Enhancing urban planning using simplified models: SIMPLAN for Ahmedabad, India

Progress in Planning 73 (2010) 113–207 www.elsevier.com/locate/pplann

Enhancing urban planning using simplified models: SIMPLAN for Ahmedabad, India Bhargav Adhvaryu Department of Architecture and Churchill College, University of Cambridge, United Kingdom

Abstract Urban planners are faced with the decision of what planning policy to pursue in order to achieve the best possible future. Many cities in developed nations use comprehensive models that simulate various aspects of the urban system, capable of predicting implications of a given set of policy inputs, to assist the planning process. However, in developing countries, demographic and socioeconomic data with appropriate spatial disaggregation are difficult to obtain. This constrains the development of such comprehensive urban models to support planning decisions. In the absence of models, the plan-making process usually inclines towards a more intuitive approach. Using simplified urban models adapted to the data constraints, this paper explores the prospects of enhancing planning in developing countries, with the aim of shifting the plan-making process from being purely intuitive towards being more scientific. The SIMPLAN (SIMplified PLANning) modelling suite has been developed for the case study city of Ahmedabad, India (the calibration per se is not discussed) to test alternative urban planning policies (combinations for land use and transport) for the year 2021. Model outputs are evaluated for key economic, environmental and social indicators. It should be noted that such a research study, in the context of developing countries, represents a first generation of studies/ models, owing to the simplicity of the model structure and its accompanying limitations and data availability constraints. The modelling framework developed in this study has a visually driven user interface. This makes the model easy to understand, operate and update. Due to this attribute, it allows local planning authorities to carry out testing of several alternative planning policies themselves, without having the need to outsource modelling work to private consulting firms, usually at much higher cost. Key model outputs indicate that dispersing cities proves to be economically beneficial to society as a whole. Compact development may prove to be better in terms of environmental and social aspects, but it may be possible to tackle the undesirable effects of dispersal by appropriate combinations of planning and management measures. The modelling outputs informed the wider debate on compact vs. dispersed urban forms. It was shown that neither of these diametrically opposite forms provide an outright ‘win–win’ solution. They are likely to perform differently in different economies and sociocultural contexts. Therefore, it would appear that each city needs to test out the pros and cons of such alterative urban planning policies before pursing a plan for the future. Learning from such modelling exercises, cities can prepare their own tailor-made policy that best satisfies their objectives, making the planning process more rigorous and transparent. # 2010 Elsevier Ltd. All rights reserved. Keywords: Urban planning; Urban modelling; Land use–transport interaction (LUTI) modelling; Urban form; Compact city; Dispersed city; Developing countries; Ahmedabad; India

E-mail address: [email protected]. 0305-9006/$ – see front matter # 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.progress.2009.11.001

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Contents 1. 2.

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Paper outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Context of developing countries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Urban development and planning . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Overview of urbanisation: India, Gujarat and Ahmedabad . . . . . . . 2.3. Background of planning in the Indian context . . . . . . . . . . . . . . . 2.4. The need and relevance of this study . . . . . . . . . . . . . . . . . . . . . General introduction of the case study city of Ahmedabad . . . . . . . . . . . 3.1. Location, topography and climate. . . . . . . . . . . . . . . . . . . . . . . . 3.2. History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction to modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Definition and types of models. . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1. Descriptive models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2. Explanatory models . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.3. Predictive models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Descriptive conceptual models of spatial organisation of land uses. 4.2.1. Concentric zone theory (1925) . . . . . . . . . . . . . . . . . . . . 4.2.2. Sector theory (1939) . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3. Multiple-nuclei theory (1945) . . . . . . . . . . . . . . . . . . . . 4.2.4. Application to Ahmedabad . . . . . . . . . . . . . . . . . . . . . . 4.3. Explanatory analytical models of location and land use . . . . . . . . 4.3.1. Isolated state (1826) . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2. Industrial location theory (1909) . . . . . . . . . . . . . . . . . . 4.3.3. Central place theory (1933) . . . . . . . . . . . . . . . . . . . . . . 4.3.4. Urban bid-rent theory (1964) . . . . . . . . . . . . . . . . . . . . . 4.4. Introduction to LUTI models . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1. The land use–transport relationship. . . . . . . . . . . . . . . . . 4.4.2. The Lowry model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.3. The MEPLAN model . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.4. The TRANUS model . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.5. The DELTA model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.6. A brief discussion on LUTI models . . . . . . . . . . . . . . . . SIMPLAN model: a brief introduction . . . . . . . . . . . . . . . . . . . . . . . . . Development of alternative policies for the future . . . . . . . . . . . . . . . . . 6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Key modelling inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3. Trend policy 2021 (TR21) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1. TR21 land use inputs . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2. TR21 transport inputs . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4. Compaction policy 2021 (CC21) . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1. CC21 land use inputs . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2. CC21 transport inputs . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5. Dispersal policy 2021 (DS21) . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.1. DS21 land use inputs . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.2. DS21 transport inputs . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of modelling outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1. Land use outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2. Transport outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1. Variation in dwellings and employment allocation . . . . . . . . . . . . 8.2. Variation in income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assessment of alternative planning policies. . . . . . . . . . . . . . . . . . . . . . 9.1. Economic assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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10.

11.

9.1.1. Housing and work travel costs . . . . . . . . . . . . . . . . . 9.1.2. Consumer and producer surplus in housing rent . . . . . 9.1.3. Consumer surplus in transport . . . . . . . . . . . . . . . . . 9.1.4. Estimates of costs. . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.5. Summary of benefits and costs. . . . . . . . . . . . . . . . . 9.2. Environmental assessment . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1. Resources: new land required for residential use . . . . 9.2.2. Emissions: vehicular CO2 . . . . . . . . . . . . . . . . . . . . 9.3. Social aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1. Mix of socioeconomic groups . . . . . . . . . . . . . . . . . 9.3.2. Social equity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.3. Accessibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4. Sensitivity analysis: assessment summary of other alternatives. 9.5. A discussion on assessment matrix . . . . . . . . . . . . . . . . . . . . 9.6. Conclusions on assessment . . . . . . . . . . . . . . . . . . . . . . . . . Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.1. Summary of key feedback and responses . . . . . . . . . 10.2. SIMPLAN application to DP making . . . . . . . . . . . . . . . . . . 10.3. SIMPLAN simplifications and its application limitations. . . . . Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1. On alternative urban forms . . . . . . . . . . . . . . . . . . . . . . . . . 11.2. On the model structure and operationality . . . . . . . . . . . . . . . 11.3. On the context of developing countries . . . . . . . . . . . . . . . . . 11.4. Summary of key research findings . . . . . . . . . . . . . . . . . . . . 11.5. Suggestions for further research . . . . . . . . . . . . . . . . . . . . . . 11.6. A final note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Paper outline This paper begins by looking at urban development and planning in the context of developing countries and how it differs from developed countries. An overview of urbanisation is presented, followed by the background of planning in the Indian context. Following from this, the necessity of the study is established. A general introduction to the case study city of Ahmedabad is presented. Since this recommends the use of models to assist planning, a general introduction to models is presented, followed by an introduction to land use– transport interaction (LUTI) models. A brief introduction to a simplified modelling suite called SIMPLAN (SIMplified PLANning) is provided. However, its calibration is a separate topic and is being considered for a shorter paper, and it is therefore not discussed here. Alternative urban planning policies for a future year (2021) are then discussed and tested using SIMPLAN. A summary of modelling outputs is presented, followed by an assessment of alternative urban planning policies, including a section on sensitivity testing. The approach

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developed in this study was presented to local authority planners and decision makers in Ahmedabad. Their feedback is provided, along with the applications for enhancing plan making. Suggestions for further research as presented, followed by overall conclusions. All sections in the paper are based on the author’s doctoral work (Adhvaryu, 2009). 2. Context of developing countries 2.1. Urban development and planning Urbanisation and urban growth (or development) are often considered synonymous. However, there is an important distinction. Urbanisation refers to the ‘relative concentration’ of people living in urban areas (in a region) compared to the total population. For example, in 2001 the total population in India was 1.029 billion, of which 0.286 billion lived in urban areas, i.e. 28% urbanisation. Urban growth refers to the ‘absolute increase’ in the physical size and population of an urban area (Potter, 1992). Urban growth is thus the combined

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effect of net urban migration, natural increase, and geographical expansion of an urban area. In this sense, urban migration may be associated with urbanisation. Thus, urban growth and urbanisation are linked, i.e. urbanisation is one of the three major components of urban growth (Jacquemin, 1999). Jacquemin (1999) argues that there is a difference between the urban growth process in the western world and in developing countries, which could be attributed mainly to the difference in the demand and supply of urban labour, and the overall population growth. In the western world, urbanisation was a direct product of the gradual process (over a century) of industrialisation and economic development. On the other hand, in developing countries, urbanisation is only partly the result of industrialisation and economic growth. In addition, it is taking place over a much shorter period, making the pace of growth comparatively rapid. Other key contributing factors to urbanisation are the ‘unfillable’ expectations of rural people migrating to cities to escape poverty, and the lack of opportunities. The recent World development report 2009 (World Bank, 2008) confirms that the absolute numbers of people being added to the urban population of today’s developing countries are much larger, even compared to the recently industrialised nations such as the Republic of Korea, Taiwan and China. Beier (1976) argues that the rapid growth of urban population in developing countries is most likely to be accommodated by expanding existing urban areas rather than by creating new settlements. This can be supported by looking at more recent data. For example, the concentration of population in cities over one million in developing countries rose from 18% to 28% from 1950 to 2005, and the population in these cities increased at a staggering rate of 4.7% per annum (calculated from United Nations, 2006). This clearly shows that one million plus cities are where most of the urban growth is taking place. Gilbert and Gugler (1992) conclude that most Third World countries have been transformed from rural to urban societies in two or three decades, with larger cities even doubling in size every 15 years—a phenomenon fuelled by changes in the countryside, high rates of fertility, falling death rates, and rapid city-ward migration. The rapid growth of urban areas in developing counties has brought serious problems, such as overcrowding, poor housing conditions, inadequate social, urban and transport infrastructure services, environmental degradation, and unemployment and poverty. These problems are not new to the developed world— they were and still are facing these problems. However,

what is new and different in developing countries is that its magnitude has been significant, owing to dramatic growth and population increase since the 1950s (Jacquemin, 1999). One of the key problems generally identified as being different in developing countries is the lack of sufficient ‘absorptive capacity’ of the urban economy in relation to the increase in the number of potential job seekers. The emergence of the informal sector in developing countries could be attributed to the mismatch between the number of potential job seekers and the number of formal jobs in the economy. There are two contrasting ways of looking at this. One school of thought argues that since urban growth produces undesirable side-effects and raises questions about the absorptive capacity of urban areas, strategies should be geared towards agricultural self-reliance, rural new town development, ‘zero urban growth’, and even ‘deurbanisation’ (Jacquemin, 1999). Others argue that, in essence, cities exist because of their ability to offer competitive advantage for industrial production and economies of scale associated with increasing urban size. For example, Alonso (1968) argues that there are good grounds for believing in increasing returns to urban size. Therefore, they conclude that, despite the disadvantages of urban growth, it is preferable to have it, from both an economic and a social development perspective. Herbert (1979), in the context of urban development in the Third World, emphasises that individuals find cities attractive for many reasons, such as greater employment and education opportunities and a wider range of amenities and opportunities for social interaction than that found in rural areas. The World Bank (2008) argues that denser concentrations of economic activity (i.e., cities) increase choice and opportunity, ensuring greater market potential for the exchange of goods, services, information and factors of production. This author also subscribes to the view that since cities or urban agglomerations offer several economic and social advantages, instead of preventing them from growing further, the emphasis should be on how to create wellplanned cities and how to manage and absorb new growth in a sustainable manner. Increasing the absorptive capacity of urban areas must be tackled at two levels: urban planning policy (i.e., city level) and national development policy (Cohen, 1976). Of course, planning is only one of the ways to address this issue and, obviously, what could be achieved in the longer run by urban planning policies is tied up with the broader aspects of regional and national economic development policies. As Todaro (1979) argues, rather than devising ways to better

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accommodate the growing population, government policy needs to focus on economic opportunity, by stressing a realistic combination of rural development and dispersed urbanisation strategy. At the level of a city, on absorptive capacity, Beier (1976) maintains that land for settling the new people would be crucial, wherein land use zoning regulations tend to play a key role. It has been observed that without control of land and its uses, existing patterns would perpetuate, to the extent of threatening the political and social stability of the city. Thus, it is important to have land use zoning regulations that accommodate the needs of the poor, rather than excluding them and further aggravating the problem. On transportation, Beier (1976) argues that journeys to work become longer as the city grows and the costs of these journeys become prohibitive for the very poor, who cannot afford to locate near to where the jobs are, thus placing them at a locational disadvantage and excluding them from the labour market. Of course, solutions have to be catered to individual cities, but it is clear that developing countries cannot afford to follow spatial patterns and capital-intensive mass transport facilities (e.g., subways) like developed countries. Jobs and residential locations will have to be contiguous and the appropriate pattern may well be cities with multiple centres. For example, Shanghai, China, ever since the first Metropolitan Plan in 1927, was planned as a metropolitan city with only one centre, with industry and housing closely located, often in inner-city areas. However, the monocentric city became impractical with population growth in Shanghai, and the Shanghai Metropolitan Government has increasingly sought to set up alternative commercial and industrial districts and residential towns and suburbs (Abelson, 2000). In the context of mid- or intermediate-sized cities (say, population ranging from one to 10 million) in developing countries, Rivkin (1976) argues that these cities have peculiar characteristics such as: (a) rapid population growth, (b) presence of growing industrial processing activities, (c) increasing modernisation (e.g., automobiles, multi-storeyed buildings and supermarkets), and (d) threat to environmental ambience. It is these characteristics that ‘jolt’ the traditional land use patterns and physical form and hence require land use control. He goes on to argue that the problems faced by such cities, namely inadequate open space, uncoordinated utilities provision, resolving competition amongst land uses, land speculation, traffic congestion, undesirable densities, and so on, must be tackled at the level of the city itself. Solutions to such problems cannot be afterthoughts or subsidiary concerns within a national/ regional planning framework.

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It is clear from the above discussion that the scholarly literature on urbanisation and urban development in developing countries acknowledges that urban planning policy can indeed play an important role in addressing the problems arising due to rapid urbanisation. It is beyond the scope of this paper to look into the broader aspects of national economic development policies that can effectively be used to address the urbanisation issue. Nonetheless, what is within the scope of this paper is to look at urban policy measures that could be interwoven into the city planning process. For example, United Nations (1970) indicate that the sharpest and most complex conflicts arise in towns and cities lacking comprehensive development plans that can harmonise the various demands on space, relate land development to transport, provide public facilities (or at least ensure there is space for them), and integrate the man-made and natural environments. Rivkin (1976) argues that developing nations should be encouraged to develop their own urban research institutions and to direct the analytical and data-gathering activities of university faculties towards building a better understanding of the social, economic and physical characteristics of urban areas. He further argues that there are practically no empirical materials extant that assess the effectiveness of different approaches or techniques of land control in developing countries. There is little material on identifying the results of a process and comparing those results with initial (planning) objectives. There is nothing, save impressionistic assessment, to provide guidance for a country or community preparing to establish new, or revise old, measures. 2.2. Overview of urbanisation: India, Gujarat and Ahmedabad Over the past three decades or so, the rate of urbanisation in India has been much higher than that in the UK or the US, and second only to China (see Fig. 1). Table 1 gives the total and urban population in India from 1901 to 2001 (and projections up to 2016). The annual growth rate of the total population in India in the last five decades up to 2001 has been 2.1%. Even more dramatic has been the grown in urban population, which in this period is around 3.1% per annum. The level of urbanisation in India has been consistently rising and is expected to continue thus (second only to China). The rate of urbanisation compared to developed countries may seem low, but the absolute numbers of people living in urban areas in India is rather staggering. For example, the 286.1 million people living in urban areas

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Fig. 1. Percentage of urban population.

and 20% to 3%, respectively, while the decline for Class VI cities was the steepest from 6% to 0.3%. This clearly shows the importance of larger cities and their growth potential. Urbanisation trends in Gujarat State (see Fig. 2) are comparable to India. For example, the annual growth rate of the total population in Gujarat in the last four decades up to 2001 has been 2.3% (as against 2.1% in India) and the annual grown rate of the urban population during the same period has been 3.2% (as against 3.3% in India). However, in terms of the level of urbanisation, Gujarat stands much higher than India. From 1961 to

in India in 2001 is even higher than the total population of the US in 2000 (US Census Bureau, 2001), which was 281.4 million. The other interesting phenomenon is the growth differential of different cities in India. Urban areas in India are divided into six classes (see Table 2). In 1901, 26% of the urban population was living in Class I cities, which grew to around 68% in 2001, whereas for Classes II and III it has remained fairly constant (in the range of 10% to 11% and 12% to 16%, respectively). For Classes IV and V, the proportion of urban population had declined from around 21% to 7% Table 1 Urbanisation trends in India 1901–2001. Year

Total population

Urban population

% Urban population

Millions

Annual growth rate (%)

Millions

Annual growth rate (%)

1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001

238.4 252.1 251.3 279.0 318.7 361.1 439.2 548.2 683.3 846.3 1,028.7

– 0.56 0.03 1.05 1.34 1.26 1.98 2.24 2.23 2.16 1.96

25.9 25.9 28.1 33.5 44.2 62.4 78.9 109.1 159.5 217.6 286.1

– 0.04 0.80 1.77 2.81 3.52 2.37 3.29 3.87 3.16 2.75

10.8 10.3 11.2 12.0 13.9 17.3 18.0 19.9 23.3 25.7 27.8

2006 2011 2016

1,094.1 1,178.9 1,263.5

0.63 0.75 0.70

332.1 377.1 425.4

1.53 1.28 1.21

30.0 32.0 34.0

Data source: Census (1991) for 1901–1991; Census (2001b) for 2001; Census (2001c) for 2006–2016 projections (shown in italics).

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Table 2 Distribution of urban population in Indian cities. Year

1901 1951 1961 1971 1981 1991 2001

Class of city I 100,000 plus population (%)

II 50,000–99,999 population (%)

III 20,000–49,999 population (%)

IV 10,000–19,999 population (%)

V 5,000–9,999 population (%)

VI Below 5,000 population (%)

Total (%)

26.0 44.6 51.4 57.3 60.6 65.3 68.3

11.3 10.0 11.2 10.9 11.6 10.9 9.6

15.6 15.7 16.9 16.0 14.3 13.2 12.4

20.8 13.6 12.8 10.9 9.5 7.8 6.9

20.1 13.0 6.9 4.5 3.6 2.6 2.6

6.1 3.1 0.8 0.4 0.3 0.1 0.3

100 100 100 100 100 100 100

Data source: Compiled from Gurumukhi (n.d.) and Jacquemin (1999).

2001, the percentage of urban population grew from 25.8% to 37.4% as against 18.0% to 27.8% in India. Gujarat is undoubtedly one of the most rapidly urbanising states in India. Gujarat has 25 districts, of which Ahmedabad District (area of 8087 km2) has the highest population (5.81 million in 2001). The annual growth rate of total population for Ahmedabad District from 1961 to 2001 was 2.7% and the annual growth rate for urban population was 3.2%. Urbanisation in Ahmedabad District stood at 65.9% in 1961, which rose to 80.1% in 2001. The population in the Ahmedabad urban agglomeration (an area of about 600 km2, covering the main city and peripheral areas) rose from 3.31 million in 1991 to 4.69 million in 2001 (at an annual rate of 3.5%). Considering

only the population of the Ahmedabad Municipal Corporation (area 191 km2), it rose from 2.88 million in 1991 to 3.52 million in 2001, at an annual rate of 2%. In terms of population, the Ahmedabad urban agglomeration ranks seventh in India, and Ahmedabad Municipal Corporation ranks sixth. The pace of growth of the Ahmedabad urban agglomeration is staggering and typifies a rapidly growing urban area in India. 2.3. Background of planning in the Indian context In general, the goals of planning human settlements are well established. Broadly speaking, these are protecting the environment and achieving economic efficiency and social equity. In order to assess whether a

Fig. 2. Location of Ahmedabad.

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plan would be able to achieve its desired goals, it is necessary to forecast the implications of a proposed plan. In the context of an urban area, at the very least, this would entail having an idea of the spatial distribution of population and employment and its interaction for the horizon year in question. Planning in the context of mid-sized Indian cities is generally driven by a development plan (DP). The DP sets out the course of development for the next 10 years, in accordance with the town planning act prevailing in the state, and has a specific set of objectives. On the land use side, the DP generally prescribes ‘broad-brush’ maps for land use zoning, in which uses like residential, commercial, industrial, etc. are specified. In addition, development control regulations are also specified, relating to plot coverage (or margins) and the height and bulk of buildings. On the transport side, road-widening proposals (if any) are formulated and the future citylevel road network is specified, along with the tentative alignment of roads and their total widths (rights of way). Other aspects of DP include specifying augmentations to the underground infrastructure, such as water supply, sewerage and drainage, and specifying civic amenities. Special interest areas such as environmental and heritage conservation and tourism development may also be incorporated in the DP if relevant. The next level of planning after the DP generally has two approaches to managing new growth in urban areas. In the first approach, planning authorities acquire agricultural and undeveloped land by buying from the owners at prevailing agricultural land prices in large quantities, and re-plan them in an appropriate manner—called the ‘land acquisition’ method. In the second approach, called the ‘land readjustment and pooling’ method, instead of acquiring land from owners, land is brought together by pooling it from a group of owners and then the area is planned by readjusting and reshaping the land parcels so as to provide regular shapes to original plots and to use a portion of the land for roads, civic infrastructure and public amenities. The key advantages of the second method are that the original owners are not displaced and, more importantly, the increment in land value accrues to the owners whenever the land is sold and developed for urban use, unlike the first method. In addition, since the role of the government is more that of a facilitator, it is less likely to be prone to corrupt practices, compared to the land acquisition method (Ballaney, 2008). Returning to the method of DP making, it uses models for forecasting population and the future population becomes the key basis for formulating proposals in the DP. For example, the Draft Development Plans for Ahmedabad (AUDA, 1988, 1997) use an

average of conversion factor method, compound interest method, and Binomial expansion method for estimating population by zones over a 20-year period. Further to this, based on a rather arbitrary choice of threshold densities for various sub-regions, land requirements for residential use are calculated, followed by formulating land use proposals. One of the key regulations that controls the intensity of development, the floor space index (FSI, which is the ration of total built-up area to plot area, also known as floor area ration (FAR) in some countries), is almost uniform across the city (or in some cases it may have two grades). Regardless of whether the land is centrally located and/or has high transport accessibility or is located at the periphery of the city, the intensity of development permissible is nearly the same. It seems rather difficult to achieve the objective of, for example, compact development with a ‘blanket-type’ FSI regulation. In addition, the problem with this is that it does not respond to the demands of the real estate market. In other words, stipulating uniform low densities across the city is likely to create land scarcity and force unauthorised development on the periphery and on ‘marginal lands’ that are unsafe, such as hillsides, flood-prone valley floors, river banks, etc. (Byahut & Parikh, 2006). This author believes that there is also a further problem that could be identified with the current method, which is lack of clarity as to how the final land use plan is arrived at. Seminal textbooks in planning dating back over four decades or so prescribe that a planning exercise has several steps between decision to plan and goal formulation to production of the final plan. For example (see Fig. 3(a) and (b)) both emphasise that a final plan should be generated from assessing a set of alternative plans, which are tested using some form of quantitative techniques. To date, this approach continues to be emphasised. For example, Healey (2007), studying conceptual development and the practical implications of spatial strategies in European cities, and using the example of the Cambridge sub-region, emphasises the role of development of options for future growth in spatial planning and strategy formulation (example from Cambridge Futures, 1999) and Webster (2010), in the context of accessible urban form, emphasises that if such accessibility within a master plan could be priced, its designers could more readily maximise the value of the plan and weigh objectively between alternative designs. With regard to the Indian DP-making practice, there does not seem to be any explicit mention of alternative plans or policies and how these are assessed in order to arrive at the final plan. In addition, as Byahut and Parikh (2006) point out, there

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Fig. 3. Scientific approach to planning. Source: (a) Chapin (1965, Figure 36, p. 458); (b) Chadwick (1971, Figure 12.1, p. 279).

are problems in the content of the Ahmedabad Development Plan itself, which are only regulatory in nature and do not translate into projects, and therefore many of the intentions of these plans remain unrealised. In general, it seems that there does not appear to be a consistent theoretical and analytical framework within which planning decisions are being made. Rather, they appear to be piecemeal and ad hoc in nature, without proper justification. In other words, the decisions appear to be generally driven by political interests and seem to reflect a ‘map of influences’ from ‘pressure groups’ of various sorts. Exploring urban and regional policy issues in developing countries, Chatterjee (1983) argues that the practical consequences of the lack of interaction between the political and scientific communities have been particularly severe in developing countries. She asserts that the gap between the two has increased rather than decreased over the years. 2.4. The need and relevance of this study Over the last four to five decades or so, many theories of how land use is organised over space, embedded in a

microeconomic framework, have been propounded. Using these theories as building blocks, many models for simulating urban development have been developed in the developed world. Such models essentially simulate where urban land uses would tend to locate over space as a function of transport accessibility (or costs), a set of user preferences, and development constraints. Further to this, land use–transport interaction models have also been developed, which actively consider the feedback from transport to land use and vice versa. Some LUTI models available commercially are also used to test policy alternatives (i.e., alternative future scenarios, such as compact development or dispersed development or major transport improvement projects, or combinations thereof) by governments in developed countries. Alternative scenarios of supply of housing and employment, land and transport are fed as inputs to a LUTI model. Based on the behavioural assumptions of how households and firms locate, a LUTI model simulates the likely distribution of land uses for a future year and produces transport outputs for all origins and destinations, such as modal split, average trip costs, trip

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lengths, passenger-kilometre travelled, and network flows and congestion. Since all outputs are quantified, they can be systematically evaluated against economic, environmental and social indicators, leading to an overall assessment of the alternatives, which are used to support the plan-making and policy-formulation process. However, developing a full-fledged LUTI model to support planning decisions in the context of developing countries is generally reported to have been constrained by the non-availability of spatially disaggregated land use data. Furthermore, no visible attempt is being made to collect relevant land use and transport data in this regard (Srinivasan, 2005). Chatterjee and Nijkamp (1983) have argued that while models and techniques for urban and regional analysis have been fruitfully used to fit quantitative data to urban social, political, economic, and geographic theories in the advanced economies, they have much less applicability in developing countries. They maintain that the key reasons for this are: (a) huge quantity of data required for validating models; (b) type and quality of data; and (c) prohibitive data collection costs. The results of applications of models for planning purposes in developing countries have been generally mediocre. This is not to say that such constraints should be a deterrent to developing models and analytical techniques for planning in developing countries. As Echenique (1983) points out, in cases of no or limited availability of data, simple and robust models could be built, followed by collecting essential data for them. Molai and Vanderschuren (2003), based on their experience of developing a (land use–transport) model for Cape Town, South Africa, argue that models from developed countries are not likely to be adopted (to developing countries) in their present form, due to different socioeconomic and environmental contexts. The key is thus to ascertain the degree of simplicity and adaptability required for the development and application of models. To this end, in this study a simplified urban modelling framework has been developed for the case study city of Ahmedabad. The scope of this framework is informed by the literature review of prevailing academic wisdom and practical knowledge and its applicability to the case study city. Current research efforts in the Indian context need to focus on deepening the understanding of the nature of urban development and the impact of current policies on it, both from a spatial and socioeconomic perspective. Hence, some form of quantitative planning framework needs to be developed which entails (a) use of simple and robust descriptive and predictive models, and (b) a

framework for assessing planning policy alternatives, which could then be compared with the current approach. A clear understanding of the implications of alternative plans to the policy makers is crucial. While developing a model for Cape Town, Molai and Vanderschuren (2003) argue that there is a pressing need for models, particularly for developing countries, that answer ‘what if’ questions about land use and transport systems and address important policy concerns of relevance to both planners and the public. In the Indian context, a possible application could be developing a modelling framework for plan making and policy formulation that can answer the ‘what if’ questions, similar to the one developed in this study, which also helps inform the debate on alternative urban forms. Lastly, it is important for researchers to interact closely with practitioners to obtain feedback on the potential applicability and usability of new approaches that are likely to affect the practice of plan making. To this end, a series of meetings and presentations to government planners and decision makers were conducted in the case study city to obtain their feedback. In a nutshell, this study attempts to demonstrate how a theoretically consistent analytical framework can be developed with due regard to both data and resource constraints and used to assist in plan making, thereby enhancing current practice, serving as a reasonable justification to support the need and relevance for such a study. 3. General introduction of the case study city of Ahmedabad 3.1. Location, topography and climate Ahmedabad is located at 23.03N 72.58E on the banks of Sabarmati river in the state of Gujarat in western India (see Fig. 2). The city is divided by the river into two physically distinct eastern and western regions. The old city (also known as the walled city) is on the eastern bank of the river and is predominantly characterised by row houses (sharing common walls, also known as terraced houses) along the streets. Ahmedabad is 53.0 m above the mean sea level, with a relatively flat topography—the range between highest and lowest point being 4.27 m. Ahmedabad is in a hot and arid region, with summer highs of around 44 8C and winter low of around 7 8C. The average rainfall, based on the past 46 years of data (1961–2006), is 791 mm, with an average of 38 rain days per year (AMC, 2007).

