 2.8 F-Statistic 1368.57 4778.56 4480.11 1710.31 (significance level) (0.000) (0.000) (0.000) (0.000) Source: Processed by author.

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10 home-to-work travel

distanceby car (kilometres) 5

0 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8 cluster code

Figure 6.8 Travel distance by car Source: Table 6.3.

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4 by motorcycle (kilometres)

home-to-work travel distance 2

0 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8 cluster code

Figure 6.9 Travel distance by motorcycle Source: Table 6.3.

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18 16 14 12 10 8 6 4 home-to-work travel distance

by public transport (kilometres) 2 0 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8 cluster code

Figure 6.10 Travel distance by public transport Source: Table 6.3.

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1 home-to-work travel distance by non-motorised transport (km) 0 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8 cluster code

Figure 6.11 Travel distance by non-motorised transport Source: Table 6.3.

Time travelled

Travel time is considered an important indicator of transport sustainability performance (e.g., Litman and Burwell, 2006). Table 6.4 and Figures 6.12 to 6.15 present the comparisons of home-to-work travel time by travel mode among the employment clusters. Travel times for car, motorcycle and public transport are derived from the integrated traffic simulation package (Arikawa, 2006) while that of non-motorised travel, which is not simulated in the traffic simulation package, is based on travel departure and arrival times as reported by trip makers. Travel time by public transport includes waiting time and on-board travel time. The average travel times for home-to-work trip by private vehicles are 28.4 minutes by car and 21.6 minutes by motorcycle. Travel time by car is consistent with findings from other cities suggesting around a half hour commuting time (Newman and Kenworthy, 1999, p. 27).

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The average travel time by public transport (55.8 minutes) is considerably higher than both those of car and motorcycle, while that by non-motorised transport takes an average of almost 20 minutes. The fact that the average travel time by public transport is almost twice as long as that by car and is 2.6 times as long as that by motorcycle is not favourable for transport sustainability. The case is worse for agricultural and desakota areas, where the lower frequency of public transport services results in longer total travel time. This makes the motorcycle a popular option over public transport modes. The regional CBD and the Jakarta urban core attract the longest travel times by car, motorcycle and public transport modes among the clusters, reflecting both longer distance travel among most of the other clusters and more severe inbound traffic condition. The “suburban gridlock” phenomenon (Cervero, 1986) seems to be experienced in the JMA as car travel speeds suggested from the figures also implies slow travel speed for traffic destined to employment clusters outside the urban core, and this, therefore, suggests a wider spatial coverage of unsustainable transport conditions (for loss in fuel consumption and higher emission in traffic congestion) than was probably assumed.

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Table 6.4 Travel time by transport modes by employment clusters Average travel time destined in cluster (minutes) By non- By By public motorised Code Cluster name By car motorcycle transport transport Factor 1: Higher job diversity urban core 1a Jakarta urban core 27.4 23.9 55.9 19.1 1b Bogor city centre 23.8 22.7 39.9 21.0 Factor 2: Higher job diversity centres 2 Four centres in Jakarta 31.1 26.6 61.4 20.7 Factor 3: Manufacturing corridors 3a Tj. Priok-Cilincing-Cakung 23.6 20.1 48.7 19.0 Penjaringan-Cengkareng- 3b 24.9 16.9 45.7 19.6 Kalideres 3c Tambun-Cibitung-Cikarang 38.3 17.3 65.0 21.2 3d Pasar Kemis-Jatiuwung-Cikupa 19.9 13.4 57.0 19.7 Cileungsi-Kalapanunggal- 3e 23.9 12.9 48.3 16.7 Citeureup 3f Ciracas-Cimanggis-Cibinong 24.9 11.6 42.1 17.0 Factor 4: Higher order services 4 Regional CBD 30.7 30.6 63.2 23.9 Factor 5: Agriculture, forestry, fishery 5 Agricultural areas 21.6 12.5 49.7 20.2 Factor 6: Local government 6a Five kotamadyas in Jakarta 28.5 23.2 55.4 18.2 6b Bogor Municipality 21.7 20.1 34.3 20.2 6c Depok Municipality 27.1 16.5 35.7 14.2 6d Tangerang Municipality 22.4 19.4 48.6 23.9 6e Bekasi Municipality 22.4 18.1 39.9 18.2 6f Bogor Regency 28.2 13.3 49.7 19.7 6g Tangerang Regency 31.8 14.0 59.6 18.7 Higher urban-rural mix 7 Desakota 38.1 14.1 62.2 19.4 LISA “high-low” quadrant 8 Serpong sub-centre 22.1 15.4 56.6 19.0 All clusters 28.4 21.6 55.8 19.6 F-Statistic 1404.89 5371.62 3609.08 315.92 (significance level) (0.000) (0.000) (0.000) (0.000) Source: Processed by author.

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45 40 35 30 25 20 by car (minutes) 15

home-to-work travel time 10 5 0 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8 cluster code

Figure 6.12 Travel time by car Source: Table 6.4.

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by motorcycle (min) 10 home-to-work travel time 5

0 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8 cluster code

Figure 6.13 Travel time by motorcycle Source: Table 6.4.

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20 by public transport (min) home-to-work travel time 10

0 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8 cluster code

Figure 6.14 Travel time by public transport Source: Table 6.4.

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transport (min) 15

5 home-to-work travel time

by non-motorised 0 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8 cluster code

Figure 6.15 Travel time by non-motorised transport Source: Table 6.4.

Travel mode

Travel mode share is another important indicator of urban transport performance because higher shares of public transport and non-motorised transport modes are associated with more sustainable transport (Newman and Kenworthy, 1999, p. 18- 19; Litman and Burwell, 2006). Table 6.5 and Figure 6.16 present the pattern of travel mode choice among the employment clusters. Average public transport share for all clusters is more than 43 per cent, which is the most dominant among the four transport modes. Highest shares of public transport mode use are found in the local government regions of Bogor Municipality (66.6 per cent), Depok Municipality (57 per cent) and Bogor Regency (51.2 per cent). Agricultural areas and desakota attract the highest shares of non-motorised transport modes of 59.6 per cent and 42.2 per cent, respectively. The regional CBD attracts the highest share of car travel (26.0 per cent) among the clusters, followed by the centres and local government regions within the Jakarta urban core. Motorcycles account for the second largest share

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(24.0 per cent) of the four transport modes among the clusters. Clusters that attract high shares of motorcycle travel include the local government region of Tangerang Regency (37.1 per cent), Pasar Kemis-Jatiuwung-Cikupa manufacturing corridor (34.4. per cent), and Serpong sub-centre (30.0 per cent). Interestingly, all these three clusters are located in Tangerang Regency areas. The lowest motorcycle shares are shown in the local government regions of Bogor Municipality (13.0 per cent) and Depok Municipality (17.9 per cent). The results are in general consistent with empirical findings on impacts of employment density (Pivo, 1993) and jobs-housing balance (Cervero, 1989) on travel mode share. The regional CBD and Bogor Municipality, whose relatively high job density and high proportion of jobs to housing (as found in Chapter 5), attract higher share of journey to work trips by public transport, while agricultural and desakota areas, characterised with very low job density, exhibit the lowest public transport ridership among the clusters. The lower frequency of public transport services in agricultural and desakota areas (mentioned previously) may explain their higher ratio of motorcycle to public transport shares, as compared to the other employment clusters.

