Adhvaryu - 2010 - Enhancing Urban Planning Using Simplified Models SIMPLAN for Ahmedabad, India

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

www.elsevier.com/locate/pplann

Progress in Planning 73 (2010) 113–207

E-mail address: [email protected].

0305-9006/$ – see front matter # 2010 Elsevier Ltd. All rights reserved.

doi:10.1016/j.progress.2009.11.001

mailto:[email protected]

http://dx.doi.org/10.1016/j.progress.2009.11.001

Contents

1. Paper outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

2. Context of developing countries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

2.1. Urban development and planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

2.2. Overview of urbanisation: India, Gujarat and Ahmedabad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

2.3. Background of planning in the Indian context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

2.4. The need and relevance of this study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

3. General introduction of the case study city of Ahmedabad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

3.1. Location, topography and climate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

3.2. History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

3.3. Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

3.4. Economy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

4. Introduction to modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

4.1. Definition and types of models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

4.1.1. Descriptive models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

4.1.2. Explanatory models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

4.1.3. Predictive models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

4.2. Descriptive conceptual models of spatial organisation of land uses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

4.2.1. Concentric zone theory (1925) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

4.2.2. Sector theory (1939) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

4.2.3. Multiple-nuclei theory (1945) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

4.2.4. Application to Ahmedabad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

4.3. Explanatory analytical models of location and land use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

4.3.1. Isolated state (1826) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

4.3.2. Industrial location theory (1909) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

4.3.3. Central place theory (1933) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

4.3.4. Urban bid-rent theory (1964) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

4.4. Introduction to LUTI models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

4.4.1. The land use–transport relationship. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

4.4.2. The Lowry model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

4.4.3. The MEPLAN model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

4.4.4. The TRANUS model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

4.4.5. The DELTA model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

4.4.6. A brief discussion on LUTI models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

5. SIMPLAN model: a brief introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

6. Development of alternative policies for the future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

6.2. Key modelling inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

6.3. Trend policy 2021 (TR21) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

6.3.1. TR21 land use inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

6.3.2. TR21 transport inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

6.4. Compaction policy 2021 (CC21) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

6.4.1. CC21 land use inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

6.4.2. CC21 transport inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

6.5. Dispersal policy 2021 (DS21) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

6.5.1. DS21 land use inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

6.5.2. DS21 transport inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

7. Summary of modelling outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

7.1. Land use outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

7.2. Transport outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

8. Sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

8.1. Variation in dwellings and employment allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

8.2. Variation in income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164

9. Assessment of alternative planning policies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164

9.1. Economic assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

B. Adhvaryu / Progress in Planning 73 (2010) 113–207114

9.1.1. Housing and work travel costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

9.1.2. Consumer and producer surplus in housing rent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

9.1.3. Consumer surplus in transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171

9.1.4. Estimates of costs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172

9.1.5. Summary of benefits and costs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

9.2. Environmental assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

9.2.1. Resources: new land required for residential use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

9.2.2. Emissions: vehicular CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

9.3. Social aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176

9.3.1. Mix of socioeconomic groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177

9.3.2. Social equity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

9.3.3. Accessibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

9.4. Sensitivity analysis: assessment summary of other alternatives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183

9.5. A discussion on assessment matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186

9.6. Conclusions on assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

10. Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

10.1. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

10.1.1. Summary of key feedback and responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

10.2. SIMPLAN application to DP making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

10.3. SIMPLAN simplifications and its application limitations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

11. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

11.1. On alternative urban forms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

11.2. On the model structure and operationality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

11.3. On the context of developing countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

11.4. Summary of key research findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

11.5. Suggestions for further research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

11.6. A final note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

B. Adhvaryu / Progress in Planning 73 (2010) 113–207 115

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 introduc-

tion 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

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


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 devel-

oping 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 industria-

lised 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 over-

crowding, poor housing conditions, inadequate social,

urban and transport infrastructure services, environ-

mental 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 well-

planned 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


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 supermar-

kets), 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, uncoordi-

nated utilities provision, resolving competition amongst

land uses, land speculation, traffic congestion, undesir-

able 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.

It is clear from the above discussion that the

scholarly literature on urbanisation and urban devel-

opment 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 comprehen-

sive 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


Fig. 1. Percentage of urban population.

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

Millions Annual growth rate (%) Millio

1901 238.4 – 25.9

1911 252.1 0.56 25.9

1921 251.3 �0.03 28.1

1931 279.0 1.05 33.5

1941 318.7 1.34 44.2

1951 361.1 1.26 62.4

1961 439.2 1.98 78.9

1971 548.2 2.24 109.1

1981 683.3 2.23 159.5

1991 846.3 2.16 217.6

2001 1,028.7 1.96 286.1

2006 1,094.1 0.63 332.1

2011 1,178.9 0.75 377.1

2016 1,263.5 0.70 425.4

Data source: Census (1991) for 1901–1991; Census (2001b) for 2001; Cen

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

population % Urban population

ns Annual growth rate (%)

– 10.8

0.04 10.3

0.80 11.2

1.77 12.0

2.81 13.9

3.52 17.3

2.37 18.0

3.29 19.9

3.87 23.3

3.16 25.7

2.75 27.8

1.53 30.0

1.28 32.0

1.21 34.0

sus (2001c) for 2006–2016 projections (shown in italics).


Table 2

Distribution of urban population in Indian cities.

Year 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 (%)

1901 26.0 11.3 15.6 20.8 20.1 6.1 100

1951 44.6 10.0 15.7 13.6 13.0 3.1 100

1961 51.4 11.2 16.9 12.8 6.9 0.8 100

1971 57.3 10.9 16.0 10.9 4.5 0.4 100

1981 60.6 11.6 14.3 9.5 3.6 0.3 100

1991 65.3 10.9 13.2 7.8 2.6 0.1 100

2001 68.3 9.6 12.4 6.9 2.6 0.3 100

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

Fig. 2. Location of

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 agglomera-

tion 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

Ahmedabad.


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 city-

level 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 agricul-

tural 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 Develop-

ment 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 regula-

tion. 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 con-

tinues 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 formula-

tion (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


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 interac-

tion 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


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 pro-

cess.

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 techni-

ques for planning in developing countries. As Echeni-

que (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 applica-

tion 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 con-

cerns 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 con-

ducted 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 ofAhmedabad

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 8Cand 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).


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

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’


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 situa-

tions 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 representa-

tions 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


Fig. 4. Concentric zone theory.

Source: Burgess (1925).

Fig. 5. Sector theory.

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 other-

wise.

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 single-

family 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

‘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.

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 wedge-

shaped 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. 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 multiple-

nuclei 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 require-

ments, different rent-paying abilities, and their agglom-

erative 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 lower-

income 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 influ-

ences. 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.


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 sub-

centres, 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 present-

day 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 beha-

vioural explanation of their constituent elements.

Therefore, they cannot be applied to cities for analysing

the evolution of their spatial structure in order to


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 Thunen (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. Isolated state (1826)

Johann Heinrich von Thunen in 1826 made the first

attempt to show the interlinkages between space and

economic activity. He developed a model that demon-

strated how production cost and the cost of transporting

production to the market affects agricultural land use

(i.e., cropping pattern) in a region. Von Thunen 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?

4.3.1.1. Concept of land rent. Von Thunen 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, administra-

tion, 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 tBonwards, 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.


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 Thunen 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.

Fig. 10. Agricultural land use patt

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 Thunen’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

Thunen shows that the limits of the isolated state are

extended markedly, concluding that transport improve-

ments have a vast effect on the welfare of a nation.

Although von Thunen’s model is for only agricultural

ern and effect of grain price.


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

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 Thunen’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

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 under-

stand 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


Fig. 12. Upper and lower limits of range.

analysing and understanding the factors responsible for

the location of an industry. With regard to its application

to developing countries (in market-oriented econo-

mies), 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

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


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.


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 char-

acteristics. 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.,


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 Thunen’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 bid-

price 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


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 bid-

price 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


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 high-

income 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 Thunen’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.


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

Although Alonso’s (or von Thunen’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 contem-

porary 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 four-

step transport model (which has trip generation, trip

distribution, modal split and route assignment, see

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


Fig. 19. The land use–transport interaction.

Table 3

Four-way classification of land use and transport.

Function Form

Land use Activities Buildings

Transport Flows 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. Full-

fledged 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


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 insignif-

icant. 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


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 popula-

tion, 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 employ-

ment. 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

Tomas 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 short-

term projections.