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3.2. History Archaeological evidence suggests that the area around Ahmedabad has been inhabited since the 11th century, when it was known as Ashaval or Ashapalli. At that time, Karandev I, the Solanki ruler of Anhilwara (modern Patan, which is the capital city of Patan District, is north of Ahmedabad District), waged a successful war against the Bhil king of Ashaval and established a city called Karnavati, located at the present area of Maninagar, close to the Sabarmati river. Solanki rule lasted until the 13th century, when Gujarat came under the control of the Vaghela dynasty of Dholka (in the southern part of Ahmedabad District) and Karnavati was conquered by the Sultanate of Delhi. In 1411, the rule of Sultan Ahmed Shah of the Muzaffarid dynasty (which ruled Gujarat from 1391 to 1583) was established, which is how the city got its current name (the word ‘abad’ means ‘founded’ or ‘populated’). In 1487, Mahmud Begada, the grandson of Ahmed Shah, fortified the city with an outer wall 10 km (six miles) in circumference. The area enclosed within it is what is now known as the walled city. The Muzaffarid dynasty’s rule in Ahmedabad ended in 1573, when Gujarat was conquered by the Mughal emperor Akbar. During the Mughal reign, Ahmedabad became one of the empire’s thriving centres of trade, mainly in textiles, which were exported as far as Europe. Ahmedabad remained the provincial headquarter of the Mughals until 1758, when the Mughals surrendered the city to the Marathas. The Marathas form an Indo-Aryan group of Hindu warriors hailing mostly from the present-day state of Maharashtra (south of Gujarat), who created the expansive Maratha Empire, covering a major part of India (north and central regions), in the late 17th and 18th centuries. During the Maratha governance, the city lost some of its past glory and was at the centre of contention between two Maratha clans—the Peshwa of Poona (also written as Pune, a city in Maharashtra about 120 km south-east of Mumbai) and the Gaekwad of Baroda (a city in Gujarat about 100 km south-east of Ahmedabad). The British East India Company took over the city in 1818 as part of the British conquest of India. A military cantonment was established in 1824 and a municipal government in 1858. India’s movement of independence (from British rule) developed strong roots in Ahmedabad when Mahatma Gandhi established two ashrams (the Kochrab Ashram near Paldi and the Satyagraha Ashram, now known as the Sabarmati Ashram) on the banks of Sabarmati river during 1915–1917. Both these Ashrams became centres of intense nationalist

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activities. Following independence and the partition of India in 1947, the city was scarred by intense communal violence that broke out between Hindus and Muslims. Unfortunately, to date this tension still exists in the city and occasionally erupts in the form of violence and rioting. In 1960, the Indian state of Bombay was split into two states—Maharashtra and Gujarat. Ahmedabad was selected to be the first capital of Gujarat. The capital was shifted from Ahmedabad to Gandhinagar in 1971, which was a new, planned city, set to rival the Le Corbusier-planned Chandighar city in Punjab State, North India. Today, Ahmedabad is very diverse in terms of its built form. The walled city has most of the older and heritage buildings, with great examples of beautiful Islamic architecture. New and modern buildings occupy most of the western part of the city, with buildings designed by noted architects like Le Corbusier, Charles Correa, and Louis Kahn. 3.3. Demographics According to the 2001 census, the area under Ahmedabad Municipal Corporation had a population of 3.5 million and the population of the Ahmedabad urban agglomeration area was 4.5 million. Ahmedabad has a literacy rate of nearly 80% (88% males and 71% females), which is the highest in Gujarat. It is estimated that around 440,000 people live in slums within the city. The sex ratio (i.e., females to 1000 males) in 2001 was 885 (AMC, 2007). 3.4. Economy In the 19th century, the textile and garments industry received strong capital investment, with the first textile mill being established in 1861. By 1905, there were about 33 textile mills in the city, which soon came to be known as the ‘Manchester’ of the east. However, by the 1980s the textile mills had closed down, which marked the end of an era of the industry’s dominance in the economy of Ahmedabad. A sectoral shift was observed in Gujarat after liberalisation of the economy in the early 1990s. A rapid growth of chemical and pharmaceutical industries was observed in that decade. The tertiary sector, which includes business and commerce, transportation and communication, construction, and other services, has grown rapidly in the decade up to 2001 (with about 64% of the jobs). This trend is continuing, with a rise in the information technology industry in Ahmedabad. A survey in 2002 on the ‘super nine Indian destinations’

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for IT-enabled services ranked Ahmedabad fifth among the top nine most competitive cities in the country. 4. Introduction to modelling 4.1. Definition and types of models The word ‘model’ is extensively used in both arts and sciences. It has several meanings that vary, depending on the context in which it is being used. Models can range from physical objects to mathematical equations. Regardless of the nature of the model and the context, it would appear that the commonality in meaning is ‘abstraction of reality’, with the aim of either better understanding a real system or being able to predict its behaviour. Echenique (1972), Torrens (2000) and DfT (2005) provide detailed descriptions of various types of model. Based on these, this author has categorised models into three main categories: descriptive, explanatory and predictive, discussed in the following sections. 4.1.1. Descriptive models Descriptive models aim to describe real-life situations by abstracting their key elements, leading to the understanding of ‘what it is’. Torrens (2000) describes these as basic models and categorises them into three sub-categories. First are scaled or iconic models, which are scaled-down versions of reality, usually without any functional or predictive capacity. Essentially, they differ from reality only in size (e.g., architectural models of buildings). Second are analogue models, in which size is transformed, but so are some of the properties of the thing that is being modelled (e.g., maps, in which size is reduced, as with the scale model, but also some of the features of real elements are symbolised). Third are conceptual models, generally attempting to express how we think a system works. Usually, conceptual models are schematic representations or diagrams of a real-life system, using boxes and arrows showing interrelationships between its various elements or highlighting key elements (e.g., schematic diagrams of a carbon cycle or a plant cell). If appropriate, the word ‘model’ in the context of conceptual models could be used interchangeably with ‘theory’. Some key conceptual urban models are described in Section 4.2. Often, descriptive models have a mathematical structure, in which case they could be termed ‘descriptive analytical models’ (e.g., density gradients (Clark, 1951), ‘dispersion index’ (Bertaud, 2001), and ‘concentration/de-concentration measure’ (SCATTER, 2005)).

4.1.2. Explanatory models Explanatory models go a bit further than descriptive models. In other words, they attempt to explain ‘why it is what it is’. In this sense, these models could be termed ‘behavioural’ models (as against descriptive models, which describe the ‘end-state’ of a system rather than the process responsible for it—also sometimes known as ‘end-state’ models). Explanatory models try to explain the phenomenon by transforming conceptual understanding to mathematical symbology. Their aim is to offer explanations as to why the phenomenon being modelled is happening, by studying behavioural aspects of the comments of a system under question (e.g., those discussed in Section 4.3). 4.1.3. Predictive models Predictive models are similar to explanatory models in terms of having an explicit mathematical structure, but they enable the testing of ideas by allowing predictions to be made. It is obvious they build on explanatory models and have active feedback loops for various elements of the system being modelled. In this sense, they are simulations of a system and output effects given a set of stimuli (or course of action). These can further be classified into two sub-categories. First are conditional models (Echenique, 1972), wherein cause and effect are modelled, i.e., ‘if x occurs y must follow’ (also termed as ‘what if’ models). Second are optimising models (DfT, 2005), which optimise urban systems rather than predict their behaviour. Examples of optimising-type LUTI models include TOPAZ (first developed in 1970 in Australia by J.F. Brotchie, R. Sharpe, and J.R. Roy) and SALOC (first developed in 1973 in Sweden by L.L. Lundqvist), see Webster and Paulley (1990). Such models are intended as tools, which can find an optimum ‘design’, as against conditional models, which respond to a ‘design’ input by the user. Optimising models may be informative for research and long-term planning, but in general they require a substantial model development effort, in order to link them to the practical planning problems of individual cities or regions (DfT, 2005). Good examples of predictive models are the land use–transport interaction models, discussed in Section 4.4. 4.2. Descriptive conceptual models of spatial organisation of land uses Essentially, there are three main models or theories, often referred to as human ecological theories, which have been advanced to offer generic descriptions of how urban land uses organise over space. These are the

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‘invasion’ and ‘succession’. As the city grew and developed over time, the CBD would exert pressure on the zone immediately surrounding it (i.e., the zone of transition). Outward expansion of the CBD would invade nearby residential neighbourhoods, causing them to move outward. The process was thought to continue, with each successive neighbourhood moving further from the CBD. Burgess suggested that inner-city housing was largely occupied by immigrants and households of low socioeconomic status. As the city grew and the CBD expanded outward, lower status residents moved to adjacent neighbourhoods, and more affluent residents moved further from the CBD. A noteworthy feature of this theory was that it observed a positive correlation between income status and place of residence, i.e., the more affluent households were observed to live at greater distances from the CBD.

Fig. 4. Concentric zone theory. Source: Burgess (1925).

concentric zone theory, the sector theory, and the multiple-nuclei theory, which are discussed in the following sections. The reviews of these theories are drawn from Chapin (1965), Carter (1995), Harvey (1996), and Torrens (2000), unless mentioned otherwise. 4.2.1. Concentric zone theory (1925) In 1925, Ernest W. Burgess put forward the theory of concentric zones (Burgess, 1925). Burgess theorised that urban land use organises itself in concentric rings around the central business district (CBD) (see Fig. 4), with each ring having a different land use. This theory was developed based on observations of the city of Chicago from the 1980s to the early 20th century. The CBD (Zone I) forms the core of the city because it is the most accessible area and has shopping, offices, hotels and restaurants, theatres, banks, etc. Encircling the CDB is an area in transition, which is being invaded by business and light manufacturing (Zone II). Zone III is inhabited by workers in industries who have escaped from the area of deterioration (Zone II) but who desire to live within easy access of their work. Beyond this are residential areas (Zone IV) of high-class apartment buildings or of exclusive ‘restricted’ districts of singlefamily dwellings. Still further, out beyond the city limits, is the commuters’ zone (Zone V)—suburban areas or satellite cities—within a 30–60 min ride of the CBD. The process of change in the spatial patterns of residential areas was described as a process of

4.2.2. Sector theory (1939) Homer Hoyt in 1939 proposed the sector theory, primarily developed to describe the structure of residential areas, by modifying the concentric zone theory. Based on his study of rent patterns in 25 widely distributed American cities, Hoyt concluded that land uses tended to conform to a pattern of sectors rather than concentric circles, i.e., a city expands essentially along transport routes (railways and highways) in wedgeshaped sectors emanating from the CBD (see Fig. 5), rather than in concentric circles. The higher the accessibility of land, the higher would be its rent. This meant that most of the commercial functions would remain in the CBD, but some manufacturing functions would develop in wedges along the transport routes. Low-income households would locate near the factories/manufacturing sector, while middle- and high-income households would tend to locate away from the factories. Hoyt observed that, over time, high-income classes expanded outward from the CBD along faster transport routes. In general, he

Fig. 5. Sector theory.

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Fig. 6. Multiple-nuclei theory.

concluded that, rather than purely the distance from the CBD, the accessibility of land was also an important determinant of rent and hence land use. Hoyt, in a way, further enhanced the distance from the centre element of Burgess, by adding the directional element. Unlike Burgess, Hoyt acknowledged that the distribution of land uses has a strong relationship with transport accessibility. In addition, Hoyt’s hypothesis allows for a more irregular pattern of development, implying that different parts of a city grow at different rates. 4.2.3. Multiple-nuclei theory (1945) Harris and Ullman (1945) proposed the multiplenuclei theory, in which they theorised that many towns and nearly all large cities did not grow simply around a single CBD, but were, rather, formed by the progressive integration of a number of separate centres (or nuclei). Although they recognised that the CBD was a major centre of commerce, they suggested that cells or clusters of specialised activities (such as sectors 2, 6 and 7 in Fig. 6) would develop according to specific requirements, different rent-paying abilities, and their agglomerative tendencies. At the centre is the CBD, with light manufacturing and wholesaling located along transport routes. Heavy industry was thought to locate near the outer edge of the city, perhaps surrounded by lowerincome households, and suburbs of commuters and smaller service centres would occupy the urban periphery. Harris and Ullman identified four factors responsible for the emergence of sub-centres, as follows: (a) interdependency amongst activities and the need to be in close proximity; (b) natural clustering tendency, which is mutually profitable (e.g., retail centres, medical centres, etc.); (c) incompatibility of functions and special area (land) requirements; and (d) high land costs (or rents), which impacted the process of nucleation.

The innovative thing about this theory was that it recognised the fact that many cities tend to be polycentric, and hence the traditional monocentric models (e.g., concentric zone and sector theories) did not explain the urban land use pattern in most large cities. In addition, it goes further than the monocentric models in recognising the fact that, apart from transport accessibility, there are other factors that affect the spatial distribution of urban land uses, such as topography, special accessibility, and historical influences. It should be noted that the multiple-nuclei theory, unlike the previous two theories (which described changes in the basic arrangement of land use patterns), describes the land use pattern at a particular point in time. 4.2.4. Application to Ahmedabad Carter (1995) argues that the key criticism of the concentric zone theory is that it lacks universality and may have been applicable to the American city of the 1920s. This author thinks that the concentric zone theory is too simplistic and too limited in historical and cultural application to lead to an understanding of land use patterns of contemporary cities in developing countries. As can be seen from Fig. 7, there is no indication of formation of concentric zones in Ahmedabad, as suggested. On the other hand, as suggested by the sector theory, the formation of wedges (or sectors) along transport routes is abstractly evident for industrial areas (see Fig. 7). Since commercial development is allowed along roads 18.0 m or higher (see Fig. 8), strong formation of commercial sectors is not evident, except for some major concentrations in western Ahmedabad (Ashram Road on the western riverbank and CG Road, which is about one kilometre west of Ashram Road commercial area). In recent times, another major commercial sector has developed in western Ahmedabad, beyond the AMC boundary (called SG Highway, see Fig. 15). Residential use is spread all across the city, with high-income households generally concentrated in the western parts (not distinguished on the map)—an observation consistent with sector theory’s view on residential location. This author believes that, as suggested by the sector theory—that distribution of land uses has a strong relationship with transport accessibility—it is quite plausible that this relationship exists in cities in developing countries. Although sector theory’s application to Ahmedabad is fairly moderate, a comprehensive study of a large number of cities in developing countries needs to be undertaken, in order to generalise its applicability to such cities.

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Fig. 7. Land use map of AMC area.

As noted before, although the sector theory provides a useful way of describing the evolution of patterns of urban spatial structure, its ability to explain the land use organisation of larger present-day cities, especially in developing countries, appears to be limited. This is because, although such urban areas have traditionally had a centre, over the past few decades they have exhibited a tendency towards a multiplicity of subcentres, like most metropolitan areas in the West. In this sense, the multiple-nuclei theory appears to be the only theoretical model that recognises this aspect of presentday larger cities. The key deviation predicted by the

multiple-nuclei theory, as against the concentric zones and sector theories, is that major cities tend to have multiple centres—this is rather true in the case of Ahmedabad. In fact, jobs are scattered all over the city, with higher concentrations in the CDB, and other commercial areas forming sub-centres (see Fig. 15). The general disadvantage of the conceptual models discussed in this section is that they do not have an explicit mathematical structure, and lack the behavioural explanation of their constituent elements. Therefore, they cannot be applied to cities for analysing the evolution of their spatial structure in order to

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4.3.1. Isolated state (1826) Johann Heinrich von Thu¨nen in 1826 made the first attempt to show the interlinkages between space and economic activity. He developed a model that demonstrated how production cost and the cost of transporting production to the market affects agricultural land use (i.e., cropping pattern) in a region. Von Thu¨nen assumes an isolated agricultural region at the centre of which is a single town. This town is the only market for the agricultural produce. The soil is capable of cultivation and has the same fertility throughout the region. The town supplies the rural area with all the manufactured products and in turn obtains all its provisions from the surrounding countryside. The key questions the theory tries to answer are: what pattern of cultivation will take place, given the above assumptions? And, how will the farming system be affected by its distance from the town?

Fig. 8. All roads with commercial development allowed.

provide a useful quantitative basis. To this end, as mentioned before, descriptive analytical models, such as density gradients (Clark, 1951), dispersion index (Bertaud, 2001), and concentration/de-concentration measure (SCATTER, 2005) could be used. These models essentially use time-series population data by spatial units of analysis (e.g., zones or census wards), creating quantitative measure of the change in spatial structure. The spatial structure of Ahmedabad has been analysed using these three measures in a forthcoming paper by this author and hence is not repeated here. 4.3. Explanatory analytical models of location and land use In Section 4.2, we looked at some key theories that provided a generic picture of the effects of economic forces in shaping the spatial structure of cities. Urban economists have tried to present a more detailed account of the effect of economic forces on location of specific land uses in the context of a land market, attempting to explain the phenomenon. The works of four authors, namely von Thu¨nen (1826), Weber (1909), Christaller (1933), and Alonso (1964) are discussed in the following sections, as their contributions could be considered unprecedented, setting a sound foundation for the development of more comprehensive models over the years (such as the ones discussed in Section 4.4.

4.3.1.1. Concept of land rent. Von Thu¨nen introduced the concept of land rent, which was defined as the portion of the farm revenue that is left after deduction of the interest on the value of buildings, timber, fences and other valuable objects separable from land, i.e., the portion that is attributable to the land itself. Thus, land rent is the surplus left after deduction of production costs (i.e., sowing, cultivation, harvesting, administration, transport, interest on buildings, etc.). Land rent (or surplus) for a particular crop being grown at a particular location can be mathematically expressed as shown in Eq. (1). S ¼ qð p  c  ktÞ

(1)

where S is the land rent (or surplus) per unit of land; q is the yield of crop per unit of land; p is the price of crop fetched at the market per unit of weight; c is the production cost per unit of weight; k is the transport cost per unit of weight per unit of distance; t is the distance from the town (or market). If we take a hypothetical example of three crops, A, B and C, each of these crops will have such an equation of their own (see Fig. 9), which will be different based on their yield and the price they fetch in the market. It can be seen that from the town/market to tA, crop A will be grown, as it fetches more land rent than any other crop. From tA to tB, crop B offers highest land rent, and hence it will be grown in this ring. Lastly, from tB onwards, crop C will be grown similarly. It should be noted that if two crops have the same yield, then the one with the lower transport cost will be grown further away from the town, and if the production costs of two crops are the same, then the one with the lower yield will be grown further away from the town.

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Fig. 9. Land rent for various crops.

4.3.1.2. Pattern of cropping for the isolated state. Based on the actual data collected by von Thu¨nen for a period of five years for Tellow town in Germany, and using the principle developed above, he calculates the distances of the different rings around the town that will grow the various types of crops as discussed below. The first ring from the town (or the market) will have crops that are perishable in nature (i.e., those that cannot survive long journeys). Examples are cauliflower, strawberries, lettuce, etc. Milk will also be produced in this ring. It should be noted that no land would ever lie fallow in this ring. It is profitable to get manure from the town for these crops. However, as distance from the town increases, a point is reached when the transport costs of fetching the manure from the town are more than the cost of producing manure in the farm. This point marks the end of the first ring and the beginning of the second.

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The second ring will have forestry, i.e., it will be engaged in growing fuel wood. The third, the fourth and the fifth rings will have various types of grains grown using the crop alternation system, the improved system, and the three-field system, respectively. The sixth ring will be used for stock farming, breweries, etc., since no grain will be grown, as the land rent here becomes zero. In summary, since farmers would try to maximise profit (which is essentially the market price minus the production and transport costs), the most productive activities (e.g., vegetables, milk, etc.) or activities having high transport costs (e.g., firewood) would locate near the market. The agricultural land use model thus generated is shown in Fig. 10(a), while (b) illustrates the effect of change in grain price on the sizes of the rings. 4.3.1.3. Comments. Von Thu¨nen’s theory establishes that land values will be highest at the centre of the town and will decrease towards the periphery. Also, the density or intensity of an activity will be higher near the centre and will decrease towards the periphery. This results in the most favourable land use pattern around an isolated town, in the form of different economic activities locating in concentric rings. Using the introduction of highways and railways as an example to signify the effect of improvements in transport, von Thu¨nen shows that the limits of the isolated state are extended markedly, concluding that transport improvements have a vast effect on the welfare of a nation. Although von Thu¨nen’s model is for only agricultural

Fig. 10. Agricultural land use pattern and effect of grain price.

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land, it can also be extended to urban land uses, as shown by William Alonso (discussed in Section 4.3.4). Comments on its application to Ahmedabad are discussed in the same section, owing to the conceptual similarities of von Thu¨nen’s and Alonso’s models. 4.3.2. Industrial location theory (1909) Alfred Weber in 1909 explored the theoretical aspects of location of a specific type of economic activity, i.e., industries. He defined location factors as those forces that operate as economic causes of location. In other words, these factors can be seen as an advantage gained by locating an economic activity at a particular place rather than elsewhere. 4.3.2.1. Classification of location factors. Factors that could be held responsible for location could be categorised into two types: general factors, which are those that apply to each and every industry, regardless of their size or what they are manufacturing (e.g., cost of transport, cost of labour, and rent) and special factors, which are those that apply to only this or that type of industry. They may be attributed to some peculiar technical requirement of an industry (e.g., perishability of materials, climatic requirements, specific inputs requirements, such as fresh water, etc.). All location factors, whether general or special, may be classified further, based on the influence they exercise, and distribute the industries regionally and agglomerate (or deglomerate) industries within the regional distribution. To distribute industries regionally means to direct industries towards places that are geographically determined and given, thus creating a fundamental framework of industrial locations. To agglomerate means to contract industry at certain points within the regional framework. Of course, a third set of location factors may also be thought to exist: natural and technical factors, on the one hand, and social and cultural factors, on the other. 4.3.2.2. Orientation of industry. Transport factors: Weber analysed the location factors by first looking at transport costs as the only influencing factor in the location of an industry. In other words, it is possible to find an optimum location with regard to transport costs, to which an industry will be attracted. This forms the basic network of industrial orientation created by the first location factor, i.e., transport costs. This could be explained by a simple example. Let M1 and M2 (see Fig. 11) be raw material deposits, from which 0.7 and 0.3 tons of material are to be transported, respectively, to the place of production. Assuming both raw materials

Fig. 11. Location of an industry in the location figure.

are of the ‘pure’ type (the one that imparts its total weight into the product), the weight to be transported from the place of production to the place of consumption is one ton. Weber here uses an analogy from mechanics, in that the weights to be transported are treated as weights hanging down from the three corners of the location figure (the actual mechanical device used is known as a Varigon’s frame). These weights represent the force with which the corner of the location figures will pull (or attract) the location towards them in order to minimise transport costs. Thus, the point at which the weights stabilise mathematically represents the location, P, where production will take place. Labour and agglomeration factors: Having had the location fixed based on least transport cost, the second factor, i.e., labour cost, is then introduced. In doing so, the ‘deviation’ caused by introducing this factor is examined to ascertain their combined effect. Finally, agglomerative factors are considered, to arrive at the final deviation. Such a method allows an elegant and simple analysis of the factors of location and how they would work when acting together. 4.3.2.3. Comments. Weber’s theory helps us understand how transport costs influence the location of an industry. Based on the location of raw material deposits and the place of consumption of a finished product, the optimum location of an industry can be easily found such that the overall transport costs are minimal. This orientation may be attracted to other places, either by cheaper availability of labour or cheaper production costs, due to agglomeration of industries. In general, this theory explains how industries locate and move to different regions (or even countries) with changes in availability of raw material and labour and in the nature of coexistence of industries. Although this theory is specific to a particular type of land use (i.e., industries), it provides a useful theoretical construct for

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analysing and understanding the factors responsible for the location of an industry. With regard to its application to developing countries (in market-oriented economies), this theory does seem to have potential. However, its application specifically to Ahmedabad requires historical data at least after India’s independence (1947), which unfortunately is not available. Therefore, it is not possible to test its application for Ahmedabad, while acknowledging that it does indeed have the potential to explain industrial location. 4.3.3. Central place theory (1933) Walter Christaller in 1933 attempted to demonstrate the spatial effects of economic laws and rules on the geography of settlements, and tried to explain the size, number and location of cities in a region, in his central place theory. Central places are defined as places (a general term used for town/city/settlement) that have localisation of function. These places act as centres of the region in which they are situated. In contrast, there are dispersed places, which are defined as places that are not central. A central place is called thus only when it performs the function of a centre, i.e. providing goods and services to the region of which it is a centre. Goods (including services) provided by central places are called central goods and similarly those provided by dispersed places are called dispersed goods. Central goods are necessarily produced and offered at few central points, in order to be consumed at many scattered points (e.g., cars, doctors’ services, etc.). On the other hand, dispersed goods are necessarily produced and offered at many scattered points, in order to be consumed at a few points (e.g., bread, milk, etc.). Lastly, the term complementary region is defined as the region for which the central place is the centre. 4.3.3.1. Range of central goods and its upper and lower limits. Christaller then defines a very useful concept of range, which forms one of the key elements of the central place theory. Range is defined as the distance up to which the population will still be willing to purchase a good offered at a central place. Christaller emphasises that, conceptually, range is an economic distance and not a mathematical one. It should be noted that range also depends on the type of demand of the central good. If the demand is inelastic (i.e., urgent, non-substitutable), then the range is larger and if the demand is elastic (i.e., not urgent, substitutable) then the range is smaller. For example, the demand for medical services is likely to stretch far

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Fig. 12. Upper and lower limits of range.

out from the central place, while that for cinema would cease at a very short distance. The other two important factors that influence range are size of the central place and the density of population. The larger the central place, the greater will be the range as compared to smaller central places. This is because in a larger central place, the production costs are relatively lower and a larger amount of sales permits a lower unit cost. Higher population density implies greater range, as again higher densities make production cheaper. The range of a good has its upper and lower limits. The upper (or outer) limit denotes an area beyond which there will be no buyer for that particular good from the central place (i.e., it will be cheaper to buy a good from some other neighbouring central place). In other words, it is the maximum distance people are willing to travel to purchase a good. The lower (or inner) limit denotes an area need for a firm/individual selling a good to exist in business and make normal profits. In other words, it denotes a minimum radius of a market area needed to generate sufficient demand to support the supply of a good. In the literature produced by the followers of Christaller, upper limit came to be known simply as range and the lower limit as threshold (see Fig. 12). 4.3.3.2. The distribution of central places. Christaller proposes three principles that could determine the distribution of central places in a region, which are discussed below. The marketing principle: if the distribution is entirely based on the range of the good, then it would result in evenly spaced central places with hexagonal markets areas (see Fig. 13(a)). The traffic principle: if any of the cities distributed as per the market principle are smaller in size than expected, then this could be attributed to it not being on a major transport route. Conversely, if a smaller city were on a major transport route, then it would be bigger in size than expected by the market principle. If distribution were to adhere solely to the transport principle, then central places would be lined up on a

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Fig. 13. A system of central places.

transport route that fanned out from central places of higher order (see Fig. 13(b)). The separation principle: unlike the previous two, which are economic, this principle is socio-political. Political considerations sometimes distort the even spacing (and size) of cities. For example, if a region bans the sale of certain types of goods, then its central place will be less developed than the one in the neighbouring region that does not have such restrictions (see Fig. 13(c)).

4.3.3.3. Observations from the case study of southern Germany. Based on the study of settlements in southern Germany, Christaller concludes that the marketing principle is the primary and chief law of distribution of central places. The transport and separation principle are only secondary laws causing deviations. In practice, these two laws are effective under certain conditions only. In short, the interplay of all three principles generally explained the distribution, size and number of central places in southern Germany.

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Fig. 14. Development of central places in Ahmedabad sub-region.

