Urban Expansion For Efficient Town Planning

Published Date: 02 Nov 2017

Introspection on the origin of towns and cities has to be traced back to the human evolution about 3 million years back. Our distant ancestors lived a very hard existence as hunters and gatherers until the early civilisations started around 8000 years ago. Sjoberg (1965) notes that the different levels of human organisation characterised by technological, economic, social and political patterns are the factors that aided the origin and evolution of cities before the modern epoch of urbanisation. In the first level, the pre-urban and pre-literate human society was mostly hunters and gatherers with little or no surplus food. Consequently, the society had little or no 10 specialisation of labour or distinction of class. Slowly through the advances in technology and organisational structure, human societies evolved into slightly complex societies through settlements in villages. Humans learnt to cultivate and subsequently communities evolved that could support more people with food as the time progressed. With the knowledge of cultivating plants, lighting fire, inventing wheel, making tools, humans advanced by leaps and bounds. This second level of human organisation is attributed to the knowledge of humans to cultivate thereby creating a surplus of food. This pre-industrial civilised society is also characterised by the art of writing to make inscriptions, maintain and record law, literature, and religious beliefs and the ability to harness energy from wind and water sources for sailing in seas to grinding grains to make use of water power. By around 2500 years BCE (before Common Era) there were towns and cities like Harappa and Mohenjodaro. Within the last 500 years BCE, kingdoms and cities emerged worldwide where most of the civilisations originated mostly in the river valleys. This included the cities of India, Mesopotamia, Egypt, China, etc. During the next one millennium the world saw a host of religions that came up to have a significant impact on human evolution. Meanwhile, cities expanded, kingdoms rose and fell, wars were fought, and the humans learnt to harness the natural resources incessantly and mercilessly. The post -industrial revolution cities was characterised by mass literacy, a fluid class system and the tremendous technological breakthrough to new sources of inanimate energy that sustained the industrial revolution from the third level of complexity in the human organisation (Sjoberg, 1965). The industrial revolution during eighteenth and nineteenth century was seen as a major cause for the current growth and sustenance of towns and cities. The developments during the industrial revolution between 1750 and 1830 CE transformed most of north-western Europe from a largely rural and agrarian population to a town-centric society engaged increasingly in factory manufacture, trade and commerce. The post industrial revolution era also saw the enormous upsurge in people moving from rural to urban settlements. As most of the north-western Europe became industrialised, the faster was the urbanisation. In the more advanced countries, urbanisation seemed as a consequence of industrialisation, although as more of these countries urbanised, industrialisation and technological innovations were also enhanced significantly. A strong positive reinforcing feedback emerged with the industrialisation and urbanisation in the advanced countries alone. Contrastingly enough, the industrialisation and subsequent urbanisation did not catch up in the developing and under-developed countries as in the developed countries until the mid-nineties. Of late, the developing countries have seen tremendous upsurge in their urbanisation and urban population growth rates, especially with the number of cities and urban agglomerations having a population of more than a million has increased significantly over the recent decades. This upsurge has not been solely as a consequence of industrialisation alone in these countries but also due to the falling agriculture produce and other factors in rural areas like livelihood inducing migration of large populations into urban areas in search of better livelihoods (Harris, 2005). In industrialised countries the growth of urban population is comparatively modest as population growth rates are low and over 80 percent of their population already live in urban areas. Conversely, developing countries with higher growth rates are in the middle of a transition. The exceptional growth of many urban agglomerations in many developing countries is the result of a threefold structural change process: the transition away from agricultural employment, high overall population growth, and increasing urbanisation rates (Grubler, 1994). Unlike in developed countries where the problem of sprawl has to be addressed in terms of transport, energy, land-use, and environment, developing countries are faced with additional problems of increasing urban poverty levels, higher population growth rates and rising numbers of slums or squatters resulting out of sprawl. It is in this context that the study on urban sprawl gains importance.