Similar to MEPLAN, the theoretical framework of

TRANUS also draws from many traditions, namely:

spatial microeconomics (Alonso, 1964; von Thunen,

1826); gravity and entropy maximisation (Lowry, 1964;

Wilson, 1970); and the input–output accounting frame-

work (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 schema-

tically 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


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 pedes-

trians. The operative supply uses the physical supply to

perform its functions.

Demand–supply equilibrium in the transport sub-

system 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 equili-

brium 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


Fig. 23. General structure of DELTA model.

activities deal with household transitions and employ-

ment 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 environ-

mental variables. Fig. 23 shows the main linkages

between the sub-models in DELTA model within a one-

year period.

DELTA consists of six urban and three regional sub-

models. The urban sub-models estimate:

1. The development of buildings on land.

2. Demographic change and economic growth (apply-

ing 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


1 All references to average housing rents in this study mean imputed

rents, unless stated otherwise.

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 profes-

sional/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. SIM-

PLAN 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

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.

Rmi j ¼ Em

j

Xmi expð�bmcm

i jÞPiX

mi expð�bmcm

i jÞ(1)

where Rmi 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; cmi 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:

cmi j ¼ rm

i þ nmi j þ f i j

where rmi is the average imputed housing rent1 paid by

SEG type m in zone i obtained as r� uniti � DFSDm

i , in Rs/

day (for details see Eqs. (2) and (3)), vmi j is the average

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 vmi j þ f i j would be an acceptable repre-


sentation of generalised work travel cost. In other

words, it is assumed that the nuances due to mode-

specific out-of-pocket expenses are insignificant insofar

as being able to change location behaviour. Xmi is a

housing attractiveness factor by SEG type m to be

calibrated, where, Xmi ¼ F

dmi

i and Fi is the theoretical

maximum supply of residential floorspace allowable in

a zone and dmi 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).

ri ¼P

mðr�uniti � DFSDm

i � Hmi Þ

Hi(2)

where

r�uniti ¼ runit

i

Di

Si

� �u

(3)

where, r�uniti is the (new) unit monthly rent (Rs/m2) in

zone i. runiti 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:PmðDFSDm

i � Hmi Þ, where DFSDm

i is the dwelling

floorspace demanded (m2/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, two-

wheelers 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 para-

transit 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).

PrðkÞi j ¼expðVk

i jÞPkexpðVk

i jÞ(4)

where Vki j is the utility of choosing mode k formulated

as:

Vki j ¼ ak þ bck

i j þ vtki j ¼ ak þ bðck

i j þ ðv=bÞtki jÞ

¼ ak þ bðcki j þ ttk

i jÞ (5)

where ak is the alternative specific constant (assumed

zero for the other modes); b is a parameter to be

calibrated (Rs�1); cki j is the cost of travel by mode k

(Rs); v is a parameter to be calibrated (min�1); tki j 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:

Rki j ¼ PrðkÞi j ðwhere Ri j

¼X

m

Rmi j; and Rm

i j is from Eq:ð1ÞÞ (6)

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


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 transpar-

ency, 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


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 thefuture

6.1. Introduction

Planning in Ahmedabad is governed by the Devel-

opment 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, com-

mitted 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.


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


Fig

.2

7.

SIM

PL

AN

scre

ensh

ot

(bas

ey

ear

20

01).


Table 4

Workers and population 2001 (modelled vs. observed) (thousands).

Zone Resident workers Households Population

Obs Mod % Diff: mod

vs. obs

Obs Mod % Diff: mod

vs. obs

Obs Mod % Diff: mod

vs. obs

1 111.3 111.3 0.0 69.5 73.1 5.2 372.6 366.5 �1.6

2 59.3 59.3 0.1 38.0 39.0 2.6 178.5 195.4 9.5

3 41.3 41.3 0.0 26.5 27.1 2.5 127.4 136.0 6.8

4 117.8 117.8 0.0 77.5 77.4 �0.1 369.5 388.2 5.1

5 62.3 62.3 0.0 37.7 40.9 8.6 205.2 205.2 0.0

6 162.6 162.7 0.0 110.6 107.0 �3.3 585.6 536.4 �8.4

7 66.3 66.3 0.0 38.7 43.6 12.6 194.1 218.7 12.6

8 180.9 180.9 0.0 105.7 118.9 12.5 557.5 595.9 6.9

9 55.2 55.2 0.0 46.8 36.3 �22.5 226.8 181.9 �19.8

10 105.0 105.0 0.1 65.5 69.0 5.5 345.3 346.0 0.2

11 109.2 109.2 0.0 75.7 71.8 �5.1 357.7 359.6 0.6

12a 6.0 6.0 0.0 2.9 3.9 36.1 14.7 19.8 34.3

13 3.5 3.5 �0.3 2.1 2.3 6.3 10.6 11.4 7.5

14 16.3 16.3 �0.3 11.1 10.7 �3.7 54.7 53.7 �1.8

15 26.1 26.0 �0.1 17.7 17.1 �3.5 84.3 85.8 1.8

16 15.2 15.2 0.0 9.1 10.0 9.7 44.4 50.1 13.0

17 14.9 14.9 0.0 9.4 9.8 4.4 48.2 49.0 1.8

18 87.0 87.0 0.0 57.5 57.2 �0.5 270.6 286.6 5.9

19 86.2 86.2 0.0 60.0 56.6 �5.6 290.4 283.9 �2.2

20 38.0 38.0 0.0 27.8 25.0 �10.0 136.8 125.3 �8.4

21 135.9 135.7 �0.1 96.1 89.1 �7.3 467.3 446.6 �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).

H2021 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:

d2021�2001 ¼ d2021 � d2001 (8)

where d2021 is dwelling units in 2021 obtained by assum-

ing 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


Table 5

Average trip distance (modelled vs. observed).

SEG bm Modelled

ATD (km)

Observed

ATD (km)a

SEG1 0.200 8.69

SEG2 0.235 7.53

SEG3 0.300 5.74

SEG4 0.510 5.12

All SEG 6.22 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

Fig. 28. Average zonal housing ren

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-

ts 2001 and 1996 land prices.


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

cussed as part of sensitivity analysis in Section 8.0).

E2021�2001i ¼ E2021�2001 SE2001

i PTQS2021iP

iSE2001i PTQS2021

i

(9)

where E2021�2001i 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 addi-

tional dwelling units (d2021�2001i ) 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.

d2021�2001i ¼ d2021�2001 Ec

i RRvi SCx

i PTQS’IP

iEci RRv

i SCxi PTQS’

I

(10)

where d2021–2001 is as calculated from Eq. (8); d2021�2001i

is the additional number of dwelling units supplied in

zone i in the period stated (which is converted to

floorspace); Eci is the employment in zone i (where,

Ei ¼ E2001i þ E2021�2001

i and E2021�2001i is from Eq. (9));

RRni is the ratio of rent in zone i to the average for


Fig. 30. SIMPLAN zones.

modelled area for base 2001; SCxi 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 automo-

biles, average travel speeds have been reduced from


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 20-

year 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-


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

B.

Ad

hva

ryu/P

rog

ressin

Pla

nn

ing

73

(20

10

)1

13–2

07

15

5Table 6

Summary of land use and transport inputs.

S# Input Base 2001 Planning policy alternatives 2021

TR21 CC21 DS21

Land use

L1 Employment (taken up

by workers resident in

modelled area, i.e.,

zones 1–21)

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)

L2 Proportion of

employment

by SEG

Based on LBGC (2001) Modified to account for increases in SEG1 and SEG2, based on trend analysis

SEG1: 8.4% SEG1: 10.2%

SEG2: 22.5% SEG2: 25.3%

SEG3: 41.2% SEG3: 38.5%

SEG4: 27.9% SEG4: 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 Same as base W-city: 3.0 W-city: 3.0

Inner: 1.8 Inner: 2.5 Inner: 1.8

Outer: 1.0 Outer: 1.0 Outer: 2.0

G’ngr: 1.8 G’ngr: 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)

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: 8,195 ha LSR inner: 8,195 ha LSR inner: 9,780 ha LSR inner: 8,195 ha

LSR outer: 5,770 ha LSR outer: 6,587 ha LSR outer: 5,770 ha LSR outer: 7,404 ha

LSR total: 13,965 ha LSR total: 14,782 ha LSR total: 15,550 ha LSR total: 15,599 ha

B.

Ad

hva

ryu/P

rog

ressin

Pla

nn

ing

73

(20

10

)1

13–2

07

15

6

Table 6 (Continued )

S# Input Base 2001 Planning policy alternatives 2021

TR21 CC21 DS21

L6 Dwelling

floorspace supply

45,684,830 m2

(966,323 dwelling units).