Deviations not explainable economically, historically or physiographically could be explained by people-related causes or military reasons. 4.3.3.4. Comments. Using this theory, it is possible to generate a network of hierarchically ordered centres in a region with predictable functional and location characteristics. Although Christaller’s framework in general applies to central places in a sub-region, this theoretical framework could also be applied to investigating the phenomenon of development of sub-centres and their spatial distribution, within an urban area. In a sub-regional context, a visual analysis of the central place theory for the Ahmedabad sub-region is

shown in Fig. 14. Taking the old city of Ahmedabad as the first order settlement, central place theory predicts six second order settlements around the first order settlement in the radius of 36 km. Indeed, in case of Ahmedabad, there are six second order settlements in a 30 km radius, albeit not forming a perfect hexagon. Boundaries of lower order settlements are also shown. It can be observed that in many instances these form hexagonal boundaries (with pentagonal or rectangular or irregular shaped boundaries as well). In addition, the spatial arrangement as predicted by central place theory seems to show formation, demonstrating all three principles at work. The various principles of the system of central places could also be applied to a smaller spatial scale (i.e.,

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Fig. 15. Development of commercial areas in Ahmedabad.

metropolitan area). In this context, looking at the distribution of centres (i.e., concentrated commercial development), Ahmedabad has traditionally had its historic CBD in the old city (see Fig. 15). Over the years, new centres developed along Ashram Road and south of the CBD in 1980s, followed by CG Road commercial development around the 1990s. In the next decade, the SG Highway in the western part (beyond the AMC boundary) was the next major commercial development. It is clear that these new centres did not follow a perfect hexagonal geometry as predicted by the range concept under the marketing principle. However, the deviation as predicted by Christaller owing to the traffic principle is evident in the occurrence of the new commercial developments (post-1980s) in Ahmedabad, which have exhibited a linear form. 4.3.4. Urban bid-rent theory (1964) 4.3.4.1. Theoretical underpinnings. William Alonso in 1964 developed the theory of location of urban land

uses based on von Thu¨nen’s theory of agricultural land uses. He considers where an individual (or household) and a firm would locate in the city. He develops a very important concept of bid-rent that is used to arrive at an overall equilibrium in the market. Essentially, a bid-price curve for a household denotes a set of land prices that the household could pay at various distances, deriving a constant level of utility (or satisfaction). In other words, an individual is indifferent with regard to choosing locations on the bid-price curve (see Fig. 16(a)). On the other hand, the opportunities available to a household can be expressed in the form of a price structure curve (see Fig. 16(b)). A household will choose a point at which its utility is maximised—this is a point where the price structure touches the lowest of the bidprice curves (see Fig. 16(c)). Alonso similarly extends the same concept to determining the location of a firm. Market equilibrium will be achieved when no user of land can increase their level of utility (in the case of a

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Fig. 16. Residential bid price, price structure and equilibrium.

household) or their profits (in the case of a firm) by moving to some other location or by buying more or less land. Equilibrium requirements for land market are similar to any other economic good, i.e. at equilibrium demand and supply quantities and prices must be equal. However, in the land market there are two goods— quantity of land and distance from the centre, but only one transaction and one price (that of land). Hence, the simple requirements of the equation of demand and supply become much more complicated in the case of land market. It follows that a consumer with the steepest bid-price curve will locate near to the centre, and the bid-price curves get flatter as the location moves away from the centre, as shown in the chain of bid-price curves (see Fig. 17).

4.3.4.2. Some applications. Alonso draws important conclusions pertaining to rising incomes, transport improvements, and zoning regulations on location behaviour, which are discussed as follows. Effect of rise in income: The effect of rise in income has two facets. Firstly, it would tend to flatten the bidprice curve, resulting in preference for more peripheral location. Secondly, on the other hand, the marginal utility of land will decrease as more land is held, while the marginal utility of distance may increase as accessibility becomes scarcer relative to land. This will lead to steeper bid-price curves, resulting in preference for more central location. Thus, the effect of rising income has a combined effect and hence the net effect cannot be generalised. What actually happens

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Fig. 17. A chain of bid-price curves.

depends on the rate at which the ratio of marginal utility of distance and land increases or decreases with regard to the size of land holdings. In other words, if land holdings are bigger, rising incomes will imply flatter bid-price curves (i.e., preference for more peripheral location, e.g. some American cities) and if land holdings are smaller, it will imply steeper bid-price curves (i.e., preference for more central location, e.g. some Indian and Latin American cities). Effect of improvements in transport: If a city goes through technical improvements in transport (i.e., making commuting easier or less expensive, thereby reducing the generalised cost of transport) then this would tend to flatten the bid-price curves. If this happens in conjunction with a marginal increase in population, then city size will increase in terms of land area (sprawl). On the other hand, if population increases without transport improvements, then the city size will increase, mainly in terms of density. This is an important economic explanation of the evolving nature of a city’s spatial structure. Effect of zoning: Alonso concludes that land use zoning results in a discontinuity in the bid-price curve for a particular user. The effects of this are simple: the highest bidder is ‘disallowed’; the second highest bidder (as allowed by the land use zoning regulation) will take precedence. In such a case, the bid price of the land will be lower than the ‘free market’ condition (i.e., had there been no zoning regulation). In other words, land use zoning reduces the supply of land available for that particular type of use, and for other allowable uses it means a slight reduction in competition. The displaced

land use locates elsewhere at a higher price with lower utility (satisfaction or profits). Density zoning of the type that states minimum plot size (i.e., the user is compelled to buy more land than necessary), means the user will bid less per unit of land. If, on the other hand, a density zoning regulation states maximum plot size (i.e., it does not permit the user to have as much land as desired), this means the user will purchase more composite good to maintain the same level of utility, in order to compensate for decreased utility by the forgone land. Higher-income people make higher bids in the periphery of the city, while lower-income people make higher bids near the centre of the city. Thus, in an area, if zoning regulation is set at minimum plot size, then highincome people would move in, and if it is set at maximum plot size, then lower-income people would move in. This strongly suggests that density zoning can be used as an effective tool for an urban renewal programme. 4.3.4.3. Comments. Alonso’s theory of urban land use and land rent derives from von Thu¨nen’s theory of agricultural land use. This theory shows how various land uses in an urban area bid to secure the optimum location—a location that maximises their utility (satisfaction, in the case of residents, and profits, in the case of firms). This theory further demonstrates the effect of planning policies such as land use and density zoning on the location of activities. Alonso’s work could be considered very important, as it triggered extensive research on urban land use location models that are widely used today.

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Although Alonso’s (or von Thu¨nen’s) framework provides a good behavioural explanation of the process, the problem with its application to present-day mid-size or mega-cities is its assumption of a central place (or a place of ‘attraction’) to which all actors in the economic process are obliged to travel. Theoretically, for a monocentric city, the land uses would arrange in concentric rings, based on their bid-rent function (e.g., see Fig. 18). However, as seen in Fig. 7, organisation of land uses in concentric rings in Ahmedabad is not evident. The reason for this could be attributed to the polycentric nature of Ahmedabad (as discussed in Section 4.2.4 and Fig. 15) and indeed other contemporary metropolitan areas in developing countries. Therefore, it becomes difficult to adapt this framework to explain the location of land uses in such cities. The multiplicity of centres in Ahmedabad implies that its historical CBD is gradually losing its importance as a main ‘attractor’, making the direct application of this theoretical framework to Ahmedabad difficult. 4.3.4.4. Discussion. It is clear from the discussions in this section that models can play an important role in city planning. Although the models discussed in these sections provide a useful theoretical way to understand and analyse cities, applications for more practical purposes in planning have become possible by embedding the theoretical constructs in larger spatial interaction modelling frameworks (discussed in the next section). 4.4. Introduction to LUTI models Predictive models have an explicit mathematical structure. As the name suggests, such models predict outcomes of a system of inter-related components, based on a set of inputs (stimuli). This section discusses the basic structure of land use–transport interaction models, which serve as a typical example of predictive models applied to urban systems, with various feedback loops embedded in their structure. The purpose of this section is limited to the extent of providing a general understanding of how land use and transport interact in an urban system using some examples of existing LUTI models. The intention is not to describe the detailed working of LUTI models, which is comprehensively covered in Wilson (1974), Echenique and de la Barra (1976), de la Barra (1989), and Torrens (2000), amongst others. In the early 1960s, the use of the conventional fourstep transport model (which has trip generation, trip distribution, modal split and route assignment, see

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Fig. 18. Bid-rent theory—land use organisation.

Fig. 19) was quite prevalent. However, the criticism of the four-step model is that it ignores the fact that transport cost (or time) affects where land uses locate (households and firms), and alterations in the spatial pattern of location of land uses change the pattern of spatial flows between origins and destinations. In general, there is a well-accepted methodology for representing the effects of changes in land use on the transport system, and this has been successfully modelled. However, there is no accepted methodology for the converse relationship, i.e., the effects of transport change on location of land use. In fact, there is not even a consensus on what the effects are (Mackett, 2002). For example, if fuel prices are increased, or if road pricing is introduced, or if free buses are provided, then in the long run, the location of land uses may change as a result. On the other hand, if the distribution of population (housing) and/or economic activity (jobs) alters because of redevelopment or new development, this influences demand for transport. LUTI models are used to study the impact either of changes in land use on transport or vice versa. In addition, LUTI models can also be used to study the impacts of alternative ‘futures’ to inform the urban development policy-formulation process. Over the past few decades, especially in developed countries, national and local governments have been using LUTI models for testing the implications of proposed planning policies. 4.4.1. The land use–transport relationship Cites may be abstracted in terms of the functions they perform and their physical form. Functions are

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Fig. 19. The land use–transport interaction.

Table 3 Four-way classification of land use and transport.

Land use Transport

Function

Form

Activities Flows

Buildings Channels

Source: Mackett (1985) (who adapts from McLoughlin, 1969).

aggregate actions of the population, such as residing, working, shopping, recreation, etc., which collectively could be termed ‘activities’. Performing these activities requires travelling from one place to another, which generates ‘flows’. The physical form of a city consists of ‘buildings’ and (transport) ‘channels’. By comparison, activities are performed in ‘buildings’ and ‘flows’ generated by the activities traverse through the ‘channels’. The four-way classification thus generated is shown in Table 3. LUTI models can be thought of as two distinct systems that are interconnected, schematically shown in Fig. 19. The land use model uses an equilibrium mechanism that balances the forces of demand and supply and simulates the processes that affect the spatial location of activities, i.e., households and firms (or employment). The transport model takes the outputs of the ‘flow’ of ‘activities’ to ascertain specific ‘channels’ and transport modes likely to be chosen. If there are changes in the transport system, then this will change the behaviour of location of ‘activities’, generating different ‘flows’, thus creating a feedback loop. Fullfledged LUTI models in practice have a complex web of several sub-models embedded in the structure of the two

systems. In some models, the location of employment is an exogenous input to the model and location of residences is usually modelled using the bid-rent theory. Models that also model the location of employment use factors such as availability of labour and its cost, and access to transport and its cost, in the process. Some LUTI models are discussed in the next section. 4.4.2. The Lowry model Ira S. Lowry in 1964 developed the first LUTI model in his seminal work, ‘The model of a metropolis’, that was based on Pittsburgh (USA) region (Lowry, 1964). Lowry’s premise is that the place of employment dictates where people live. He divided the employment sector into two components: basic sector that caters to non-local demands of goods and services (i.e., those exported outside the urban area) and service sector that caters to the needs of the local population (i.e., retail shops, schools, etc.). In addition, Lowry identifies a household sector, which constitutes the residents who are directly related to the number of jobs available. Their choice of a place of residence is closely linked to their place of employment. The location of employment in the basic sector is exogenously inputted into the model, based on the assumption that its location is not constrained by local factors. This is used to estimate the location of (employed) residents, based on a gravity model, which uses distance (or transport costs) between various employment (zones) as a deterring function. The resident population so created will require further employment to provide them with local services. This

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Fig. 20. General structure of Lowry model.

Fig. 21. General structure of MEPLAN model.

estimate of service sector employees is added to the total employment, and the model proceeds iteratively to estimate population in each of the zones, until further changes in the population estimates become insignificant. Fig. 20 schematically shows the process in the Lowry model. The Lowry model is important in the history of LUTI modelling as it triggered development of several Lowry-type models in the decades to follow, each with specific improvements. 4.4.3. The MEPLAN model The MEPLAN suite of models stems from the work of Marcial Echenique and Partners, which was based on the original work carried out at the Martin Centre, University of Cambridge. The initial work by Echeni-

que and others in 1969 took the Lowry model (Lowry, 1964) as a starting point and extended it to include an explicit representation of the building stock that existed in an area. Further refinement of the model in terms of its calibration, and detailed development of the transport side, took place in the 1970s. By 1977, the basic structure of MEPLAN was nearly complete and was developed into flexible software (Echenique, 1994). MEPLAN applications to various cities over the years are covered in Echenique (1983, 1986), Echenique et al. (1990) and Echenique, Jin, Burgas, and Gil (1994). The MEPLAN modelling package is designed as a general abstract modelling framework to represent socioeconomic phenomena with a spatial dimension. It

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has a sophisticated and consistent mathematical structure, embedded in the influential school of discrete choice models and random utility theory (Domencich & McFadden, 1975). The general structure of MEPLAN is schematically shown in Fig. 21. It shows that the relationship between land use and transport is treated as a market relationship. As in any market, there are actors who demand—in this case land/ floorspace/buildings and transport—and actors who supply these. The interaction between the demand and supply determines the equilibrium prices of buildings and transport. Prices thus act as a key measure of the way in which land and transport networks are assigned to potential users, and determine the density of occupation of both land and transport. If the demand for capacity of buildings or transport exceeds supply, then prices go up, reducing demand until equilibrium is established. Land and transport networks with higher demand end up being used at higher density, implying higher land values (rents) and congestion, respectively. Using the concept of land use and transport as interacting markets provides three advantages: 1. Modelling results produced can be justified on the basis of economic behaviour. 2. The model is suitable for analysis of policy alternatives, by allowing policy options to change the demand and supply of land and transport elements (i.e., by using policy tools such as investment, regulation and pricing, or combinations thereof). 3. The outputs from the model are produced as a set of prices and quantities, and therefore provide a basis for formulating a system of economic evaluation of alternative policy options. The MEPLAN package has four interrelated modules (Echenique, 1994; Williams, 1994). The first is the land use module, which estimates the spatial location of activities such as employment and population, and produces trade between zones. It incorporates three elements: an input–output model; an elastic consumption model that allows the consumption of goods, services and space to vary with prices and incomes; and a spatial choice model that predicts the location of activities such as households and employment. It contains a trip distribution stage. The second is the land use transport interface module, which converts the matrices of flows of trade from the land use model into trip matrices disaggregated by purpose, and also covers transport disutilities of travel from the transport module into trade disutilities or

accessibilities for use in the land use model. It contains the trip generation stage. The third is the transport module, which assigns the flow matrices to different modes and routes, and carries out capacity restraint on links to represent congestion on roads and overcrowding on railways. It contains the modal split and assignment stages. The last is the evaluation module, which carries out the cost-benefit analysis of a particular policy compared to a base case. It represents both land use and transport benefits, and produces further indicators on the performance of the system, such as average speeds, energy use, pollution emissions, and distribution of benefits by socioeconomic groups. 4.4.4. The TRANUS model TRANUS (de la Barra, 1989) is an integrated land use and transport modelling package developed by Toma´s de la Barra in 1989, and can be considered conceptually similar to the MEPLAN model. The system combines a state-of-the-art model of activities location and interaction, land use and the real estate market, with a comprehensive multi-modal transport model. The combination of these two models produces the highest benefits, but the transport model may be used as a stand-alone component, especially for shortterm projections. Similar to MEPLAN, the theoretical framework of TRANUS also draws from many traditions, namely: spatial microeconomics (Alonso, 1964; von Thu¨nen, 1826); gravity and entropy maximisation (Lowry, 1964; Wilson, 1970); and the input–output accounting framework (Leontief, 1962). Like MEPLAN, TRANUS is also embedded in the school of discrete models and random utility theory. The general structure of the model, shown schematically in Fig. 22, has two main sub-systems: activities and transport. Within each sub-system, a distinction is made between demand and supply elements that interact to generate a state of equilibrium. The location and interaction of activities represent the demand side in the activities sub-system. Activities such as industries or households locate in specific places and interact with other activities to perform their functions. Activities also require land and floorspace in order to perform their functions. Such spaces are provided by developers in the real estate market, thus representing the supply side. The interaction between these two elements must lead to a state of equilibrium. If the demand for space is greater than the supply in a specific place, land rent will increase to reduce demand. Consequently, land rents or real estate prices are the

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Fig. 22. General structure of TRANUS model.

variable elements that lead the system to a state of equilibrium. In turn, the interaction between activities generates travel requirements. In the transport sub-system, demand is represented by the need for travel, which may take the form of people travelling to their places of work or services, or goods that are produced in one place and consumed in another. A distinction is made between physical supply and operative supply. The physical supply is made of roads, railways, maritime routes or any other relevant component. The operative supply is made of a set of transport operators that supply transport services, such as bus companies, truck companies, airlines, or even automobiles and pedestrians. The operative supply uses the physical supply to perform its functions. Demand–supply equilibrium in the transport subsystem is achieved in two ways: prices and time. If the demand becomes greater than the supply for a particular service, the price of the service may increase, but it is mainly the travel time that increases to achieve equilibrium. For example, if the number of passengers boarding a bus is greater than the spare capacity of the service, then the waiting time will increase. Similarly, if the number of vehicles along a road gets close to the capacity of the road, congestion is generated, thus increasing travel times. In other words, time is an important component in the demand–supply equilibrium in the transport system. The result of such equilibrium is synthesised in the concept of accessibility. It is the friction imposed by the transport system that inhibits the interaction between

activities. Consequently, accessibility feeds back into the activities system, affecting the location and interaction between activities and the prices in the real estate market. Because it is a cost function, accessibility may also be called transport disutility. 4.4.5. The DELTA model DELTA is a more recent model developed by David Simmonds of David Simmonds Consultancy, originally developed in the mid-1990s (Simmonds & Feldman, 2007) and formally published in 1999 (Simmonds, 1999). The overall aim of DELTA is to allow the development of land use models, which, in combination with appropriate transport models, enable users to study the future effects of both land use and transport policies, singly or in combination, on both the land use and transport markets. DELTA represents land use change over periods of time, linked to a transport model, which is run to model the performance of the transport system at a particular point in time. The transport model is therefore run several times in any one test, rather than just once for a horizon year. DELTA calculates all information about households, population, employment and floorspace, which the transport model requires to generate travel. DELTA thus replaces what is otherwise a process of preparing exogenous ‘planning data’ input. The processes modelled in DELTA can be divided into those that primarily affect spaces and those that primarily affect activities. For those affecting space, it predicts changes in the quantity and quality of floorspace available for occupation. Those affecting

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Fig. 23. General structure of DELTA model.

activities deal with household transitions and employment growth or decline, location or relocation and competition for space (the property market), and the employment status of individuals. The location or relocation model is the main locus of interactions, both between activities and space and between land use and transport. The influence of transport operates through sets of accessibility measures and through environmental variables. Fig. 23 shows the main linkages between the sub-models in DELTA model within a oneyear period. DELTA consists of six urban and three regional submodels. The urban sub-models estimate: 1. The development of buildings on land. 2. Demographic change and economic growth (applying growth rates which are either exogenous or predicted in the regional components of DELTA). 3. Changes in car ownership. 4. Location and relocation of households and jobs. 5. Employment and status changes. 6. Changes in the quality of urban areas. The regional sub-models represent: 7. Migration between different labour market areas. 8. Investment in the regional economy (long-term decisions affecting the future location of employment). 9. Production and trade in the regional economy (shorter-term effects on employment and freight transport). 4.4.6. A brief discussion on LUTI models As seen in the preceding sections, LUTI models allow the planning process to be carried out in a more

scientific manner by modelling the behaviour of urban ‘actors’ as against a more ‘intuitive’ approach (or ‘informal commonsense’ approach, as Breheny and Foot (1986) call it) without models. However, as already reported in Section 2.4, developing such comprehensive LUTI models in developing countries is problematic, considering the dearth of availability of appropriate data. Based on his experience in developing countries, Echenique (1983) points out that simple and robust models could be built in situations with limited data. In this study, a simplified suite of models has been developed for the case study city of Ahmedabad (discussed in the next section) that uses the available data to the best possible extent. 5. SIMPLAN model: a brief introduction SIMPLAN is a suite of four modules for informing the process of city planning. Its development and calibration is a subject matter for a separate paper (forthcoming) and is therefore not discussed here. However, a brief introduction, along with key equations and a comparison of modelled outputs and observed data for base year 2001, is provided in this section. The first module, called the trend analysis module (TAM), is concerned with analysing the evolution of the spatial structure of a city. This module currently uses three spatial analysis tools, such as density gradients (Clark, 1951), dispersion index (Bertaud, 2001), and concentration/de-concentration measure (SCATTER, 2005), see Appendix D. (Its application to Ahmedabad is discussed in a forthcoming paper.) Such analysis not only provides a quantitative understanding of the spatial evolution of a city, but also helps inform the process of

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formulating alternative future planning policies for testing. The second module is an econometric residential location model (RLM), as it uses average housing rents as part of the generalised cost (in addition to transport costs) in a gravity-type allocation function and currently deals with work trips. This module uses the microeconomic theory of demand and supply to ascertain the consumption of residential floorspace in each zone, based on the income and price elasticity of demand for housing floorspace. The study area workers are divided into four socioeconomic groups (SEGs) or income groups (SEG1–SEG4, representing professional/managerial, administrative/clerical, semi-skilled, and unskilled workers, respectively). The demand and supply in each zone determines the average housing rent, which is part of the location cost for households. As mentioned earlier, it was not possible to develop a full-fledged land LUTI model that considers all activities in an urban system, due to data availability constraints. However, it is believed that modelling residential location would be a significant step, considering that it is the single most dominant land use in most urban areas (about 45–50% in Ahmedabad). The work trips are then split by mode, using a multinomial logit modal split model (MSM), which forms the third module of SIMPLAN. After calibration, SIMPLAN can be used to test alternative planning policy alternatives for a future year, with appropriate employment, dwelling floorspace, and transport inputs. The fourth module, called ASM, is concerned with the assessment of alternative planning policies against key economic, environmental and social indicators. LUTI models usually have various stages. For example, de la Barra (1989) conceptualises the stages and its hierarchical sequence as location choice, trip choice, mode choice, and route choice; while Echenique (2004) conceptualises it as location choice, mode choice, time-of-day choice, and route choice. SIMPLAN considers two stages: location choice and mode choice. The reason for eliminating the trip choice stage is because the key determinant of where households locate is primarily driven by job location, and hence, in this context, modelling only the work trips would suffice. The route choice and time-of-day choice stages have also been eliminated because generating traffic volumes by time of day is beyond the scope of a standard development plan-making exercise (at which SIMPLAN is primarily aimed), in addition to the fact that this stage requires modelling of non-work trips, such as shopping, education, recreational, etc. With regard to the sequence of stages within SIMPLAN, although in theory it could be argued that the mode

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choice decision could either occur simultaneously with location choice or could precede it, the conventional hierarchy (i.e., location choice followed by mode choice) has been adopted. This is because doing so does not appear to have any inherent advantage over the conventional hierarchy. The structure of the RLM is shown in Eq. (1), which is similar to Mackett and Mountcastle (1997), but it has two crucial differences: firstly, it is by SEG, and secondly and more importantly, it uses housing rents as part of the location cost in addition to the generalised cost of travel. This aspect is important because housing rents represent a substantial portion of the location cost and are influential in determining location behaviour. Xim expðbm cm i jÞ m P Rm ¼ E ij j m m m i Xi expðb ci j Þ

(1)

where Rm i j is resident worker of SEG type m locating in zone i with a job in zone j; Emj is employment in zone j by SEG type m; cm i j is the a composite measure of generalised cost converted to Rs/day to avoid huge magnitude of values. It is calculated as shown below: m m cm i j ¼ ri þ n i j þ f i j

where rim is the average imputed housing rent1 paid by SEG type m in zone i obtained as ri unit  DFSDm i , in Rs/ day (for details see Eqs. (2) and (3)), vm is the average ij time cost for a round trip from zone i to j by SEG type m, in Rs/day. Notes: (1) Modal split is not carried out at this stage and hence average (harmonic mean) observed speed matrix is used in the calculation. Because of this current limitation, congestion is not being modelled. (2) In this study, for a future transport policy to be tested (e.g., public transport-oriented, highway capacity expansion, or a combination), this matrix is modified accordingly (not discussed in this paper). (3) The value of time used is 50% of hourly wage of a resident worker of SEG type m based on the literature review of travel time estimates. f ij is the average out-of-pocket expense (i.e., fuel, fare, etc.) for a round trip from zone i to j, in Rs/day. Notes: (1) As modal split is not carried out at this stage, average out-of-pocket expenses are used in the calculation. (2) In addition, it is not possible to create a feedback loop after modal split, as modal split is carried out at an aggregate level (i.e., not by SEG type m, see Eq. (6)). In light of these limitations, it is believed that vm i j þ f i j would be an acceptable repre1

All references to average housing rents in this study mean imputed rents, unless stated otherwise.

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sentation of generalised work travel cost. In other words, it is assumed that the nuances due to modespecific out-of-pocket expenses are insignificant insofar as being able to change location behaviour. Xim is a housing attractiveness factor by SEG type m to be dm calibrated, where, Xim ¼ Fi i and F i is the theoretical maximum supply of residential floorspace allowable in a zone and dm i is a parameter to be calibrated for each of the zones by SEG type m (which is set to unity initially). The purpose of this parameter is to factor for the unexplained variation in making a zone more or less attractive for housing. bm a parameter by SEG type m to be calibrated. The average housing rent in each zone is obtained using Eq. (2) and the new unit rent is calculated iteratively using Eq. (3) (which is conceptually similar to Echenique, 2004). P unit m ðr  DFSDm i  Hi Þ ri ¼ m i (2) Hi where riunit

¼

riunit

 u Di Si

(3)

where, riunit is the (new) unit monthly rent (Rs/m2) in zone i. riunit is the (previous) unit monthly rent (Rs/m2) in zone i. Di is the total residential floorspace demanded (m2) in zone i which is calculated as: P m m m m ðDFSDi  Hi Þ, where DFSDi is the dwelling 2 floorspace demanded (m /dwelling) in zone i by SEG type m (obtained from the equations of the respective demand curves) and H denotes households. Si is the total residential floorspace supplied in zone i (obtained by applying the average dwelling size to the dwellings in 2001). Note: The Census of India does not provide information on dwellings; however, the numbers of households are provided, and assuming a vacancy of 2%, dwellings for each of the model zones in 2001 are estimated accordingly. u is a control parameter estimated to be 0.10 (the purpose of this parameter is merely to control the 2

It is acknowledged that in terms of safety and comfort, twowheelers and cars are perceived differently. However, these have been amalgamated based on their common characteristics of being ‘private’ (i.e., available on demand). In addition, it should be noted that paratransit modes are rarely used for work trips on a regular basis and hence have not been included in the model. However, for some work trips within and between peripheral areas of Ahmedabad, a particular type of para-transit mode called chakda does exist (see Fig. 46). In the future, such special modes could be included in the model should observed data on their usage become available.

oscillations in the demand–supply ratio, enabling the model to converge quickly). From the resident workers in a zone, the households and population are calculated using observed resident workers per household (w) and population each residential worker supports (also known as inverse activity rate) (g), respectively; for a future year both w and g are forecasted. The structure of MSM is shown in Eq. (4), which calculates the proportion (or probability), PrðkÞi j , of resident workers residing in zone i having a job in zone j using mode k for travel to work. The modal split model developed here involves three modes: private automobile (PA) (two-wheelers and cars2); public transport (PT) (bus); and slow (SL) (bicycling and walking) and uses the standard multinomial logit (MNL) formulation (Domencich & McFadden, 1975). expðVikj Þ PrðkÞi j ¼ P k k expðVi j Þ

(4)

where Vikj is the utility of choosing mode k formulated as: Vikj ¼ ak þ bckij þ vtikj ¼ ak þ bðckij þ ðv=bÞtikj Þ ¼ ak þ bðckij þ ttikj Þ

(5)

where ak is the alternative specific constant (assumed zero for the other modes); b is a parameter to be calibrated (Rs1); ckij is the cost of travel by mode k (Rs); v is a parameter to be calibrated (min1); tikj is the time of travel by mode k (min); t is a new parameter, which is v=b (Rs/min) and hence by definition is the value of time. The proportion of work trips by mode k from zone i to j is given as: Rkij ¼ PrðkÞi j ðwhere Ri j X m ¼ Rm i j ; and Ri j is from Eq:ð1ÞÞ

(6)

m

Data from an origin–destination survey carried out by DMRC (2004) has been used to calibrate the model. It should be noted that the survey is aggregated over all income groups and hence this modal split model is applied to the total trips by all SEG type. SIMPLAN is developed in spreadsheet, with all key operations controlled by pressing ‘buttons’ linked with macros (macros are sub-routines written in Visual Basic Application code, within the spreadsheet). This provides a visually driven user interface, making the

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Fig. 24. SIMPLAN modelling suite.

model simple to understand, operate and update. Therefore, it allows planners to prepare several policy alternatives with drastic variations, and to test these to see future implications, enabling them to make more informed decisions before arriving at the final plan. In terms of computing times, running alternative policy options (like those discussed in the next section) takes about five minutes on a standard personal computer. Secondly, all testing can be carried out in-house by city

planning officials, which not only lends more transparency, usually not associated with planning projects involving mathematical modelling (wherein specific tasks are outsourced to private consulting firms), but also implies less financial burden on local authorities for outsourcing work. The interrelationship between the four SIMPLAN modules is shown schematically in Fig. 24, and the overall structure of second and third modules, which

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Fig. 25. Structure of SIMPLAN core.

constitute the core of SIMPLAN, is shown in Fig. 25. The working of the RLM is shown in Fig. 26 and a screen shot of the spreadsheet is shown in Fig. 27. Key comparison of population, average trip distances and housing rents between modelled outputs and observed data is shown in Tables 4 and 5 and Fig. 28, respectively. 6. Development of alternative policies for the future 6.1. Introduction Planning in Ahmedabad is governed by the Development Plan, which is a statutory document enforceable by law. The DP is revised every 10 years. The current DP, which was first published in November 1997 and revised in May 1999, was for horizon year 2011. The next DP, due in the next couple of years, would be for the horizon year 2021. Therefore, for the sake of consistency with local planning agencies in Ahmeda-

bad, year 2021 has been adopted as the horizon year for the urban planning policy alternatives in this study. An urban planning policy generally has two key components: the urban form and transport. There can be a variety of theoretical possibilities for these two components themselves and how they can be combined, as shown schematically in Fig. 29. In this study, it was thought prudent to examine two extreme urban planning policies: compaction and dispersal. As Banister (2005) puts it, even making no change needs to be placed in the same context (of other potential choices), as this would have important implications. Therefore, in addition, a trend policy is also developed, which, by and large, represents continuation of current trends both in terms of spatial development and transport policies. However, committed projects like the Bus Rapid Transit System for Ahmedabad (BRTS), the implementation of which began in 2007, has been included in all future policies. Thus, three alternative urban planning policies have been developed, as described in Sections 6.3–6.5.