2. Urban Growth, Urbanisation and Urban Sprawl

It is very much apparent that cities had evolved more than a few millenniums ago, while

some grew and perished, urban growth was prevalent and not urbanisation. It is essential to

clearly distinguish from the growth of cities from the thousands of years to the more recent

urbanisation. On the distinction of urbanisation and urban growth, several authors have put

forward their viewpoints. Cautioning that attributing simply the growth of cities to urbanisation, Davis (1965) notes that urbanisation refers to the proportion of the total

population concentrated in urban settlements, or else the rise in this proportion. It is argued

that since urbanisation would account for the total population composed of both urban and

rural, the proportion of urban is a function of both of them. Accordingly, cities can grow

without urbanisation provided the rural population grows at an equal or greater rate. The

transformation of human settlements from a spread-out to compact urban centres is a change

that can be traced but the growth of cities has no inherent limit and so are the boundaries.

Such growth could continue even after everyone was living in cities, as in cities of already

urbanised developed countries, through sheer excess of births over deaths (Davis, 1965).

The process of urbanisation is fairly contributed by rural-urban migration leading to the higher proportional population growth of urban-rural and infrastructure initiatives, resulting in the growth of villages into towns, towns into cities and cities into metros. In developed countries of north-western Europe and North America, urbanisation is already at its peak, with little or no further urbanisation possible, as the scope for rural-urban migration is minimal. Furthermore, it is the urban population per se that grows and not the proportion of

urban to rural population. With the extensive urbanisation followed by industrialisation, the

compact and densely populated cities emerged during the last century. Over the last century,

these countries saw the emergence of large metropolitan cities. What has been intriguing to

the urban planners, researchers, managers and administrators is that in spite of the saturated

and stagnated urbanisation, the cities are continuing to spread (Batty, Besussi, and Chin,

2003). As the cities grew in population, transportation got affected. The affluent aided by

individual transportation, moved towards the outskirts thereby minimising costs at the central business districts, inducing the spread of cities (Marathe, 2001). At times, the city authorities provided better transportation from the core to the outskirts and along the periphery, which encouraged people to move outskirts also inducing sprawl. In other words, be it either better transportation or the population growth, the cities expanded consuming neighbouring agricultural lands and affecting ecologically sensitive habitats. This phenomenon of urban sprawl is being witnessed, studied and documented in several cities of north-western Europe and North America even after reaching the stagnation and saturation levels of urbanisation. The problem of sprawl is now being addressed through extensive studies and policy recommendations in the European Union (Gayda et al., 2005) and United States of America (Transportation Research Board, 2002). In 1800, only 3 percent of the world’s population lived in urban areas. By 1900, almost 14 percent were living in urban centres, and only 12 cities had 1 million or more inhabitants. In 1950, 30 percent of the world’s population resided in urban centres and the number of cities with over 1 million people had grown to 83. The world has experienced unprecedented urban growth in the recent decades. In 2000, about 47 percent of the world’s population lived in urban areas. Now, there are 411 cities over 1 million population. The prevalently developed nations are about 76 percent urban, while 40 percent of residents of less developed countries live in urban areas. However, urbanisation is occurring rapidly in many less developing countries. According to Population Research Bureau (2005), it is expected that 60 percent of the world population will be urban by 2030, and that most urban growth will occur in less developed countries. The implications of sprawl are not only on the surrounding neighbourhood with loss of agricultural lands or ecological habitats but also on the access to basic amenities and

infrastructure like health care, water supply and sanitation, transportation, energy, etc. within

the inner core of the city. As sprawl advances, the city notifies overtime these areas as a part

of the extended city itself, thus it will become the onus of the city administration to cater for