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Table 6.5 Travel mode share by employment clusters Share of travel mode destined in cluster (percentage) By By non- By public motorised Code Cluster name By car motorcycle transport transport Sum Factor 1: Higher job diversity urban core 1a Jakarta urban core 17.6 23.9 45.3 13.2 100 1b Bogor city centre 6.6 13.7 68.3 11.4 100 Factor 2: Higher job diversity centres 2 Four centres in Jakarta 21.4 23.2 45.5 9.9 100 Factor 3: Manufacturing corridors 3a Tj. Priok-Cilincing-Cakung 12.5 27.2 45.2 15.1 100 Penjaringan-Cengkareng- 3b 8.2 26.8 37.0 27.9 100 Kalideres 3c Tambun-Cibitung-Cikarang 5.5 28.1 48.9 17.5 100 Pasar Kemis-Jatiuwung- 3d 3.8 34.4 33.4 28.4 100 Cikupa Cileungsi-Kalapanunggal- 3e 3.5 27.6 44.6 24.4 100 Citeureup 3f Ciracas-Cimanggis-Cibinong 6.2 24.4 49.2 20.2 100 Factor 4: Higher order services 4 Regional CBD 26.0 20.2 49.2 4.7 100 Factor 5: Agriculture, forestry, fishery 5 Agricultural areas 3.1 18.7 18.6 59.6 100 Factor 6: Local government 6a Five kotamadyas in Jakarta 17.7 23.5 47.2 11.5 100 6b Bogor Municipality 7.1 13.0 66.6 13.3 100 6c Depok Municipality 6.8 17.9 57.0 18.3 100 6d Tangerang Municipality 10.1 25.6 47.8 16.5 100 6e Bekasi Municipality 9.9 26.7 41.2 22.1 100 6f Bogor Regency 5.7 22.8 51.2 20.3 100 6g Tangerang Regency 7.6 37.1 33.6 21.7 100 Higher urban-rural mix 7 Desakota 2.9 24.6 30.1 42.4 100 LISA “high-low” quadrant 8 Serpong sub-centre 12.0 30.0 40.1 17.8 100 All clusters 14.8 24.0 43.4 17.8 100 Chi-square 1113569.34 (significance level) (0.000) Source: Processed by author.

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100 90 80 70 60 non-motorised 50 40 public transport 30 motorcycle 20 mode share (percentage) car 10 0 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8 cluster code

Figure 6.16 Shares of transport modes Source: Table 6.5.

Measure of “local trips”: share of trips by distance by travel mode

Job-housing imbalance and spatial-mismatch are used to indicate lack of local spatial balance between jobs and workers. This study investigates the share of trips made within a 5 kilometre distance (that can be considered “local”), and some distances beyond this, destined to each of the employment clusters. Moreover, the share by travel mode of each distance segment is also identified. The patterns are presented in Figures 6.17 to 6.20, respectively, for the manufacturing corridors outside the urban core as one group (i.e., clusters 3-c, 3-d, 3-e and 3-f), the regional CBD (cluster 4), local government regions of the four municipalities outside Jakarta as one group (i.e., clusters 6-b, 6-c, 6-d and 6-e) and the desakota areas (cluster 7). The results contrast the regional CBD to the other employment clusters in attracting local trips. While the share of “local trips” destined to manufacturing corridors, municipality regions and desakota reached 58 per cent, 54 per cent and 67 per cent, respectively, those destined to the regional CBD were less than 20 per cent. 209

Moreover, the highest share was contributed by the distance segment between 10-20 kilometres, accounting for 32 per cent of the total home-to-work trips to the regional CBD. Although desakota areas attract the highest share of non-motorised travel among the clusters, their ratio of private to public transport shares is alarming. For example, within 5 kilometres distance, the ratio of private to public transport mode shares is around 50:50 for the manufacturing corridors and the regional CBD, while that for desakota areas is around 60:40.

The importance of promoting a higher share of “local trips” is shown in terms of the high share of non-motorised transport use within this short distance band. Policy measures should be aimed at reducing the share of private vehicle use for all distance bands, while promoting the share of non-motorised transport for the “local trips” distance and that of public transport for distance bands beyond it. Policy indicators should also include, among others, the total share of trips for each band (i.e., higher share for closer distance bands is better).

70%

60%

50% 20% 40% non-motorised

trip share 30% 18% public transport 20% private vehicle 1% 10% 20% 8% 9% 6% 5% 4% 0% 4% 2% 1% 0-5 5-10 10-20 20-30 >30 trip distance (kilometres)

Figure 6.17 Share of trips by distance and by transport modes to manufacturing corridors outside the Jakarta urban core Source: Processed by author.

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70%

60%

50%

40% non-motorised

trip share 30% public transport 15% 20% private vehicle 4% 10% 8% 10% 10% 17% 8% 10% 9% 6% 0% 2% 0-5 5-10 10-20 20-30 >30 trip distance (kilometres)

Figure 6.18 Share of trips by distance and by transport modes to the regional CBD Source: Processed by author.

70%

60%

50% 16% 40% non-motorised

trip share 30% 22% public transport 20% 1% private vehicle 12% 10% 16% 11% 7% 4% 2% 0% 5% 2% 1% 0-5 5-10 10-20 20-30 >30 trip distance (kilometres)

Figure 6.19 Share of trips by distance and by transport modes to local government regions of four municipalities outside the Jakarta urban core Source: Processed by author.

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70%

60%

50% 39% 40% non-motorised

trip share 30% public transport 11% 20% private vehicle 3% 10% 17% 5% 6% 4% 4% 0% 4% 3% 1% 2% 0-5 5-10 10-20 20-30 >30 trip distance (kilometres)

Figure 6.20 Share of trips by distance and by transport modes to desakota areas Source: Processed by author.