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Fig. 26. SIMPLAN RLM stage operation.

6.2. Key modelling inputs The key land use inputs to running SIMPLAN for each of the urban planning policy alternatives are employment per zone and dwellings floorspace supplied per zone. The study area is divided into 21 zones (modelled zones) and 22–26 are external zones (see Fig. 30). The totals for employment and dwelling floorspace supply for the study area remain the same for all alternative policies, but their

spatial allocation per zone may be different, depending on the alternative. It should be noted that the alternative planning policy inputs are deliberately extreme or exaggerated, in order to amplify their effects. The total employment for 2021 has been obtained by interpolation from LBGC (2001). The total dwellings required in 2021 are derived as follows. The census data from 1971 to 2001 shows that resident workers per household has been growing at an annual rate of

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Fig. 27. SIMPLAN screen shot (base year 2001).

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149

Table 4 Workers and population 2001 (modelled vs. observed) (thousands). Zone

Resident workers Obs

Mod

Households

Population

% Diff: mod vs. obs

Obs

Mod

% Diff: mod vs. obs

Obs

Mod

% Diff: mod vs. obs

1 2 3 4 5 6 7 8 9 10 11 12 a 13 14 15 16 17 18 19 20 21

111.3 59.3 41.3 117.8 62.3 162.6 66.3 180.9 55.2 105.0 109.2 6.0 3.5 16.3 26.1 15.2 14.9 87.0 86.2 38.0 135.9

111.3 59.3 41.3 117.8 62.3 162.7 66.3 180.9 55.2 105.0 109.2 6.0 3.5 16.3 26.0 15.2 14.9 87.0 86.2 38.0 135.7

0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.3 0.3 0.1 0.0 0.0 0.0 0.0 0.0 0.1

69.5 38.0 26.5 77.5 37.7 110.6 38.7 105.7 46.8 65.5 75.7 2.9 2.1 11.1 17.7 9.1 9.4 57.5 60.0 27.8 96.1

73.1 39.0 27.1 77.4 40.9 107.0 43.6 118.9 36.3 69.0 71.8 3.9 2.3 10.7 17.1 10.0 9.8 57.2 56.6 25.0 89.1

5.2 2.6 2.5 0.1 8.6 3.3 12.6 12.5 22.5 5.5 5.1 36.1 6.3 3.7 3.5 9.7 4.4 0.5 5.6 10.0 7.3

372.6 178.5 127.4 369.5 205.2 585.6 194.1 557.5 226.8 345.3 357.7 14.7 10.6 54.7 84.3 44.4 48.2 270.6 290.4 136.8 467.3

366.5 195.4 136.0 388.2 205.2 536.4 218.7 595.9 181.9 346.0 359.6 19.8 11.4 53.7 85.8 50.1 49.0 286.6 283.9 125.3 446.6

1.6 9.5 6.8 5.1 0.0 8.4 12.6 6.9 19.8 0.2 0.6 34.3 7.5 1.8 1.8 13.0 1.8 5.9 2.2 8.4 4.4

Tot.

1500.1

1500.1

0.0

986.0

986.0

0.0

4941.9

4941.9

0.0

Key: diff: difference; mod: modelled; and obs: observed. Note: [1] Observed values are from Census (2001a). [2] Although modelled and observed resident workers match within 0.5%, the households and population have a discrepancy because an overall average of resident workers per household and inverse activity rate (or household size) has been applied to the modelled resident workers in all zones. a It should be noted that zone 12 is a military cantonment area and hence most of the land is not in the open market and thus this zone is not being properly modelled.

0.024%. Using this rate, w2021 is calculated and then the total number of households in the modelled area for 2021 is calculated as shown in Eq. (7). The dwellings required from 2021 to 2001 are calculated as shown in Eq. (8). H 2021 ¼

R2021 w2021

(7)

where H2021 is the total households in 2021 in the modelled area (zones 1–21); R2021 is estimate of workers both with residence and job in the modelled area (about 96% to total jobs in the modelled area); w2021 is the projected resident workers per household.Using the total households obtained in Eq. (7), the estimate of dwelling units required in the 20-year period is then given as: d20212001 ¼ d2021  d 2001

(8)

where d2021 is dwelling units in 2021 obtained by assuming one household consumes one dwelling and 2% vacancy rate of dwellings; d2001 is dwelling units in 2001.

It is assumed in this 20-year period that average incomes will increase in real terms. In reality, this is reflected by households moving up the SEG ladder. This is done by increasing the proportion of SEG1 and SEG2 households in 2021 based on trend analysis. In addition, in the case of Ahmedabad, to simulate a rapidly grown economy, incomes have been assumed to increase at a rate slightly higher than inflation. This is achieved by assuming the increase in income per annum (5.5%) to be higher than the discount rate (5%). As a result of the increase in average incomes in real terms, the 2021 demand curves, as compared to 2001, shift to the right, as shown in Fig. 31. The equations of these curves are used to calculate the dwelling floorspace demanded for alternative policies. 6.3. Trend policy 2021 (TR21) 6.3.1. TR21 land use inputs As the name suggests, this policy represents a continuation of trends, both in terms of land use and

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Table 5 Average trip distance (modelled vs. observed). SEG

bm

Modelled ATD (km)

SEG1 SEG2 SEG3 SEG4

0.200 0.235 0.300 0.510

8.69 7.53 5.74 5.12

All SEG

6.22

Observed ATD (km) a

6.00–6.50

a

Data by SEG is not available; the aggregate range is based on LBGC (2001) and CEPT (2006).

transport. LBGC (2001) report has employment projections up to 2035, which are interpolated to 2021 for SIMPLAN zones. However, this employment produces a proportion split of 65% for inner zones (zones 1–11) and 35% for outer zones (zones 12–21), as against the 2001 proportion of 80%–20%, respectively. On the other hand, if all of the new employment for period 2001–2021 hypothetically occurs only in outer zones, then this produces an employment proportion spilt by inner and outer zones of 60%–40%, respectively, and thus the LBGC (2001) employment projections could be considered a more radical scenario. Therefore, the zonal employment was appropriately modified to achieve employment proportion of 75%–25%, respectively (details are

presented in Table 17 and zone-wise values are shown in Appendix A). It should be noted that using total employment for allocation resulted in a huge reduction in employment in certain zones (depending on the policy), implying that employment will move to different zones, dipping below the 2001 level in some zones. This is unlikely, given the current growth potential of Ahmedabad region and Gujarat as a whole. In other words, regardless of the policy, employment will still grow in absolute terms (albeit in varying magnitudes) in all zones. Therefore, it was felt appropriate to deal with increments only. Total employment is obtained by adding the increments to the base 2001 employment. The allocation of employment increments (by inner and outer zones) is carried out using Eq. (9)—an approach similar to Hansen (1959). In theory, it is possible to control new jobs locations by planners, but this requires very strict land use regulations. Given the current statutory scope of the Ahmedabad Development Plan (e.g., commercial development is allowed on roads with right of way of 18 m or more), this is very unlikely to happen in Ahmedabad. Therefore, employment allocations are kept the same for all alternatives, and only dwelling allocations are varied for alternative policies. Nonetheless, the effects of extreme versions of compaction and dispersal policies, with different employment allocations, have also been tested (dis-

Fig. 28. Average zonal housing rents 2001 and 1996 land prices.

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151

Fig. 29. Schematic policy alternatives—urban form and transport.

cussed as part of sensitivity analysis in Section 8.0). SE2001 PTQS2021 i Ei20212001 ¼ E20212001 P i 2001 2021 SE PTQS i i i

(9)

where Ei20212001 is the additional employment in zone i in the period stated; E2021–2001 is the total employment increment for the period stated (which was divided into inner and outer zones); SEi is the share of employment in zone i in the year indicated; PTQSi is the public transport quality score in zone i in the year indicated for the option under question.The allocation of the additional dwelling units (di20212001 ) to the zones is carried using a similar equation to employment allocation, as

shown in Eq. (10). The floor space index (i.e. the ratio of total built-up area to plot area) for each zone is kept the same as base 2001, as this remains unchanged for trend policy. Ec RRv SC x PTQS’I di20212001 ¼ d 20212001 P i c i v i x ’ i Ei RRi SCi PTQSI

(10)

where d2021–2001 is as calculated from Eq. (8); di20212001 is the additional number of dwelling units supplied in zone i in the period stated (which is converted to floorspace); Eic is the employment in zone i (where, Ei ¼ Ei2001 þ Ei20212001 and Ei20212001 is from Eq. (9)); RRni is the ratio of rent in zone i to the average for

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Fig. 30. SIMPLAN zones.

modelled area for base 2001; SCix is spare capacity in zone i (depends on the FSI in that zone, which changes depending on the policy under question); PTQS’i is the public transport accessibility score in zone i; c; n; x; ’ are parameters which are currently set to unity (but can easily be changed, based on the value judgements of local planners). The allocation of dwellings was modified marginally for zone 1 (walled city) because of the trend in population decline, and zone 12 being a special zone (i.e., a military cantonment). It should be noted that if local authority planners are confident enough to use a

more ‘intuitive’ approach, then they could directly input the dwellings by zone. Eq. (10) is used for allocating dwellings for all 2021 alternative policies, which is then converted to floorspace. The average dwelling unit size in 2001 increases in 2021, based on a household’s income elasticity of demand for housing. 6.3.2. TR21 transport inputs The changes in transport systems are being represented by changes in the average travel times for all origin–destination pairs. For private automobiles, average travel speeds have been reduced from

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153

Fig. 31. Change in demand curves for alternative policies.

base 2001, to account for congestion in inner zones, and increased marginally in outer zones to reflect augmentation of existing road capacity and new roads. In addition, in cases where information has been available, the network distances have been changed to represent changes in the road network, such as flyovers and underpasses. For public transport, the bus rapid transit system (which is now being implemented in Ahmedabad), has been considered for all planning alternatives (with a superior version in compaction policy). The public transport speeds have been increased, based on the predominant BRTS type (i.e., exclusive BRTS, normal BRTS, or ordinary bus). The current fare policy of the AMC (at 2001 prices) has been adopted for the public transport system. Since private automobiles and slow modes usually share the same road infrastructure, travel speed changes are in line with private automobiles, as discussed above. Such assumptions have been made for trend and other alternative policies described in the subsequent sections, because developing a network-based transport model was beyond the scope of this study. However, any commercially available transport model with network modelling capability could be easily dovetailed with SIMPLAN, to better simulate the transport system.

Average speeds assumed for all modes across all policies are shown in Appendix C, Tables C1–C3. 6.4. Compaction policy 2021 (CC21) 6.4.1. CC21 land use inputs This policy represents an alternative urban form, in which most of the new residential development in the 20year period to 2021 takes place within inner zones (i.e., the AMC 2001 boundary, zones 1–11). The aim is to concentrate dwellings, as far as possible, within the existing footprint of the city, to reduce the overall travel distance to work and to create a modal shift in favour of public transport. Corresponding changes in FSI are made, in which FSI in inner zones is increased to 2.5, while in outer zones it is retained at 1.0. In addition, the land suitable for residential area has been increased to take into account conversion of non-residential uses to residential use. For example, there is a lot of derelict old textile mills’ land in eastern Ahmedabad that could be put to residential use under the compaction policy. Employment by zone for 2021 is the same as trend policy. 6.4.2. CC21 transport inputs Travel speeds for private automobiles have been reduced compared to trend policy to represent conges-

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Fig. 32. Dwelling inputs for alternative policies (2001–2021).

tion owing to the higher amount of population in inner zones, while for outer zones they are at par with trend policy. Network distances are the same as trend policy. For the public transport system, a superior version (i.e., better than the trend policy) has been assumed, to reflect more investments in public transport. Therefore, travel speeds by public transport are more than the trend policy, based on the type of bus service available in that zone (i.e., exclusive BRTS, normal BRTS, or ordinary bus). It is assumed that the pedestrian infrastructure would be better than trend policy, but since bicycling

infrastructure is part of roads, it will be of lower quality than trend policy. The combined effect on the infrastructure for slow modes is that slightly better speeds are assumed than trend policy in inner zones. 6.5. Dispersal policy 2021 (DS21) 6.5.1. DS21 land use inputs This policy represents an alternative urban form, in which most of the new residential development in the 20-year period to 2021 takes place in outer zones. The

Table 6 Summary of land use and transport inputs. S#

Input

Base 2001

Planning policy alternatives 2021 TR21

Land use L1 Employment (taken up by workers resident in modelled area, i.e., zones 1–21)

Proportion of employment by SEG

DS21

1,500,068

2,038,434

Per zone: see Appendix A for details

Per zone: calculated based on Eq. (9), see Appendix A for details (different employment distribution per zone tested for sensitivity analysis, see Section 9)

Based on LBGC (2001)

Modified to account for increases in SEG1 and SEG2, based on trend analysis

SEG1: SEG2: SEG3: SEG4:

8.4% 22.5% 41.2% 27.9%

SEG1: SEG2: SEG3: SEG4:

10.2% 25.3% 38.5% 26.0%

L3

Distribution of employment by SEG by zone

Assumed based on local knowledge, but adjusted to match totals in L2

Unchanged

L4

Floor space index (FSI)

W-city: 3.0 Inner: 1.8 Outer: 1.0 G’ngr: 1.8

Same as base

W-city: 3.0 Inner: 2.5 Outer: 1.0 G’ngr: 2.0

W-city: 3.0 Inner: 1.8 Outer: 2.0 G’ngr: 1.8

L5

Land suitable for residential use (LSR)

Estimated based on existing land use map and satellite images

Changed for outer zones based on local knowledge to account for conversions of greenfield sites to residential use (a phenomenon naturally occurring as the city expands) LSR inner: 8,195 ha LSR outer: 6,587 ha LSR total: 14,782 ha

Changed for inner zones based on local knowledge to account for conversion of brownfield sites to residential use (by way of market response to higher FSI)

Changed for outer zones based on local knowledge to account for conversions of greenfield sites to residential use (by way of market response to higher FSI)

LSR inner: 9,780 ha LSR outer: 5,770 ha LSR total: 15,550 ha

LSR inner: 8,195 ha LSR outer: 7,404 ha LSR total: 15,599 ha

LSR inner: 8,195 ha LSR outer: 5,770 ha LSR total: 13,965 ha

B. Adhvaryu / Progress in Planning 73 (2010) 113–207

L2

CC21

155

156

Table 6 (Continued ) S#

Input

Base 2001

Planning policy alternatives 2021 TR21

L6

Dwelling floorspace supply

45,684,830 m (966,323 dwelling units). Calculated based on observed households (Census, 2001a) Per zone: see Appendix B for details

Calculated from map

CC21

DS21

2

67,602,643 m (1,306,880 dwelling units)

Per zone: different for each zone calculated based on Eq. (10), see Fig. 32 and Appendix B for details (Also, different dwellings’ distribution per zone tested for sensitivity analysis)

Calculated from map (revising base year values after considering network changes)

T2

Average travel speeds O–D matrix

Harmonic mean of zonal speeds (see Appendix C, Tables C1–C3).

T3

Average travel time O–D matrix

Calculated from T1 and T2 above

T4

Out of pocket expenses

PA: Rs 1.86/km

PA: Rs 2.13/km

PT: 2001 fares PT: 2001 fares (as advised by AMC) SL: Rs 0.08/km SL: Rs 0.09/km (see Appendix C, Tables C4 and C5 for details) T5

Generalised cost of travel

Calculate from T3 and T4 using value of time estimated in T6

T6

Proportion of trips by PA, PT, SL for calibration of modal split

MNL modal split model calibrated based on survey data from LBGC (2001)

P Rij (i.e., m Rm i j ) from the residential location model is fed in to the modal split model to obtain person work trips by mode

Key: W-city: walled city (zone 1); inner: area within AMC 2001 boundary (i.e., zones 1–11); outer: area outside AMC 2001 boundary (i.e., zones 12–21); G’ngr: Gandhinagar city (zone 21); O–D: origin–designation pair (of zones); PA: private automobile (two-wheeler, car); PT: public transport (bus); SL: slow (bicycle, walk); and MNL: multinomial logit.

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Transport T1 Average (network) distance O–D matrix

2

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aim is to increase dwelling supply in outer zones to achieve a better balance in housing rents over the modelled area. Corresponding changes in FSI are made, in which FSI in outer zones is increased to 2.0, while for inner zones it is held the same as trend policy at 1.8. Employment by zone for 2021 is the same as trend policy. In addition, the land suitable for residential use in outer zones has been increased higher than trend policy, to account for conversion of greenfield sites. 6.5.2. DS21 transport inputs Travel speeds for private automobile have been increased for all zones over trend policy (with slightly more increase in outer zones) to represent the higher level of investments in road infrastructure (i.e., capacity expansion of existing roads and new roads). Network distances are the same as trend policy. The public transport system is assumed to be the same as trend policy, as the BRTS is a committed project already under implementation. Since this policy is private automobileoriented, the bicycling infrastructure (which uses roads) also benefits from capacity expansion, while the pedestrian infrastructure remains the same as base 2001. However, the combined effect on the infrastructure for slow modes is that speeds decrease in walled city, and increase in inner and outer zones over base 2001. In other words, better speeds are assumed in inner zones and much better in outer zones, as compared to trend policy. Key attributes of the inputs for all policies are summarised in Table 6. Details of employment and dwelling inputs by zones are shown in Appendices A and B and details of transport inputs are shown in Appendix C (with base 2001 included in all for comparison) (Fig. 32). 7. Summary of modelling outputs SIMPLAN model has been run for the various urban planning policies. However, for simplicity, only one of the variations for each of the 2021 alternatives is reported in detail: these are trend (TR21 ED63-37), compaction (CC21 D90-10), and dispersal (DS21 D1090) (see Table 17). Key outputs of other policies are presented in the section on sensitivity analysis (Section 8.0), with base 2001 outputs included for comparison. 7.1. Land use outputs It can be seen from Fig. 33, which presents percentage change in average housing rents in trend policy compared to base 2001 and compaction and dispersal compared to trend, that the overall average housing rents have increased in the range of eight to 10% in the period

157

from 2001 to 2021. In trend policy, rents have increased in all except two zones, which have marginal reductions. In zone 3, the most affluent zone in Ahmedabad, rent has gone down by about 0.9% (see Table 7). The reason for this is that the adjoining zones (i.e., zones 18 and 19) have become rather preferred zones for the affluent and hence the overall demand for housing in zone 3 has gone down. In similar vein, the development of zones 13 and 20 (mainly underdeveloped areas in 2001) over the 20-year period has reduced the demand in zone 21. An interesting spatial pattern of percentage change in rents emerges when the two diametrically opposite policies are compared to trend policy. Rents increase in the outer zones in compaction policy. This is because the inner zones have a huge supply of dwellings, causing the rents to reduce, with the opposite effect in outer zones created due to lesser dwelling supply. A similar pattern is observed in dispersal policy, but the pattern is more of an east–west divide, rather than inner and outer zones, owing to a larger supply of housing in western zones (especially outer zones). Examining Table 8 (part A), which presents average housing rents by SEGs, it can be seen that in trend policy, the effect of rent changes over base year is getting more pronounced as one moves from SEG1 to SEG4. In terms of average housing rents compared to trend policy, compaction policy is beneficial for SEG3 and SEG4, while dispersal policy is beneficial to SEG1. This is because in the inner zones in compaction policy, where there is more supply of dwellings, there are about 79% of SEG3 and SEG4 households locating, bringing down the average rents (as against 76% and 67% in trend and dispersal policies, respectively (see Table 9). On the other hand, in dispersal policy, wherein the supply of dwellings is more in outer zones, 53% of the SEG1 and SEG2 households are locating, pushing the rents down (as against 39% and 32%, respectively, for trend and compaction policies). In general, the pattern of housing floorspace consumption (see Table 8, part B) is the reverse of that of rent, as in pure economic terms these are inversely proportional, ceteris paribus. A summary of the key overall demographics is included in Table 10; the population per zone for the alternative policies by sub-regions and zones is presented in Table 11; and gross population densities are presented in Table 12. It can be seen that, in general, in terms of percentage change, population increases more in trend policy in the outer zones than in the inner zones. This is in tune with the observed trend of dispersal tendency of Ahmedabad. As expected, the population increases more in the inner zones in compaction policy and in the outer zones in dispersal

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Fig. 33. Housing rents—base 2001 and alternatives policies.

policy. Indicators to measure the change in spatial structure from 1971 to 2001 and 2021 modelled values are shown in Fig. 34. Calculation details are shown in Appendix D. 7.2. Transport outputs Transport outputs are presented in Table 13 as passenger-kilometres travelled (by SEG and by mode), average trip distance and time (by SEG and by mode),

and modal split. Expectedly, both the passengerkilometre and average trip distance and time are lowest in compaction policy and highest in dispersal policy. Although the average trip time (ATT) is highest in dispersal policy, its percentage change with respect to trend is much lower than average trip distance (ATD), because of higher average travel speeds. The pattern reverses in compaction policy, but not with a corresponding decrease in ATT, due to lower speeds. However, in case of public transport the proportionate

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Table 7 Average housing rents by zones. Zone

Base 2001

TR21

% Change: TR21 vs. base 2001

CC21

% Change: CC21 vs. TR21

DS21

% Change: DS21 vs. TR21

1 2 3 4 5 6 7 8 9 10 11 12a 13 14 15 16 17 18 19 20 21

2,380 2,957 3,531 2,566 2,355 1,950 2,447 2,282 1,438 2,152 2,045

2,675 3,195 3,499 2,718 2,519 2,156 2,542 2,507 1,689 2,165 2,072

12.4 8.1 0.9 5.9 7.0 10.6 3.9 9.9 17.5 0.6 1.3

2,663 3,187 3,731 2,670 2,502 2,073 2,524 2,392 1,558 2,023 2,052

0.4 0.3 6.6 1.8 0.7 3.8 0.7 4.6 7.8 6.5 1.0

2,663 2,904 3,482 2,653 2,439 2,207 2,722 2,551 1,787 2,318 2,263

0.4 9.1 0.5 2.4 3.2 2.4 7.1 1.8 5.8 7.1 9.2

2,606 1,705 1,913 2,196 1,783 2,828 2,199 2,378 2,386

2,711 1,950 2,169 2,335 2,103 3,121 2,987 2,663 2,344

4.0 14.4 13.4 6.3 17.9 10.4 35.8 12.0 1.8

2,594 1,976 2,173 2,672 2,353 3,559 3,344 2,565 2,411

4.3 1.3 0.2 14.4 11.9 14.0 12.0 3.7 2.9

3,001 1,997 2,214 2,246 1,728 2,875 2,741 2,364 2,338

10.7 2.4 2.1 3.8 17.8 7.9 8.2 11.3 0.3

Avg.

2,313

2,520

8.9

2,538

0.7

2,508

0.5

a

Values for zone 12 are not reported.

Table 8 Housing rents and dwelling floorspace consumed by SEG. Zone

Base 2001

TR21

% Change: TR21 vs. base 2001

A. Average monthly household’s rent (Rs, 2001 prices) SEG1 3,800 3,958 4.2 SEG2 2,743 2,894 5.5 SEG3 2,214 2,377 7.4 SEG4 1,665 1,801 8.2 ALL 2,313 2,520 8.9

CC21

% Change: CC21 vs. TR21

DS21

% Change: DS21 vs. TR21

4,188 2,929 2,359 1,776 2,538

5.8 1.2 0.8 1.4 0.7

3,726 2,903 2,401 1,803 2,508

5.9 0.3 1.0 0.1 0.5

B. Average floorspace/dwelling consumed (m2) SEG1 70.8 77.0 8.7 SEG2 54.5 58.9 8.2 SEG3 45.2 48.1 6.6 SEG4 34.5 36.2 4.7 ALL 46.4 50.7 9.1

75.0 58.5 48.5 36.8 50.7

reduction in ATD is substantial, benefiting from a superior public transport system (see Table 14). It should be noted that the overall ATD from base to trend has reduced. This is unusual, but it may be attributed to a combined effect of two factors. Firstly, the dispersal of jobs to outer areas has meant a reduction in ATD for outer to inner and outer to outer zones’ work trips (see Table 15). Secondly, there have been improvements in the road network in the trend policy, especially in outer zones (e.g., some new road links and better intra-zonal

roads), which has slightly reduced the network distance as compared to the base year. The modal split, both overall and by sub-regions, is presented in Table 16 (along with the average trip lengths for reference) and Fig. 35. The overall modal split for the alternative policies is as expected. In that, the share of private automobile is increasing from 2001 to 2021 for both trend and dispersal policies. Owing to a better public transport system in all alternative policies than the base year, the share of public

2.5 0.8 0.7 1.9 0.0

79.0 58.7 47.6 36.3 50.7

2.6 0.3 1.0 0.4 0.0

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Table 9 Households by income groups in sub-regions. Sub-regions

Base 2001

Trend 2021

Compaction 2021

Dispersal 2021

High-income group (SEG1 and SEG2) Inner zones 192,953 (63%) Outer zones 111,380 (37%) Sub-total 304,333

290,784 (61%) 182,349 (39%) 473,133

323,032 (68%) 150,101 (32%) 473,133

220,813 (47%) 252,320 (53%) 473,133

Low-income group (SEG3 and SEG4) Inner zones 511,309 (75%) Outer zones 170,402 (25%) Sub-total 681,711

648,728 (75%) 211,690 (25%) 860,418

679,725 (79%) 180,693 (21%) 860,418

570,235 (66%) 290,183 (34%) 860,418

Total

986,043

1,333,552

1,333,552

1,333,552

Table 10 Summary of key demographics. Item

2001

2021

Employment Resident workers Households Population

1,570,399 1,500,068 986,043 4,941,905

2,131,828 2,038,434 1,333,552 6,410,819

Table 11 Population—base 2001 and alternative policies (thousands). Base 2001

TR21

CC21

DS21

% Change TR21 vs. base 2001

By sub-regions Inner 3,520 Outer 1,422 Total 4,942 By zones 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Total

372.63 178.53 127.35 369.48 205.16 585.63 194.11 557.47 226.77 345.28 357.67 14.71 10.61 54.73 84.28 44.37 48.17 270.57 290.36 136.77 467.26 4,942

4,517 1,894 6,411 425.16 278.53 214.60 533.65 227.75 562.74 314.87 682.88 217.44 509.81 549.12 27.11 22.89 68.87 108.71 86.79 87.29 447.31 315.12 205.78 524.40 6,411

4,821 1,590 6,411 434.40 298.20 261.46 576.32 231.17 569.52 348.85 708.21 216.76 563.54 612.14 21.84 17.32 46.23 81.93 57.14 62.69 396.06 307.22 153.24 446.56 6,411

3,803 2,608 6,411 407.56 204.16 159.80 411.27 218.09 531.55 245.81 646.41 195.64 377.59 404.95 25.06 41.89 99.84 143.59 139.51 128.11 792.20 374.87 278.73 584.18 6,411

CC21 vs. TR21

DS21 vs. TR21

28 33 30

7 16 0

16 38 0

14 56 69 44 11 4 62 22 4 48 54 84 116 26 29 96 81 65 9 50 12

2 7 22 8 2 1 11 4 0 11 11 19 24 33 25 34 28 11 3 26 15

4 27 26 23 4 6 22 5 10 26 26 8 83 45 32 61 47 77 19 35 11

30

0

0

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161

Table 12 Population densities—base 2001 and alternative policies (gross density in persons per hectare). Sub-region

Base 2001

Trend 2021

Compaction 2021

Dispersal 2021

Inner zones Outer zones Overall

190 40 92

243 54 119

260 45 119

205 74 119

Table 13 Summary of transport outputs by SEG. Item

Base 2001

Work trips (same as no. of resident workers) SEG1 125,857 SEG2 337,125 SEG3 618,931 SEG4 418,155 ALL 1,500,068