the rising travel demands, water supply and sanitation, energy needs, etc. The magnitude and nature of urban sprawl is quite different in the developed countries than to that of a rapidly developing and largely rural-agrarian populated country like India. The problem of sprawl is magnified in the developed countries after reaching saturation levels of urbanisation. Conversely, most of the developing and under -developed countries are now urbanising rapidly and already prone to the problem of sprawl at an even worse magnitude. A significant difference in the urbanisation patterns of developed and developing countries is that of population densities. The developed countries embraced urbanisation after industrialisation wherein the population growth rates and densities were lower, with a prosperous economy and technology to support. Conversely, developing countries are having high population growth rates and densities, in the midst of economic development, with lack of basic amenities and urbanisation taking place at a rapid rate. In India, already 28 percent of the population live in urban areas and these cities are expanding like never before, with inadequacies in facilities for transportation, water supply and sanitation, energy demands, etc. With a booming economic activity on the one side and large population in unorganised sectors of employment with inadequate housing on the other, rise of slums and squatters in urban areas seems inevitable.

3 Studies on Urban Sprawl

Until 1960s, the problem of urban sprawl was not studied / documented in already urbanised

economically advanced countries. Although, Davis (1962) notes the ‘deconcentration of cities

as they become more urbanised’, this was not formally termed as sprawl then. The Transportation Research Board (TRB) of the United States of America (USA), in one of the

recent and authoritative definitions, ascribe sprawl to exhibit ‘deconcentrated centres’ and

grow in the neighbourhood (Transportation Research Board, 2002). The problem gained

importance only in late 60s and early 70s, mostly in the USA and north-western Europe. And

since then there has been significant research and debates on this topic. Several authors: Batty, Xie, and Sun (1999); Batty, Chin, and Besussi (2002); Torrens and Alberti (2000); and Transportation Research Board (2002) and other organisations have attempted to define ‘sprawl’ since the problem of urban sprawl has been acknowledged for nearly fifty years. Sierra Club (1998) defines sprawl as a low-density development beyond the edge of service and employment, which separates where people live from where they shop, work, recreate, and educate thus requiring cars to move between zones. This definition ascribes sprawl as induced directly by the location of work-home and aided by individual transport ation (cars), and this phenomenon is more prevalent in the United States of America. Batty, Xie, and Sun (1999) consider urban sprawl in relation to the contemporary urban growth consisting of three interrelated problems of spatial dynamics: the decline of central or core cities which usually mark the historical origins of growth; the emergence of edge cities which compete with and complement the functions of the core; and the rapid suburbanisation of the periphery of cities - core and edge - which represent the spatially most extensive indicator of such growth. Further, Torrens and Alberti (2000) note that sprawl is characterised by uniform low-density development, which is often uncoordinated and extends along the fringes of the metropolitan areas invading prime agricultural and resource lands. Also, they indicate that such areas are over reliant on the automobiles for access to resource and community facilities with these areas regarded as aesthetically displeasing. The study of urban sprawl and its implications have been addressed by Transportation Research Board (1998; 2002) and Sierra Club (1998). TRB (Transportation Research Board, 2002) explains sprawl as the spread-out development that consumes significant amounts of natural and man-made resources, including land and public works infrastructure of various types. Sprawl also adds to overall travel costs due to increasing use of the automobile to access work and residence locations more widely spaced due to the sprawl phenomenon. Furthermore, sprawl appears to deconcentrate centres and takes away from the multiplicity of

purpose that neighbourhoods once delivered. Yet sprawl has benefits. It offers access to less-expensive housing and opportunities for homeownership at the periphery of metropolitan

areas. It provides congestion management in automobile-dominated metropolitan areas by

creating the suburban-to-suburban trip and by better equalising the percentages of the commuting population involved in reverse and forward commutes. Ciscel (2001) examined sprawl by quantifying three components: the jobs, business and housing; commuting; and government infrastructure capital costs. He notes that sprawl raises the costs of operating urban infrastructure and hence leads to economic inefficiency. Brueckner (2001) attributes the spatial growth in the USA to: rise in population, rise in incomes and falling commuting costs. It is further argued that urban growth is in response to these fundamental forces and hence urban growth is not socially undesirable. Any market failures distorting the fundamental forces, can lead to improper allocation of land between agricultural and urban uses. Recently Batty, Besussi, and Chin (2003) termed sprawl as ‘uncoordinated growth’,

the unplanned incremental urban growth, which is unsustainable. Noting that sprawl is a consequence of simultaneous population growth and better transportation from the core to

edge, they question it as a typical chicken and egg conundrum of what comes first: better

transportation or population growth; or po pulation growth followed by better transportation?