Calculated based

on observed

households (Census, 2001a)

67,602,643 m2 (1,306,880 dwelling units)

Per zone: see Appendix B

for details

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)

Transport

T1 Average (network)

distance O–D matrix

Calculated from map 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)

Rij (i.e.,P

mRmi 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.


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 automobile-

oriented, 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 D10-

90) (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

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


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 passenger-

kilometre 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


Table 8

Housing rents and dwelling floorspace consumed by SEG.

Zone Base 2001 TR21 % Change: TR21

vs. base 2001

CC21 % Change: CC21

vs. TR21

DS21 % Change: DS21

vs. TR21

A. Average monthly household’s rent (Rs, 2001 prices)

SEG1 3,800 3,958 4.2 4,188 5.8 3,726 �5.9

SEG2 2,743 2,894 5.5 2,929 1.2 2,903 0.3

SEG3 2,214 2,377 7.4 2,359 �0.8 2,401 1.0

SEG4 1,665 1,801 8.2 1,776 �1.4 1,803 0.1

ALL 2,313 2,520 8.9 2,538 0.7 2,508 �0.5

B. Average floorspace/dwelling consumed (m2)

SEG1 70.8 77.0 8.7 75.0 �2.5 79.0 2.6

SEG2 54.5 58.9 8.2 58.5 �0.8 58.7 �0.3

SEG3 45.2 48.1 6.6 48.5 0.7 47.6 �1.0

SEG4 34.5 36.2 4.7 36.8 1.9 36.3 0.4

ALL 46.4 50.7 9.1 50.7 0.0 50.7 0.0

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 2,380 2,675 12.4 2,663 �0.4 2,663 �0.4

2 2,957 3,195 8.1 3,187 �0.3 2,904 �9.1

3 3,531 3,499 �0.9 3,731 6.6 3,482 �0.5

4 2,566 2,718 5.9 2,670 �1.8 2,653 �2.4

5 2,355 2,519 7.0 2,502 �0.7 2,439 �3.2

6 1,950 2,156 10.6 2,073 �3.8 2,207 2.4

7 2,447 2,542 3.9 2,524 �0.7 2,722 7.1

8 2,282 2,507 9.9 2,392 �4.6 2,551 1.8

9 1,438 1,689 17.5 1,558 �7.8 1,787 5.8

10 2,152 2,165 0.6 2,023 �6.5 2,318 7.1

11 2,045 2,072 1.3 2,052 �1.0 2,263 9.2

12a

13 2,606 2,711 4.0 2,594 �4.3 3,001 10.7

14 1,705 1,950 14.4 1,976 1.3 1,997 2.4

15 1,913 2,169 13.4 2,173 0.2 2,214 2.1

16 2,196 2,335 6.3 2,672 14.4 2,246 �3.8

17 1,783 2,103 17.9 2,353 11.9 1,728 �17.8

18 2,828 3,121 10.4 3,559 14.0 2,875 �7.9

19 2,199 2,987 35.8 3,344 12.0 2,741 �8.2

20 2,378 2,663 12.0 2,565 �3.7 2,364 �11.3

21 2,386 2,344 �1.8 2,411 2.9 2,338 �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.

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


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%) 290,784 (61%) 323,032 (68%) 220,813 (47%)

Outer zones 111,380 (37%) 182,349 (39%) 150,101 (32%) 252,320 (53%)

Sub-total 304,333 473,133 473,133 473,133

Low-income group (SEG3 and SEG4)

Inner zones 511,309 (75%) 648,728 (75%) 679,725 (79%) 570,235 (66%)

Outer zones 170,402 (25%) 211,690 (25%) 180,693 (21%) 290,183 (34%)

Sub-total 681,711 860,418 860,418 860,418

Total 986,043 1,333,552 1,333,552 1,333,552

Table 10

Summary of key demographics.

Item 2001 2021

Employment 1,570,399 2,131,828

Resident workers 1,500,068 2,038,434

Households 986,043 1,333,552

Population 4,941,905 6,410,819

Table 11

Population—base 2001 and alternative policies (thousands).

Base 2001 TR21 CC21 DS21 % Change

TR21 vs. base 2001 CC21 vs. TR21 DS21 vs. TR21

By sub-regions

Inner 3,520 4,517 4,821 3,803 28 7 �16

Outer 1,422 1,894 1,590 2,608 33 �16 38

Total 4,942 6,411 6,411 6,411 30 0 0

By zones

1 372.63 425.16 434.40 407.56 14 2 �4

2 178.53 278.53 298.20 204.16 56 7 �27

3 127.35 214.60 261.46 159.80 69 22 �26

4 369.48 533.65 576.32 411.27 44 8 �23

5 205.16 227.75 231.17 218.09 11 2 �4

6 585.63 562.74 569.52 531.55 �4 1 �6

7 194.11 314.87 348.85 245.81 62 11 �22

8 557.47 682.88 708.21 646.41 22 4 �5

9 226.77 217.44 216.76 195.64 �4 0 �10

10 345.28 509.81 563.54 377.59 48 11 �26

11 357.67 549.12 612.14 404.95 54 11 �26

12 14.71 27.11 21.84 25.06 84 �19 �8

13 10.61 22.89 17.32 41.89 116 �24 83

14 54.73 68.87 46.23 99.84 26 �33 45

15 84.28 108.71 81.93 143.59 29 �25 32

16 44.37 86.79 57.14 139.51 96 �34 61

17 48.17 87.29 62.69 128.11 81 �28 47

18 270.57 447.31 396.06 792.20 65 �11 77

19 290.36 315.12 307.22 374.87 9 �3 19

20 136.77 205.78 153.24 278.73 50 �26 35

21 467.26 524.40 446.56 584.18 12 �15 11

Total 4,942 6,411 6,411 6,411 30 0 0


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 190 243 260 205

Outer zones 40 54 45 74

Overall 92 119 119 119

Table 13

Summary of transport outputs by SEG.

Item Base 2001 Trend 2021 Compaction 2021 Dispersal 2021

Work trips (same as no. of resident workers)

SEG1 125,857 208,381 208,381 208,381

SEG2 337,125 514,838 514,838 514,838

SEG3 618,931 784,663 784,663 784,663

SEG4 418,155 530,552 530,552 530,552

ALL 1,500,068 2,038,434 2,038,434 2,038,434

Work trip passenger-km [millions] (one-way/day)

SEG1 1.09 1.59 1.35 2.45

SEG2 2.54 3.48 3.27 3.61

SEG3 3.55 4.08 3.93 4.17

SEG4 2.14 2.60 2.60 3.67

ALL 9.32 11.75 11.15 13.91

% Change vs. base – 26% 20% 49%

% Change vs. trend – – �5% 18%

Average work trip distance [km] (one-way)

SEG1 8.69 7.63 6.46 11.77

SEG2 7.52 6.75 6.36 7.01

SEG3 5.74 5.20 5.00 5.32

SEG4 5.12 4.90 4.89 6.93

ALL 6.21 5.76 5.47 6.82

% Change vs. base – �7% �12% 10%


Average work trip time [min] (one-way)

SEG1 55.37 49.40 40.76 65.27

SEG2 49.00 45.54 41.60 43.65

SEG3 40.15 37.78 35.74 35.58

SEG4 38.86 37.30 36.19 44.96

ALL 43.06 40.81 37.85 43.09

% Change vs. base – �5% �12% 0%


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 conges-

tion assignment model is beyond the scope of this study,

this effect is not modelled accurately, and is therefore a

limitation. However, any standard commercially avail-

able 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


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-


Table 15

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

Policy Living in Job in ALL

Inner Outer

Base 2001 Inner 4.04 12.19 4.05

Outer 21.30 7.43 11.64

ALL 5.91 7.44 6.21

Trend 2021 Inner 4.49 14.24 4.52

Outer 19.10 6.48 8.72

ALL 5.51 6.54 5.76

Compaction 2021 Inner 4.77 16.70 4.96

Outer 17.75 6.32 7.01

ALL 5.03 6.83 5.47

Dispersal 2021 Inner 3.73 12.97 3.74

Outer 18.37 6.68 11.33

ALL 6.87 6.68 6.82

Table 14

Summary of transport outputs by mode.