Trend 2021

Compaction 2021

Dispersal 2021

208,381 514,838 784,663 530,552 2,038,434

208,381 514,838 784,663 530,552 2,038,434

208,381 514,838 784,663 530,552 2,038,434

Work trip passenger-km [millions] (one-way/day) SEG1 1.09 SEG2 2.54 SEG3 3.55 SEG4 2.14 ALL 9.32 % Change vs. base – % Change vs. trend –

1.59 3.48 4.08 2.60 11.75 26% –

1.35 3.27 3.93 2.60 11.15 20% 5%

2.45 3.61 4.17 3.67 13.91 49% 18%

Average work trip distance [km] (one-way) SEG1 8.69 SEG2 7.52 SEG3 5.74 SEG4 5.12 ALL 6.21 % Change vs. base – % Change vs. trend –

7.63 6.75 5.20 4.90 5.76 7% –

6.46 6.36 5.00 4.89 5.47 12% 5%

11.77 7.01 5.32 6.93 6.82 10% 18%

Average work trip time [min] (one-way) SEG1 SEG2 SEG3 SEG4 ALL % Change vs. base % Change vs. trend

49.40 45.54 37.78 37.30 40.81 5% –

40.76 41.60 35.74 36.19 37.85 12% 7%

65.27 43.65 35.58 44.96 43.09 0% 6%

55.37 49.00 40.15 38.86 43.06 – –

transport has increased markedly, with highest in compaction policy (attributed to a superior public transport system than trend and dispersal policies). Slow modes have shown an overall decrease over the 20-year period, which is generally as expected, because of increase in incomes (translating to either higher vehicle ownership or higher affordability for using public transport). Though dispersed policy has higher highway capacity (and thus higher average travel speed, especially for private automobile (see Table 14)), the share of private automobile compared to trend policy has not increased significantly (only about 2%). In theory, this analysis be more, owing to lower generalised costs due to

higher speeds. However, since a network-based congestion assignment model is beyond the scope of this study, this effect is not modelled accurately, and is therefore a limitation. However, any standard commercially available transport model with network modelling capability could be used for this purpose. The variations by sub-regions are also generally as expected. For shorter commutes (i.e., inner to inner zones) the share of private automobile and slow modes has decreased for all alternative policies, compensated by and attributable to a better public transport system than base year. For the second category of shorter commutes (i.e., outer to outer zones) the simulated existence of a

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Fig. 34. Spatial indicators for alternative policies.

superior public transport system has not decreased the share of private automobile; however, it has had a substantial reduction in the share of slow modes. This could be attributed to better speeds in outer zones in general. With regard to the longest commuting trips (i.e., outer to inner zones), the share of private automobile for all alternative policies has gone down, compensated for by an increase in public transport and slow modes. On the other hand, for the second longest commuting category (i.e., inner to outer zones), the superior public transport system has not had an effect on reducing the share of private automobile, except for compaction policy. It would therefore appear that public transport is a more preferred mode for journeys beyond a certain threshold. In the case of Ahmedabad, the inner to outer zone commutes (averaged over all modes) for all alternatives range from 13 to 17 km, while the average outer to inner zone commutes range from 18 to 19 km (see Table 15). Therefore, such a threshold could be around 17 km in the case of Ahmedabad. 8. Sensitivity analysis Sensitivity on two accounts has been tested. The first is with regard to variation in physical aspects, such as

allocation of dwellings and employment, and the second is income variation, discussed in the following sections. 8.1. Variation in dwellings and employment allocation As mentioned before, several variations of the alternative planning policies for 2021 were tested to see the effects of variations in dwellings and employment distribution. For each of compaction and dispersal policies, three other alternatives were developed with the same employment and different dwelling inputs and one set of inputs with both employment and dwelling inputs different from trend policy. As mentioned before, several variations of employment and dwelling inputs were tested, but only key input sets (nine) are presented in Table 17. A summary of key outputs is presented in Table 18. In compaction policy with same employment but different dwelling allocations (columns d–f), it can be seen that an ‘extreme’ version (i.e., having all of the new dwelling in inner zones, CC D100-0) is more favourable in terms of overall passenger-kilometres and average trip distance and time, but least favourable in terms of speed, which is attributable to more conges-

B. Adhvaryu / Progress in Planning 73 (2010) 113–207

163

Table 14 Summary of transport outputs by mode. Item

Base 2001

Trend 2021

Compaction 2021

Dispersal 2021

3.68 4.07 4.00 11.75

2.98 4.63 3.54 11.15

4.87 4.46 4.59 13.91

5.61 7.31 4.84 5.76

4.99 7.21 4.43 5.47

7.03 8.57 5.56 6.82

Average work trip distance [min] (one-way) Private auto 29.02 Public transport 64.66 Slow 44.29 ALL 43.05

26.23 54.97 42.82 40.81

24.83 47.47 39.85 37.85

27.06 60.67 45.47 43.09

Average work trip time [min] (one-way) Private auto 13.82 Public transport 7.03 Slow 6.95 ALL 8.66

12.82 7.98 6.79 8.47

12.05 9.12 6.67 8.67

15.58 8.47 7.33 9.50

Work trip passenger-km [millions] (one-way/day) Private auto 3.65 Public transport 2.41 Slow 3.26 ALL 9.32 Average work trip length [km] (one-way) Private auto 6.68 Public transport 7.58 Slow 5.13 ALL 6.21

Table 15 Average work trip lengths by origin–destination (km). Policy

Living in

Job in

ALL

Inner

Outer

Base 2001

Inner Outer ALL

4.04 21.30 5.91

12.19 7.43 7.44

4.05 11.64 6.21

Trend 2021

Inner Outer ALL

4.49 19.10 5.51

14.24 6.48 6.54

4.52 8.72 5.76

Compaction 2021

Inner Outer ALL

4.77 17.75 5.03

16.70 6.32 6.83

4.96 7.01 5.47

Dispersal 2021

Inner Outer ALL

3.73 18.37 6.87

12.97 6.68 6.68

3.74 11.33 6.82

tion. In terms of average housing rents, this policy is least favourable but most favourable in terms of work travel costs, due to lowest average trip distance and time. An exact mirror image is depicted in dispersal policy. In other words, the ‘mildest’ version of dispersal policy (i.e., having 80% of new dwellings in outer zones, DS D20-80) is more favourable in more aspects than an ‘extreme’ version. However, interestingly, in terms of economic benefits, the picture is different (discussed in Section 9.4 as part of the assessment of other alternative policies for sensitivity analysis).

With regard to those versions of compaction and dispersal policies (in which employment is also altered compared to trend policy, i.e., CC ED92-08 and DS ED22-78, respectively), dispersal policy has lower passenger-kilometres, ATD and ATT as compared to trend. This may seem counter-intuitive at first, but the reason for this is that dispersing employment to outer zones has resulted into shorter commutes (i.e., more people are living as well as working in outer zones). On the other hand, concentrating most of the new employment in inner zones in compaction policy has

164

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Table 16 Modal split aggregated by O–D (work trips). Item

Modal split a

Average trip length (km)

BS01 (%)

TR21 (%)

CC21 (%)

DS21 (%)

BS01

TR21

CC21

DS21

28.9 17.4 53.8

32.2 27.3 40.5

29.3 31.5 39.3

34.0 25.5 40.5

6.68 7.58 5.13

5.61 7.31 4.84

4.99 7.21 4.43

7.03 8.57 5.56

Inner to inner zones PA 35.9 PT 21.1 SL 43.0

32.6 28.1 39.3

29.0 32.7 38.3

33.6 24.1 42.2

4.37 4.33 3.62

4.53 5.58 3.68

4.38 6.33 3.73

3.91 4.06 3.41

Inner to outer zones PA 25.1 PT 16.8 SL 58.1

28.4 40.1 31.6

19.3 60.0 20.7

35.3 30.2 34.4

12.21 12.17 12.19

13.74 15.10 13.62

14.69 18.12 14.46

12.93 13.27 12.74

Outer to inner zones PA 52.1 PT 28.7 SL 19.1

29.2 40.4 30.4

23.5 50.1 26.4

36.6 36.0 27.4

21.16 23.66 18.98

18.38 20.94 17.35

16.29 19.61 15.52

18.31 19.53 16.92

Outer to outer zones PA 28.3 PT 10.8 SL 60.9

31.7 22.2 46.1

31.0 24.9 44.2

33.1 22.0 44.9

7.55 9.62 6.50

6.19 8.15 5.87

5.93 7.93 5.67

6.47 8.66 5.86

Overall PA PT SL

Key: BS01: base 2001; PA: private automobile (two-wheeler, car); PT: public transport (bus); and SL: slow (bicycle, walk). a Base year values are from LBGC (2001).

resulted in longer commutes than its counterpart dispersal policy. This is because some of the outer zones have higher housing attractiveness, implying that SEG1 and SEG2 households prefer to locate in these zones but have jobs in inner zones. However, both these policies do not fare well in the economic benefits (discussed in Section 9.4). 8.2. Variation in income As shown in Fig. 31, it was assumed that incomes increase in real terms. However, if a scenario were envisaged where the income levels in 2021 remained the same at 2001 level in real terms, then these would have some variation on the outputs. These have been presented in Table 19. It should be noted that for simplicity this has been tested only for policies CC D9010 and DS D10-90 (i.e., the policies presented in detail in Section 7.0). From Table 19, it can be seen that if incomes do not increase in real terms, then as a consequence, the average housing consumption reduces slightly more for higher income groups and less for lower income groups, with a corresponding reduction in average housing rents for all alternative policies. A slight increase in average

trip distance and time is noticed. A plausible explanation that could be offered for this is that to compensate for lower incomes, households locate a bit further (implying cheaper housing rents) in order to satisfy their total household budget, whilst deriving the same level of satisfaction (or utility). The increase in work travel costs are in line with the increase in average trip distance and time. The change in modal split is insignificant. Changes in the economic benefits for both the scenarios are discussed in Section 9.4. 9. Assessment of alternative planning policies Assessment, in the context of planning policies, is the process in which various pro and cons of the outcomes of alternative policies are estimated (quantitatively and/or qualitatively), in order to create a comparative picture of the alternative policies. The term ‘assessment’ is usually used ex-ante, while ‘evaluation’ is preferred ex-post. The assessment process produces distilled information that helps improve the decisionmaking process by providing decision makers with an objective framework from which a desired policy could be chosen for adoption, or combinations thereof can be developed for further testing.

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165

Fig. 35. Modal split aggregated by O–D (work-trips).

9.1. Economic assessment 9.1.1. Housing and work travel costs Key economic outputs from the model are average housing rents and work travel costs—which together could be seen as constituting the bulk of the cost of living—presented in Table 20. Other costs, like nonwork travel, food, clothing, etc., are assumed to be the same across alternative policies for the purpose of assessment in this study. It can be seen from Table 20 that, as expected, the average housing rents per household have increased in absolute terms (in the range of eight to 10%). Although the differences in average housing rent for 2021 policies are marginal, the highest rent is in compaction policy

and the lowest is in dispersal policy. The work travel cost includes out-of-pocket expense and value of time based on the income of workers. The average transport cost per household in trend policy has reduced. The prime reason for this is that the introduction of the BRTS (which is present in all alternative policies, but did not exist in the base year) has contributed to the overall travel time savings. Comparing the three alternative policies, since the average work trip distance and time are highest in dispersal policy and lowest in compaction policy, expectedly, the work travel cost per household is also highest and lowest, respectively. The total costs (i.e., rents plus work travel, which constitute the bulk of the cost of living) as compared to trend policy, are lower in compaction and higher in dispersal,

166

Table 17 Summary of inputs for 2021 policies. Items

TR21 ED63-37 (%)

CC21 variations (with same employment, but different dwellings)

DS21 variations (with same employment, but different dwellings)

CC21 Diff emp and dwellings

DS21 Diff emp and dwellings

CC D80-20 (%)

CC D90-10 (%)

DS D20-80 (%)

DS D10-90 (%)

CC ED92-08 (%)

DS ED22-78 (%)

CC D100-0 (%)

DS D0-100 (%)

–

63

63

63

63

63

63

63

92

22

–

37

37

37

37

37

37

37

8

78

80

75

75

75

75

75

75

75

83

65

20

25

25

25

25

25

25

25

17

35

–

63

80

90

100

20

10

0

92

22

–

37

20

10

0

80

90

100

8

78

70

68

73

75

78

57

55

52

76

58

30

32

27

25

22

43

45

48

24

42

Totals: Employment 2001 = 1,500,068; 2021 = 2,038,434; dwellings 2001 = 966,323; 2021 = 1,301,806. Increments: Employment 2021 = 538,366; dwellings 2021 = 335,483; Key: diff = different; emp = employment.

B. Adhvaryu / Progress in Planning 73 (2010) 113–207

Employment increment: inner zones Employment increment: outer zones Employment total: inner zones Employment total: outer zones Dwelling increment: inner zones Dwelling increment: outer zones Dwelling total: inner zones Dwelling total: outer zones

Base 2001 (%)

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167

the latter being attributed to higher work travel costs. However, dispersal policy yields higher economic benefits, as explained in the next section. 9.1.2. Consumer and producer surplus in housing rent In the above section, the costs to the citizens of Ahmedabad were analysed. However, as a society, these costs are incurred by consumers and accrued to suppliers (or producers). Therefore, these costs do not give a complete picture of the net economic benefits or welfare to society as a whole. In order to do so, the surplus to society has to be estimated. This surplus can be split into two, based on which group it accrues to: the consumers or the producers. Consumer surplus is the difference between what consumers are willing to pay for a good (or service) and what they actually pay (represented by the area labelled ‘consumer surplus’ in Fig. 36). The producer surplus can be defined as the difference between the price for which a producer would be willing to provide a good (or service) and the actual price at which the good (or service) is sold (represented by the area labelled ‘producer surplus’ in Fig. 36). Consumer surplus and producer surplus definitions are adapted from Samuelson and Nordhaus (2001), Perloff (2004), Katz and Rosen (2005), and Krugman and Wells (2005). To estimate the total consumer surplus, the demand curves in Fig. 31 can be used. However, unfortunately, there are no past studies in Ahmedabad available on the elasticity of housing supply from which supply curves can be estimated. Therefore, a rather simplistic assumption is made: if the price is zero there would be no supply of housing.3 In other words, the supply curve, assumed to be a straight line, would pass through the origin and point E (see Fig. 36). In this case, the producer surplus in zone i by household of SEG type m is simply half of the expenditure (or revenue). In SIMPLAN, the housing demand is given by the following equation (see Fig. 31): pm i ¼ pmax expðbqÞ

(11)

Fig. 36. Consumer and producer surplus.

Fig. 37. Change in consumer surplus.

The consumer surplus (for zone i by household of SEG type m) can be calculated as: CSm i

¼

Zqe

pmax expðbqÞdq  pe qe

(12)

0 3

This may not be entirely true, as even at some unit price greater than zero, producers would not be willing to supply housing if that unit price is lower than unit production costs. However, this threshold value (which is the intercept of the supply curve on the unit price axis) will vary depending on the location, as the land cost is one of the biggest components of unit price (while construction costs are usually fairly uniform across the city). In addition, there could be changes in the threshold value if zoning regulations vary by location. Therefore, it would not be appropriate to have an average threshold for the study area as a whole. In absence of any substantial information, based on which such a threshold for each zone can be estimated, this rather simplistic assumption has been made.

Producer surplus as explained above is calculated as: PSm i ¼

1 pq 2 e e

(13)

The total consumer and producer surplus can be estimated, respectively, as: XX CSm (14) i m

i

168

Table 18 Sensitivity analysis—dwellings and employment variations. Base 2001

TR21 ED63-37

CC21 variations (with same employment, but different dwellings)

DS21 variations (with same employment, but different dwellings)

CC21 Diff emp and dwellings

DS21 Diff emp and dwellings

a

b

c

CC D80-20 d

CC D90-10 e

CC D100-0 f

DS D20-80 g

DS D10-90 h

DS D0-100 i

CC ED92-08 j

DS ED22-78 k

Passenger-km [millions] % Change vs. trend ATL [km] % Change vs. trend ATL [min] % Change vs. trend Speed [km/h] % Change vs. trend Modal split: PA [%] Modal split: PT [%] Modal split: SL [%] Rent [Rs/month] % Change vs. trend Transport cost [Rs/month] % Change vs. trend Cost of living [Rs/month] % Change vs. trend

9.32 – 6.21 – 43.05 – 8.66 – 28.9% 17.4% 53.8% 2,313 – 2,749 – 5,061 –

11.75 – 5.76 – 40.81 – 8.47 – 32.2% 27.3% 40.5% 2,520 – 2,613 – 5,132 –

11.49 2.2% 5.64 2.2% 38.7 5.1% 8.73 3.1% 29.3% 31.5% 39.2% 2,531 0.5% 2,469 5.5% 5,000 2.6%

11.15 5.1% 5.47 5.1% 37.8 7.2% 8.67 2.3% 29.3% 31.5% 39.3% 2,538 0.7% 2,412 7.7% 4,950 3.5%

11.06 5.9% 5.42 5.9% 37.7 7.6% 8.64 1.9% 29.2% 31.8% 39.0% 2,544 1.0% 2,401 8.1% 4,945 3.6%

13.46 14.6% 6.60 14.6% 42.1 3.1% 9.42 11.1% 33.9% 25.4% 40.6% 2,512 0.3% 2,761 5.7% 5,273 2.7%

13.91 18.4% 6.82 18.4% 43.1 5.6% 9.50 12.1% 34.0% 25.5% 40.5% 2,508 0.5% 2,832 8.4% 5,339 4.0%

14.40 22.6% 7.07 22.6% 44.3 8.5% 9.58 13.1% 34.0% 25.7% 40.3% 2,502 0.7% 2,912 11.4% 5,414 5.5%

11.71 0.3% 5.75 0.3% 39.7 2.6% 8.68 2.4% 29.1% 32.3% 38.6% 2,522 0.1% 2,527 3.3% 5,049 1.6%

11.43 2.7% 5.61 2.7% 36.3 11.0% 9.27 9.4% 33.6% 24.3% 42.1% 2,525 0.2% 2,373 9.2% 4,899 4.5%

Note: Results for CC D90-10 and DS D10-90 are given for comparison; Key: diff: different; emp: employment.

B. Adhvaryu / Progress in Planning 73 (2010) 113–207

Items

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169

Table 19 Sensitivity analysis—income variation. Items

2

SEG1: dwelling size consumed (m ) SEG2: dwelling size consumed (m2) SEG3: dwelling size consumed (m2) SEG4: dwelling size consumed (m2) Passenger-km [millions] ATL [km] ATL [min] Speed [km/h] Modal split: private auto [%] Modal split: public transport [%] Modal split: slow [%] Rent [Rs/month] Transport cost [Rs/month] Cost of living [Rs/month]

Real income increase scenario

No real income increase scenario

% Change

TR21

TR21

No real income increase vs. real income increase

77.0 58.9 48.1 36.2 11.75 5.76 40.81 8.47 32.2% 27.3% 40.5% 2,520 2,613 5,132

CC21

75.0 58.5 48.5 36.8 11.15 5.47 37.8 8.67 29.3% 31.5% 39.3% 2,538 2,412 4,950

DS21

79.0 58.7 47.6 36.3 13.91 6.82 43.1 9.50 34.0% 25.5% 40.5% 2,508 2,832 5,339

71.6 54.9 45.0 34.0 12.11 5.94 41.78 8.53 32.1% 27.6% 40.2% 2,361 2,676 5,037

CC21

69.9 54.5 45.4 34.5 11.42 5.60 38.5 8.72 29.2% 31.8% 39.0% 2,376 2,456 4,832

DS21

73.3 54.7 44.6 34.0 14.27 7.00 44.0 9.55 34.0% 25.7% 40.2% 2,349 2,894 5,244

TR21

CC21

DS21

7.0% 6.9% 6.4% 6.0% 3.1% 3.1% 2.4% 0.7% 0.1% 1.1% 0.7% 6.3% 2.4% 1.9%

6.9% 6.8% 6.4% 6.3% 2.5% 2.5% 1.8% 0.6% 0.2% 0.9% 0.6% 6.4% 1.8% 2.4%

7.2% 6.8% 6.3% 6.2% 2.6% 2.6% 2.1% 0.5% 0.1% 0.8% 0.6% 6.3% 2.2% 1.8%

Table 20 Summary of housing rent and work travel costs (Rs, 2001 prices). Indicator

Base 2001

TR21

CC21

DS21

[a] Monthly household’s rent cost Total % Change vs. base % Change vs. trend

2,313 – –

2,520 8.9% –

2,538 9.7% 0.75%

2,508 8.4% 0.47%

[b] Monthly household’s transport cost for work trips (incl. time) Total 2,749 % Change vs. base – % Change vs. trend –

2,613 4.9% –

2,412 12.3% 7.7%

2,832 3.0% 8.4%

[c] Monthly household’s cost of living [a + b] Total 5,061 % Change vs. base – % Change vs. trend –

5,132 1.4% –

4,950 2.2% 3.5%

5,339 5.5% 4.0%

XX m

PSm i

(15)

i

However, it is not necessary to calculate the total consumer surplus as discussed above, since only the change in these quantities is important. Alternatively, the change in consumer surplus can be calculated by using the rule of a half (see Fig. 37) as a reasonably accurate approximation. It can be shown that the area labelled change in consumer surplus in Fig. 37 is given by Eq. (16). For the sake of consistency with consumer surplus in transport, which has been calculated using the rule of a half (see Section 9.1.3), the consumer surplus in housing rent is also calculated by the same method. 1 DCS ¼ ðq0 þ q1 Þð p0  p1 Þ 2

(16)

where DCS is the change in consumer surplus; q, p are demand and price, respectively; 0, 1 are sub-scripts indicating a reference (datum) policy and an alternative policy, respectively. Eq. (16) has to be suitably modified to Eq. (17), to include the households to calculate overall quantities of change in housing rent consumer surplus for the modelled area. 1 m m DCSm i ¼ ðqT Hi;T þ qA Hi;A Þð pT  pA Þ 2

(17)

where DCSm i is the change in housing rent consumer surplus in zone i by SEG type m; q, p are demand (m2/ dwelling) and price (monthly unit rent in Rs/m2), respectively (from Fig. 31); Him is the households in zone i by SEG type m; T, A are sub-scripts indicating

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Table 21 Change in housing rent consumer and producer surplus (million Rs/ month, 2001 prices). Indicator

CC21 vs. TR21

DS21 vs. TR21

Change in consumer surplus SEG1 38.01 SEG2 16.30 SEG3 12.12 SEG3 12.81 ALL 29.38

50.24 9.88 15.16 12.00 32.96

Change in producer surplus SEG1 15.68 SEG2 5.96 SEG3 –4.63 SEG3 –4.44 ALL 12.57

–15.83 1.56 5.94 0.35 7.98

16.81

24.98

Total (welfare)

trend policy and an alternative policy (compaction or dispersal policy in this case), respectively. The total change in housing rent consumer surplus is then given by: XX DCSm (18) i m

i

The change in consumer and producer surpluses (using Eqs. (17) and (15), respectively) for each alternative policy against the trend policy is shown in Table 21, with differences between trend and alternative policies by SEG shown in Fig. 38. It can be seen that the change in consumer surplus with respect to trend is highest in dispersal policy and lowest in compaction policy. From the graphical comparison presented in Fig. 38, as compared to trend policy, dispersal turns out to be significantly better for SEG1 and SEG2, while compaction is better for SEG3 and SEG4. The overall explanation that could be offered

Fig. 38. Housing consumer and producer surplus by SEG.

B. Adhvaryu / Progress in Planning 73 (2010) 113–207 Table 22 Average housing rent by SEG and sub-region (Rs/month, 2001 prices). SEG and sub-region

Base 2001

TR21

CC21

DS21

SEG1 Inner zones Outer zones ALL

3,993 3,564 3,800

3,989 3,924 3,958

4,030 4,435 4,188

4,097 3,639 3,726

SEG2 Inner zones Outer zones ALL

2,797 2,634 2,743

2,927 2,832 2,894

2,856 3,111 2,929

3,067 2,679 2,903

SEG3 Inner zones Outer zones ALL

2,235 2,162 2,214

2,393 2,336 2,377

2,311 2,518 2,359

2,474 2,241 2,401

SEG4 Inner zones Outer zones ALL

1,673 1,634 1,665

1,802 1,801 1,801

1,753 1,883 1,776

1,834 1,752 1,803

Overall Inner zones Outer zones ALL

2,292 2,364 2,313

2,466 2,648 2,520

2,425 2,883 2,538

2,496 2,525 2,508

for this is that, in dispersal policy, a higher proportion of wealthier households (SEG1 and SEG2) prefer to live in peripheral areas (see Table 9) as depicted by their ATD (see Table 13) and hence benefit from the lower rents (see Table 22) and consequently more per capita space

171

(see Table 9). The scenario is reversed in compaction policy, in which a higher proportion of poorer households (SEG3 and SEG4) prefer to live in inner zones, thereby benefiting from lower rents in inner zones as compared to dispersal policy. If the change in consumer and producer surplus by inner and outer zones is graphed, then an interesting pattern emerges (see Fig. 39). Consumer surplus is positive for compaction in inner zones and negative for outer zones, while for dispersal, it is negative in inner zones and positive in outer zones. The change in producer surplus nearly exhibits the same patterns as consumer surplus, but reverses for inner and outer zones. In overall terms, housing suppliers benefit more in compaction because of spatial monopoly powers, which is not the case in dispersal policy. 9.1.3. Consumer surplus in transport The change in transport consumer surplus can be given using the rule of a half (see Fig. 37), which requires passenger-kilometre and average generalised cost per trip as tabulated in Table 23 (base 2001 values are presented for comparison). It should be noted that since this calculation uses the aggregated passengerkilometre, there is no need to carry out the calculation by household (or trip makers), as in the case of consumer surplus in housing rent. Eq. (16) can be suitably modified to Eq. (19) for calculating the change in transport consumer surplus. The overall change is

Table 23 Summary of consumer surplus in transport. Indicator

TR21

CC21

DS21

[q] Passenger-km (millions, one-way per day) Private auto Public transport Slow ALL

3.68 4.07 4.00 11.75

2.98 4.63 3.54 11.15

4.87 4.46 4.59 13.91

[p] Generalised cost per trip including timea (Rs/km) (2001 prices) Private auto 5.60 Public transport 6.24 Slow 6.65 ALL 6.18

5.82 5.56 6.77 6.01

4.98 5.87 6.16 5.65

Change in transport consumer surplus (million Rs one-way per day, 2001 prices)

CC21 vs. TR21

DS21 vs. TR21

Private auto Public transport Slow

0.74 2.98 0.44

2.63 1.60 2.09

1.80

6.32

Total a

Value of time (VOT) is from the MSM. This is different from the VOT in RLM, which is by SEG. As a sensitivity test, using the weighted average VOT from RLM, the change in transport consumer surplus (vs. trend) for compaction and dispersal works out to be 1.59 and 4.29, compared to 1.80 and 6.32, respectively. Although the magnitudes are different, as expected, the direction of change is the same.

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Fig. 39. Housing consumer and producer surplus by SEG and sub-region.

then given as

P

k DCS

k

users in outer areas, where normal buses would share the road infrastructure with other modes.

.