Obviously, it is a difficult paradigm to ascertain whether it is population growth or transportation that leads to sprawl. In an already urbanised and developed country like England, the sprawl can be thought of chicken or egg problem. However, in the context of a

rapidly urbanising and developing country like India wherein the population is largely agrarian with high urban population growth rates and a booming economic activity, sprawl is

not only a chicken and egg problem but there are a host of factors that are contributing to the

complexity for sprawling towns and cities. Review of various studies concerning urban sprawl indicates that sprawl has been attributed in various contexts, while characterising / quantifying sprawl has always been a contentious issue. Noting the same, Galster et al. (2001) have observed that literature on sprawl is ‘lost in semantic wilderness’, and hence they categorise sprawl as being ascribed under six broad categories:

a) By example that embodies characteristics of sprawl, such as Los Angeles

b) Aesthetic judgement and general development pattern

c) Cause of an externality

d) Consequence or effect independent variables

e) Pattern of development

f) Process of development

Extending Torrens and Alberti (2000)’s notion of urban sprawl, Galster et al. (2001)

define sprawl as a pattern of land-use in an urban agglomeration that exhibits low levels of

some combination of eight distinct dimensions: density, continuity, concentration, clustering, centrality, nuclearity, mixed uses and proximity. Ascribing sprawl as a pattern of land-use alone would not throw light on the underlying processes, causal mechanisms and hence consequences. In a developing country like India, where population density is high with significant urbanisation rates, urban sprawl obviously cannot be characterised by pattern

alone but processes, causes and their consequences. Hence, we suggest a modification to the

definition of urban sprawl as the pattern of outgrowth emergent during the process of urban

spatial expansion over time caused by certain externalities and a consequence of inadequate

regional planning and governance. The sequence of patterns, processes, causes and consequences sets the research agenda in the current thesis. In India, several studies have addressed urbanisation and urban growth in relation to transportation linking energy (Reddy B. S., 2000), land-use (Srinivasan, 2002) and vehicular emissions (Rama Krishna and Reddy 2001; Sikdar, 2001), etc. However, not many studies have addressed the problem of urban sprawl (Behera, et al., 1985; Jothimani, 1997; Lata et al., 2001; Subudhi and Maithani, 2001; Sudhira et al., 2003). Furthermore, there are fewer studies on modelling urban sprawl in India (Subudhi and Maithani, 2001; Sudhira et al., 2004b). Similar to trends in research on urban sprawl in advanced countries, the problem of sprawl has been largely addressed in isolation in India. Of late, there have been concerns of integrative modelling by synchronising different domains concerned with urban issues and making planning more effective for taking policy decisions. For undertaking the public transport planning, Mohan (2001) has attempted to integrate safety, environment and economic issues. Padam and Singh (2001) highlight the need for an urban transport policy in the wake of rapid urbanisation owing to transportation problems, while taking into account of the economic contributions of the urban areas. Gakenheimer (2002) notes that currently land use planning and transportation planning are not synchronous although well undertaken individually, thus leading to significant concerns and problems in urban areas. However, the need for integrative approach linking land-use and transportation planning is now being suggested (Gakenheimer, 2002). Subsequently, as with the studies and definitions on urban sprawl, the metrics and methods to quantify sprawl are still vague. This necessitates arriving at appropriate metrics to address the problem of urban sprawl considering the rates of urbanisation and population densities apart from the spatial extents amassed by urban areas.