Item Base 2001 Trend 2021 Compaction 2021 Dispersal 2021

Work trip passenger-km [millions] (one-way/day)

Private auto 3.65 3.68 2.98 4.87

Public transport 2.41 4.07 4.63 4.46

Slow 3.26 4.00 3.54 4.59

ALL 9.32 11.75 11.15 13.91

Average work trip length [km] (one-way)

Private auto 6.68 5.61 4.99 7.03


Slow 5.13 4.84 4.43 5.56

ALL 6.21 5.76 5.47 6.82

Average work trip distance [min] (one-way)

Private auto 29.02 26.23 24.83 27.06


Slow 44.29 42.82 39.85 45.47

ALL 43.05 40.81 37.85 43.09

Average work trip time [min] (one-way)

Private auto 13.82 12.82 12.05 15.58


Slow 6.95 6.79 6.67 7.33

ALL 8.66 8.47 8.67 9.50

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


Table 16

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

Item Modal split Average trip length (km)

BS01a (%) TR21 (%) CC21 (%) DS21 (%) BS01 TR21 CC21 DS21

Overall

PA 28.9 32.2 29.3 34.0 6.68 5.61 4.99 7.03

PT 17.4 27.3 31.5 25.5 7.58 7.31 7.21 8.57

SL 53.8 40.5 39.3 40.5 5.13 4.84 4.43 5.56

Inner to inner zones

PA 35.9 32.6 29.0 33.6 4.37 4.53 4.38 3.91

PT 21.1 28.1 32.7 24.1 4.33 5.58 6.33 4.06

SL 43.0 39.3 38.3 42.2 3.62 3.68 3.73 3.41

Inner to outer zones

PA 25.1 28.4 19.3 35.3 12.21 13.74 14.69 12.93

PT 16.8 40.1 60.0 30.2 12.17 15.10 18.12 13.27

SL 58.1 31.6 20.7 34.4 12.19 13.62 14.46 12.74

Outer to inner zones

PA 52.1 29.2 23.5 36.6 21.16 18.38 16.29 18.31

PT 28.7 40.4 50.1 36.0 23.66 20.94 19.61 19.53

SL 19.1 30.4 26.4 27.4 18.98 17.35 15.52 16.92

Outer to outer zones

PA 28.3 31.7 31.0 33.1 7.55 6.19 5.93 6.47

PT 10.8 22.2 24.9 22.0 9.62 8.15 7.93 8.66

SL 60.9 46.1 44.2 44.9 6.50 5.87 5.67 5.86

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 D90-

10 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 explana-

tion 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 (quanti-

tatively 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 decision-

making 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.


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 non-

work 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,

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Table 17

Summary of inputs for 2021 policies.

Items 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

CC

D80-20

(%)

CC

D90-10

(%)

CC

D100-0

(%)

DS

D20-80

(%)

DS

D10-90

(%)

DS

D0-100

(%)

CC

ED92-08

(%)

DS

ED22-78

(%)

Employment increment:

inner zones

– 63 63 63 63 63 63 63 92 22

Employment increment:

outer zones

– 37 37 37 37 37 37 37 8 78

Employment total:

inner zones

80 75 75 75 75 75 75 75 83 65

Employment total:

outer zones

20 25 25 25 25 25 25 25 17 35

Dwelling increment:

inner zones

– 63 80 90 100 20 10 0 92 22

Dwelling increment:

outer zones

– 37 20 10 0 80 90 100 8 78

Dwelling total:

inner zones

70 68 73 75 78 57 55 52 76 58

Dwelling total:

outer zones

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.


Fig. 37. Change in consumer surplus.

Fig. 36. Consumer and producer surplus.

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):

pmi ¼ pmaxexpð�bqÞ (11)

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.

The consumer surplus (for zone i by household of

SEG type m) can be calculated as:

CSmi ¼

Zqe

0

pmaxexpð�bqÞdq� peqe (12)

Producer surplus as explained above is calculated as:

PSmi ¼

1

2peqe (13)

The total consumer and producer surplus can be

estimated, respectively, as:Xm

Xi

CSmi (14)

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Table 18

Sensitivity analysis—dwellings and employment variations.

Items Base 2001 TR21 ED63-37 CC21 variations (with same employment,

but different dwellings)

DS21 variations (with same employment,


CC21 Diff emp

and dwellings

DS21 Diff emp

and dwellings

CC D80-20 CC D90-10 CC D100-0 DS D20-80 DS D10-90 DS D0-100 CC ED92-08 DS ED22-78

a b c d e f g h i j k

Passenger-km [millions] 9.32 11.75 11.49 11.15 11.06 13.46 13.91 14.40 11.71 11.43

% Change vs. trend – – �2.2% �5.1% �5.9% 14.6% 18.4% 22.6% �0.3% �2.7%

ATL [km] 6.21 5.76 5.64 5.47 5.42 6.60 6.82 7.07 5.75 5.61

% Change vs. trend – – �2.2% �5.1% �5.9% 14.6% 18.4% 22.6% �0.3% �2.7%

ATL [min] 43.05 40.81 38.7 37.8 37.7 42.1 43.1 44.3 39.7 36.3

% Change vs. trend – – �5.1% �7.2% �7.6% 3.1% 5.6% 8.5% �2.6% �11.0%

Speed [km/h] 8.66 8.47 8.73 8.67 8.64 9.42 9.50 9.58 8.68 9.27

% Change vs. trend – – 3.1% 2.3% 1.9% 11.1% 12.1% 13.1% 2.4% 9.4%

Modal split: PA [%] 28.9% 32.2% 29.3% 29.3% 29.2% 33.9% 34.0% 34.0% 29.1% 33.6%

Modal split: PT [%] 17.4% 27.3% 31.5% 31.5% 31.8% 25.4% 25.5% 25.7% 32.3% 24.3%

Modal split: SL [%] 53.8% 40.5% 39.2% 39.3% 39.0% 40.6% 40.5% 40.3% 38.6% 42.1%

Rent [Rs/month] 2,313 2,520 2,531 2,538 2,544 2,512 2,508 2,502 2,522 2,525

% Change vs. trend – – 0.5% 0.7% 1.0% �0.3% �0.5% �0.7% 0.1% 0.2%

Transport cost [Rs/month] 2,749 2,613 2,469 2,412 2,401 2,761 2,832 2,912 2,527 2,373

% Change vs. trend – – �5.5% �7.7% �8.1% 5.7% 8.4% 11.4% �3.3% �9.2%

Cost of living [Rs/month] 5,061 5,132 5,000 4,950 4,945 5,273 5,339 5,414 5,049 4,899

% Change vs. trend – – �2.6% �3.5% �3.6% 2.7% 4.0% 5.5% �1.6% �4.5%

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


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 2,313 2,520 2,538 2,508

% Change vs. base – 8.9% 9.7% 8.4%

% Change vs. trend – – 0.75% �0.47%

[b] Monthly household’s transport cost for work trips (incl. time)

Total 2,749 2,613 2,412 2,832

% Change vs. base – �4.9% �12.3% 3.0%

% Change vs. trend – – �7.7% 8.4%

[c] Monthly household’s cost of living [a + b]

Total 5,061 5,132 4,950 5,339

% Change vs. base – 1.4% �2.2% 5.5%

– �3.5% 4.0%

Table 19

Sensitivity analysis—income variation.

Items Real income increase scenario No real income increase scenario % Change

TR21 CC21 DS21 TR21 CC21 DS21 No real income increase vs.

real income increase

TR21 CC21 DS21

SEG1: dwelling size consumed (m2) 77.0 75.0 79.0 71.6 69.9 73.3 �7.0% �6.9% �7.2%




Passenger-km [millions] 11.75 11.15 13.91 12.11 11.42 14.27 3.1% 2.5% 2.6%

ATL [km] 5.76 5.47 6.82 5.94 5.60 7.00 3.1% 2.5% 2.6%

ATL [min] 40.81 37.8 43.1 41.78 38.5 44.0 2.4% 1.8% 2.1%

Speed [km/h] 8.47 8.67 9.50 8.53 8.72 9.55 0.7% 0.6% 0.5%

Modal split: private auto [%] 32.2% 29.3% 34.0% 32.1% 29.2% 34.0% �0.1% �0.2% 0.1%

Modal split: public transport [%] 27.3% 31.5% 25.5% 27.6% 31.8% 25.7% 1.1% 0.9% 0.8%

Modal split: slow [%] 40.5% 39.3% 40.5% 40.2% 39.0% 40.2% �0.7% �0.6% �0.6%

Rent [Rs/month] 2,520 2,538 2,508 2,361 2,376 2,349 �6.3% �6.4% �6.3%

Transport cost [Rs/month] 2,613 2,412 2,832 2,676 2,456 2,894 2.4% 1.8% 2.2%

Cost of living [Rs/month] 5,132 4,950 5,339 5,037 4,832 5,244 �1.9% �2.4% �1.8%

Xm

Xi

PSmi (15)

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.