1 DCSk ¼ ðqkT þ qkA Þð pkT  pkT Þ 2

(19)

where DCSk is the change in transport consumer surplus by mode k; qk is the demand (in passenger-kilometre, one-way per day) by mode k; pk is the price or generalised cost of travel including time per trip (in Rs/km) by mode k; T, A are sub-scripts indicating trend policy and an alternative policy (compaction or dispersal policy in this case), respectively. It can be seen that in compaction policy, owning to the superior public transport system, the consumer surplus in public transport in much higher than in trend policy, but road-based modes are less beneficial than in trend. In dispersal policy, road-based modes are more beneficial, owing to higher investments in road transport infrastructure. In addition, this road infrastructure investment has proved beneficial to public transport

9.1.4. Estimates of costs It is important to estimate the costs for the 2021 alternative planning policies to calculate the net economic benefit. Since the population is the same for all alternative policies, the overall cost of population-based infrastructure—such as water supply, sewerage treatment, other civic amenities like public schools, parks and gardens, etc.—is assumed to remain more or less the same across the alternative policies. For example, theoretically, although in compaction policy, the underground infrastructure (such as water supply, sewerage, telecom, etc.) could be shorter, its installation in already well-developed and populated areas is more expensive. On the other hand, in dispersal policy it could be lengthier, but with lower installation costs, due to vacant or less developed areas. A similar argument would also apply to roads. In addition, it is acknowl-

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Table 24 Estimates of transport costs (million Rs, 2001 prices). Item BRTS costs Capital cost (basic) [2006] Capital cost (basic) [2001] Cost increase factor Capital cost (modified) [a] O and M cost (@5%) [b] Total cost [a +b] Total additional BRTS cost (vs. trend) [I] Road costs Length in 2001 (km) Average width in 2001 (m) Road area in 2001 (m2) Capacity increase factor Road capacity enhancement per annum (values in brackets are for 2001–2021) New road area required 2001–2021 (m2) Capital cost of new roads (@Rs 781.75/m2)b [c] O and M cost (@5%) [d] Total cost [c + d] Total additional road costs (vs. trend) [II] Total additional transport costs (vs. trend) [I + II] a b

TR21

CC21

9,901.49 7,758.08 1.00 7,758.08 387.90 8,145.98 –

1.86a 14,446.47 722.32 15,168.80 7,022.81

3,111 25.00 77,770,000 1.00 0.26% (5.36%) 4,171,819 3,261.32 163.07 3,424.38 3,424.38 –

0.00 0.00% (0.00%) 0 0.00 0.00 0.00 3,598.43 3,598.43

DS21

1.00 7,758.08 387.90 8,145.98 0.00

2.00 0.52% (11.00%) 8,555,672 6,688.39 334.42 7,022.81 3,598.43

Estimated to achieve the same difference in total costs vs. trend as dispersal. Rs 1110/m2 in 2008 prices discounted to 2001 prices @5%.

edged that there would be subtle variations in the manner of provision of these facilities, but these are insignificant insofar as being able to create substantial cost differences. Therefore, civic infrastructure costs, both hard and soft, are assumed to be the same across alternative policies. However, transport infrastructure is the single most important element that is different across alternatives. This includes the public transport system and road capacity (new and augmentation). During this author’s visit to Ahmedabad for data collection and obtaining feedback on the proposed approach, meetings were held with city engineers and planners to obtain block cost estimates. Based on this information and discussions with them, transport costs estimates have been prepared, which are presented in Table 24. The total BRTS cost from the report (CEPT, 2006) has been adopted and converted to 2001 prices (at 5% discount rate). With regard to the differences of transport costs amongst alternative policies, it was assumed that in compaction policy, most of the BRTS routes would be totally grade-separated with better frequency. From the discussions held with the city officials of Ahmedabad in August 2008, it was learnt that such upgrading of the BRTS could roughly translate to about 1.5–2.0 times the cost of a basic BRTS (which is part of both trend and dispersal policies). For this exercise, the cost increase factor is estimated, such that the total transport

investment cost increase, over trend policy, is the same for both compaction and dispersal policies (which turned out to be 1.86, i.e., within the acceptable range). Since the non-BRTS public transport routes run on normal roads in mixed traffic, the overall cost of these are the same across all 2021 alternative policies. It is acknowledged that there would be variations in the overall fleet size for buses, routing, frequencies and administrative setup, depending on the location of the zone amongst alternative policies. However, it is presumed that these variations will be subtle enough not to significantly affect the overall costs estimates. In terms of road capacity enhancement, the AMC trend data (AMC, 2007) for three decades was analysed and the per annum growth in road capacity was calculated. This was then projected for the decades 2001–2011 and 2011–2021. The growth rate per annum in the 20-year period from 2001 to 2021 turns out to be 0.26%. For dispersal policy, this is assumed to be double that of trend, and for compaction policy, no new road network growth is assumed. It should be noted (as explained before) that only those costs that are different across alternative policies are estimated. 9.1.5. Summary of benefits and costs The summary of benefits and costs of compaction and dispersal policies compared to trend policy are presented in Table 25. Benefits (from Table 21) are

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Table 25 Annual estimates of benefits and costs vs. trend (million Rs, 2001 prices). Item Annual benefits Change in housing rent consumer surplus Change in housing rent producer surplus Change in transport consumer surplus Total Annual costs BTRS additional cost (values in brackets are total costs) New roads and capacity augmentation additional cost (values in brackets are total costs) Total Net benefit (benefits–costs) a b

Compaction 2021

Dispersal 2021

352.55 (29.38a  12) 150.81 (12.57  12) 1,037.53 (1.80  48b  12) 835.79

395.24 (32.96  12) 95.75 (7.98  12) 3,638.87 (6.32  48b  12) 3,938.66

563.53 (7,022.81) 274.78 (3,424.38)

0.00 (0.00) 288.75 (3,598.43)

288.75

288.65

547.04

3,649.91

Monthly values are rounded so it will not give exact annual values. Two work trips per day  24 working days in a month = 48 trips in a month.

converted to annual values. Costs are converted to equal annual instalments, such that the sum of the present value of all 20 instalments (i.e., 2001–2021) equals the total cost in 2001 prices. In P other words, this is done by finding x in C, where C ¼ n x=ðð1 þ rÞn Þ, is the cost difference in 2001 prices (including operation and maintenance), r is the discount rate and n is the number of years (20 in this case). For example, the annual cost difference of Rs 563.53 million for BTRS costs in compaction policy in 2001 prices (in Table 25), add up to Rs, 7022.81 million after discounting at 5% for each year to 2021 (in Table 24). The idea is to see how the alternative planning policies fare against trend policy, which allows decision makers to see its pros and cons in a more objective manner. It should be noted that, since detailed estimation of all the costs is not carried out, it was not possible to calculate the net present value of costs and hence the internal rate of return. Nonetheless, it is believed that in the absence of a detailed and sophisticated financial analysis, the estimates presented in Table 25 would provide a reasonable comparison. It can be seen from Table 25 that both the alternative policies turn out to be better compared to trend in economic terms. However, compared to trend policy, dispersal policy has a substantially huger net benefit than compaction policy. There are other benefits to the government, such as property tax. However, since the total supply of residential floorspace in the model is the same across all policies, the totals would be same. In the case of a tax structure based on SEGs, this would produce different revenues, as consumption of floorspace is slightly

different across alternative policies (see Table 8). However, this can easily be calculated from the modelled outputs should Ahmedabad civic authorities decide to adopt such a structure in the future, as against their current practice of a flat property tax structure based on floor area. The other item that could be considered in economic assessment is the fuel tax revenue. However, this has not been considered, as currently fuel tax is collected by the state government and the magnitude of ploughing some or all of it back into the municipal authorities’ treasury is not known. 9.2. Environmental assessment 9.2.1. Resources: new land required for residential use The most important resource in urban development is land. Based on the FSI and density estimates, the total land required for new dwellings from 2001 to 2021 is estimated and then converted to annual values, as presented in Table 26. As expected, compaction requires the lowest amount of land for new dwellings and dispersal the highest. Although not presented here, the estimates shown in Table 26 are available at a zone level and hence the authorities can use them in preparing detailed zoning and development control regulations. This would be useful in introducing caveats into the development plan, where the estimated amount for residential use is expected to be met with difficulty, thereby enabling the authorities to alter the zoning and development control regulations at a more local level than is currently being

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Table 26 Estimate of land required for new dwellings (annual estimates in ha). Sub-region

Trend 2021

Compaction 2021

Dispersal 2021

Inner zones Outer zones Total

195 188 384

245 39 285

31 388 419

26

9

% difference with trend 2021

done. If necessary, alterations to development control regulations could be fine-tuned iteratively before finalising the development plan. With regard to use of building materials, it is acknowledged that there would be subtle changes across the alternative policies, depending on building typology (i.e. high-rise vs. low-rise). However, since the total supply of dwelling floorspace is the same across all alternative policies, the changes are assumed to be insignificant. Lastly, energy use in buildings (i.e., heating, ventilation and air conditioning, elevators, etc.) could vary, depending on building typology. However, since plot-level base year information is not available, it was not possible to assess this aspect across the alternative policies. 9.2.2. Emissions: vehicular CO2 In addition to CO2, there are several other types of emissions, such as carbon monoxide, volatile organic compounds, nitrogen oxides, nitrous oxide, hydrocar-

bons, particulate matter, and methane. However, all of these except CO2 can be controlled through catalytic converters and other add-on technologies. At present, there is no technological means to reduce CO2 emissions, other than through the use of alternative fuels, such as electricity and hydrogen (Banister, 2005). Therefore, only CO2 emissions have been considered in this study. The CO2 emissions for base year and each of the policies are estimated, based on the passenger-kilometre outputs from the model, by converting them to vehicle-kilometres, using average vehicle occupancy. It is expected that by 2021 all buses will be running on compressed natural gas fuel and therefore only private automobiles are considered. It should be noted that, due to lack of availability of data on para-transit modes used for work trips (known locally as chakda, see Fig. 46), which currently run on diesel, these are not included in the model and hence their emissions cannot be estimated. However, in the future it is quite likely that

Table 27 Estimate of CO2 emissions for private automobiles (annual estimates, except mentioned otherwise). Item Passenger-km

Units 10

Base 2001

TR21

CC21

DS21

6

2104.61

2117.47

1714.82

2803.96

6

1940.09 1670.05 270.04

1922.96 1529.54 393.42

1557.30 1238.69 318.61

2546.39 2025.42 520.97

133,604 43,206 176,810 124.23

122,363 62,948 185,311 100.37

Vehicle-km Of which, two-wheeler (2W) Of which, car

10 106 106

2W CO2 Car CO2 Total CO2 Daily CO2 per capita Difference with trend 2021

ton ton ton g ton (%)

99,095 50,978 150,073 81.28 35,238 (19%)

162,034 83,356 245,389 132.91 60,078 (32%)

Notes and assumptions  Annual passenger-km is obtained by converting values from Table 14 by multiplying them by 576 (i.e., 24 working days/month  2 work trips/ day  12 months).  Average vehicle occupancy of two-wheeler (2W): 1.05.  Average vehicle occupancy of car: 1.30.  Share of 2W and car (2001): 86% and 14%, respectively.  Share of 2W and car (2021): 80% and 20%, respectively [projected based on 1961–2006 trends (AMC, 2007)].  Weighted average vehicle occupancy: 1.08 (2001) and 1.10 (2021).  CO2 emission rates: 80 g/km (2W) and 160 g/km (car). Adapted from two recent studies of cities in the Indian context: Bhajracharya (2008) and Hickman, Saxena, and Banister (2008). The latter study has value in the range of 120–240 for most popular cars in India. Values for 2021 are reduced by 10% to account for improvement in vehicle technology in the 20-year period.

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these, like auto-rickshaws (a three-wheeler, predominantly para-transit mode), would be compulsorily converted to compressed natural gas-fuelled engines. The CO2 emissions for private vehicles are presented in Table 27 and the associated assumptions are shown below the table. It can be seen from Table 27 that, as expected, CO2 emissions are highest in dispersal policy, due to a higher vehicle-kilometre figure. In terms of percentage change with regard to trend, it is about 32% higher, while for compaction policy it is about 19% lower. However, it should be noted that, as mentioned earlier, network congestion is not modelled, which makes these estimates indicative. For example, in compaction policy, in certain already well-developed inner zones, it is quite likely that the traffic in peak times could be ‘start–stop’, resulting in more CO2 emissions. On the other hand, a compensating effect in dispersal policy could take place, wherein higher travel occurs, but at higher speeds, thereby reducing CO2 emissions. It is

acknowledged that these two factors could change the estimates slightly and thus this is a limitation of the study. However, this could be overcome by collecting data for Indian roads and establishing a relationship between average vehicular speeds and CO2 emissions. 9.3. Social aspects Assessing the social aspects of alternative policies quantitatively has always remained a challenge in the realm of public policy. The key reason for this is the lack of agreement amongst experts on what factors constitute social wellbeing and how to measure them. In this study, the following aspects have been considered, based on the outputs available, which can be quantified per zone and, if appropriate, aggregated for the modelled area: (1) mix of socioeconomic groups in a zone and sub-regions and its total effect; (2) social equity in distribution of change in housing rent

Table 28 Distribution of each SEG by sub-region. SEG1 (%)

SEG2 (%)

SEG3 (%)

SEG4 (%)

Base 2001 Walled city (zone 1) Inner West (zones 2–4) Inner East (zones 5–11) Outer East (zones 12–16) Outer West (zones 17–20) Gandhinagar city (zone 21)

3 27 25 2 25 18

7 20 40 4 15 14

8 13 51 5 15 8

8 10 62 6 12 3

Trend 2021 Walled city (zone 1) Inner West (zones 2–4) Inner East (zones 5–11) Outer East (zones 12–16) Outer West (zones 17–20) Gandhinagar city (zone 21)

2 25 26 3 32 13

5 20 40 4 17 14

9 13 51 6 14 7

6 13 60 5 13 3

Compaction 2021 Walled city (zone 1) Inner West (zones 2–4) Inner East (zones 5–11) Outer East (zones 12–16) Outer West (zones 17–20) Gandhinagar city (zone 21)

4 30 27 3 26 10

7 23 42 3 15 11

9 14 54 4 13 6

4 14 64 4 11 3

Dispersal 2021 Walled city (zone 1) Inner West (zones 2–4) Inner East (zones 5–11) Outer East (zones 12–16) Outer West (zones 17–20) Gandhinagar city (zone 21)

0 9 10 2 60 19

5 19 34 6 21 15

10 11 47 7 18 7

5 8 51 10 24 3

Notes: (1) Columns total 100%. (2) Grey cells in trend denote values higher than base 2001, while in compaction and dispersal they indicate values higher than trend policy. (3) Based on trend projections, the overall proportions in 2021 of SEG1 and SEG2 have increased and those of SEG3 and SEG4 have decreased.

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177

Table 29 Proportion of SEGs in each sub-region. Sub-region

SEG2 (%)

SEG3 (%)

SEG4 (%)

4 2 6 0

21 20 25 20

47 53 53 61

28 25 16 19

Inner West (zones 2–4) Base 2001 Trend 2021 Compaction 2021 Dispersal 2021

15 16 17 8

30 32 32 39

36 32 30 36

19 21 21 17

Inner East (zones 5–11) Base 2001 Trend 2021 Compaction 2021 Dispersal 2021

4 5 5 2

18 21 21 21

43 41 41 44

35 33 33 32

Outer East (zones 12–16) Base 2001 Trend 2021 Compaction 2021 Dispersal 2021

4 7 8 3

18 21 20 20

42 44 41 39

36 29 31 38

Outer West (zones 17–20) Base 2001 Trend 2021 Compaction 2021 Dispersal 2021

14 20 19 25

23 26 26 22

41 33 36 28

22 21 20 25

Gandhinagar city (zone 21) Base 2001 Trend 2021 Compaction 2021 Dispersal 2021

17 16 15 21

36 42 41 42

38 34 35 29

10 8 9 8

Walled city (zone 1) Base 2001 Trend 2021 Compaction 2021 Dispersal 2021

SEG1 (%)

Notes: (1) Rows total 100%. (2) Based on trend projections, the overall proportions in 2021 of SEG1 and SEG2 have increased and those of SEG3 and SEG4 have decreased.

consumer surplus; and (3) job and workforce accessibility. 9.3.1. Mix of socioeconomic groups SIMPLAN outputs households by SEG for each zone. For better comprehension, these were amalgamated into six sub-regions of the study area. Table 28 shows the proportion of each SEG by the six subregions, while Table 29 shows the proportion of SEG in each of the six sub-regions (also mapped in Fig. 40). A key observations from Table 28 is that, in general, compared to trend policy, in compaction, the proportion of households is increasing in the inner zones (zones 1– 11), with a gradual decrease in magnitude from SEG1 to SEG4, with a corresponding decrease in the outer zones (zones 12–21), albeit not that steep. This pattern is nearly reversed in dispersal policy. However, most

notably the magnitude of increase in SEG1 in Outer West is staggering. Overall, it would appear that SEG1 are the most mobile in response to changes in spatial policy. The other significant observation (see Table 29) is that, as compared to trend policy, in all sub-regions, except Outer West and Gandhinagar city, SEG1 in dispersal policy has declined, with the strongest decline in Inner West, with a reversed pattern in compaction policy. Although the mix of SEGs by zone (or sub-region) can be examined, an attempt has been made to obtain an overall picture across alternative policies. To do so, it is proposed to use Gini coefficients (Gini, 1912). Proportions of SEG1–SEG4 households for each zone are compared to the total proportions of SEGs for the study area (which remain the same for all alternative policies). This is achieved by calculating the Gini

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Fig. 40. Proportion of SEGs in each sub-region.

B. Adhvaryu / Progress in Planning 73 (2010) 113–207

179

Table 30 Gini coefficients of mix of socioeconomic groups. Zone

Zone name

TR21

CC21

DS21

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Walled city Vasna-Paldi Navrangpura-Gandhigram Naranpura-Vadaj-Sabarmati Dudheshwar-Madhupura-Girdharnagar Saraspur-Asarwa Naroda-Sardarnagar Bapunagar-Rakhial-KokhraMehmdabad Nikol-Odhav Maninagar-Kankaria Vatva-Badodara Cantonment Bhat-Chiloda-Nabhoi Kathwada-Muthiya Singarva-Vastral-Ramol Aslali-Lambha-Piplaj Sharkej-Gyaspur-Okaf Thaltej-Vastrapur-Vejalpur-Makarba-Ambli-Shilaj Sola-Gota-Chandlodia-Ghatlodia-Ranip Adalaj-Chandkheda-Kali-Motera-Zundal-Khoraj Gandhinagar City

0.11 0.23 0.06 0.11 0.15 0.08 0.04 0.19 0.11 0.31 0.09 0.06 0.13 0.07 0.07 0.10 0.33 0.18 0.25 0.06 0.28

0.02 0.25 0.16 0.09 0.12 0.09 0.01 0.18 0.13 0.39 0.12 0.21 0.28 0.03 0.07 0.09 0.30 0.17 0.27 0.07 0.25

0.09 0.14 0.34 0.03 0.20 0.10 0.09 0.18 0.13 0.22 0.08 0.02 0.08 0.21 0.12 0.33 0.68 0.20 0.34 0.04 0.33

Sum of Gini coefficients

3.02

3.30

3.95

Variations by inner and outer zones Inner zones total (weighted) Outer zones total (weighted)

1.48 1.54

1.57 1.73

1.60 2.34

Note: [1] Value as nearer to zero indicate SEG mix in a zone is closer to SEG mix of the study area.

coefficients for each of the zones using Eq. (20), as the first step. X m1 Gi ¼ 1  ðxm  xm1 Þðym Þ (20) i þ yi m where Gi is the Gini coefficient for zone i; x is the cumulative proportion of SEG type m in the study area; ym i is the cumulative proportion of SEG type m in zone i. In Eq. (20), the absolute value is taken into account, as by definition the Gini coefficient ranges from zero to one, with zero denoting total equality of distribution (i.e., in this case the SEG mix in a zone is identical to the study area) and one denoting total inequality of distribution (i.e., in this case the SEG mix in a zone is in stark contrast to the study area). Table 30 shows the value for each of the alternative policies by zone. In this case, the problem with Gini coefficients is that these are given for each of the zones. Although each zone can be compared across alternatives, this does not give an overall effect of the distribution of households by SEG, as can be seen for the shaded cells in Table 30. Therefore, as the second step, to obtain an overall picture, it proposed to sum the Gini coefficients for the study area (and also by inner and outer

zones). Since, by definition, the values range from zero to one, the lowest total value would imply a socioeconomic mix closest to that of the study area. In this sense, trend policy is the first, followed by compaction and dispersal policies. This could imply that altering the urban form to a preconceived structure (e.g., compact or dispersed) leads to a suboptimal SEG mix as compared to trend, with dispersal policy being the least favourable, created by a significantly lopsided SEG mix in the outer zones as compared to trend. However, for the inner zones, the alternative policies do not deviate much more than the trend (which could be attributed to fact that inner zones are already well developed compared to outer zones, creating an ‘inertia’ effect). 9.3.2. Social equity The distribution of economic benefits spatially and across SEGs can be viewed as an aspect of social equity. In this case, only the consumer surplus in housing rent is used, as it is output from the model by zone and by SEG. It should be noted that transport consumer surplus is an aggregated value across SEGs and hence its distribution is not output from the model.

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Table 31 Distribution of change in consumer surplus in housing rent (million Rs/month, 2001 prices, vs. trend 2021). SEG

% of households a

Compaction 2021

Dispersal 2021

SEG1 SEG2 SEG3 SEG3

10.2 25.3 38.5 26.0

38.01 16.30 12.12 12.81

50.24 9.88 15.16 12.00

ALL

100.0

29.38

32.96

a

Total households = 1,333,558.

The overall distribution of benefits of change in consumer surplus is shown in Table 31 (repeated from Table 21), which shows a very interesting pattern. The higher income households (SEG1 and SEG2) benefit in dispersal policy but are worse off in compaction policy, with a reversed pattern with regard to low-income households (SEG3 and SEG4). In theory, some form of ‘ideal’ distribution across SEGs could be assumed and both compaction and dispersal policies could be compared to it. However, each society would view the importance (weight) of benefit or loss accruing to a particular SEG differently, and hence boiling down this

distribution to a single number across alternatives would not be appropriate. Decision makers could further comprehend this aspect by looking at the spatial distribution of change in consumer supply in housing rent by SEG and by zone, as shown in Fig. 41. Access to private gardens could also be included as a social indicator in assessment. However, unlike most developed countries, the housing typology in Ahmedabad, which is predominantly flats and row houses (and presumably in other developing countries as well) does not allow for private gardens. Even high-income households live in flats, with proportionately very few

Fig. 41. Distribution of economic benefits in housing rent.

B. Adhvaryu / Progress in Planning 73 (2010) 113–207

living in bungalows. In addition, the data required to establish a baseline status of people having access to private gardens are not available. Therefore, on these accounts, this aspect cannot be included in the assessment. 9.3.3. Accessibility Hansen (1959) in his seminal paper, ‘How accessibility shapes land use’, provides a very useful definition of accessibility as ‘the potential of opportunities of interaction’ (for more details, the reader is referred to Geurs & van Wee, 2004, who provide a useful summary of the various accessibility definitions that have been propounded over the years, and Ingram, 1971, and Harris, 2001, for discussions on conceptual and operational aspects of accessibility). However, it is important to specify accessibility to what and by whom. With regard to urban areas, it is useful to denote accessibility for people at location A to opportunities at location B. In terms of measurement, accessibility has been conceptualised as being a function of the number of opportunities and the ‘distance’ separating them. However, it is better to use a generalised cost measure, rather than distance, as doing so enables accessibility to be measured over time and/or across alternative spatial configurations of location of people and opportunities. An accessibility measure can be seen as an indicator of the impact of land use and transport developments and policy plans on the functioning of society in general. In other words, it provides a measure of the potential access to opportunities experienced by individuals or groups of individuals (Geurs & van Wee, 2004). Therefore, in assessing alternative policies, it is useful to know the measure of accessibility offered by each policy. There are many approaches to measuring accessibility. Harris (2001) reviews these approaches and opines that a more flexible method would be to use a continuous declining function of separation between A and B; the same method has been adopted in this study. The second aspect to measuring accessibility is deciding the As and Bs. With regard to comparing alternative planning policies, workforce and employment are the two key elements. In this study, both these have been considered, i.e., accessibility to jobs by workforce and accessibility to workforce by employers. Geurs and van Eck (2001) term these as supplied and demanded activities, respectively. DfT (2003, 2004) also propose these two types of accessibility in assessing the wider economic benefits of transport schemes. The first measure is residence-based (denoted as zone i in this study), and the second is employment-

181

based (denoted as zone j). Both these accessibility measures enable decision makers to see how alternative dispositions of employment and dwellings and transport policies affect accessibility. A popular general structure for accessibility measure is: X Ai ¼ W j f ðci j Þ (21) j where Ai is accessibility of zone i with respect to the opportunity W under question; f(cij) is a function of the generalised cost of travel from i to j (which could be expressed either in monetary terms per trip or time per trip). Note: Depending on whether the accessibility is resident-zone based or employment-zone based, the sub-scripts would change accordingly. 9.3.3.1. Workforce’s accessibility to jobs. This measure denotes how accessible employment is for the workforce resident in zone i and can be measured by suitably modifying Eq. (21) as follows: X JAi ¼ E expðbci j Þ (22) j j where JAi is the accessibility to jobs for workforce in zone i; Ej is employment in zone j; cij is a generalised cost per trip (Rs/trip); b is the aggregate distance decay parameter estimated in the multinomial modal split model. It should be noted that part of accessibility, as expressed by Eq. (22), is indirectly already built into the SIMPLAN allocation equation (see Eq. (1)) and therefore is reflected in the location of households and ultimately in the consumer surplus in housing rent. However, the purpose here is to create a graphical representation of accessibility to gain a better understanding of its magnitude and spatial distribution by zones at a more aggregate level. 9.3.3.2. Employers’ accessibility to workforce. This measure denotes how accessible the workforce is for employers located in zone j and can be measured by suitably modifying Eq. (21) as follows: X WA j ¼ R expðbci j Þ (23) i i where WAj is the accessibility to workforce for employers in zone j; Ri is resident workers in zone i; other parameters are the same as Eq. (22). Kwok and Yeh (2004) suggest that overall accessibility could be determined by weighting zonal accessibility by share of population (i.e., resident workers or employment). Mathematically, the overall accessibility measure for the study area (given by

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Eq. (24)), for both workers and employers, is identical.  X Ri P JAi JA ¼ or WA i i Ri ! X Ej (24) WA j P ¼ j jE j The overall accessibility calculated using Eq. (24), represented as index values (with trend as 100), is presented in Table 32. It can be seen that, overall, compaction policy offers higher accessibility, and expectedly, it is higher in inner zones and lower in outer zones, with vice versa for dispersal policy. Fig. 42 gives accessibility calculated by Eqs. (22) and (23), for each of the zones, converted to index values. It can be seen that, as compared to trend,

Table 32 Accessibility indexes (index numbers). Sub-region

Trend 2021

Compaction 2021

Dispersal 2021

Inner zones Outer zones Overall

100 100 100

121 97 117

95 155 104

compaction policy offers higher job accessibility than dispersal policy in about 64% of inner zones and about half of outer zones. In terms of workforce accessibility, as expected, dispersal policy is much higher in outer zones and compaction is much higher in inner zones. Accessibility for each of the zones by mode has also been calculated, as it shows the effects of changes in the generalised cost of travel across alternative policies (see

Fig. 42. Accessibility indexes by zone.

B. Adhvaryu / Progress in Planning 73 (2010) 113–207

183

Fig. 43. Accessibility indexes by zone and by mode.

transport for the population and in this regard, it could be seen as a social objective. Owing to a much higher quality of public transport infrastructure in the inner zones, the overall PTQS is appreciably higher in compaction. In dispersal policy, this is lower than trend, but this is only a marginal difference.

Fig. 43). It can be seen that, in general, both job and workforce accessibility by private automobile in compaction policy is much lower, and vice versa for dispersal. Conversely, for public transport, compaction policy offers much higher job and workforce accessibility, and vice versa for dispersal. Lastly, based on the public transport quality in each zone, a public transport quality score (PTQS) was assigned (used in Eqs. (9) and (10)), ranging from one to six, with one denoting very poor and six, excellent. A weighted average score for the study area, calculated using the share of population as the weight, is presented in Table 33. This score in a sense denotes an aggregate effect of the potential of distribution of access to public

9.4. Sensitivity analysis: assessment summary of other alternatives As mentioned in Section 8.0, several other variations of the alternative policies were tested as part of the sensitivity analysis. Land use and transport outputs have

Table 33 Public transport quality score. Item

Base 2001

TR21

CC21

DS21

Average PTQS Percentage change (TR vs. BS and Alts vs. TR) (%)

2.70 –

3.72 38

4.79 29

3.57 4

184

Table 34 Assessment indicators from sensitivity analysis (part 1). Dwellings and employment variations. Items

TR21 ED63-37

a

c

CC21 variations (with same employment, but different dwellings)

DS21 variations (with same employment, but different dwellings))

CC21 Diff emp. and dwellings

DS21 Diff emp. and dwellings

CC D80-20 d

DS D20-80 g

DS D10-90 h

DS D0-100 i

CC ED92-08 j

DS ED22-78 k

CC D90-10 e

CC D100-0 f

– – – –

1.220 0.093 1.335 0.208

0.353 0.151 1.038 0.836

0.769 0.196 0.863 0.290

0.526 0.058 3.259 3.728

0.396 0.096 3.639 3.939

0.125 0.137 3.997 3.986

0.320 0.023 1.053 1.396

0.587 0.047 2.576 3.210

Costs (vs. trend) [billion Rs/year] Public transport New roads and capacity augmentation Costs total (B) Net benefits (A–B)

– – – –

0.564 0.275 0.289 0.081

0.564 0.275 0.289 0.547

0.564 0.275 0.289 0.002

0.000 0.289 0.289 3.439

0.000 0.289 0.289 3.650

0.000 0.289 0.289 3.697

0.564 0.275 0.289 1.108

0.564 0.289 0.852 2.358

Resources and environment Residential land for new development (ha/year) % Change (vs. trend) Annual CO2 emission (thousand tons) % Change (vs. trend)

384

300

285

275

425

419

415

284

418

– 185 –

22 154 17

26 150 19

28 148 20

11 237 28

9 245 32

8 254 37

26 154 17

9 197 6

Social indicators Social P equity squared deviations

–

53

78

122

47

100

196

6

35

SEG mix: sum of Gini coefficients Inner zones Outer zones Overall Accessibility indexes Inner zones Outer zones Overall Public transport quality score

1.48 1.54 3.02

1.53 1.57 3.10

1.57 1.73 3.30

1.63 1.92 3.56

1.58 2.28 3.86

1.60 2.34 3.95

1.61 2.39 4.00

1.72 1.53 3.24

1.36 1.96 3.32

100.0 100.0 100.0 3.72

118.2 102.5 115.9 4.77

120.6 97.4 117.3 4.79

121.9 91.6 117.5 4.80

97.7 147.2 104.9 3.59

95.2 155.1 103.9 3.57

92.3 163.1 102.5 3.54

135.2 69.4 125.6 4.82

81.7 204.6 99.5 3.57

Note: Results for CC D90-10 and DS D10-90 are given for comparison.