Considering the various studies and prevailing conditions of urban fabric in India, it is found that lack of effective urban governance have resulted in unplanned and uncoordinated urban outgrowth. Urban governance requires keeping track of various processes, activities, services and functions of the urban local body, which is possible through an information system. In the absence of any such system, at the basic level, there is a strong and pressing need for an information system to cater to all these. In the next level, it becomes essential to build models based on the information systems involving simulation and analysis for specific urban contexts. The subsequent level involves evolving different strategy and policy options using the models and information systems. Thus, at the outset, there are three essential steps to address the problem of sprawl and to strengthen planning and decision-making, namely,

information systems, models and policies.

i. Metrics of Urban Sprawl

Evolving appropriate measures to quantify urban sprawl is a prerequisite to undertake modelling of urban sprawl dynamics. Often, there is a lack of appropriate indicators and

information concerning the cities or its status, from a holistic perspective that captures not

only the economic aspects but also ecological and socio-economic aspects including livelihood of people. Given the problem of urban sprawl and it’s in adequate understanding to

precisely determine its nature, pattern and rate of growth, there is an urgent need to characterise urban sprawl, more so from the perspective of achieving sustainable urbanisation

in developing countries. Thus, a significant challenge is to understand the processes that

cause such growth, for which, there is a pressing need to identify the appropriate indicators

towards achieving sustainable urbanisation. Torrens and Alberti (2000) note that despite the level of importance given to the problem of sprawl, there remains little understanding of its determinants and its constituents, since sprawl is most often confused with sub-urbanisation. However, some researchers in the recent past have attempted to characterise urban sprawl (Barnes et al., 2001; Hurd et al., 2001; Epstein, Payne, and Kramer, 2002; Sudhira et al., 2004b) using spatial metrics. Essentially, the urban sprawl metrics aid in quantifying the process, monitoring the extent of urban sprawl and also become useful as indicators fo r measuring the implications of policy decisions. Although some of the indicators for achieving sustainable development have been evolved by Meadows (1998), still there is not any broad consensus on the appropriate indices representing all of the factors and disciplines. For managing urban sprawl in north-western European cities, Gayda et al. (2003) have evolved metrics, adopted as indicators to achieve sustainable development. Furthermore, on the lines of sustainable development framework, there also exists quantification of metrics based on the carrying capacity approach. In this case, the carrying capacity of an urban system is evaluated based on the different functions and activities of the urban systems and accordingly a certain threshold for development is set, beyond which it is detrimental to the entire system itself. The concept of carrying capacity has been in news since the seminal work by Meadows et al. (1972) on the notion of ‘Limits to growth’. In India, the NIUA (National Institute of Urban Affairs, 1996) has evolved a framework for the carrying capacity based regional planning. The essence of carrying capacity based approach on the lines of achieving sustainable development lies in the fact that a host of factors (such as assimilative and supportive capacities) are under consideration in the planning processes. Some of the existing works on sprawl ascribe spatial extent of built -up areas derived from remote sensing data or other geospatial data as the measure of sprawl. On the spatial metrics for sprawl, entropy, patchiness and built-up density have been suggested (Yeh and Li, 2001; Sudhira et al. 2004b; Torrens and Alberti, 2000). In addition to this, the percentage of population residing over the built-up area to arrive at population-built-up density was considered metric for sprawl (Gayda et al., 2005; Sudhira et al., 2003). However, it still remains largely unanswered as to how and what are the appropriate metrics or indicators of urban sprawl that are sufficient to represent the process of sprawl. Although some attempts are made to capture sprawl in its spatial dimensions, still they fail to capture sprawl in other dimensions (like travel times, pollution, resource usage, etc.) and neither do they indicate their intensity (density metrics). It is thus imperative for research to address intensity of sprawl through appropriate metrics or indicators for effective regional planning.