The influence of physical features

Travel dimensions, explanatory variables and model estimation

The impacts of the physical features of urban structure on home-to-work trip patterns destined to the employment clusters identified in this study are investigated in terms of travel distance by car, travel distance by motorcycle and private vehicle versus public transport mode choice. All the three travel dimensions investigated are regarded as important indicators of transport sustainability so that insights from the results are expected to contribute to policy recommendations which aim to promote more sustainable transport in the JMA. The various influences of the physical features of the employment clusters on travel distance by car and travel distance by motorcycle are investigated using ordinary linear regression models, while those on the probability of choosing private vehicle (car and motorcycle) over public transport modes (bus and train) are investigated using logistic regression. Socioeconomic characteristics of workers and physical features of the trip origin zones (i.e., workers’ residential zones) are included in the model as control variables. Variables derived from the dataset that may explain the three travel dimensions in the models were considered but some of them were dropped from the

212 models because they did not contribute significantly to model estimation. Examples of variables excluded for this reason are types of workers’ occupation and zonal share of housing by type (see Chapter 4). Tables 6.6 to 6.8 present the list of explanatory variables used and their parameter estimates for travel distance by car, travel distance by mode and private vehicle versus public transport mode choice models, respectively.

Results of the empirical investigation on impacts of urban forms on the journey to work are presented as follows: First, results of each of the models are reported briefly (along with Tables 6.6 to 6.8). Then, a summary of the three models in terms of the influences (negative or positive) of explanatory variables on travel distance by car, travel distance by motorcycle, and the probability of choosing the private vehicle over public transport is presented in Table 6.9. Finally, the empirical findings are discussed in terms of their implications on urban and transport sustainability in the JMA.

The ordinary linear regression model for travel distance by car explains 26.2 per cent of the variance in home-to-work trips made by workers to employment clusters. All of the parameter estimates are significant at the 0.01 level, except for jobs per household at the trip origin, which is significant at the 0.05 level. The model confirms the strong positive influence of income levels on home-to-work travel distance by car. Males are shown to travel longer by car than females. Physical features of employment clusters (i.e., workplaces) that have positive impact on travel distance by car include job density, land-use diversity index, distance to nearest toll ramp and job accessibility. Higher job-to-household ratios, further distances to MONAS, higher road density and being within a 3 kilometre distance of the nearest railway station, on the other hand, are shown to have negative impacts on travel distance by car. Characteristics of residential areas contributing to longer car travel distance include higher job-to-household ratio, further distance to nearest toll ramp and higher land-use diversity, while those with a negative influence on car travel distance include higher job density, further distance to MONAS, higher road density, higher job accessibility index and being within a 5 kilometre distance to the nearest railway station.

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Table 6.6 Ordinary linear regression model of home-to-work travel distance by car (in kilometers) Parameter estimate t-stat Constant 10.134 65.59 Socioeconomic characteristics Dummy age (reference category: 15 to 30 years old) over 50 years old -0.541 -15.90 Dummy gender (reference category: female) Male 0.904 28.91 Dummy income (ref. category: below Rp. 1 million) Medium (Rp. 1,000,000 – 3,999,999) 2.651 68.70 High (Rp. 4 million or more) 3.555 75.22 Zonal characteristics of workplace Job density (in 1,000 jobs per hectare) 7.815 52.15 Job per household (in 10 jobs per household) -0.012 -2.87 Zonal centroid Euclidean distance to MONAS (kilometres) -0.183 -62.10 Land-use diversity index (non-unitary) 3.435 37.32 Zonal centroid Euclidean distance to nearest toll ramp 0.028 3.66 (kilometres) Road density (in 10 kilometres per square kilometres) -2.463 -74.97 Job accessibility index by car (in 100,000) 0.516 23.26 Job accessibility index by public transport (in 100,000) 0.372 20.45 Whether zonal centroid is within 3 km straight-line distance from -0.162 -4.72 nearest railway station (yes = 1, no = 0) Zonal characteristics of residential Job density (in 1,000 jobs per hectare) -3.537 -9.49 Job per household (in 10 jobs per household) 0.038 2.09 Zonal centroid Euclidean distance to nearest toll ramp 0.421 147.59 (kilometres) Zonal centroid Euclidean distance to MONAS (kilometres) -3.913 -39.56 Land-use diversity index (non-unitary) 2.184 70.84 Road density (in 10 kilometres per square kilometres) -2.910 -83.60 Job accessibility index by car (in 100,000) -0.330 -14.88 Job accessibility index by public transport (in 100,000) -0.994 -27.59 Whether zonal centroid is within 5 km straight-line distance from -3.537 -9.49 nearest railway station (yes = 1, no = 0) Adjusted R-Square: 0.263 F-statistic 8883.74 Number of cases 547,621 Source: Processed by author.

The model for travel distance by motorcycle (Table 6.7) explains 30.3 per cent of the variance, and all the parameter estimates are significant at the 0.01 level. The model reveals the positive influences of family size, income levels and motorcycle ownership on home-to-work travel distance by motorcycle. As in the case of the car mode, males are also shown to travel longer by motorcycle than females. Physical features of employment clusters that have positive influences on travel distance by motorcycle include job density, job-to-household ratio, land-use diversity index and job accessibility, while those having negative impacts on the travel distance are

214 distance to MONAS, road density and being within a 3 kilometre distance from the nearest railway station. Characteristics of residential areas associated with longer travel distance by motorcycle include further distance to MONAS and higher road density, while those influencing negatively on the travel distance include higher job density, higher job-to-household ratio, higher job accessibility index and being within a 5 kilometre distance to the nearest railway station.

