dynamic modelling of spatial development of ...

DYNAMIC MODELLING OF SPATIAL DEVELOPMENT OF AHMEDABAD Sustainability of Asian Cities: Urban Form and Development

Deepty Jain1, Mark Zuidgeest2, Johannes Flacke3, Talat Munshi4 ABSTRACT Cities are complex systems that comprise of various interacting sub-systems [2]. To be able to take appropriate policies and strategies to attain a desired future it is required to understand the behaviour of the city in question and foresee the changes that are likely to occur. Given the complexity of cities their behaviour is highly unpredictable. Hence it is required to develop tools that can be used to study the impacts of the strategies taken up today on the emerging structure of the city in question. The human’s zeal and the requirements laid by various organizations and institutes have resulted in development of various modelling tools through time to represent spatial behaviour of cities. Ahmedabad city, India, is in the developing stage and is experiencing unseeingly high growth rates demographically, economically and spatially. To set up a model relevant to represent this behaviour the city’s land use is modelled from the year 1983 to 2001 using SL-METRONAMICA. The simulated land use map of 2001 is compared with the actual land use map of 2001. The model is able to represent the behaviour of changing land use patterns of the city. However, disparities lie between the actual and simulated land use map. This can be accounted to the fact that Ahmedabad city represents a heterogeneous structure that this particular model is not able to deal with. The paper in the end discusses the need to develop a model that can simultaneously deal with the other dimensions of city structure like built up density and mix intensity of land uses. 1 INTRODUCTION Cities are centres of trade, commerce, manufacturing, etc. i.e. to put in one phrase they are economic engines. The fast changing economic trends of the world (often named globalization) invites every city to participate in the competition, to provide quality living standards and fetch the benefits from the changing economy. In this scenario every city especially those of developing countries are seeing an unprecedented growth and changes both demographically and spatially. Most of the growth is due to the migration into a city either from rural areas or other urban centres resulting in urbanization. It is expected that in next 20 to 25 years, the rate of urbanization will be highest in the developing countries and Asian & African countries will have the highest number of urban dwellers by then [4]. The continual growth in urban population had so far exerted pressure on the responsible authorities to meet the growing needs of space to perform activities and infrastructure to support them that are beyond their provision and management capacity. In Indian cities, development is controlled by development plans that are revised and developed on interval basis. As discussed earlier, the last decadal growths had been and are going to be unseeingly high and rapid and therefore the growing demand for space is many times not realized. Hence the development plans are either not able to cop up with these growing needs or are prepared on ad-hoc basis without realizing the impacts of the same over the city and its environment resulting in unmanaged, uncontrolled and haphazard development.

1

Deepty Jain, Research Associate, TRIPP, IIT Delhi, Delhi, India.

2

Mark Zuidgeest, Assistant Professor, ITC, Enschede, The Netherlands

3

Johannes Flacke, Assistant Professor, ITC, Enschede, The Netherlands

4

Talat Munshi, Associate Professor, CEPT University, Ahmedabad, India

Cities are dynamic and complex in nature and exhibit self organizing behaviour [5]. This means that cities comprise of various sub-systems that further have sub-sub systems. These systems interact with each other along with the externalities, and change in any sub-system is the result of these interactions [2]. Any changes made in a part or part thereof has thus impact on the all over development of cities. Moreover, cities evolve over time and grow in terms of activities defined as population and jobs adding to the complexity of cities. Hence, development plans, growth, interactions between the subsystems and changing socio-economic profile of citizens have impact over the type of development that a city might see. Plans, policies and strategies are prepared to attain a desired future. Under the set purview it becomes difficult to predict how the city will emerge in future and how the activities will be distributed in space and what will be the effects of the strategies taken up today on the development of the city. Therefore, the promise of attaining the perceived future is very doubtful and once a development takes place it is difficult and costly to reverse the process [6]. To be able to better mange the growing demand for space and have appropriate policies in place it is necessary to be able to perceive the changing structure of cities. Therefore, it is required to model the city and simulate its changing structure that takes into account the complex behaviour in the most simplistic way. This would allow decision makers to know the impacts of the development plans, current development pattern, and individual choices over future developments and hence be able to take appropriate decisions. The paper thus deals in developing such a model for Ahmedabad city. The paper majorly has three sections. First one dealing with different modelling techniques available, second dealing with modelling land use pattern of the city and the last one discussing the simulated results of the model developed. 2 MODELS AND MODELLING TECHNIQUES Models are methods or tools used to represent reality in the simplistic way and yet maintaining its relevance. When these models are run over a period of time with given parameters of constants, factors and constraints to understand the behaviour of a system it is known as simulation [7]. Models can be used to extrapolate; forecast & predict the future. Models and simulations are useful tools to see the implication of our actions taken up today on the future emerging situations. Owing to the need of modelling cities and understanding its complexity, various models have been developed from time to time. Moreover, with the regular enhancement in the computer handling capacities the new age of models are able to represent more complex behaviour of cities. Reliability of the models however depends upon how well the reality is represented. Though, it is not necessary that a complex model is better able to represent the true behaviour of cities. Models are meant to represent reality in the simplest way and hence the inputs, outputs and its structure should be clear to the modellers, decision makers and its users as well. The development of models can be broadly classified into two eras as traditional or aggregated models and new generation of models. Aggregated models as the name suggests use single data and information to represent a group of individuals defined by their location. Some of the aggregated models are Von-Thunen model, Henderson’s model, spatial interaction models, etc. This kind of approach is generally counteracted with the question of ecological fallacy [8] which can be resolved by defining the level of aggregation. Moreover, these models are based on the different type of theories developed about cities that aimed to achieve an equilibrium state for a city which is highly hypothetical. Also, the models are based on the application of single science like political or economical, etc. whereas cities can be better understood by a multi-disciplinary approach. Thus to sum up traditional models had limitations of dealing with a centralised structure, bad at handling dynamics, lack of detail, little usability, no flexibility and realism (cited in [9]). Owing to the limitations of the then developed models, a need had come up to develop models in which the components of city that exhibit different behaviours is also modelled and also integrates various disciplines together that have impact on the organization of the city [10]. However, the behaviour exhibited by each of its components may be different from what the cities represent. As