ii. Capturing the Dynamics of Urban Sprawl

a. Approaches to Model the Dynamics of Urban Sprawl

The urban sprawl phenomenon is very dynamic in nature. Although it is often considered

endemic, the phenomenon has impacts on the structure and growth of any city or town. Development of suburbs because of increased population growth and infrastructure facilities

around cities is a well-established reasoning for urban sprawl. Several approaches and methods originating from the disciplines of urban planning, engineering, management, geography and artificial intelligence have been used for modelling urban systems. The key

approaches include operations research (OR) methods, system dynamics (SD) framework,

geospatial modelling using the tools of GIS and more recently the use of agent -based models

in conjunction with geospatial models to capture the dynamics and modelling of urban sprawl. A review of different OR methods were done by (Catanese, 1972). Among the

predominantly used methods for operational planning and decision-making are probabilistic

models, optimisation techniques, linear, non-linear, dynamic and stochastic programming

methods. More recently, simulation tools are being used extensively to capture and emulate

urban system and its dynamics. These simulations are based on the concepts of discrete-event

system simulation approaches. With the emergence of multi-agent systems from artificial

intelligence domain, these are now being used to aid in simulation of urban systems. The SD

framework captures the system based on complexity involving dynamic relations represented

by stocks and flows determined by various activity volumes in the city, which were synthesised from casual knowledge and observations. Although OR approaches and SD framework have been applied quite rigorously in urban systems, but in the recent times, geospatial modelling aided by visualisation has been ver y effective. The origins of GIS date

back to the late 1960s with the creation of a spatial database for urban areas. Mapping urban

sprawl provides a "picture" of the location and extent of growth that helps to identify the

environmental and natural resources threatened by such sprawls. Analysing the sprawl over a

period of time will help in understanding the nature and growth of this phenomenon, which

helps to suggest the likely future directions and patterns of growth. Availability of spatio temporal data with GIS are very useful to study sprawl. The spatial patterns of urban sprawl

on temporal scale can be analysed and monitored using the remotely sensed satellite imageries. They can be used in identifying urban growth pattern from spatial and temporal

data. These help in delineating the growth patterns of urban sprawl such as linear growth and

radial growth patterns.

b. Modelling Urban Sprawl

Modelling urban sprawl dynamics has closely followed traditional urban growth modelling

approaches. Subsequently, with the need to manage urban sprawl, modelling urban sprawl by

relating to nature of growth and its implications has been undertaken since sixties. Urban

development models were developed much earlier, however modelling dynamics of urban

sprawl has been undertaken only recently (Batty, Xie, and Sun, 1999; Torrens and Alberti,

2000). The key initial studies in the developed countries on urban growth and urban development models (Lowry, 1967, In: Batty and Torrens, 2001; Helly, 1975; Allen and

Sanglier, 1979; and Pumain et al., 1986). Most of these studies followed the traditional

approaches of urban model building. The traditional approach of model building involved

linking independent to dependent variables, which were statistically significant, additive as in a linear model or a non-linear model but tractable in a mathematical way. However, these

models were used mostly for policy purposes, but they could not be useful when processes

involved rule-based systems, which in practice cannot be tractable mathemat ical operations

(Benenson and Torrens, Geosimulation: Automata-based modeling of urban phenomena,