Table 6.7 Ordinary linear regression model of home-to-work travel distance by motorcycle (in kilometers) Parameter estimate t-stat Constant 2.899 43.96 Socioeconomic characteristics Dummy age (reference category: 15 to 30 years old) 31 to 50 years old -0.386 -23.79 over 50 years old -1.399 -44.22 Dummy gender (reference category: female) Male 1.529 67.47 Family size (persons) 0.101 18.92 Dummy income (ref. category: below Rp. 1 million) Medium (Rp. 1,000,000 – 3,999,999) 1.672 103.04 High (Rp. 4 million or more) 1.365 21.17 The number of motorcycles owned by household (units) 0.747 56.53 Zonal characteristics of workplace Job density (in 1,000 jobs per hectare) 11.526 99.78 Job per household (in 10 jobs per household) 0.034 9.69 Zonal centroid Euclidean distance to MONAS (kilometres) -0.431 -227.41 Land-use diversity index (non-unitary) 5.923 123.24 Road density (in 10 kilometres per square kilometres) -0.765 -37.39 Job accessibility index by car (in 100,000) 0.275 17.47 Job accessibility index by public transport (in 100,000) 0.630 56.51 Whether zonal centroid is within 3 km straight-line distance from -0.409 -20.99 nearest railway station (yes = 1, no = 0) Zonal characteristics of residential Job density (in 1,000 jobs per hectare) -7.725 -31.48 Job per household (in 10 jobs per household) -0.072 -4.55 Zonal centroid Euclidean distance to MONAS (kilometres) 0.449 235.62 Road density (in 10 kilometres per square kilometres) 1.321 64.98 Job accessibility index by car (in 100,000) -2.536 -116.74 Job accessibility index by public transport (in 100,000) -0.555 -50.00 Whether zonal centroid is within 5 km straight-line distance from -0.494 -24.16 nearest railway station (yes = 1, no = 0) Adjusted R-Square: 0.303 F-statistic 20285.36 Number of cases 1,027,295 Source: Processed by author.

Table 6.8 presents the estimation results of the private vehicle versus public transport logistic regression mode choice model. The model explains 28.6 per cent (based on Nagelkerke R-square) of actual travel mode choices made by workers. A

215 more intuitive interpretation of results is offered by the odds ratio. Odds in this model are the ratio of the probability of choosing the private vehicle to the probability of choosing public transport. The odds ratio is the ratio of the odds resulting from a one unit increase in the explanatory variable to the original odds (i.e., before a one unit increase in the explanatory variable) (Field, 2005, p.225). Odds ratios of higher than one mean that an increase in the explanatory variable results in an increase in the probability of choosing car, and vice versa.

The socioeconomic characteristics of workers including dummy age groups (being above 30 years old), gender (being male), medium to high income levels and higher car and motorcycle ownership are shown to contribute to a higher probability of choosing the private vehicle over public transport modes. The very high odds ratio of the dummy higher income shows the inferiority of public transport to this income segment. Higher travel impedance between the trip origin and destination (represented as road transport network distance) reduces the probability of using private vehicle modes. Characteristics of workplaces contributing to a higher probability of choosing private vehicle include a further distance to the nearest toll ramp, higher zonal road density and a higher job accessibility index by public transport. Those contributing to less probability of choosing private vehicles are higher job density, higher job-to-household ratios, higher degree of job diversity, higher job accessibility by car and being within a 3 kilometre distance from the nearest railway station. Characteristics of residential areas contributing to a higher probability of choosing private vehicle modes are a further distance to the nearest toll ramp and a higher job accessibility index by car, while those favouring public transport modes are higher job density, higher job-to-household ratio, higher population density, further distance to MONAS, higher degree of job diversity, higher job accessibility index by public transport and being within a 5 kilometre distance to the nearest railway station.

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Table 6.8 Logistic linear regression model of private vehicle versus public transport mode choice Parameter Odds estimate ratio Wald Constant -0.126 0.881 51.365 Socioeconomic characteristics Dummy age (reference category: 15 to 30 years old) 31 to 50 years old 0.690 1.993 27648.689 over 50 years old 0.310 1.363 1879.304 Dummy gender (reference category: female) Male 1.770 5.874 172848.405 Dummy income (ref. category: below Rp. 1 million) Medium (Rp. 1,000,000 – 3,999,999) 0.492 1.635 12610.938 High (Rp. 4 million or more) 1.406 4.080 13598.534 The number of cars owned by household (units) 0.641 1.899 29316.050 The number of motorcycles owned by household 0.608 1.837 30921.335 (units) Travel distance Road transport network distance (km) -0.043 0.958 50338.622 Zonal characteristics of workplace Job density (in 1,000 jobs per hectare) -0.414 0.661 283.161 Job per household (in 10 jobs per household) -0.006 0.994 74.480 Land-use diversity index (non-unitary) -0.286 0.751 512.960 Zonal centroid Euclidean distance to nearest toll ramp 0.027 1.028 754.686 (km) Road density (in 10 kilometres per square kilometres) 0.068 1.070 195.789 Job accessibility index by car (in 100,000) -0.027 0.973 95.145 Job accessibility index by public transport (in 100,000) 0.011 1.011 15.099 Whether zonal centroid is within 3 km straight-line -0.018 0.982 13.974 distance from nearest railway station (yes = 1, no = 0) Zonal characteristics of residential Job density (in 1,000 jobs per hectare) -0.311 0.733 25.412 Job per household (in 10 jobs per household) -0.029 0.971 83.988 Population density (in 1,000 persons per hectare) -1.169 0.311 2087.760 Zonal centroid Euclidean distance to MONAS (km) -0.016 0.984 2361.867 Land-use diversity index (non-unitary) -0.666 0.514 2430.399 Zonal centroid Euclidean distance to nearest toll ramp 0.026 1.026 860.657 (km) Job accessibility index by car (in 100,000) 0.200 1.222 1423.214 Job accessibility index by public transport (in 100,000) -0.079 0.924 834.092 Whether zonal centroid is within 5 km straight-line -0.316 0.729 3572.557 distance from nearest railway station (yes = 1, no = 0) Nagelkerke R-Square 0.286 Likelihood-ratio 405079.26 Number of cases 1,888,343 Source: Processed by author.

Summary and implications of the models estimations

Table 6.9 summarises the impacts (positive or negative) of the explanatory variables on each of the three travel dimensions investigated. These results imply a set of policy measures that should be formulated and implemented to promote more

217 sustainable transport in the JMA. The inferiority of public transport modes to higher income segments emphasises the need to improve public transport quality of service. The dominance of public transport in the JMA seems to be mainly caused by the very high proportion of lower income workers across the region (see Appendix 1) who are the captive market (due to unaffordable private vehicle modes) or are more willing to accept lower quality public transport services than higher income people are. The results of the models suggest that higher income group should be targeted for policy measures because the calibration of the models has shown strong positive influences of income on travel distance by private vehicles and probability of choosing private vehicles over public transport modes. The preference towards public transport modes for long travel distance shown in the model should be seen as the opportunity to target the suburbanised higher income people as a potential market for a significantly improved long haul public transport system.