DCS ¼ 1

2ðq0 þ q1Þð p0 � p1Þ (16)

% Change vs. trend –

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.

DCSmi ¼

1

2ðqT Hm

i;T þ qAHmi;AÞð pT � pAÞ (17)

where DCSmi 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); Hmi is the households in

zone i by SEG type m; T, A are sub-scripts indicating


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 50.24

SEG2 �16.30 9.88

SEG3 12.12 �15.16

SEG3 12.81 �12.00

ALL �29.38 32.96

Change in producer surplus

SEG1 15.68 –15.83

SEG2 5.96 1.56

SEG3 –4.63 5.94

SEG3 –4.44 0.35

ALL 12.57 �7.98

Total (welfare) �16.81 24.98

Fig. 38. Housing consumer and

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:Xm

Xi

DCSmi (18)

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

producer surplus by SEG.


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 3,993 3,989 4,030 4,097

Outer zones 3,564 3,924 4,435 3,639

ALL 3,800 3,958 4,188 3,726

SEG2

Inner zones 2,797 2,927 2,856 3,067

Outer zones 2,634 2,832 3,111 2,679

ALL 2,743 2,894 2,929 2,903

SEG3

Inner zones 2,235 2,393 2,311 2,474

Outer zones 2,162 2,336 2,518 2,241

ALL 2,214 2,377 2,359 2,401

SEG4

Inner zones 1,673 1,802 1,753 1,834

Outer zones 1,634 1,801 1,883 1,752

ALL 1,665 1,801 1,776 1,803

Overall

Inner zones 2,292 2,466 2,425 2,496

Outer zones 2,364 2,648 2,883 2,525

ALL 2,313 2,520 2,538 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

Table 23

Summary of consumer surplus in transport.

Indicator TR21

[q] Passenger-km (millions, one-way per day)

Private auto 3.68

Public transport 4.07

Slow 4.00

ALL 11.75

[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

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

Private auto

Public transport

Slow

Total

a Value of time (VOT) is from the MSM. This is different from the VOT in R

VOT from RLM, the change in transport consumer surplus (vs. trend) for com

and 6.32, respectively. Although the magnitudes are different, as expected,

(see Table 9). The scenario is reversed in compaction

policy, in which a higher proportion of poorer house-

holds (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 passenger-

kilometre, 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

CC21 DS21

2.98 4.87

4.63 4.46

3.54 4.59

11.15 13.91

5.82 4.98

5.56 5.87

6.77 6.16

6.01 5.65

prices) CC21 vs. TR21 DS21 vs. TR21

�0.74 2.63

2.98 1.60

�0.44 2.09

1.80 6.32

LM, which is by SEG. As a sensitivity test, using the weighted average

paction and dispersal works out to be 1.59 and 4.29, compared to 1.80

the direction of change is the same.


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

then given asP

kDCSk.

DCSk ¼ 1

2ðqk

T þ qkAÞð pk

T � pkTÞ (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 general-

ised 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

users in outer areas, where normal buses would share

the road infrastructure with other modes.

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 popula-

tion-based infrastructure—such as water supply, sew-

erage 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-


Table 24

Estimates of transport costs (million Rs, 2001 prices).

Item TR21 CC21 DS21

BRTS costs

Capital cost (basic) [2006] 9,901.49

Capital cost (basic) [2001] 7,758.08

Cost increase factor 1.00 1.86a 1.00

Capital cost (modified) [a] 7,758.08 14,446.47 7,758.08

O and M cost (@5%) [b] 387.90 722.32 387.90

Total cost [a +b] 8,145.98 15,168.80 8,145.98

Total additional BRTS cost (vs. trend) [I] – 7,022.81 0.00

Road costs

Length in 2001 (km) 3,111

Average width in 2001 (m) 25.00

Road area in 2001 (m2) 77,770,000

Capacity increase factor 1.00 0.00 2.00

Road capacity enhancement per annum

(values in brackets are for 2001–2021)

0.26% (5.36%) 0.00% (0.00%) 0.52% (11.00%)

New road area required 2001–2021 (m2) 4,171,819 0 8,555,672

Capital cost of new roads (@Rs 781.75/m2)b [c] 3,261.32 0.00 6,688.39

O and M cost (@5%) [d] 163.07 0.00 334.42

Total cost [c + d] 3,424.38 0.00 7,022.81

Total additional road costs (vs. trend) [II] �3,424.38 3,598.43

Total additional transport costs (vs. trend) [I + II] – 3,598.43 3,598.43

a Estimated to achieve the same difference in total costs vs. trend as dispersal.b 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


Table 25

Annual estimates of benefits and costs vs. trend (million Rs, 2001 prices).

Item Compaction Dispersal

2021 2021

Annual benefits

Change in housing rent consumer surplus �352.55 (�29.38a � 12) 395.24 (32.96 � 12)

Change in housing rent producer surplus 150.81 (12.57 � 12) �95.75 (�7.98 � 12)

Change in transport consumer surplus 1,037.53 (1.80 � 48b � 12) 3,638.87 (6.32 � 48b � 12)

Total 835.79 3,938.66

Annual costs

BTRS additional cost (values in brackets are total costs) 563.53 (7,022.81) 0.00 (0.00)

New roads and capacity augmentation additional cost

(values in brackets are total costs)

�274.78 (�3,424.38) 288.75 (3,598.43)

Total 288.75 288.65

Net benefit (benefits–costs) 547.04 3,649.91

a Monthly values are rounded so it will not give exact annual values.b 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 other words, this is done by

finding x in C, where C ¼P

nx=ðð1þ rÞnÞ, 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


Table 26

Estimate of land required for new dwellings (annual estimates in ha).

Sub-region Trend 2021 Compaction 2021 Dispersal 2021

Inner zones 195 245 31

Outer zones 188 39 388

Total 384 285 419

% difference with trend 2021 �26 9

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-

Table 27

Estimate of CO2 emissions for private automobiles (annual estimates, exce

Item Units Base 2001

Passenger-km 106 2104.61

Vehicle-km 106 1940.09

Of which, two-wheeler (2W) 106 1670.05

Of which, car 106 270.04

2W CO2 ton 133,604

Car CO2 ton 43,206

Total CO2 ton 176,810

Daily CO2 per capita g 124.23

Difference with trend 2021 ton (%)

Notes and assumptions

� Annual passenger-km is obtained by converting values from Table 14 by m

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 base

� 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

Hickman, Saxena, and Banister (2008). The latter study has value in the

reduced by 10% to account for improvement in vehicle technology in th

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-kilo-

metre 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

pt mentioned otherwise).

TR21 CC21 DS21

2117.47 1714.82 2803.96

1922.96 1557.30 2546.39

1529.54 1238.69 2025.42

393.42 318.61 520.97

122,363 99,095 162,034

62,948 50,978 83,356

185,311 150,073 245,389

100.37 81.28 132.91

�35,238 (�19%) 60,078 (32%)

ultiplying them by 576 (i.e., 24 working days/month � 2 work trips/

d on 1961–2006 trends (AMC, 2007)].

recent studies of cities in the Indian context: Bhajracharya (2008) and

range of 120–240 for most popular cars in India. Values for 2021 are

e 20-year period.


these, like auto-rickshaws (a three-wheeler, predomi-

nantly 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

Table 28

Distribution of each SEG by sub-region.

SEG1 (%)

Base 2001

Walled city (zone 1) 3

Inner West (zones 2–4) 27

Inner East (zones 5–11) 25

Outer East (zones 12–16) 2

Outer West (zones 17–20) 25

Gandhinagar city (zone 21) 18

Trend 2021







Compaction 2021







Dispersal 2021







Notes: (1) Columns total 100%. (2) Grey cells in trend denote values higher t

higher than trend policy. (3) Based on trend projections, the overall proportio

SEG4 have decreased.

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

SEG2 (%) SEG3 (%) SEG4 (%)

7 8 8

20 13 10

40 51 62

4 5 6

15 15 12

14 8 3

5 9 6

20 13 13

40 51 60

4 6 5

17 14 13

14 7 3

7 9 4

23 14 14

42 54 64

3 4 4

15 13 11

11 6 3

5 10 5

19 11 8

34 47 51

6 7 10

21 18 24

15 7 3

han base 2001, while in compaction and dispersal they indicate values

ns in 2021 of SEG1 and SEG2 have increased and those of SEG3 and


Table 29

Proportion of SEGs in each sub-region.