2004). Among the path-breaking models developed to capture urban systems, Forrester (1969) attempted to model urban dynamics based on complexity involving dyn amic relations represented by stocks and flows that determined the various activity volumes in the city, which were synthesised from knowledge and observation of causal factors. A key distinction of this model was its ability to represent emergent behaviour of the system originating out of complexity. However, this model could not be represented spatially. Batty et al. (1999) provided spatially aggregate model for the urban sprawl phenomenon. Cheng and Masser (2003) report spatial logistic regression techniques for analysing urban growth pattern, which was applied for a city in China. This study also includes extensive exploratory data analyses considering the causal factors. Later, Sudhira et al. (2004b) attempted modelling urban sprawl in a non-spatial domain. In an interesting analysis on regional industrialisation in a province in China, Huang and Leung (2002) have employed geographically weighted regression to identify spatial interaction between level of regional industrialisation and various factors affecting industrialisation. It is argued that conventional regression analysis would only produce the ‘average’ and ‘global’ parameter estimates, which vary over space depending on the respective spatial systems. Thus, they suggest using the geographic weighted regression technique for analysing spatial non-stationarity of different factors affecting regional industrialisation. Allen et al. (1986), Couclelis (1987) and Engelen (1988) assert modelling urban systems as complex systems, while acknowledging the self-organisation in urban systems. Capturing urban systems as discrete models gained further momentum with the popularity of the cellular automata (CA) based techniques. Ulam developed CA in the 1940s, and it was later used by von Neumann to investigate the logical nature of self-reproducible systems (White and Engelen, 1993; Li and Yeh, 2000) and extensive experiments were done by Wolfram (2002). The most pioneering work in simulating urban growth using CA was done by Couclelis (1987) and Batty and Xie (1994). Now, most models of spatial dynamics rests with land cover and land-use change studies (Yang and Lo, 2003), urban growth models (Batty, 1998; Batty and Xie, 1994; Clarke and Gaydos, 1998; Clarke, Hoppen, and Gaydos, 1996; Couclelis, 1997; Jianquan and Masser, 2002; White and Engelen, 1993; White and Engelen, 1997) and in urban simulation (Li and Yeh, 2000; Torrens and O’Sullivan, 2001; Torrens, 2000; Waddell, 2002). Urban growth modelling considering the spatial and temporal analyses of land-use / land cover changes like LUCAS (Land Use Change Analysis System) model (Berry, Flamm, Hazen, and MacIntyre, 1996), GIGALOPOLIS (Clarke, Hoppen, and Gaydos, 1996), and California Urban Futures (CUF-II) model (Landis and Zhang, 1997). Li and Yeh (2000) develop and demonstrate the constrained CA model for sustainable urban development modelling. Some of these models conceptualise the causal factors, such as the

availability of land and proximity to city centres and highway, driving the sprawl. CA has been used for simulating urban growth quite successfully, mostly considering various driving forces that are responsible for sprawl. However, some issues like the impact on ecology, energy, environment and economy for taking policy decisions have not been addressed effectively. To counter the shortcomings of CA, different approaches are being suggested. Among them is the integration of agent -based models and CA models, where agent-based models are used to capture the externalities driving the processes. Models developed using CA and agent-based models would prove beneficial to pinpoint where sprawl takes place, which would help in effective visualisation and understanding of the impacts of urban sprawl. Further to achieve an efficient simulation of urban sprawl, modelling has to be attempted in both spatial and non-spatial domains. Modelling urban sprawl in non-spatial domain is mainly done by the application of statistical techniques while CA models and agent -based modelling are known to complement modelling in the spatial domain. For achieving the integration of CA and agent -based models to simulate urban sprawl phenomenon, Benenson and Torrens (2004) have evolved the Geographic Automata Systems (GAS) framework, while Sudhira et al. (2005) have developed the Dynamic Geo-Spatial Simulation (DGSS) framework. Although research in geospatial modelling has matured towards arriving at simulation frameworks, this is yet to be graduated into an effective spatial planning support system.

iii. The Integrated Spatial Planning Support System

For effectively managing, testing of different hypothesis, building and visualising scenarios, it is imperative to have a robust Spatial Planning Support Systems (SPSS) for addressing the

problem of urban sprawl. An ideal SPSS would not only aid in managing but also in planning, organising, coordinating, monitoring and evaluation of the system in question. These systems include instruments relating to geoinformation technology that have been primarily developed to support different aspects of the planning process, including problem

diagnosis, data collection, mining and extraction, spatial and temporal analysis, data modelling, visualisation and display, scenario-building and projection, plan formulation and

evaluation, report preparation, enhanced participation and collaborative decision-making

(Geertman and Stillwell, 2004). Integration of different processes associated with the dynamics of sprawl phenomenon is required for addressing the problem of urban sprawl.