The models suggest that higher job density, job diversity and higher job-housing balance in employment centres should be promoted to increase public transport ridership. Findings from the models showing that employment clusters located nearby railway stations are associated with higher public transport ridership and shorter travel distance by private vehicles should be seen as an opportunity to promote such higher job density and diversity of employment centres around railway stations in the JMA. Currently most railway stations in the JMA are located further away from activity centres (ROI BAPPENAS-JICA, 2004a) so that a considerable amount of effort is needed to implement such a policy. Employment clusters located close to toll ramps, as expected, attract longer travel distance by car and a higher share of commuting by car. The models provide the evidence for revisiting the already legalised JABODETABEKPUNJUR plan, which indicate and recommend centres to locate mostly along toll road and arterial road corridors and intersections (as shown in Figure 3.19 in Chapter 3).

From the residential side, the results suggest that higher population density, higher job density and diversity and higher job-housing balance should be promoted in residential areas for their associations with less travel distance and higher public transport ridership. A spatial mismatch problem would appear to be highlighted

218 from the model as a higher jobs to household ratio is associated with longer travel distance by car. In other words, car users (i.e., higher income group) are currently not advantaged by the availability of jobs around their residential areas due to a mismatch with the types of jobs. This is most likely the case for most of the employment clusters outside the Jakarta urban core, given their lower share of private offices and higher order services jobs. The association between being nearby a railway station at the trip origin to less travel distance by private vehicles and higher public transport share also suggests advantages in promoting residential centres around railway stations in the JMA. Job accessibility by public transport should be used as one of the indicators in land-use and transport policy and development in the JMA. Proximity to toll ramps is shown to encourage longer travel distance by car and a lower share of public transport ridership. This is another difficult challenge facing urban planners and decision makers in the JMA, given the trend toward large scale housing development that form sprawl-like corridor pattern along the toll road, as shown in Figure 3.5 (Chapter 3).

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Table 6.9 Summary of impacts of the physical features on home-to-work travel Impacts on the travel dimensions (positive or negative) Probability of choosing Travel Travel private distance distance by vehicle over by car motorcycle public (km) (km) transport Socioeconomic characteristics Dummy age (reference category: 15 to 30 years old) 31 to 50 years old - + over 50 years old - - + Dummy gender (reference category: female) Male + + + Family size (persons) + Dummy income (ref. category: below Rp. 1 million) Medium (Rp. 1,000,000 – 3,999,999) + + + High (Rp. 4 million or more) + + + The number of cars owned by household (units) + The number of motorcycles owned by household + + (units) Travel distance Road-based journey to work distance (km) - Zonal characteristics of workplace Job density (in 1,000 jobs per hectare) + + - Job per household (in 10 jobs per household) - + - Zonal Euclidean distance to MONAS (kilometres) - - Land-use diversity index (non-unitary) + + - Zonal centroid Euclidean distance to nearest toll ramp + + (km) Road density (in 10 kilometres per square kilometres) - - + Job accessibility index by car (in 100,000) + + - Job accessibility index by public transport (in 100,000) + + + Whether zonal centroid is within 3 km straight-line - - - distance from nearest railway station (yes = 1, no = 0) Zonal characteristics of residential Job density (in 1,000 jobs per hectare) - - - Job per household (in 10 jobs per household) + - - Population density (in 1,000 persons per hectare) - Zonal centroid Euclidean distance to MONAS (km) - + - Land-use diversity index (non-unitary) + - Zonal centroid Euclidean distance to nearest toll ramp + + (km) Road density (in 10 kilometres per square kilometres) + Job accessibility index by car (in 100,000) - - + Job accessibility index by public transport (in 100,000) - - - Whether zonal centroid is within 5 km straight-line - - - distance from nearest railway station (yes = 1, no = 0)

Source: Table 6.6, Table 6.7, Table 6.8.

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Transport sustainability and policy implications

This section aims to discuss the transport sustainability performance of the JMA given the above empirical findings of home-to-work trip patterns across the region. Newman and Kenworthy (1999, p. 7-14) have shown that urban sustainability should be assessed based on performance indicators of “resource inputs”, “waste outputs” and the “livability” of the city (or region) as an “extended metabolism”. While the scope of this study does not allow such comprehensive assessments, transport sustainability issues are discussed in terms of how journey to work patterns in the JMA, as revealed from the empirical analysis, may be promoted so that they are more sustainable (i.e., less use of resources and less waste). The approach is to synthesise the empirical results of this study with theories and practices from the literature.

Desireline analysis shows the dominance of the Jakarta urban core and the regional CBD in terms of their “catchment areas” for home-to-work trips. Jakarta urban core accounts for more than 40 per cent of total home-to-work trips in the JMA. The regional CBD in the heart of the urban core accounts for almost 14 per cent of the total home-to-work trips in the region. Transport sustainability problems are associated with the increasing number of home-to-work private vehicle trips that originate from areas outside the Jakarta urban core. As indicated in ROI BAPPENAS-JICA (2004a), from 1985 to 2002, the radial journey to work trips destined to Jakarta from its surroundings had increased by 10 times. The empirical results in this study show that 60 per cent of home-to-work trips attracted to the regional CBD are generated by areas located beyond 10 kilometres distance from the CBD. Moreover, higher car trips are also associated with further distance bands, particularly that in the range of 10-20 kilometres from the CBD (Figure 6.18).

The policy formulation should focus on the provision of a medium to long haul rapid transit system connecting residential centres outside the Jakarta urban core to the regional CBD. The target market of the system should include higher income workers who, as suggested by the model, have very strong preferences for private vehicles. As argued by Newman and Kenworthy (1999, p. 130) the opportunity to capture the higher public transport ridership of higher income people is enhanced when public transport quality of service is improved and traffic congestion is severe

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(as in the case of JMA). The strong preference of higher income households towards the private vehicle mode implies the inferiority of public transport for this income segment. A policy of promoting transit use for higher income groups should be directed not only to limit the use of private vehicles by limiting access, imposing higher fuel taxes and higher parking fees and limiting parking spaces in the central city, but also to increase public transport level of service that suits the demands by this particular market segment. For the shorter distance band (less than 5 kilometres), the regional CBD only attracts a small fraction of non-motorised home- to-work trips. Pedestrian oriented development or “urban villages” within the CBD and the Jakarta urban core should be promoted to increase the share of non- motorised trips destined in higher order services employment clusters.

Employment density (and also population density) has been regarded as the most important variable that influences travel mode choice (Bernick and Cervero, 1997; Newman and Kenworthy, 1999). The logistic regression model of private versus public transport modes (Table 6.8) shows that the JMA is not an exception. In Chapter 5, however, it has been shown that in terms of job density distribution, the JMA still resembles a monocentric urban structure in which employment clusters outside the Jakarta urban core (with an exception of Bogor Municipality) are characterised by low job densities. Referring to the advantages of a polycentric urban structure as set out by Robinson (1995, p. 87), the policy implication would be to promote higher job density employment clusters (i.e., sub-centres) outside the Jakarta urban core. Job density should be used as one of the most important indicators for the success of sub-centre formation. In this case, the planned sub- centre of Serpong has been shown as having failed to emerge as a set of higher job density contiguous zones.