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Economic indicators Benefits (vs. trend) [billion Rs/year] DCS in housing rent DPS in housing rent DCS in transport Benefits total (A)

Table 35 Assessment indicators from sensitivity analysis (part 2). Income variation. Items

Real income increase scenario TR21

CC21

DS21

No real income increase scenario

% Change

TR21

No real income increase vs. real income increase

CC21

DS21

CC21

DS21

Economic indicator: benefits [billion Rs/month] DCS in housing rent (vs. trend) DPS in housing rent (vs. trend) DCS in transport (vs. trend) Benefits total

– – – –

0.353 0.151 1.038 0.836

Environmental indicators Residential land for new development (ha/year) Annual Co2 emission (thousand tons)

185

150

Social indicators Social equity P squared deviations

0.396 0.096 3.639 3.939

– – – –

0.365 0.128 1.023 0.786

0.474 0.088 3.617 4.002

– – – –

3% 15% 1% 6%

20% 8% 1% 2%

2%

3%

No change as supply is same across alternative policies for both scenarios 245 190 153 252 3%

–

78

100

–

59

73

–

24%

27%

SEG mix: sum of Gini coefficients Inner zones Outer zones Overall

1.48 1.54 3.02

1.57 1.73 3.30

1.60 2.34 3.95

1.53 1.71 3.24

1.60 1.84 3.44

1.63 2.39 4.02

2.9% 11.2% 7.2%

2.1% 6.3% 4.3%

1.9% 2.0% 2.0%

Accessibility indexes Inner zones Outer zones Overall

100.0 100.0 100.0

120.6 97.4 117.3

95.2 155.1 103.9

100.0 100.0 100.0

121.2 96.9 117.6

94.8 155.6 103.8

– – –

0.5% 0.6% 0.3%

0.4% 0.3% 0.1%

3.72

4.79

3.57

3.71

4.78

3.56

–

0.1%

0.2%

Public transport quality score a

B. Adhvaryu / Progress in Planning 73 (2010) 113–207

TR21 a

Costs remain the same in both income scenarios.

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already been presented in Tables 18 and 19. In this section, a summary of the assessment of key aspects is presented (see Tables 34 and 35). It can be seen from Table 34, that in terms of economic benefits, the ‘extreme’ version of dispersal (DS D0-100) is the best, while compaction (CC ED9208) works best if employment is also concentrated in inner zones. In other words, if dispersal policy is to be pursued, then releasing more land (leading to a higher supply of residential floorspace) in the outer areas proves best economically, and if compaction policy is pursued, then compacting both residential development and jobs proves most beneficial. The new residential land required is least in the ‘extreme’ version of compaction (CC D100-0) and dispersal (DS D0-100), as expected (as in both cases no new land is required in the inner and outer zones, respectively). The emissions are in line with the passenger-kilometres (see Table 18). In terms of social indicators, the results are mixed. Both the ‘mildest’ versions of compaction (CC D80-20) and dispersal policies (CC D20-80) are better in terms of socioeconomic mix of households. Social equity in distribution of change in housing rent consumer surplus, for both compaction and dispersal policies, wherein employment is also altered (i.e., CC ED92-08 and DS ED22-78, respectively), appears to perform the best overall. The overall accessibility and PTQS is best in the ‘extreme’ version of compaction policy (CC D100-0) and ‘mildest’ version of dispersal policy (DS D20-80). However, as expected, altering employment inputs in compaction policy (CC ED92-08) proves to be most beneficial in terms of public transport aspects. It can be seen from Table 35 that if income does not increase in real terms, then the overall effect on the benefits is not significant and the same is the case with regard to environmental indicators. If incomes increase in real terms for all households, then it seems to create a better mix of SEGs. Lastly, if incomes do not increase in real terms, the pattern of distribution of benefits is the same for both compaction and dispersal policies, but the overall benefits reduce in compaction policy and increase in dispersal policy. The variations in sum of Gini coefficients for SEG mix and accessibility indexes are not significant. Overall, it would appear that changes in income in real terms obviously affects magnitudes of outputs, but the direction of the outputs does not alter significantly. 9.5. A discussion on assessment matrix It can be a daunting task for planners and decision makers to choose the ‘best’ or ‘optimum’ outcome for

the society from a set of alternatives, as this could become a very subjective process. This problem grows in importance if the actions under consideration ultimately determine the welfare and wellbeing of a region, as is often the case in development planning. Often matters could be compounded when there are mutually conflicting sets of criteria or objectives within the alternatives (Nijkamp & Voogd, 1983). They consider multicriteria analysis (known popularly as MCA in recent literature) as an important assessment tool in this process. Further to this, they distinguish two MCA approaches: discrete and continuous. Discrete MCA implies that there is a finite number of explicitly formulated alternatives that are being considered. Continuous MCA means that the alternatives themselves are not explicit, but only their dimensions are known, and then from a ‘feasible area’, the optimum solution is sought. They briefly explain about nine discrete MCA methods and it is clear from the discussion that each of these methods has its own merits and demerits. The choice of method essentially depends on the context and type of modelling outputs available for assessment. In practice, usually a local expert group is convened and the various indicators and the weights to be attached to each indicator are finalised. Such an approach could take several months to finalise. Considering the time limitation in the study, it was not possible to arrange for a local expert group. Therefore, it has not been possible to prepare a detailed assessment matrix comparing the alternative planning policies. However, since the alternatives are precisely known, the discrete MCA approach could be adopted for this study, bearing in mind that assigning relative importance to the various aspects of assessment indicators (i.e., its weights) is a highly political process. On the other hand, planners could employ an approach wherein key outcomes of alternative policies are presented to decision makers under broad headings, such as economic, environment, and social (similar to those shown in Section 9.1). As van Wee (2002) puts it, policy makers can explicitly ask for evaluation criteria and indicators that they consider relevant (and have the same assessed). Healey (2007) argues that plan making and agreed strategies of one period have been pushed to the sidelines or deliberately overridden by shifts in political priorities or by the force of particular interests. This author thinks that it is important for planners to make the technical outputs of the assessment process available to policy- and decision makers (who are usually politically appointed, but have a reasonable technical background). Based on such outputs, the latter

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group could then make more informed judgements, translating into policy decisions issued to the local authorities. 9.6. Conclusions on assessment The outputs of SIMPLAN were assessed under three key headings: economic, environmental and social. It was shown that dispersal policy is much better from an economic perspective. The general economic argument is that releasing more land for development in the outer areas of an existing city (which consequently implies a higher supply of dwellings) reduces average housing rents. The downside of dispersing cities is that the monetary cost of travel increases, owing to higher average trip lengths. However, in general, the cost of housing rents is a transfer of payments in the city system from consumers to producers. A more important economic effect is captured by looking at the consumer surplus to the society, in which the dispersal policy proves to be substantially better. In terms of environmental resources, in this study, the only variable output from the model is land required for new residential development. It was seen that, compared to trend policy, compaction policy consumes less land (26%) and dispersal policy consumes more land (9%), both of which are as expected. Some consider that using a smaller amount of new land for development is an advantage in itself. This author’s view is that consuming more or less land is beside the point, as essentially land use is being transferred from one economic use to another (e.g., say from agriculture to urban residential, in this case). Obviously, agricultural land is lost in the process, but dispersing cities implies that agricultural land use is faced with higher competition from urban land uses, implying that the agriculture sector needs to become more efficient in terms of yield per square unit of land (e.g., through technological advancement in cultivation). Because of the global influences on cities (such as rapidly globalising food markets), a city’s reliance solely on its hinterland for food supply is decreasing. Of course, exploring the complex economic relationships between a city and its regions (and beyond) is beyond the scope of the current research and hence conclusive remarks cannot be made. What can be said, though, is that when assessing alternative policies, the consumption of new land for development should not be seen, a priori, as having any negative effect on the wellbeing of society. It was shown that the CO2 emissions from private vehicles were highest in dispersal policy (i.e., 32% higher than trend policy). This is an issue that needs to be tackled

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at many levels, such as ‘greener’ fuels, better vehicle technology, and appropriate travel demand management measures within each of the alternative policies. As mentioned before, as a limitation of this study, better representation of network congestion can have a deteriorating effect in compaction policy and a compensating effect in dispersal policy, with regard to the total CO2 emissions from private vehicles at the city level. It should be noted that changes relating to fuel and vehicle technology and associated costs are related to national and global economic policies and standards, and therefore are usually beyond the scope of local planners. At best, they can anticipate such changes and build alternative scenarios for the sensitivity testing of planning policies. The socioeconomic mix of households turned out to be most favourable in trend policy, followed by compaction and dispersal. Social equity in distribution of change in consumer surplus in housing rent appears to be best for both compaction and dispersal policies, when employment inputs are also altered (with respect to trend). Considering overall accessibility as a social indicator, compaction policy is the most favourable, followed by dispersal. Lastly, potential of access to public transport service is highest in compaction policy, with dispersal policy only marginally lower than trend policy. In general, it appears that it may be possible to address some of the environmental and social shortcomings of dispersal policy by travel demand management measures and stricter zoning norms for location of new employment, respectively. As can be appreciated from the above discussion, it is nearly impossible to ‘pin down’ one alternative as the most desirable. However, what this exercise demonstrates is that it is possible to evaluate the pros and cons of each of the alternative policies, thereby allowing planners and decision makers to gain more scientific knowledge on their implications. The next step to follow from such an academic exercise is that the local authorities responsible for preparing the city plan devise a policy that includes the best bits from all alternatives tested. For example, the principles of compaction and dispersal policies could be tried out in certain zones, producing a combined policy. In essence, more insightful planning policy alternatives specific to the local context could then be tested and evaluated, before finalising the plan. 10. Feedback 10.1. Background It was felt necessary to present the approach developed in this study to obtain feedback from the

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local government planners and decision makers responsible for preparation of the DP. A local quasigovernment agency kindly agreed to coordinate the presentations and meetings. In addition, the trip to Ahmedabad in August 2008 was used as an opportunity to interact with academics and professionals and to discuss various aspects of the research. Eight presentations and meetings were held, as listed below. Presentations and meetings with government planners and decision makers.

rigour and to take a scientific approach in making city plans. They could start with a simplified urban simulation model, and once they have adopted it, they can then enhance and update the model, as more disaggregated data become available, to make it a more powerful tool to aid their decision-making capabilities, over the years. A summary of the key concerns raised in the above presentations and meetings, and this author’s responses to them, are presented in the next section.

1. Presentation to staff of Ahmedabad Municipal Corporation, Ahmedabad Urban Development Authority, and Gandhinagar Urban Development Corporation, and other governmental organisations involved in DP making (2 August, Ahmedabad). 2. Meeting with Dr J.G. Pandya, Manager, Bhaskaracharya Institute for Space Applications and GeoInformatics (BISAG), Gandhinagar and formerly CEO of Ahmedabad Urban Development Authority (26 August, Gandhinagar). 3. Meeting with Mr P.L. Sharma, Officer on Special Duty, Urban Development and Urban Housing Department, Government of Gujarat (26 August, Gandhinagar). 4. Presentation to the special taskforce on urban planning at the Ahmedabad Municipal Corporation, headed by the Municipal Commissioner (28 August, Ahmedabad).

10.1.1. Summary of key feedback and responses During the presentations and meetings, many questions and concerns were raised by government planners and decision makers, and practitioners. The key concerns and this author’s responses (in italics) are discussed below.

Presentation with academics and professionals. 1. Presentation to practising planning professionals and academics at HCP Desing and Project Management (8 August, Ahmedabad). 2. Presentation to academics and practising planning professionals at the Centre for Environmental Planning and Technology University (11 August, Ahmedabad). 3. Presentation to academics at the Sardar Vallabhbhai National Institute of Technology, Surat (22 August, Surat). 4. Presentation to academics at the Centre for Social Studies, Surat, with representation from Surat Municipal Corporation (22 August, Surat). Overall, it was acknowledged by the decision makers and government planners that such an analytical approach should indeed form the basis of all planning exercises, and therefore is highly welcomed. Practitioners were of the opinion that it is imperative for urban local government authorities to impart more analytical

1. Currently housing schemes by private developers are constructed and then provision of transport facilities follows. This approach is not appropriate. There should be an interactive process adopted while making city plans. Yes. This model takes into account transport costs as part of the location cost. Therefore, alternative transport policies could be tested (with accompanying urban form policy) to arrive at an appropriate combination. 2. We may want to allow compact development in certain areas and dispersed development in other areas. Yes. It is possible to test alternatives wherein some zones in the model are treated for compact development and other zones for dispersed development. In fact, the purpose of such a model is precisely to test such combinations as felt appropriate by local government planners to see their implications before finalising the DP. 3. We have to identify the civic needs of the population in various parts of the city and then decide the land requirements for such facilities. What facilities are needed is an aspect that has to be ascertained by the urban local body in charge of preparing the city plan (and hence is external to the model). However, since the model outputs population at a zone level, once the civic facilities needed are identified, based on population estimates, land can be easily earmarked zone-wise in the plan for such facilities. Currently, this is not possible as the AMC area is treated as one zone in the DP. 4. We should be able to update the model, as new data is available.

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5.

6.

7.

8.

Yes. This model is spreadsheet-based and hence all aspects of it are easily changeable because of the visually driven user-friendly interface. Various inputs’ worksheets can be easily updated as and when new data and information are available. Local authority planners, with basic computer literacy, can be quickly trained to operate and update the model with ease. Can issues like flood- or earthquake-proneness be taken into account in the model, as these are likely to change for different parts of the city? Yes. Such aspects make a zone less attractive for residential location, which, in theory, is captured in the housing attractiveness factors for each zone calibrated for the base year 2001. However, for example, it is likely that effects on residential location due to the 26 January 2001 earthquake were not fully captured in the 2001 Census. In addition, if there is information available based on sample survey, the model could be re-calibrated between census years (which are every 10 years). Is the carrying capacity (of each zone) taken into account for future years? Yes. The model allocates dwelling floorspace based on an allocation equation that includes the spare capacity of each zone. This is estimated based on the FSI in each zone and the land potentially available for residential use. The base year rent patterns produced by the model seem to be very realistic (see Fig. 28). However, is the model capable of capturing property speculation in housing rents in the future? The phenomenon of property speculation decreases the potential supply of dwellings, with a resultant increase in prices. If there are more policy constraints (e.g., in compaction policy there would be constraints on the release of new land) then the magnitude of speculation is likely to increase in pursuit of more profits (because of higher price increases). In this sense, speculation is a function of the policy constraints. Can the outputs of the model be transformed into a land use map, as this is what the local planning authorities ultimately make? Yes. Since the population distribution for a future urban policy is at a zonal level, all land-consuming activities, such as residential, commercial, civic amenities, etc., can be estimated and shown on a land use map. In the current study, this step in not demonstrated, as detailed plot level information is not available. However, if a base year map at plot level is created, exact locations of new land uses required for the future can be easily marked on a

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map. In addition, based on the policy adopted, zoning of residential use into sub-categories (as per the current practice of DP) can also be included. 9. Certain areas of the city are highly susceptible to communal violence. There have been cases of mass movement of people from one area to the other after episodes of communal violence. How does the model deal with such situations? The population is segregated by income groups and not religious groups (although there might be a weak to moderate correlation between income and religion). Therefore, housing location preferences, purely based on religious aspects, are not modelled. The housing attractiveness factors for each zone are calibrated for year 2001, which reflects an aggregated behaviour. However, if there are enough sample survey data available to track movements of people over the years based on religious preferences, then it should be possible to include this in the model. Currently, no such information is available for Ahmedabad. 10.2. SIMPLAN application to DP making One of the key outputs of the Ahmedabad Development Plan is a land use zoning plan for the horizon year. In the current method of preparing the DP, population projections are carried out without any reference to future employment location. At the level of an urban area, this is not appropriate. Since all proposals in the DP follow from the population projections, it could be said that the proposals are based on an ‘unsound’ foundation. As discussed in Section 9.2.1, estimates of new residential land required can be made by zone. Based on these, residential zoning regulations can be prepared more accurately compared with the current method of using blanket-type zoning. This gives the local authorities more flexibility in zoning the land for residential use. Similarly, new land required for commercial use can be estimated, based on the employment inputs for each zone. For other areas in the zoning plan, SIMPLAN outputs of resident workers, households and population by each zone, can be used to estimate infrastructure services at a more detailed level than is currently being done. From the requirements estimated, the areas of existing facilities (2001) are to be deducted to calculate the infrastructure required for 2001–2021. This can be easily turned to land areas based on the local norms and can be shown on a spatially more disaggregated scale on a map compared to the current practice.

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With regard to transport infrastructure, since the inputs are based on network distances, new road alignments and capacity augmentations proposals can be checked using SIMPLAN until satisfactory outputs are achieved. This is completely different from the current approach, wherein transport infrastructure is simply ‘imposed’ on the plan without reference to its future implications. It should be noted that in this study a detailed land use plan for the horizon year of 2021 has not been developed from the modelling outputs. The reason for this is twofold: firstly, this is something that needs to be done in close collaboration with the local authorities, or alternatively by the authorities themselves, and secondly, being an academic exercise, taking this route would be inappropriate considering the time limitation of the study. However, as discussed above, given the outputs from SIMPLAN a much more detailed land use zoning plan than the current one can be prepared. 10.3. SIMPLAN simplifications and its application limitations The first simplification is spatial disaggregation, in which zones larger than the census wards are used. This

will certainly make the outputs coarser than what the available data could best provide. However, this limitation does not appear to be an imminent issue, given the scope and level of detail addressed in the current DP-making practice. Of course, once such a model is adopted, the local authority can always make it more spatially disaggregated, in order to use the outputs for a neighbourhood level of planning (the second tier after the DP). A possible approach could be to use much smaller zones (or grid-based cells, if plot level base year map is available), but this would increase the computational requirements. Because of the spreadsheet-based structure, a quick run using different spatial levels of disaggregation could be tried out to ascertain the magnitude of accuracy gained at the cost of adding the computational complexity, based on which the local authority can make a decision as to what level of spatial disaggregation should be adopted. The second simplification is modelling only residential location. The limitation of this is that only journey-to-work trips are output and other trips, such as education, shopping, social, etc.) are ignored. The outputs cannot therefore be used to represent the entire urban system. For example, the total CO2 emission is not modelled and therefore is not useful for cross-cities,

Table 36 SIMPLAN simplifications limitations, and possible solutions. S. No.

Simplification

Limitations and/or caveats for use of outputs

Possible solutions

1

Lower level of spatial disaggregation of zones (than Census wards).

2

Modelling of only residential location (employment location modelling not carried out but is given as external inputs to the model).

Given the simplicity of operation of the model, it would be possible to quickly ‘try out’ different spatial scales to weigh the accuracy gained at the cost of computational complexity. As and when more observed data to calibrate the model are collected, addition of non-work travel can be carried out incrementally without major structural changes to the model, given its spreadsheet-based structure.

3

Modal split model being calibrated based on all trips without SEG disaggregation (due to lack of observed data for work travel trips by SEG). Network assignment of journey to work trips is not carried out, on the grounds that for an academic study it is impractical, both in terms of funding and time constraint, to build a network model of Ahmedabad. Employment inputs are only by SEG without sub-categorisation by industry sector (e.g., primary, secondary and tertiary), due to lack of economic census data.

More aggregation of employment, dwellings, and planning inputs, hence interpreting results with regard to smaller areas with the model would be coarser. Only journey to work trips are output (other trips such as education, shopping, and social/recreation are not modelled). Therefore, outputs cannot be used to represent the entire system, e.g., estimates of CO2 emissions are with regard to only work trips. Calculations of consumer surplus in transport by mode are indicative.

Network congestion is not modelled and hence cannot be fed back into the residential location model. Estimates of CO2 emissions cannot be made at a local level.

Commercially available transport models with network assignment capability could be easily dovetailed with SIMPLAN.

Economic vitality (usually measured by the mix of jobs by sector) and its social implications cannot be estimated.

Employment inputs by sector and by SEG (creating a two-way matrix) could be easily introduced into the model with minor structural changes, should such data be made available.

4

5

It is fairly easy to recalibrate the modal split model if such data are available.

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comparison. However, for comparing alternative policies for the same city, this limitation is not particularly significant. As and when more observed data are collected to calibrate the model, the addition of nonwork travel can be carried out incrementally without major structural changes to the model, given its spreadsheet-based structure. The third simplification is lack of calibration of the modal split model without SEG disaggregation. However, this is simply an issue related to lack of availability of observed data by SEG. This limitation implies that the estimates of consumer surplus in transport are indicative. However, it is fairly easy to recalibrate the modal split model if such data is available. The fourth simplification is ignoring the assignment of trips onto actual transport network. Again this simplification is sought on the grounds that, for academic study, it was thought impractical (both in terms of funding and time constraints) to build a transport network of Ahmedabad. This limitation implies that network congestion cannot be modelled and hence cannot be fed back into the residential location model. In addition, localised estimates of CO2 emissions cannot be made. This limitation can be easily overcome (if adequate funds are available with the local authority) by dovetailing SIMPLAN with commercially available transport model with network assignment capability (which includes the highly resource-consuming task of building the transport network, say in a GIS environment). The fifth simplification is that employment inputs are only by SEG, without sub-categorisation by industry sector (e.g., primary, secondary and tertiary), due to a lack of economic census data. This limitation implies that economic vitality (usually measured by the mix of jobs by sector) and its social implications cannot be estimated. However, if an economic census (or even a sample survey on a regular basis) is carried out by the local authority, then employment inputs by SEG and sector (creating a two-way matrix) could be easily introduced into the model with minor structural changes. The above discussion is summarised in Table 36. 11. Conclusions 11.1. On alternative urban forms At this point, an understanding of the merits and demerits of alternative urban forms, as reported by academics and professionals, would provide a useful

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background. Since the literature on this debate is vast, only a brief discussion is included here. In theory, cities could be categorised as being either compact or dispersed (and, of course, there could be cities that may exhibit both properties). A theoretical manifestation of a compact form could be thought of as people living at high densities, with high levels of public transport, walking and bicycling use, and perhaps shorter average trip distances. On the other hand, a theoretical manifestation of a dispersed city could be thought of as people living at lower densities, with most trips being performed by private automobiles, and perhaps longer average trip distances. Burchell et al. (2002) in their report, Cost of sprawl—2000, conclude that sprawl has both positive and negative effects. Amongst the key benefits reported in this study are: affordable housing, as land further out is cheaper; housing with larger per capita interior and exterior space; lesser travel times for suburban-to-suburban commuters; and lesser intensity of traffic congestion in low-density areas. On quality of life aspects, lower crime rates and better quality of schools are reported. The key negative effects of sprawl are higher costs of infrastructure and public services’ operations; more vehicle miles travelled; longer travel times; higher per capita travel costs; higher reliance on private automobiles; excessive transport energy use; and loss of agricultural and environmentally fragile land. On quality of life aspects, the negatives reported are: more air pollution; weakened sense of community and fostering of social exclusion; and spatial mismatch. (Spatial mismatch is a phenomenon described first by Kain in 1968, in which the poor are forced to live in central cities owing to exclusionary land use zoning policies, which limits their access to suburban bluecollar jobs (see Anas, Arnott, & Small, 1998; Ihlanfeldt, 1992; Kain, 1992).) Newman and Kenworthy (1989a, 1989b), from their study of 32 cities in North America, Australia, Europe, Canada and Asia, concluded that as urban density decreases, gasoline consumption increases markedly, with 30 persons per hectare being suggested as the cutoff mark (see Fig. 44). They report negative correlations of land use and transport variables, such as land use intensity, traffic restraint, and public transport use, with gasoline use, suggesting that if a city wanted to lower its gasoline use and automobile dependence, it ought to increase land use intensity and degree of centralisation, and improve its public transport. However, Newman and Kenworthy’s notion of correlation between density and energy use in transport has been refuted; for example, Gordon and Richardson (1989) say that their

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Fig. 44. Relationship between density and gasoline use.

analysis is faulty, the problems are wrongly diagnosed, and that their policy and planning prescriptions are inappropriate and unfeasible. In addition, a later study by Gordon (1997) uses data from Newman and Kenworthy (1989a), and recalculates the correlation after incorporating fuel prices. He finds that it is indeed the price of fuel that accounts for variations in transport energy use, rather than density, and opines that higher fuel prices would also tend to generate more compact settlement patterns (a finding similar to Clark (1951). A recent study (Brownstone & Golob, 2009) concentrating on California (based on a sample National Household Survey) concludes that density directly influences vehicle usage, and both density and vehicle usage influence fuel consumption. Comparing two households that are similar in all respects except residential density, a lower density of 1000 (roughly 40% of the mean value) housing units per square mile implies a positive difference of almost 1200 miles per year (4.8%) and about 65 more gallons of fuel per household (5.5%). From a broader perspective, Kahn (2006) reports that there is no correlation between quality of life and a city’s spatial structure. He further concludes that compact cities, with all employment located in the CBD, limit economic opportunities. Firms that need large parcels of land to operate and people who have a

strong preference for their own large private plots of land face significant tradeoffs if they must locate in compact cities. Sprawled cities offer both firms and households more choices, while the diversity of consumer choices for firms and households is likely to shrink in compact cities. Findings from a study of English cities (Burton, 2000), investigating the validity of the claims that a higher-density urban form promotes social equity, indicate that compactness (compact city) is likely to be negative for certain aspects, e.g. less domestic living space, lack of affordable housing, and increased crime levels. However, it may offer benefits, such as improved public transport use, reduced social segregation, and better access to facilities. In general, these conclusions are similar to the benefits of sprawl as reported by Burchell et al. (2002), but in contrast with those of Newman and Kenworthy (1989a, 1989b) and Brownstone and Golob (2009). On commuting times, Kahn (2006) concludes that compact cities feature greater congestion and higher commute times, while in sprawled cities certain global environmental externalities, such as greenhouse gas production, are likely to be exacerbated (but technological advance has mitigated many of the environmental problems associated with sprawl). An empirical study of US cities by Gordon, Kumar, and Richardson (1989) concludes that a polycentric and dispersed metropolitan area facilitates

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shorter commuting times. Both these conclusions are similar to Burchell et al. (2002). A study by Lin and Yang (2006) of medium- and small-sized cities in Taiwan suggests that the influence of the compact-city paradigm (i.e., a high-density pattern and intensification) has a direct negative effect on environmental and social sustainability, but positively affects economic sustainability (indirectly, i.e. via (creation of a) mix of (land) uses)—the latter being in contrast with what Kahn (2006) concludes. Lin and Yang acknowledge that their findings do not present a full and accurate picture of the sustainability of the compact-city paradigm in Taiwan, owing to sample size and data limitations; nonetheless, they do cast doubt on whether the compact-city paradigm is good for all sustainability issues. In contrast, Gordon (2008) opines that the social and political implications of sustained efforts to promote higher densities by means of severely restricting greenfield development, which would raise dwelling prices and restrict access to housing, would be unacceptable. On similar lines, Brueckner (2000), in the context of US cities, concludes that greatly restricting urban expansion might needlessly limit the consumption of housing space, depressing the standard of living of American consumers. Rather, the approach to adopt would be one that recognises the damage done by an unwarranted restriction of urban growth, such as development taxes and congestion tolls, which attack sprawl at its source by correcting specific market failures. Specifically researching social interaction and urban sprawl, a recent study by the same author (Brueckner & Largey, 2008) indicates that density and social interaction may be negatively correlated. From the above discussion, it appears that both forms of urban development have merits and demerits reported in the literature, but neither form has a set of settled arguments as to which form is absolutely better than the other, which was corroborated by the finding of this study. It would also appear that cities across the globe exhibit different responses to urban form. This perpetual debate, on which city form is ideal, was addressed in this study in the context of developing countries. It showed that indeed a compact or dispersed form does not appear to be an outright ‘win–win’ proposition. As shown in Section 9.1, in economic terms, a dispersed form offers more benefits. This finding is in line with Burchell et al. (2002) and Kahn (2006), but in contrast with Lin and Yang (2006), Newman and Kenworthy (1989a, 1989b), and Brownstone and Golob (2009). Purely from the perspective of travel time savings, it was shown that a compact form achieves more, but suffers when it comes to the

193

consumer surplus in both housing rent and transport. This is because of higher housing rents and lower proportionate change in generalised travel costs, as compared to average trip distances for private and slow modes (implying higher generalised cost per km—see Table 23). As expected, in terms of land requirements, a compact form consumes less. With regard to CO2 emissions, compaction policy was the most beneficial, but it needs to be borne in mind that congestion effects could tilt the balance. In terms of social aspects, the SEG mix of households achieved in compaction is better than dispersed policy (albeit not so compared to trend policy). This is in contrast with the findings of Lin and Yang (2006), who conclude that compact form has a negative effect on social aspects. In summary, it therefore appears that the performance of urban forms has a strong bearing on the specific attributes of the context, such as type of economy, cultural preferences and political environment, and does not appear to have globally generalisable merits or demerits. 11.2. On the model structure and operationality Some of the LUTI modelling approaches prevailing in developed countries were discussed in Section 4.4. Literature with specific references to developing countries opines that although full-fledged LUTI models are difficult to develop given the data availability constraints, it is possible to build simplified models from available data to inform planning policymaking. In this study, such an approach was demonstrated for Ahmedabad and it was shown how the current approach to planning can be enhanced to better inform the plan-making process. With regard to operating the model, a spreadsheetbased approach was adopted to reinforce the simplicity. This approach offers a visually driven user-interface and therefore improves the understanding of the processes within the model and makes it more flexible for operating and updating the model (which was also corroborated by government decision makers and planners, to whom it was presented in August 2008). The other advantage is the speed of running the model. As discussed in Section 11.1, it is apparent that neither of the alternative urban forms is optimum in an absolute sense and that each of them offers different benefits. Planners must therefore test alternative ‘designs’ and learn from the quantified merits and demerits of alternative urban forms (like the ones tested in this study), or combinations of such forms, to pursue the optimum outcome for the local context in question. This is precisely possible given the speed advantage in

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addition to the in-house running capability of the model owing to its simplicity. For example, planners can quickly and reliably test different FSI norms by zone (i.e., can have an appropriate mix of compaction and dispersal) to arrive at a final plan. The spatial disaggregation of outputs would allow planners to make more detailed land use zoning plans and accompanying DCRs than is currently possible. In addition, preliminary testing of a transport policy package (e.g., new arterial roads, capacity expansion of existing roads, dedicated busways, etc.) in conjunction with a land use/spatial policy package can be carried out quickly. By associating SIMPLAN with any commercially available transport network modelling software, the reliability of outputs from testing transport policies could be enhanced.