Moreover, a key challenge for technology is to facilitate collaborative decision-making for

evaluating different policy options through participatory simulations by different stakeholders. Most of the existing simulation framework allows simulations only on stand-alone systems, wherein each stakeholder has to choose / decide the options on the same system / platform. This would suggest that all the stakeholders have to meet physically to evaluate and decide. Moreover, such initiatives are not normal and very difficult to moderate. In this context, it becomes necessary for a distributed simulation framework to support SPSS, so that all the stakeholders and managers / administrators are able to interact, organise, plan, evaluate and decide through a network. Then the challenges are twofold: one, to integrate different models that are required to carry out the simulations and secondly, to synchronise the model’s inputs, feedbacks and outputs over space and time. Currently, there are a few popular frameworks that try to emulate SPSS with an objective to make planning interactive and participatory. Among such existing SPSS are What-If? (Klosterman, 1999), RAMCO (Uljee, Engelen, and White, 1999) etc. What-If? (Klosterman, 1999) is an interactive GIS-based planning support system that responds directly to both achieving the ideals of communicative rationality and traditional comprehensive land-use planning. It uses geographic data sets to support community-based efforts to evaluate the likely implications of alternative public policy choices. The package can be customised to a community’s existing geographic data, concerns, and desires that provides outputs in easy to understand maps and reports which can be used to support community-based collaborative planning efforts. The system requires that, given a set of factors and factor weights for determining the suitability, projections for future land -use and subsequent allocation can be based on user requirements. Although this system is claimed to be interactive, the dynamics of the factors and hence their interactions are less captured with only a final land-use scenario obtained as output, which does not support a distributed (simulation) framework. The RAMCO (Rapid Assessment for Management of COastal zones) is a prototype information system for regional planning in a generic decision support environment for the management of coastal zones through the rapid assessment of problems (Uljee, Engelen, and White, 1999). The system was developed integrating GIS, CA and system dynamics. Subsequently, White and Engelen (2000), the developers of RAMCO, also supported the integration of GIS, CA and system dynamics with the usage of multi-agent systems for a high-resolution integrated modelling of spatial dynamics of urban and regional systems. This has currently set the standard of technology that can be used for achieving an integrated spatial planning support system. However, this also does not support a distributed framework. UrbanSim and OBEUS are two other established frameworks and supporting packages for integrated modelling of urban systems. UrbanSim is implemented as a set of packages under Open Platform for Urban Simulation (OPUS) (Center for Urban Simulation and Policy Analysis, 2006). This is fairly comprehensive in the sense that the framework integrates land-use, transportation, economic, demographics and environmental variables. However, this framework does not support participatory simulations. The OBEUS (Object-Based Environment for Urban Systems) is more robust and is an emerging trend to integrate various processes as agent-based models to simulate them spatially and hence it is termed as geosimulation (Benenson and Torrens, 2004). The notion of GAS (Geographic Automata Systems), formalising the fusion of agent -based models and cellular automata models in a spatial framework is demonstrated here. However, again the key drawback here is that this does not support participatory simulations. Also, if one may wish to consider each agent -based model as an individual discrete-event simulation model, then OBEUS addresses it using synchronous or asynchronous updating. It may well be a good frame of reference to build a distributed simulation framework for enabling participatory decision-making possible. Typically the planning machinery and administrators are less equipped to address the issues of sprawl. Concentrated economic developmental activities in a few localities have implications of rural-urban migrations that lead to skewed growth. The city planning is mostly addressed at catering to the future projected population and the facilities that the civic authorities need to cater for that forecast of population, which are normally static master plans or development plans. These plans are also less equipped to review and evaluate any policy decisions dynamically so as to visualise the potential implications of a policy directive and also the regions of potential sprawl. It is in this context that the planning machinery and administrators need to be informed of the possible areas of sprawl to take corrective actions to mitigate the implications. In this regard, the present thesis attempts to contribute towards a deeper understanding of the urban sprawl phenomenon, capturing the dynamics, modelling it and designing a spatial planning support system to visualise, review and evaluate the various policy options so as to have effective methods and tools to mitigate the problem of sprawl.