The empirical results show the advantages of employment clusters located nearby railway stations in terms of higher public transport share and less private vehicles travel distance. The spatial structure of employment identified in the JMA shows that some areas of manufacturing corridors and local government regions outside the Jakarta urban core are located within the existing railway corridor. In average, zones within the regional CBD and the Jakarta urban core are within less than 2 kilometres to the nearest railway stations. Local government regions outside the

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Jakarta urban core are located between 1 to 2 kilometres to the nearest railway station. Higher job density and job diversity sub-centres should be promoted around the railway stations, along with significant improvement in railway level of service as discussed above. The association of higher jobs-to-household ratios to a higher probability of choosing public transport as found in the model suggests that a higher jobs-housing balance should also be promoted in the development of sub-centres.

Desakota areas have been identified as being very low in their job density, yet contain a high mix of manufacturing and agricultural workers. While the share of non-motorised transport mode, as in the case for agricultural areas, is very high, travel distances of private vehicle is also high. Travel distance by car is higher than that destined to the Jakarta urban core, most likely due to cross commuting, which may include commuting between urban and rural settings (Greene and Meyer, 1997), which is found to cover longer distances than that inside rural areas. The desireline analysis shows that desakota areas attract home-to-work trips both from the agricultural areas and urban areas, including from the Jakarta city. Without proper policy intervention, it is most likely that the low job density type of urbanisation will continue in desakota, with the motorcycle as the most preferred motorised transport mode, increase its share in the cost of reduction in the share of non-motorised transport mode. Desakota would be a difficult challenge for urban and transport policy makers as the nature of low job density in the extensive region would make it difficult to develop proper public transport system and relieve heavy reliance on the individually owned rental system minibuses that have been identified as disadvantageous in previous studies (ROI BAPPENAS-JICA, 1999). Low road density in desakota areas also prevents road-based public transport to develop and hence favours more flexible transport mode such as motorcycle. Lower hierarchy of sub-centres in desakota should be promoted around the local government regions of the three regencies and “deep-pocket” manufacturing sites. The later should be promoted to replace the sprawl-like manufacturing corridors that tend to have lower job density and job diversity while occupying more lands. In fact, this type of urban penetration has been associated to loss of agricultural land in the JMA in the past (ROI MPW, 1993).

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Planning initiatives in the JMA, however, have not favoured railway transport as the trunk transport for the region. The “bundled deconcentration” conceptual spatial structure and transport configuration for JMA proposed in the beginning of JMDP study has been replaced by heavy reliance on road based transport system, toll road in particular (as shown in ROI MPW, 1985), while the identification of sub-centres have not gone far from the study, except perhaps for manufacturing clusters, that, as shown from the empirical analysis in this study, more resemble sprawl than sub-centering, for their low job density, low job diversity and corridor- based development. The subsequent JMDPR, motivated by concerns on the mounting environmental sustainability problems facing the region, proposed a more balanced combination of toll road system and heavy and light railway along the east-west direction. The expanded toll road system recommended in JMDPR, however, showed the favour towards the road-based transport which was “reasonably” adopted to facilitate foreign direct investment based industry that reach its golden period at the time. JMDPR was ineffective due to economic crisis that had paralised infrastructure development in the region from mid-1990s to early 2000s. Concerns on environmental degradation in the region have not been seen in a comprehensive way in that water related problems, including flooding and drought, seem to have dominated the development of the recent JABODETABEKPUNJUR development zoning, while transport sustainability problems do not seem to be appropriately considered. This is clearly seen in the transport network recommendation under JABODETABEKPUNJUR (see Figure 3.20 in Chapter 3), which again favour the expansion of toll road both in the forms of outer-outer ring (i.e., another ring outside the current Jakarta Outer Ring Road toll network) and extensions both in the west and east parts of JMA, passing through the agricultural and desakota areas as identified in this study. Furthermore, the centres identified in the JABODETABEKPUNJUR are located mostly along the current and proposed toll road system in the JMA, which, according to practices in many cities in the world, have failed to encourage higher density development associated with shorter travel distance and higher share of non- motorised and public transport modes. Overall, the proposed transport configuration seems to lead JMA towards an auto-dependent region, where further

224 low density development will be triggered by the dominating toll road based trunk transport system.

Past planning efforts in the JMA and the recent planning initiative in the form of JABODETABEKPUNJUR have not properly considered transport sustainability issue facing the region. The empirical results of this study already indicate that the average commuting distance by car and motorcycle is comparable to that experienced by auto-dependent American and Australian cities, while average commuting time by public transport is far left behind to that by car and motorcycle. Policy formulations to promote transport sustainability in the JMA should focus on promoting employment sub-centres characterised with high job density, high job diversity and proper jobs-housing balance, increasing public transport level of service and promoting higher share of public transport and non-motorised transport modes. The empirical findings also reveal the importance of spatial characteristics at trip origins (i.e., residential areas of workers) in promoting higher public transport share. These include higher population density, higher job density and diversity, higher jobs-to-household ratio, higher job accessibility and being in short distance to railway stations. The later two are also associated to shorter home-to- work travel distance by private vehicles. The overall results seem to favour the significant improvement of public transport level of service, in particular the medium to long haul transit system, along with the development of higher density, higher diversity sub-centres and residential centres, including pedestrian friendly “urban villages” within the regional CBD. Ideally, these centres are developed in the forms of Transit Oriented Development (TOD) around the railway stations of the significantly improved railway system as recommended in Newman and Kenworthy (1999, p. 94, 102). Lower hierarchy sub-centres should be promoted in desakota areas to avoid further sprawl urbanisation pattern and to allow provision of better public transport level of service, in order to prevent from the shift of travel mode use pattern towards higher share of private vehicles, in particular motorcycle.

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Conclusions

This chapter extends the previous results on the spatial structure of employment in the JMA by investigating the impacts the spatial structure and its physical features on journey to work. The previous chapter shows that spatial structure of employment in the JMA conforms largely to what suggested in the Southeast Asian EMR concept. It has also been suggested that outside the Jakarta urban core, employment clusters in the JMA are characterised with low job density and low job diversity and therefore are regarded as inefficient in land uses. The enquiries are continued in this chapter with regard to how such employment spatial pattern influences home-to-work pattern in the JMA. Furthermore, policy implications relevant to transport sustainability issues are suggested based on the empirical results and findings from the literature.