3.

4.

11.3. On the context of developing countries Scholarly literature on urban development and planning in developing countries indicates that disaggregated temporal and spatial data limitations make the application of model-based planning approaches challenging (Chatterjee & Nijkamp, 1983; Srinivasan, 2005). In addition, it maintains that interaction between the scientific community and the administrative and political personnel concerned with city planning has decreased over the years (Chatterjee, 1983). This study demonstrated that, although challenging, it is possible to apply analytical tools and develop simplified urban models to inform plan making using available data. This author’s interaction with decision makers and planners during the course of this study indicated that they are open and willing to adopt the path of a more scientific approach to planning. Overall, the interaction was very welcoming, and indicates their willingness to bridge the gap between theory and practice, if appropriate efforts are made. 11.4. Summary of key research findings The findings from this study have been mentioned in the text as appropriate. However, the purpose of this section is to summarise the key ones, as follows. 1. The current plan-making approach followed by the planning authorities in Ahmedabad lacks a quantitative framework, insofar as being able to test and assess alternative planning policies before arriving at the final plan. 2. It is possible to apply urban modelling approaches prevalent in the developed world, rooted in the spatial

5.

6.

interaction tradition (e.g., Lowry, etc.) and microeconomic theory of demand–supply (e.g., MEPLAN, etc.) to developing countries with data availability constraints. Applying the classical theories of spatial organisation to Ahmedabad, it was seen that Ahmedabad does not conform to the concentric zone theory, but does exhibit the formation of wedges of sectors along transport routes, as suggested by sector theory. The formation of multiple centres is also evident in Ahmedabad, as suggested by the multiple-nuclei theory. Though Ahmedabad is relatively more compact compared to some other cities of the developed world, analysis of the past 30-year data indicates that the city has a tendency towards dispersal. In addition, a reduction in population density in central areas and an increase in peripheral areas is observed for Ahmedabad. These trends are likely to continue for some time in the future. Analytical models of location and land use were applied to Ahmedabad. In that, it was shown that the monocentric bid-rent theory was not applicable directly to Ahmedabad, owing to its polycentric character (see Fig. 15). However, the distribution of settlements in the Ahmedabad sub-region did show some sort of formation, as suggested by the central place theory. In the context of Ahmedabad, dispersing the city in terms of dwellings is more beneficial economically, and the more ‘extreme’ the dispersal policy, the better it is. If compaction needs to be pursued, then it appears to perform better when both dwellings and jobs are considered. In terms of consumption of land for new development, obviously, by definition compaction policy consumes least land. In addition, CO2 emissions are least in compaction, because of shorter average trip distances (but bearing in mind that road congestion is not modelled). With regard to the mix of households by income group, trend policy is optimum, followed by compaction and then dispersal (with ‘milder’ versions of both performing relatively better). In terms of social equity, it appears that compaction and dispersal policies perform relatively better if both dwellings and employment are altered. Fig. 45 provides a snapshot summary of key assessment indicators.

11.5. Suggestions for further research With regard to the perpetual debate on compact vs. dispersed city form, it was seen in the previous section

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Fig. 45. Snapshot summary of key assessment indicators.

that neither of these urban forms offers an outright adoptable urban policy to pursue. This author is therefore led to believe that the intricacies of how cities work and sustain themselves successfully are more to do with economic factors, rather than just physical factors such as the city form and transport network geometry. Many scholars appear to be critical of the tacit assumption that high density promotes lower energy use or that low-density, dispersed settlements have a negative effect on the environment. It is clear that a deeper understanding of the way people locate and travel is the key to solving the energy use problem. In

addition, by dispersing employment for Ahmedabad, it was learnt that indeed work trip distances do reduce (see Table 18). However, more case studies need to be conducted in developing countries to explore the performance of different physical forms and transport networks when combined with economic factors (such as the generalised cost of location and travel, and the cost of employing people (not addressed in this study)). In recent times, the view that IT-based communication technology (such as the internet, mobile wi-fi, videoconferencing, etc.) has an impact on travel behaviour, is gathering research momentum. It is not

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clear that a strong connection exists, but nonetheless it is worth exploring in developing countries like India, where the IT sector has been booming for the last decade or so. In this study, this connection was not addressed, due to lack of data on such activities. However, the next Census is round the corner (i.e., 2011) and with the possibility of supplementing data gathering on employment activities with sample surveys, this aspect can be included in urban modelling. Another debate gathering momentum in recent times is the connection between the built environment and health, both in terms of air quality and physical activity. Handy, Boarnet, Ewing, and Killingsworth (2002) conclude that the available evidence lends itself to the argument that a combination of urban design, land use patterns and transportation systems that promotes walking and bicycling will help create active, healthier and more liveable communities. However, they indicate that collaborative research efforts that build on the research paradigms of the fields of both urban planning and public health are essential to making further progress in the effort to build healthier and more liveable communities. On similar lines, Frumkin (2002), while investigating the relationship between sprawl and health, concludes that data show both health benefits and costs. Frumkin particularly picks up on the unequal distribution of the adverse health effects of sprawl. Frank (2000) concludes that although there are studies that show the existence of a relationship between the built environment and physical activity and health, their findings have been refuted, based on methodological grounds and inaccurate interpretation of data. Frank, Engelke, Schmid, and Killingsworth (n.d.) carried out an extensive literature review on the relationship between physical activity and the built form. They concluded that in the American context, empirical research supports the claim that important relationships exist between urban form and travel behaviour. However, the general dearth of good empirical literature on the effects of these variables on physical activity patterns is problematic. Part of the problem lies in the inherent complexity involved in adequately measuring many of the urban form and demographic variables and in disentangling the causeand-effect relationships between them. Findings based on a more recent study (Frank, Saelens, Powell, & Chapman, 2007) that used 2000 samples in neighbourhoods in the metropolitan region of Atlanta, USA, suggest that creating walkable environments may result in higher levels of physical activity. It is clear from the above discussion that further research on fine-tuning the methodology for ascertain-

ing the strength of this relationship needs to be undertaken. Also, given the fact that developing countries have a higher level of non-motorised travel, it would be interesting to compare with developed countries its implication on physical activity, in addition to the differences in the pace of growth, economic and socio-cultural factors. Suggestions for specific further work in the context of Ahmedabad that crop up from the various limitations outlined in the study are summarised as follows. It is likely that these could apply to other Indian cities, and cities in other developing countries. 1. City-region analysis of economic activity needs to be undertaken, in order to initiate the modelling of employment location, with the possibility of integrating modelling techniques from new economic geography (usually associated with Paul Krugman, Anthony Venables, Masahisa Fujita, JacquesFranc¸ois Thisse, amongst others; see Mikkelsen, 2004, and Lafourcade & Thisse, 2008). If employment is output from the model by SEG then it could be used in making more detailed land use regulations pertaining to commercial use. In addition, the SEG mix of jobs in each zone could be used to calculate a measure of ‘vitality’, which could be a useful social indicator. 2. If employment location is not modelled, then sample surveys need to be undertaken at zone level to ascertain employment mix by SEG to create more accurate inputs and enhanced economic outputs. 3. The distinction between basic and service (local) sector employment (as is usually done in Lowry-type models, see Section 4.4.2) was not possible in this study, owing to the lack of data availability on the proportion of employment strictly due to local population. On the other hand, in recent years, this author has witnessed a new, emerging phenomenon in Ahmedabad, of people doing ‘local’ shopping in places much further away from their residences (though not supported by quantitative evidence)— dubbed ‘mall culture’ by the local media. The prime reason for this is the rapid springing up of shopping malls all over the city in the last eight years or so. The propensity of citizens to shop in malls could be attributed to products being available more cheaply than at the local grocers, and the ability of malls to combine entertainment with shopping in the form of cine-multiplexes, cafes, game arcades, etc. It is very likely that this phenomenon exists in other cities of India and the world. A similar pattern of ‘non-local’ access exists in Ahmedabad with private schools

B. Adhvaryu / Progress in Planning 73 (2010) 113–207

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Fig. 46. Chakda—a ride-shared para-transit mode.

(traditionally, the employees of which are treated as serving the local population). Therefore, studies need to be undertaken to establish the proportion of service employment strictly servicing the local population and its trend in the future to better inform the modelling process. 4. Sample surveys to ascertain modal split by SEG and by trip purpose need to be undertaken, in order to directly apply modal split by SEG to journey-to-work trips by resident workers rather than at an aggregated level, to correct the discrepancy between value of time (VOT) from the modal split model and residential location model. In addition, this would also improve the estimation of change in transport consumer surplus (see note below Table 23). 5. Para-transit modes (usually ride-shares for work trips used by low-income people, like the one shown in Fig. 46) are not considered. However, based on this author’s local knowledge, this mode is increasing in preference for work trips within and between peripheral areas of Ahmedabad, owing to lower restrictions imposed by authorities with regard to operation, and low frequency or no bus routes. Therefore, such modes in the future need to be incorporated into the model, supported by adequate sample surveys on its usage. Researching motorisation in developing countries, Kutzbach (2009) suggests that it is important to include all available modes in modelling.

6. Surveys to establish the relationship between average vehicular speeds and emissions in the Indian context (wherein both vehicular and road conditions are very likely to be different from other countries) need to be undertaken. Using such a relationship, accurate estimates of the impact of vehicular emissions can be made (see Section 9.2.2), which also ties in with the following point. 7. Local authorities should develop a road network at least of the main roads of the city in a GIS environment to enable network modelling. This would enable appropriate land use–transport feedback, making the modelling outputs more realistic (see Section 7.2). In addition, this creates a feedback loop that is useful in modelling network congestion, making transport outputs more realistic and enabling more accurate estimation of CO2 emissions (see Section 9.2.2). 8. Economic studies investigating the price elasticity of housing supply need to be undertaken to improve the estimation of producer surplus (see Section 9.1.2). 9. Economic studies looking at the role of agricultural land in city-regions, and its significance in the light of rapidly globalising food markets, need to be carried out in developing countries to better inform the debate on conversion of agricultural land to urban uses (see Section 9.6). Given that Ahmedabad has well-reputed academic institutions for architecture and planning and manage-

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ment studies most of the further research suggestions discussed above could be carried out by post-graduate students as part of their master’s/doctoral dissertation, in close collaboration with the Ahmedabad Urban Development Authority and the Ahmedabad Municipal Corporation. 11.6. A final note This research study set out to explore how a more scientific and transparent approach could be introduced to enhance planning in the context of developing countries, where data constraints pose significant challenges. Based on the census data and past studies commissioned by the government of Gujarat, a simplified modelling suite called SIMPLAN was developed. A spreadsheet environment was used to develop the model to provide visually driven userinterface, making it simpler to understand, operate and update the model. SIMPLAN was used to test and assess alternative urban planning policies for year 2021 and it was demonstrated how to use the model outputs to enhance the plan-making process. In addition, the modelling outputs allowed us to inform the wider debate on compact vs. dispersed urban forms. It was shown that, in the context of the case study city of Ahmedabad, neither policy provides an outright ‘win–win’ solution. This study demonstrates that each city has to test out the pros and cons of such policy alternatives for themselves before forming macro-level plans for the future. A series of presentations and meetings was held in August 2008 in Ahmedabad with government decision makers and planners, and planning professionals, in order to obtain feedback on the proposed approach. Overall, it was acknowledged by the decision makers and government planners that such an analytical approach should indeed form the basis of all planning exercises, and therefore is greatly welcomed. Practitioners were of the opinion that it is imperative for urban local government authorities to impart more analytical rigour and a transparent approach in making city plans. In that, they could start with such a simplified urban simulation model, and once they have adopted it, they can then enhance and update the model, as more

disaggregated data become available, to make it a more powerful tool to aid their decision-making capabilities over the years. Overall, the simplicity of operating and updating SIMPLAN and its low resource intensiveness (in terms of both time and money), allowing the testing of several planning alternatives, make this approach, in the Indian context, innovative in its own right. The proposed approach goes beyond the conventional realm, by using simple yet robust tools, developed with appropriate consideration to both data and resource constraints posed by the local context. However, in the realm of applied research, this study is not an end in itself, owing to the limitations outlined above (see Table 36). Rather, it represents a first step in trying to bring a more scientific temperament and transparency to planning in developing countries, by introducing a model-based approach. A study attempting to link land use and transportation, based on the case study of Delhi, India (Srinivasan, 2005) reaches a similar conclusion, suggesting that the idea of a data-based land use and transportation plan, instead of one based on ideology alone, must be incorporated into the planning process. This study could serve as a useful precedent to researchers working on developing countries for furthering contributions to both the theory and the practice of urban planning. Acknowledgements The author wishes to acknowledge the advice of Professor Marcial Echenique and Dr Ying Jin, Department of Architecture, University of Cambridge. Gratitude is expressed to Cambridge Commonwealth Trust, Hinduja Cambridge Trust, Churchill College, and Kettle’s Yard Travel Fund for part funding support. All tables and figures are created by the author unless mentioned otherwise. The views expressed in this paper are solely those of this author. Appendix A. Employment inputs—2001 and 2021 See Table A1

Table A1 Employment inputs—2001 and 2021. Zone Zone name

PTQS

Base 2001

Trend Compaction and dispersal Trend 2021 (LBGC, 2001) policy 2021 policies (with same employment) not useda

BS01 TR21; DS21 CC21

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

21

Walled city Vasna-Paldi Navrangpura-Gandhigram Naranpura-Vadaj-Sabarmati Dudheshwar-MadhupuraGirdharnagar Saraspur-Asarwa Naroda-Sardarnagar Bapunagar-RakhialKokhraMehmdabad Nikol-Odhav Maninagar-Kankaria Vatva-Badodara Cantonment Bhat-Chiloda-Nabhoi Kathwada-Muthiya Singarva-Vastral-Ramol Aslali-Lambha-Piplaj Sharkej-Gyaspur-Okaf Thaltej-Vastrapur-VejalpurMakarba-Ambli-Shilaj Sola-Gota-ChandlodiaGhatlodia-Ranip Adalaj-ChandkhedaKali-MoteraZundal-Khoraj Gandhinagar City

Dispersal with different employment DS 22-78

4 4 3 3 2

5 5 4 4 3

6 6 5 5 4

211,805 49,169 129,851 92,779 75,891

272,829 52,344 133,660 98,684 79,592

296,477 65,109 162,515 117,185 90,806

296,477 65,109 162,515 117,185 90,806

313,478 72,842 181,924 129,841 100,225

236,965 54,900 141,871 101,556 81,339

2 4 3

3 5 4

4 6 5

124,326 66,315 209,700

131,074 71,861 219,251

149,112 88,202 263,422

149,112 88,202 263,422

164,342 98,304 293,910

133,374 74,310 229,883

4 4 2 2 1 1 1 2 3 2

5 5 3 3 2 2 2 3 4 3

6 6 4 4 3 3 3 4 5 4

46,597 93,205 100,730 13,862 7,532 6,480 13,894 10,523 15,086 78,538

49,495 97,321 108,845 15,794 16,471 16,008 35,649 26,136 42,224 182,128

61,631 122,680 121,743 15,774 11,562 10,588 23,165 17,876 28,195 129,192

61,631 122,680 121,743 15,774 11,562 10,588 23,165 17,876 28,195 129,192

69,079 137,890 133,162 15,775 8,380 7,209 15,457 12,097 17,904 90,270

52,218 104,234 108,070 15,298 15,160 13,042 27,963 26,687 46,157 198,543

2

3

4

57,702

165,071

106,304

106,304

66,874

147,086

1

2

3

26,534

75,550

46,849

46,849

29,518

53,401

2

3

4

69,548

148,444

110,047

110,047

79,954

176,378

1,500,068 2,038,434

2,038,434

2,038,434 2,038,434

2,038,434

Total

B. Adhvaryu / Progress in Planning 73 (2010) 113–207

1 2 3 4 5

CC CC CC DS DS DS 80-20 90-10 100-0 20-80 10-90 0-100

Compaction with different employment CC 92-08

Key: PTQS = Public transport quality score; BS01 = Base 2001. a Not used for inputs but is shown just for comparison. 199

200

Table B1 Dwelling inputs—2001 and 2021. Zone

Zone name

Base (total dwellings)

Dwelling increments 2001–2021 Trend policy

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Total

Walled city Vasna-Paldi Navrangpura-Gandhigram Naranpura-Vadaj-Sabarmati Dudheshwar-MadhupuraGirdharnagar Saraspur-Asarwa Naroda-Sardarnagar Bapunagar-RakhialKokhraMehmdabad Nikol-Odhav Maninagar-Kankaria Vatva-Badodara Cantonment Bhat-Chiloda-Nabhoi Kathwada-Muthiya Singarva-Vastral-Ramol Aslali-Lambha-Piplaj Sharkej-Gyaspur-Okaf Thaltej-Vastrapur-VejalpurMakarba-Ambli-Shilaj Sola-Gota-ChandlodiaGhatlodia-Ranip Adalaj-ChandkhedaKali-Motera-Zundal-Khoraj Gandhinagar City

Dispersal variations (with same employment)

CC80-20

CC90-10

CC100-0

DS20-80

DS10-90

DS0-100

Compaction with different employment CC92-08

Dispersal with different employment DS22-78

68,140 37,262 25,947 75,977 36,944

3,192 18,205 7,979 32,876 3,482

10,871 20,773 14,755 38,261 4,770

15,288 22,250 18,653 41,359 5,511

10,000 24,211 23,828 45,471 6,495

1,000 3,042 9,698 7,503 1,846

1,000 1,498 4,777 3,696 909

0 0 0 0 0

10,000 22,889 20,347 42,616 5,803

2,000 3,486 5,000 8,823 2,243

108,414 37,962 103,567

12,809 22,162 11,682

15,889 27,507 18,898

17,662 30,581 23,049

20,014 34,662 28,559

3,828 5,674 7,112

1,885 2,795 3,503

0 0 0

18,351 31,885 24,824

4,646 6,486 8,421

45,909 64,149 74,139 2,840 2,096 10,909 17,379 8,943 9,188 56,314

8,585 27,559 64,715 1,085 1,567 5,314 6,301 8,726 6,835 33,004

9,592 33,979 77,151 100 746 605 1,180 2,328 3,297 20,108

10,172 37,673 84,305 100 365 292 584 1,161 1,646 10,048

10,941 42,576 93,801 0 0 0 0 0 0 0

962 6,709 20,738 1,000 5,000 10,000 12,000 15,000 12,000 111,579

474 3,305 10,214 1,000 5,000 10,000 12,000 15,000 12,000 126,209

0 0 0 1,000 5,000 10,000 12,000 15,000 12,000 140,839

10,428 39,339 86,920 78 308 235 449 898 1,193 7,996

1,106 7,734 24,977 1,000 5,000 10,000 12,000 15,000 12,000 113,939

58,833

9,128

3,185

1,590

0

25,464

31,124

36,783

1,140

22,434

27,221

15,954

5,083

2,539

0

27,689

33,485

39,280

1,825

17,863

94,187

39,398

31,481

15,732

0

52,714

60,685

68,656

13,033

56,399

966,323

340,558

340,558

340,558

340,558

340,558

340,558

340,558

340,558

340,558

B. Adhvaryu / Progress in Planning 73 (2010) 113–207

1 2 3 4 5

Compaction variations (with same employment)

B. Adhvaryu / Progress in Planning 73 (2010) 113–207

Appendix B. Dwelling inputs—2001 and 2021 See Table B1 Appendix C. Transport inputs—2001 and 2021 C.1. Private automobile (PA) speeds C.2. Public transport speeds C.3. Slow mode speeds (bicycling and walking) C.4. Vehicle operating and maintenance costs calculations and assumptions C.5. Public transport (bus) fares Appendix D. Spatial change indicators Indicators to measure the change in the spatial structure using population data by SIMPLAN zones, are described below. D.1. Dispersion index Alan Bertaud (Bertaud, 2001; Bertaud & Malpezzi, 2003) proposed a measure to describe the ‘shape

201

performance’ of a city. The argument is that the spatial structure of a city can be defined by two complementary components: (a) the distribution of population over space; and (b) the pattern of trips made by people from their residences to any other destination. Bertaud (2001) maintains that the pattern of trips could be encapsulated in the average distance per person to the centre. This is a weighted average using the population of each ward as the weight. Bertaud (2001) argues that, everything else being equal, in a city with a small built-up area the distance per person to the centre will be shorter than in a city with a larger built-up area. Therefore, in order to have a comparative measure of shape between cities, it is necessary to have a measure independent of the area of the city. This could be achieved by taking the ratio of the average distance per person to the centre and the average distance per person to the centre of a circle whose area would be equal to the built-up area. Such a measure, called the dispersion index r, can be mathematically expressed as: P P di wi n d i wi p ffiffiffiffiffiffiffiffiffi or r ¼ n r¼ (A1) 2=3r 2=3 A=p where di is the distance of the centroid of the ith tract (or ward or zone) from the CBD, weighted by the tract’s share of population wi; A is the built-up area of the city; r is the radius of a circle with area A; n is the total number of tracts.

Table C1 Private automobile (PA) speeds (base values in km/h and alternative policies in % change over base). Zone

Zone name

Base 2001a

TR21 (%)

CC21 (%)

DS21 (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Walled city Vasna-Paldi Navrangpura-Gandhigram Naranpura-Vadaj-Sabarmati Dudheshwar-Madhupura-Girdharnagar Saraspur-Asarwa Naroda-Sardarnagar Bapunagar-Rakhial-KokhraMehmdabad Nikol-Odhav Maninagar-Kankaria Vatva-Badodara Cantonment Bhat-Chiloda-Nabhoi Kathwada-Muthiya Singarva-Vastral-Ramol Aslali-Lambha-Piplaj Sharkej-Gyaspur-Okaf Thaltej-Vastrapur-Vejalpur-Makarba-Ambli-Shilaj Sola-Gota-Chandlodia-Ghatlodia-Ranip Adalaj-Chandkheda-Kali-Motera-Zundal-Khoraj Gandhinagar City

10.0 15.5 15.5 15.5 12.0 12.0 12.0 12.0 12.0 12.0 12.0 15.0 18.0 18.0 18.0 18.0 18.0 18.0 18.0 18.0 20.0

10.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 0.0 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5

15.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2.5 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 0.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 10.0

a

Based on CEPT (2006) and Adhvaryu (1995).

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Table C2 Public transport speeds. Base year 2001

Horizon year 2021

Code

Base

Code

TR21 (%)

CC21 (%)

DS21 (%)

1 = Good PT 2 = Moderate PT 3 = Poor PT

85% of PA 80% of PA 75% of PA

1 = Exclusive BRTS 2 = Normal BRTS 3 = Ordinary bus

20 10 5

50 35 15

20 10 5

Notes: [1] Each zone is assigned a code and accordingly the speeds are calculated. [2] For year 2021, percentage change over base is applied as shown.

Table C3 Slow mode speeds (bicycling and walking). Base year 2001

Horizon year 2021

Code

Base

Code

TR21 (%)

CC21 (%)

DS21 (%)

1 = Walled city 2 = AMC 3 = AUC-AMC

85% of PA 60% of PA 50% of PA

1 = Walled city 2 = AMC 3 = AUC-AMC

10.0 5.0 2.5

7.5 2.5 0.0

5.0 2.5 5.0

Notes: [1] Each zone is assigned a code and accordingly the speeds ares calculated. [2] For year 2021, percentage change over base is applied as shown.

The numerator (i.e., the actual distance) in Eq. (A1) is the average distance per person to the centre (CBD or the geometric centre, as the case may be) and the denominator (i.e., the theoretical

distance) is the average distance to the centre of a circle (or cylinder with unit height) of equivalent area and uniform population density (see Fig. D1).

Table C4 Vehicle operating and maintenance costs calculations and assumptions. Item

Unit

2W

Car

Bicycle

Life Average km driven in vehicle life Capital cost Salvage value a [a] Capital cost/km Maintenance and repairs [b] Unit maintenance cost Mileage Fuel cost [c] Unit fuel cost Final unit cost [a + b + c] % Share Average unit cost (weighted) Average unit cost (2001 prices)

years km

7 60,000

12 100,000

5 10,000

Rs Rs Rs/km Rs/year

20,000 7,519 0.21 500

275,000 51,399 2.24 1,000

600 298 0.03 100

Rs/km

0.06

0.12

0.05

km/l Rs/l Rs/km Rs/km

30 31 1.02 1.29

10 31 3.06 5.42

0 0 0 0.08

Rs/km

86 1.86

14 0.08

Rs/km

1.86

0.08

a

2001

Vehicle depreciation per year is assumed to be 15%.

% Increase assumed

Period

2W

Car

Bicycle

10 10

20 years 20 years

8 66,000

13 110,000

6 11,000

6 6

pa pa

6

pa

64,143 24,114 0.61 1,604

881,962 164,845 6.52 3,207

1,924 957 0.09 321

0.19

0.38

0.16

35 98 2.84 3.64

15 98 6.54 13.45

0 0 0 0.25

80 0.25

20

5.64 2.13

0.09

15 6

20 years pa

2021

B. Adhvaryu / Progress in Planning 73 (2010) 113–207

203

Table C5 Public transport (bus) fares (2001 prices). Distance (km)

Fare (Rs)

Distance (km)

Fare (Rs)

Distance (km)

Fare (Rs)

Distance (km)

Fare (Rs)

0–2 2–4 4–6 6–8 8–10

1 3 4 5 6

10–12 12–14 14–16 16–18 18–20

7 7 8 8 9

20–22 22–24 24–26 26–28 28–30

9 10 10 11 11

30–32 32–34 34–36 36–38 38–40

11 12 12 12 13

Fig. D1. Calculation of dispersion index.

D.2. H-indicator (concentration/de-concentration measure) Inspired by physics, the H indicator (SCATTER, 2005) in discrete terms, i.e., if the area under study is divided into n zones is defined as: X X 2 H¼ r r A or H ¼ P r2 (A2) i i i i i i i where Pi is the population of the ith zone; Ai is the area of the ith zone; ri is the population density of the ith zone (i.e., Pi =Ai ); ri is the straight-line distance of centroid of the ith zone from the centroid of the CBD (or centroid of the study area). By definition, if H is increasing over time, then it can be concluded that the city is dispersing and vice versa. Further to this, a relative concentration measure Hrel is introduced to assess how outer areas are changing in relation toPcentral areas. Hrel is calculated by using Pi =ðð1=nÞ i Pi Þ instead of Pi in Eq. (A2). Thus, if Hrel is increasing over time, it indicates that the outer urban ring is growing faster in relative terms than the urban ˆ (or Hˆ rel ) can be calculated as the centre. Lastly, H percentage difference between H (or Hrel) for the year

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Bhargav Adhvaryu completed his PhD at the Martin Centre for Architectural and Urban Studies, Department of Architecture, University of Cambridge, UK in May 2009, and was a member of Churchill College. He has about 11 years of experience in teaching, research and consulting. From December 2004 to January 2007, he worked as

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Research Associate at the same department. Prior to that, he taught post-graduate planning students at CEPT University, Ahmedabad, India for three years; undergraduate architecture students at SCET, Surat, India for one year, and undergraduate civil engineering and post-graduate planning students at SVRCET, Surat for a year. He was also Project Manager at EPC, Ahmedabad, an urban planning consulting firm, for four years. Dr Adhvaryu’s additional qualifications are: MSc Transport & DIC (Imperial College London & University College London, UK); MTech Planning (CEPT University, Ahmedabad, India); BEng Civil (SVRCET, Surat, South Gujarat University, India), and DipCEng (Surat, India).