Sub-region SEG1 (%) SEG2 (%) SEG3 (%) SEG4 (%)

Walled city (zone 1)

Base 2001 4 21 47 28

Trend 2021 2 20 53 25

Compaction 2021 6 25 53 16

Dispersal 2021 0 20 61 19

Inner West (zones 2–4)

Base 2001 15 30 36 19

Trend 2021 16 32 32 21

Compaction 2021 17 32 30 21

Dispersal 2021 8 39 36 17

Inner East (zones 5–11)

Base 2001 4 18 43 35

Trend 2021 5 21 41 33

Compaction 2021 5 21 41 33

Dispersal 2021 2 21 44 32

Outer East (zones 12–16)

Base 2001 4 18 42 36

Trend 2021 7 21 44 29

Compaction 2021 8 20 41 31

Dispersal 2021 3 20 39 38

Outer West (zones 17–20)

Base 2001 14 23 41 22

Trend 2021 20 26 33 21

Compaction 2021 19 26 36 20

Dispersal 2021 25 22 28 25

Gandhinagar city (zone 21)

Base 2001 17 36 38 10

Trend 2021 16 42 34 8

Compaction 2021 15 41 35 9

Dispersal 2021 21 42 29 8

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 accessi-

bility.

9.3.1. Mix of socioeconomic groups

SIMPLAN outputs households by SEG for each

zone. For better comprehension, these were amalga-

mated into six sub-regions of the study area. Table 28

shows the proportion of each SEG by the six sub-

regions, 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


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


Table 30

Gini coefficients of mix of socioeconomic groups.

Zone Zone name TR21 CC21 DS21

1 Walled city 0.11 0.02 0.09

2 Vasna-Paldi 0.23 0.25 0.14

3 Navrangpura-Gandhigram 0.06 0.16 0.34

4 Naranpura-Vadaj-Sabarmati 0.11 0.09 0.03

5 Dudheshwar-Madhupura-Girdharnagar 0.15 0.12 0.20

6 Saraspur-Asarwa 0.08 0.09 0.10

7 Naroda-Sardarnagar 0.04 0.01 0.09

8 Bapunagar-Rakhial-KokhraMehmdabad 0.19 0.18 0.18

9 Nikol-Odhav 0.11 0.13 0.13

10 Maninagar-Kankaria 0.31 0.39 0.22

11 Vatva-Badodara 0.09 0.12 0.08

12 Cantonment 0.06 0.21 0.02

13 Bhat-Chiloda-Nabhoi 0.13 0.28 0.08

14 Kathwada-Muthiya 0.07 0.03 0.21

15 Singarva-Vastral-Ramol 0.07 0.07 0.12

16 Aslali-Lambha-Piplaj 0.10 0.09 0.33

17 Sharkej-Gyaspur-Okaf 0.33 0.30 0.68

18 Thaltej-Vastrapur-Vejalpur-Makarba-Ambli-Shilaj 0.18 0.17 0.20

19 Sola-Gota-Chandlodia-Ghatlodia-Ranip 0.25 0.27 0.34

20 Adalaj-Chandkheda-Kali-Motera-Zundal-Khoraj 0.06 0.07 0.04

21 Gandhinagar City 0.28 0.25 0.33

Sum of Gini coefficients 3.02 3.30 3.95

Variations by inner and outer zones

Inner zones total (weighted) 1.48 1.57 1.60

Outer zones total (weighted) 1.54 1.73 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.

Gi ¼ 1�X

mðxm � xm�1Þðym

i þ ym�1i Þ (20)

where Gi is the Gini coefficient for zone i; x is the

cumulative proportion of SEG type m in the study area;

ymi 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 coeffi-

cients 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 sub-

optimal 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.


Table 31

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

SEG % of householdsa Compaction 2021 Dispersal 2021

SEG1 10.2 �38.01 50.24

SEG2 25.3 �16.30 9.88

SEG3 38.5 12.12 �15.16

SEG3 26.0 12.81 �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

Fig. 41. Distribution of econom

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 Ahme-

dabad, 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

ic benefits in housing rent.


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 acces-

sibility shapes land use’, provides a very useful

definition of accessibility as ‘the potential of opportu-

nities 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 accessi-

bility. 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 employ-

ment 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-

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:

Ai ¼X

jW j f ðci jÞ (21)

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 mea-

sure denotes how accessible employment is for the

workforce resident in zone i and can be measured by

suitably modifying Eq. (21) as follows:

JAi ¼X

jE jexpð�bci jÞ (22)

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 under-

standing 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:

WA j ¼X

iRiexpð�bci jÞ (23)

where WAj is the accessibility to workforce for employ-

ers 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 accessi-

bility 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


Table 32

Accessibility indexes (index numbers).

Sub-region Trend 2021 Compaction 2021 Dispersal 2021

Inner zones 100 121 95

Outer zones 100 97 155

Overall 100 117 104

Eq. (24)), for both workers and employers, is identical.

JA ¼X

iJAi

RiPiRi

� �or WA

¼X

jWA j

E jPjE j

!(24)

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,

Fig. 42. Accessibility

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

indexes by zone.


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

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 accessi-

bility, 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

Table 33

Public transport quality score.

Item Base 2

Average PTQS 2.70

Percentage change (TR vs. BS and Alts vs. TR) (%) –

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.

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

001 TR21 CC21 DS21

3.72 4.79 3.57

38 29 �4

B.

Ad

hva

ryu/P

rog

ressin

Pla

nn

ing

73

(20

10

)1

13–2

07

18

4

Table 34

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

Items TR21 ED63-37 CC21 variations (with same employment,


DS21 variations (with same employment,

but different dwellings))

CC21 Diff emp.

and dwellings

DS21 Diff emp.

and dwellings

CC D80-20 CC D90-10 CC D100-0 DS D20-80 DS D10-90 DS D0-100 CC ED92-08 DS ED22-78

a c d e f g h i j k

Economic indicators

Benefits (vs. trend) [billion Rs/year]

DCS in housing rent – �1.220 �0.353 �0.769 0.526 0.396 0.125 0.320 0.587

DPS in housing rent – 0.093 0.151 0.196 �0.058 �0.096 �0.137 0.023 0.047

DCS in transport – 1.335 1.038 0.863 3.259 3.639 3.997 1.053 2.576

Benefits total (A) – 0.208 0.836 0.290 3.728 3.939 3.986 1.396 3.210

Costs (vs. trend) [billion Rs/year]

Public transport – 0.564 0.564 0.564 0.000 0.000 0.000 0.564 0.564

New roads and capacity augmentation – �0.275 �0.275 �0.275 0.289 0.289 0.289 �0.275 0.289

Costs total (B) – 0.289 0.289 0.289 0.289 0.289 0.289 0.289 0.852

Net benefits (A–B) – �0.081 0.547 0.002 3.439 3.650 3.697 1.108 2.358

Resources and environment

Residential land for new development

(ha/year)

384 300 285 275 425 419 415 284 418

% Change (vs. trend) – �22 �26 �28 11 9 8 �26 9

Annual CO2 emission (thousand tons) 185 154 150 148 237 245 254 154 197

% Change (vs. trend) – �17 �19 �20 28 32 37 �17 6

Social indicators

Social equityPsquared deviations – 53 78 122 47 100 196 6 35

SEG mix: sum of Gini coefficients

Inner zones 1.48 1.53 1.57 1.63 1.58 1.60 1.61 1.72 1.36

Outer zones 1.54 1.57 1.73 1.92 2.28 2.34 2.39 1.53 1.96

Overall 3.02 3.10 3.30 3.56 3.86 3.95 4.00 3.24 3.32

Accessibility indexes

Inner zones 100.0 118.2 120.6 121.9 97.7 95.2 92.3 135.2 81.7

Outer zones 100.0 102.5 97.4 91.6 147.2 155.1 163.1 69.4 204.6

Overall 100.0 115.9 117.3 117.5 104.9 103.9 102.5 125.6 99.5

Public transport quality score 3.72 4.77 4.79 4.80 3.59 3.57 3.54 4.82 3.57

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

B.