Empirical analysis performed in this chapter include desireline analysis, home-to- work trip pattern comparisons (ANOVA) by the employment clusters, and investigations on the influences of physical features on travel distance and mode choice using ordinary linear regression and logistic regression models. The results largely answer the second and third sets of research questions on the spatial interaction and journey to work impacts of the employment structure identified and the policy implications of the empirical findings. As shown in the discussions, results of the empirical analysis may provide a basis for policy formulations to promote urban and transport sustainability in the JMA.

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7 Conclusions

Thesis summary and conclusions

The thesis begins with the finding from the literature showing that major developing cities in Southeast Asia have suffered severely from transport related problems, which include traffic congestion, air pollution, traffic noise, traffic accident, rapid motorisation and the decreasing share of public transport. While efforts in promoting transport sustainability in the developed world have included policy measures involving urban spatial structure and its physical features as a consequence of the understanding on strong link between land use and transport, there has been lack of understandings on the spatial structure in major cities in Southeast Asia. The literature suggests that urbanisation processes in the Southeast Asia differ significantly from that in the developed world. It has been shown that the unique urbanisation processes in Southeast Asia have led to formation of extended metropolitan regions (EMRs).

The thesis has highlighted from the literature that urban spatial structure suggested by the Southeast Asian EMR concept is distinctive and more complex than that suggested by Western city concept, so that the use of the Western city models on the identification of the spatial structure, physical features and their impacts on travel for the case of Southeast Asian cities can be misleading (and so are the policy and decision makings). Paradoxical evidence on compactness of major cities in Southeast Asia and their sustainability problems is just an example. While the

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EMR concept suggest a more complex spatial structure than that shown in the developed cities (i.e., the monocentric and polycentric urban structure dichotomy), there has been lack of empirical investigations on the spatial structure of Southeast Asian EMRs. Empirical investigations to date have been limited to defining EMRs into the three broad zones of the urban core, the inner ring (or peri-urban areas) and the outer ring (or extended metropolitan areas). Such broad division is hardly enough to use as a basis for investigating the impacts of spatial structure on travel and hence use the results to promote more sustainable transport conditions. This thesis argues that the Southeast Asian EMR concept can be used as the basis for empirical investigations on urban spatial structure and its impacts on travel at a level of detail comparable to those conducted in the developed cities. Furthermore, the thesis argues that the results can be used as evidence to promote more sustainable transport in the region.

In response to the problem statements and gaps in the literature as stated above, the aim of thesis is to conduct empirical investigation on the spatial structure of employment and its physical features (using the Jakarta Metropolitan Area as the study area), with reference to the Southeast Asian EMR concept, and follow up the findings with empirical investigations on their impacts on journey to work, and assessments on transport sustainability. The aim can be seen in three respects: first, it can be seen as an inquiry on the actual spatial structure of employment in the JMA, given the Southeast Asian EMR concept on the one hand and suggestions made that major developing cities have been transformed into polycentric urban structure (for example, Alpkokin et al., 2008) on the other hand; second, it can be regarded as a test of the hypothesis proposed by this thesis on the applicability of the Southeast Asian EMR as the theoretical ground for empirical analysis on the spatial structure and travel impacts for major developing cities in Southeast Asia; and third, the aim can also be viewed as a contribution to efforts to alleviate transport sustainability problems facing the JMA, as practical findings and a direct response to the problems identified.

Three sets of research questions with regard to the spatial structure of employment, its impacts on journey to work patterns and policy implications of the findings have been formulated to guide the process of the empirical analysis that serves as the tool

228 to fulfil the aim of the thesis and address the problem statements. Identification of employment clusters is conducted using a combination of factor analysis and ESDA, and the impacts of the spatial structure of employment and its physical features on journey to work are investigated using desireline analysis, group comparison method and regression analysis. The three sets of reseach questions have been addressed in Chapter 5 and Chapter 6, from which the following conclusions are drawn as follows:

Methods applied to identify the urban spatial structure and to identify travel impacts of the urban spatial structure and its physical features can be adapted and utilised in the Southeast Asian EMR case: This thesis has demonstrated that the adapted methods, i.e., the combination of factor analysis and ESDA, and the application of urban-rural diversity index to ESDA, have been able to identify major components of the spatial structure of employment in the JMA, as suggested in the Southeast Asian EMR concept. This thesis has also shown that the identified spatial structure of employment can be followed up with investigations on its impacts on journey to work and transport sustainability.

Conformation of the JMA’s spatial structure of employment to the Southeast Asian EMR concept: It has been found from the empirical analysis in this thesis that polycentricity, that has been suggested in a few recent studies as the emerging spatial structure of developing cities (e.g., Alpokin et al., 2008; Vichiensan, 2007) is not part of the spatial structure of employment in the JMA. The JMA exhibits a very dominant urban core with a single, expanded CBD at the heart of it. Manufacturing corridors follow largely toll roads radiating out of the urban core. Local government regions have not been developed into sub-centres. Desakota areas are found overlapping the manufacturing corridors and the agricultural areas. Portions of agricultural areas are found in the outer parts of the regencies in the JMA. Some of these components conform largely to the mega-urban region formation shown by McGee (2008), including the single regional CBD as the place of higher order services and major industrial estates located along the trunk transport corridors. Much smaller portions of agricultural areas than those suggested in McGee (2008) are found in the JMA, due to aggressive land conversion to urban land uses during the past decades. In addition, dispersed small

229 industries within agricultural areas shown in McGee (2008) have not been detected in the JMA in this study. As such, desakota areas are found in the form of bands around the manufacturing corridors and do not dominate the outer parts of the JMA, compared to the model shown in McGee (2008).

The spatial structure of employment identified suggests a hierarchy of spatial interaction in terms of journey to work in the JMA: The pattern of desireline of journey to work trips reveal the hierarchy of the employment clusters identified, in which the Jakarta urban core and the regional CBD are at the top of the hierarchy (in terms of the spatial extent and the number of commuting trips drawn), followed respectively by the manufacturing corridors, the local government regions, desakota, and the agricultural areas.

Planning initiatives in the JMA that have favoured toll road over public transport as the trunk transport systems of the region have promoted the JMA into more auto-dependent city characteristics: The empirical findings show that travel patterns in the JMA, in particular commuting distance by car, exhibit similarities to auto-dependent American and Austrian cities.