Ad

hva

ryu/P

rog

ressin

Pla

nn

ing

73

(20

10

)1

13–2

07

18

5

Table 35

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

Items Real income increase scenario No real income increase scenario % Change

TR21 CC21 DS21 TR21 CC21 DS21 No real income increase vs. real

income increase

TR21 CC21 DS21

Economic indicator: benefitsa [billion Rs/month]

DCS in housing rent (vs. trend) – �0.353 0.396 – �0.365 0.474 – 3% 20%

DPS in housing rent (vs. trend) – 0.151 �0.096 – 0.128 �0.088 – �15% �8%

DCS in transport (vs. trend) – 1.038 3.639 – 1.023 3.617 – �1% �1%

Benefits total – 0.836 3.939 – 0.786 4.002 – �6% 2%

Environmental indicators

Residential land for new development (ha/year) No change as supply is same across alternative policies for both scenarios

Annual Co2 emission (thousand tons) 185 150 245 190 153 252 3% 2% 3%

Social indicators

Social equityPsquared deviations – 78 100 – 59 73 – �24% �27%

SEG mix: sum of Gini coefficients

Inner zones 1.48 1.57 1.60 1.53 1.60 1.63 2.9% 2.1% 1.9%

Outer zones 1.54 1.73 2.34 1.71 1.84 2.39 11.2% 6.3% 2.0%

Overall 3.02 3.30 3.95 3.24 3.44 4.02 7.2% 4.3% 2.0%

Accessibility indexes

Inner zones 100.0 120.6 95.2 100.0 121.2 94.8 – 0.5% �0.4%

Outer zones 100.0 97.4 155.1 100.0 96.9 155.6 – �0.6% 0.3%

Overall 100.0 117.3 103.9 100.0 117.6 103.8 – 0.3% �0.1%

Public transport quality score 3.72 4.79 3.57 3.71 4.78 3.56 – �0.1% �0.2%

a Costs remain the same in both income scenarios.


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 ED92-

08) 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 them-

selves 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


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, agricul-

tural 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

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 compen-

sating 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 short-

comings of dispersal policy by travel demand manage-

ment 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 demon-

strates 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


local government planners and decision makers

responsible for preparation of the DP. A local quasi-

government 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 presenta-

tions and meetings were held, as listed below.

Presentations and meetings with government plan-

ners and decision makers.

1. Presentation to staff of Ahmedabad Municipal

Corporation, Ahmedabad Urban Development Au-

thority, and Gandhinagar Urban Development Cor-

poration, and other governmental organisations

involved in DP making (2 August, Ahmedabad).

2. Meeting with Dr J.G. Pandya, Manager, Bhaskar-

acharya Institute for Space Applications and Geo-

Informatics (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).

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. Practi-

tioners were of the opinion that it is imperative for urban

local government authorities to impart more analytical

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.

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.

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 accompa-

nying 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 develop-

ment. 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.


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.

5. 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).

6. 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.

7. 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.

8. 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

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 aggre-

gated 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 Devel-

opment 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.


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 sec-

ondly, 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

Table 36

SIMPLAN simplifications limitations, and possible solutions.

S. No. Simplification Limitations and/or ca

1 Lower level of spatial disaggregation of

zones (than Census wards).

More aggregation of

dwellings, and plann

interpreting results w

smaller areas with th

coarser.

2 Modelling of only residential location

(employment location modelling not

carried out but is given as external

inputs to the model).

Only journey to work

(other trips such as e

shopping, and social/

modelled). Therefore

be used to represent

e.g., estimates of CO

with regard to only w

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).

Calculations of consu

transport by mode ar

4 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.

Network congestion

and hence cannot be

residential location m

CO2 emissions canno

local level.

5 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.

Economic vitality (u

by the mix of jobs b

social implications c

estimated.

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 spread-

sheet-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 resi-

dential 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,

veats for use of outputs Possible solutions

employment,

ing inputs, hence

ith regard to

e model would be

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.

trips are output

ducation,

recreation are not

, outputs cannot

the entire system,

2 emissions are

ork trips.

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.

mer surplus in

e indicative.

It is fairly easy to recalibrate the

modal split model if such data

are available.

is not modelled

fed back into the

odel. Estimates of

t be made at a

Commercially available transport

models with network assignment

capability could be easily dovetailed

with SIMPLAN.

sually measured

y sector) and its

annot be

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.


comparison. However, for comparing alternative poli-

cies 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 non-

work 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-con-

suming 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

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 auto-

mobiles; 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 blue-

collar 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 cut-

off 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


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) concentrat-

ing 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 external-

ities, 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


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 posi-

tively 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 consump-

tion 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 Brown-

stone 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

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 policy-

making. In this study, such an approach was demon-

strated 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 spreadsheet-

based 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


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 commer-

cially available transport network modelling software,

the reliability of outputs from testing transport policies

could be enhanced.

11.3. On the context of developing countries

Scholarly literature on urban development and

planning in developing countries indicates that dis-

aggregated 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 quanti-

tative 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

interaction tradition (e.g., Lowry, etc.) and micro-

economic theory of demand–supply (e.g., MEPLAN,

etc.) to developing countries with data availability

constraints.

3. Applying the classical theories of spatial organisa-

tion 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.

4. 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.

5. 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.

6. 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


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 communica-

tion technology (such as the internet, mobile wi-fi,

videoconferencing, etc.) has an impact on travel

behaviour, is gathering research momentum. It is not


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 cause-

and-effect relationships between them. Findings based

on a more recent study (Frank, Saelens, Powell, &

Chapman, 2007) that used 2000 samples in neighbour-

hoods 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 inte-

grating modelling techniques from new economic

geography (usually associated with Paul Krugman,

Anthony Venables, Masahisa Fujita, Jacques-

Francois Thisse, amongst others; see Mikkelsen,

2004, and Lafourcade & Thisse, 2008). If employ-

ment 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


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 motorisa-

tion 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-


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 user-

interface, 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. Practi-

tioners 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, Depart-

ment 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

B.

Ad

hva

ryu/P

rog

ressin

Pla

nn

ing

73

(20

10

)1

13–2

07

19

9

Table A1

Employment inputs—2001 and 2021.

Zone Zone name PTQS Base

2001

Trend 2021

(LBGC, 2001)

not useda

Trend

policy 2021

Compaction and dispersal

policies (with same employment)

Compaction

with

different

employment

Dispersal

with

different

employment

BS01 TR21; DS21 CC21 CC

80-20

CC

90-10

CC

100-0

DS

20-80

DS

10-90

DS

0-100

CC 92-08 DS 22-78

1 Walled city 4 5 6 211,805 272,829 296,477 296,477 313,478 236,965

2 Vasna-Paldi 4 5 6 49,169 52,344 65,109 65,109 72,842 54,900

3 Navrangpura-Gandhigram 3 4 5 129,851 133,660 162,515 162,515 181,924 141,871

4 Naranpura-Vadaj-Sabarmati 3 4 5 92,779 98,684 117,185 117,185 129,841 101,556

5 Dudheshwar-Madhupura-

Girdharnagar

2 3 4 75,891 79,592 90,806 90,806 100,225 81,339

6 Saraspur-Asarwa 2 3 4 124,326 131,074 149,112 149,112 164,342 133,374

7 Naroda-Sardarnagar 4 5 6 66,315 71,861 88,202 88,202 98,304 74,310

8 Bapunagar-Rakhial-

KokhraMehmdabad

3 4 5 209,700 219,251 263,422 263,422 293,910 229,883

9 Nikol-Odhav 4 5 6 46,597 49,495 61,631 61,631 69,079 52,218

10 Maninagar-Kankaria 4 5 6 93,205 97,321 122,680 122,680 137,890 104,234

11 Vatva-Badodara 2 3 4 100,730 108,845 121,743 121,743 133,162 108,070

12 Cantonment 2 3 4 13,862 15,794 15,774 15,774 15,775 15,298

13 Bhat-Chiloda-Nabhoi 1 2 3 7,532 16,471 11,562 11,562 8,380 15,160

14 Kathwada-Muthiya 1 2 3 6,480 16,008 10,588 10,588 7,209 13,042

15 Singarva-Vastral-Ramol 1 2 3 13,894 35,649 23,165 23,165 15,457 27,963

16 Aslali-Lambha-Piplaj 2 3 4 10,523 26,136 17,876 17,876 12,097 26,687

17 Sharkej-Gyaspur-Okaf 3 4 5 15,086 42,224 28,195 28,195 17,904 46,157

18 Thaltej-Vastrapur-Vejalpur-

Makarba-Ambli-Shilaj

2 3 4 78,538 182,128 129,192 129,192 90,270 198,543

19 Sola-Gota-Chandlodia-

Ghatlodia-Ranip

2 3 4 57,702 165,071 106,304 106,304 66,874 147,086

20 Adalaj-Chandkheda-

Kali-Motera-

Zundal-Khoraj

1 2 3 26,534 75,550 46,849 46,849 29,518 53,401

21 Gandhinagar City 2 3 4 69,548 148,444 110,047 110,047 79,954 176,378

Total 1,500,068 2,038,434 2,038,434 2,038,434 2,038,434 2,038,434

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

B.

Ad

hva

ryu/P

rog

ressin

Pla

nn

ing

73

(20

10

)1

13–2

07

20

0

Table B1

Dwelling inputs—2001 and 2021.