Sub-centres development along the significantly improved railway transport should be prioritised to promote transport sustainability in the JMA: The empirical findings suggest that higher job density, higher job diversity and higher jobs to housing balance are associated with higher public transport ridership. The JMA is characterised with very high job density and relatively higher job diversity on its urban core, while employment clusters in the rest of the area is largely characterised by low job density and low job diversity. Density has been indicated as one of the most important physical attributes of the spatial structure of employment that influence travel patterns, in particular public transport share. Promotion of employment sub- centres within and outside the urban core should include density and diversity variables (job density, population density, job diversity, jobs-housing balance) as indicators of success of implementation of that policy. The empirical results in this study shows the benefits of promoting the sub-centres of employment around the railway stations given their associations with higher public transport ridership and less private vehicles distance and the fact that the employment clusters identified, including the regional CBD, the Jakarta urban core and some of the manufacturing

230 corridors and local government regions are located relatively close to the existing railway stations and

Limitation of the thesis and further research

This thesis is limited in several respects: the urban spatial structure investigated is limited to that of employment; the travel impacts investigated are limited to one- way journey to work trip; and the travel impacts are examined from the trip destination point of view. A straightforward extension of this research would involve an extension of the scope of the research with respect to one or more of those limitations.

The spatial structure of population and housing in Southeast Asian EMR may reveal the formation, characteristics and travel impacts of the distinctive features suggested in the concept such as new towns, large scale housing development, kampungs (both urban and rural kampungs) and desakota (as residential areas). Investigations on the spatial structure of population and housing can be followed by empirical investigations on their impacts on travel from trip origin point of view. Trips investigated may include those of non-work trips, as literature suggests that the spatial structure of population and housing and the physical features of residential areas have significant influences on non-work trip patterns.

This thesis is a cross-sectional study that investigates the spatial structure of employment and its impacts on journey to work at a particular point in time. This type of study lacks of insights into temporal changes of urban structure and travel patterns, which are important indicators of urban and transport sustainability. Furthermore, causal relationships can be better explained when changes of travel patterns are investigated as impacts of changes in the spatial structure and its physical characteristics. This dynamic type of analysis, however, requires consistent more than one point in time dataset that may be still hard to find for developing cities.

This thesis is conducted at a metropolitan-wide scale, with the smallest administrative zone level of kelurahan and desa is used as the unit of observation for the identification and the spatial structure of employment. More detailed empirical

231 analysis at local level will allow the investigation of the influences of neighbourhood level land use attributes on travel patterns. Advances in GIS have allowed such detailed analysis, as demonstrated for cities in developed countries but much richer dataset is required to do so.

The urban spatial structure suggested from the Southeast Asian EMR concept includes features that have not included in this study, due to data or time limitation. The literature suggests that informal jobs play an important role in Southeast Asian EMRs, yet the dataset used in this study does not contain information on informal employment. Availability of the data would provide insights into the spatial distribution and intensity of this important yet currently marginalised type of employment.

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Appendix 1

Table A.1 Workers by income group by cluster of employment

Number of Share by income group workers Code Cluster name (persons) Low Medium High 1a Jakarta urban core 2,469,930 22% 56% 22%

3a Tj. Priok-Cilincing-Cakung 586,681 22% 60% 17% Penjaringan-Cengkareng- 3b 348,631 37% 51% 12% Kalideres 3c Tambun-Cibitung-Cikarang 306,130 28% 62% 10%

3d Pasar Kemis-Jatiuwung-Cikupa 183,513 31% 61% 8% Cileungsi-Kalapanunggal- 3e 166,260 36% 58% 6% Citeureup 3f Ciracas-Cimanggis-Cibinong 141,659 27% 63% 9%

4 Regional CBD 864,727 13% 55% 33%

5 Agricultural areas 411,839 59% 35% 6%

6b Bogor Municipality 79,166 37% 51% 12%

6c Depok Municipality 11,947 26% 62% 11%

6d Tangerang Municipality 85,332 28% 56% 16%

6e Bekasi Municipality and Regency 78,354 25% 61% 14%

6f Bogor Regency 66,480 34% 56% 10%

6g Tangerang Regency 11,830 39% 51% 9%

7 Desakota 436,012 49% 45% 5%

8 Serpong sub-centre 47,677 33% 54% 13% Note: Low: below Rp. 600,000 Medium: Rp. 600,000 – Rp. 1,999,999 High: Rp. 2,000,000 or more Source: Processed by author

100% 90% 80% 70% 60% High 50% Medium 40% Low 30% 20% 10% 0% 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8

Figure A.1 Workers by income group by cluster of employment

Source: Table A.1

Table A.2 Workers by occupation group by cluster of employment

Share by occupation group Number of workers professional/ administrative/ clerk/ Code Cluster name (persons) business technical labourer 1a Jakarta urban core 2,469,930 23% 30% 47%

3a Tj. Priok-Cilincing-Cakung 586,681 18% 27% 55% Penjaringan-Cengkareng- 3b 348,631 14% 19% 67% Kalideres Tambun-Cibitung- 3c 306,130 10% 20% 70% Cikarang Pasar Kemis-Jatiuwung- 3d 183,513 7% 14% 79% Cikupa Cileungsi-Kalapanunggal- 3e 166,260 7% 13% 80% Citeureup Ciracas-Cimanggis- 3f 141,659 13% 21% 66% Cibinong 4 Regional CBD 864,727 26% 39% 35%

5 Agricultural areas 411,839 10% 9% 81%

6b Bogor Municipality 79,166 18% 23% 59%

6c Depok Municipality 11,947 29% 21% 50%

6d Tangerang Municipality 85,332 17% 24% 60% Bekasi Municipality and 6e 78,354 23% 23% 54% Regency 6f Bogor Regency 66,480 15% 23% 63%

6g Tangerang Regency 11,830 16% 17% 67%

7 Desakota 436,012 9% 11% 80%

8 Serpong sub-centre 47,677 15% 22% 63% Note: Professional/business: occupation categories 1 to 4 (please refer to Table 4.4 in Chapter 4) Administrative/technical: occupation categories 5 and 6 Clerk/labourer: 7 to 16 Source: Processed by author

100% 90% 80% 70% 60% Clerk/labourer 50% Administrative/technical 40% Professional/business 30% 20% 10% 0% 1a 3a 3b 3c 3d 3e 3f 4 5 6b 6c 6d 6e 6f 6g 7 8

Figure A.2 Workers by occupation group by employment cluster

Source: Table A.2

Appendix 2

Home Interview Survey Forms (see the following pages)