Zone Zone name Base (total

dwellings)

Dwelling increments 2001–2021

Trend policy Compaction variations (with same

employment)

Dispersal variations

(with same employment)

Compaction with

different

employment

Dispersal with

different

employment

CC80-20 CC90-10 CC100-0 DS20-80 DS10-90 DS0-100 CC92-08 DS22-78

1 Walled city 68,140 3,192 10,871 15,288 10,000 1,000 1,000 0 10,000 2,000

2 Vasna-Paldi 37,262 18,205 20,773 22,250 24,211 3,042 1,498 0 22,889 3,486

3 Navrangpura-Gandhigram 25,947 7,979 14,755 18,653 23,828 9,698 4,777 0 20,347 5,000

4 Naranpura-Vadaj-Sabarmati 75,977 32,876 38,261 41,359 45,471 7,503 3,696 0 42,616 8,823

5 Dudheshwar-Madhupura-

Girdharnagar

36,944 3,482 4,770 5,511 6,495 1,846 909 0 5,803 2,243

6 Saraspur-Asarwa 108,414 12,809 15,889 17,662 20,014 3,828 1,885 0 18,351 4,646

7 Naroda-Sardarnagar 37,962 22,162 27,507 30,581 34,662 5,674 2,795 0 31,885 6,486

8 Bapunagar-Rakhial-

KokhraMehmdabad

103,567 11,682 18,898 23,049 28,559 7,112 3,503 0 24,824 8,421

9 Nikol-Odhav 45,909 8,585 9,592 10,172 10,941 962 474 0 10,428 1,106

10 Maninagar-Kankaria 64,149 27,559 33,979 37,673 42,576 6,709 3,305 0 39,339 7,734

11 Vatva-Badodara 74,139 64,715 77,151 84,305 93,801 20,738 10,214 0 86,920 24,977

12 Cantonment 2,840 1,085 100 100 0 1,000 1,000 1,000 78 1,000

13 Bhat-Chiloda-Nabhoi 2,096 1,567 746 365 0 5,000 5,000 5,000 308 5,000

14 Kathwada-Muthiya 10,909 5,314 605 292 0 10,000 10,000 10,000 235 10,000

15 Singarva-Vastral-Ramol 17,379 6,301 1,180 584 0 12,000 12,000 12,000 449 12,000

16 Aslali-Lambha-Piplaj 8,943 8,726 2,328 1,161 0 15,000 15,000 15,000 898 15,000

17 Sharkej-Gyaspur-Okaf 9,188 6,835 3,297 1,646 0 12,000 12,000 12,000 1,193 12,000

18 Thaltej-Vastrapur-Vejalpur-

Makarba-Ambli-Shilaj

56,314 33,004 20,108 10,048 0 111,579 126,209 140,839 7,996 113,939

19 Sola-Gota-Chandlodia-

Ghatlodia-Ranip

58,833 9,128 3,185 1,590 0 25,464 31,124 36,783 1,140 22,434

20 Adalaj-Chandkheda-

Kali-Motera-Zundal-Khoraj

27,221 15,954 5,083 2,539 0 27,689 33,485 39,280 1,825 17,863

21 Gandhinagar City 94,187 39,398 31,481 15,732 0 52,714 60,685 68,656 13,033 56,399

Total 966,323 340,558 340,558 340,558 340,558 340,558 340,558 340,558 340,558 340,558


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

Table C1

Private automobile (PA) speeds (base values in km/h and alternative polici

Zone Zone name B

1 Walled city 1

2 Vasna-Paldi 1

3 Navrangpura-Gandhigram 1

4 Naranpura-Vadaj-Sabarmati 1

5 Dudheshwar-Madhupura-Girdharnagar 1

6 Saraspur-Asarwa 1

7 Naroda-Sardarnagar 1

8 Bapunagar-Rakhial-KokhraMehmdabad 1

9 Nikol-Odhav 1

10 Maninagar-Kankaria 1

11 Vatva-Badodara 1

12 Cantonment 1

13 Bhat-Chiloda-Nabhoi 1

14 Kathwada-Muthiya 1

15 Singarva-Vastral-Ramol 1

16 Aslali-Lambha-Piplaj 1

17 Sharkej-Gyaspur-Okaf 1

18 Thaltej-Vastrapur-Vejalpur-Makarba-Ambli-Shilaj 1

19 Sola-Gota-Chandlodia-Ghatlodia-Ranip 1

20 Adalaj-Chandkheda-Kali-Motera-Zundal-Khoraj 1

21 Gandhinagar City 2

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

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:

r ¼P

ndiwi

2=3ffiffiffiffiffiffiffiffiffiA=p

p or r ¼P

ndiwi

2=3r(A1)

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.

es in % change over base).

ase 2001a TR21 (%) CC21 (%) DS21 (%)

0.0 �10.0 �15.0 2.5

5.5 �5.0 �10.0 10.0

5.5 �5.0 �10.0 10.0

5.5 �5.0 �10.0 10.0

2.0 �5.0 �10.0 10.0

2.0 �5.0 �10.0 10.0

2.0 �5.0 �10.0 10.0

2.0 �5.0 �10.0 10.0

2.0 �5.0 �10.0 10.0

2.0 �5.0 �10.0 10.0

2.0 �5.0 �10.0 10.0

5.0 0.0 0.0 0.0

8.0 2.5 0.0 25.0

8.0 2.5 0.0 25.0

8.0 2.5 0.0 25.0

8.0 2.5 0.0 25.0

8.0 2.5 0.0 25.0

8.0 2.5 0.0 25.0

8.0 2.5 0.0 25.0

8.0 2.5 0.0 25.0

0.0 2.5 0.0 10.0


Table C2

Public transport speeds.

Base year 2001 Horizon year 2021

Code Base Code TR21 (%) CC21 (%) DS21 (%)

1 = Good PT 85% of PA 1 = Exclusive BRTS 20 50 20

2 = Moderate PT 80% of PA 2 = Normal BRTS 10 35 10

3 = Poor PT 75% of PA 3 = Ordinary bus 5 15 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 85% of PA 1 = Walled city �10.0 �7.5 �5.0

2 = AMC 60% of PA 2 = AMC �5.0 �2.5 2.5

3 = AUC-AMC 50% of PA 3 = AUC-AMC 2.5 0.0 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

Table C4

Vehicle operating and maintenance costs calculations and assumptions.

Item Unit 2001 %

2W Car Bicycle

Life years 7 12 5 1

Average km driven in

vehicle life

km 60,000 100,000 10,000 1

Capital cost Rs 20,000 275,000 600

Salvage valuea Rs 7,519 51,399 298

[a] Capital cost/km Rs/km 0.21 2.24 0.03

Maintenance

and repairs

Rs/year 500 1,000 100

[b] Unit maintenance

cost

Rs/km 0.06 0.12 0.05

Mileage km/l 30 10 0 1

Fuel cost Rs/l 31 31 0

[c] Unit fuel cost Rs/km 1.02 3.06 0

Final unit cost

[a + b + c]

Rs/km 1.29 5.42 0.08

% Share 86 14

Average unit cost

(weighted)

Rs/km 1.86 0.08

Average unit cost

(2001 prices)

Rs/km 1.86 0.08

a Vehicle depreciation per year is assumed to be 15%.

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).

Increase assumed Period 2021

2W Car Bicycle

0 20 years 8 13 6

0 20 years 66,000 110,000 11,000

6 pa 64,143 881,962 1,924

6 pa 24,114 164,845 957

0.61 6.52 0.09

6 pa 1,604 3,207 321

0.19 0.38 0.16

5 20 years 35 15 0

6 pa 98 98 0

2.84 6.54 0

3.64 13.45 0.25

80 20

5.64 0.25

2.13 0.09


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 1 10–12 7 20–22 9 30–32 11

2–4 3 12–14 7 22–24 10 32–34 12

4–6 4 14–16 8 24–26 10 34–36 12

6–8 5 16–18 8 26–28 11 36–38 12

8–10 6 18–20 9 28–30 11 38–40 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:

H ¼X

irir

2i Ai or H ¼

XiPir

2i (A2)

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 to central areas. Hrel is calculated by using

Pi=ðð1=nÞP

iPiÞ 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

centre. Lastly, H (or Hrel

) can be calculated as the

percentage difference between H (or Hrel) for the year

under question and the start year of analysis. If H> 0

then dispersion is likely to occur, and for H< 0,

concentration effects may dominate.

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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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http://www.census.gov/prod/2001pubs/c2kbr01-2.pdf


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).