such in the case of cities the whole is not the sum of its parts. The city rather emerges due to the cofunctioning of the uncoordinated sub-systems. This is what the complexity is all about [11]. In the purview of the limitations laid by the traditional models and the advantages and difficulties that might be there with disaggregated models a new generation of models is developed. These models have been developed to study the patterns that emerge in the city at the plausible disaggregated level. The traditional models have been largely incorporated in the new generation of models as theories of cities. The clear distinction between theories and model states that theory is a statement that explains the process of a system whereas models are an idealised and structured representation of realism or experimental design of theories [7]. These theories are like central place theory, concentric zone theory, multi-nuclei theory, wedge or sector theory, etc. There are two types of modelling techniques developed under this era as cellular automata model and agent based models that use either cells or agents to represent the real world land dynamics. Cellular Automata (CA) models comprise of three components - cells that define one unit of the study area that is lattice, neighbourhood defining local action and transition rules that determine the change of state of cell based on IF-THEN statements. Every cell has a state that defines the stage of development or type of development, etc. and neighbourhood factor computes the potential of every cell to take another state based on the state of other cells in the vicinity. Along with neighbourhood factor other factors like accessibility, suitability, etc. can be taken into account by computing algorithms. “Cellular automata are computable objects existing in time and space whose characteristics usually called states change discretely and uniformly as a function of the states of neighbouring objects, those that are in their immediate vicinity” [2]. Agent Based Modelling (ABM) on the contrary consists of agents where an agent represents a group of individuals or object having similar characteristics and goals that determine their decision making behaviour. Thus an agent based model can have such finite agents in a city that have significantly different behaviours and are important decision makers. Depending on the objectives of modelling the level of disaggregation and type of agents are selected. Cellular automata tools can model the complexities of changing land use at a disaggregated level using comparatively less data and information than agent based models. The major limitation of agent based models also lie in indentifying the group of stakeholders and knowing their decision making behaviour. However a city can be better modelled by using an agent based approach or an integrated cellular automata and agent based approach [12, 13]. Given the limitations of the agent based models the research applies cellular automata tool to model the development of Ahmedabad city. 3 CHANGING STRUCTURE OF AHMEDABAD CITY Table 1 represents an overview of as to how, where and when the major changes in the structure of Ahmedabad city had taken place. Table 1: Ahmedabad in transition; Source: [14‐16] 

Period 1901 – 1930 1931 – 1946 1947 – 1960 1960 – 1980 1980 – 2001

Walled city

East Ahmedabad

Expansion textile mills

West Ahmedabad New residential development

of Further expansion of residential area Commercial Establishment of Gujarat development University Commercial along Ashram road Industrial along Transformation from major roads residential to commercial & densification of residential

Major change Construction of first bridge Two bridges constructed Decline in textile industry Construction of Nehru bridge Land ceiling act

The city’s development can thus be regarded as a result of the growing activities and can be classified as organic, except in the later years, the development has been more of the ribbon type along the major roads. The improvement in transport services provided more and more accessibility to far off areas and hence increase in potential of far off lands for development could be perceived. The eastern areas have continued to attract industrial growth and the walled city emerged as an important commercial hub. Western Ahmedabad has developed into residential areas with emerging commercial centres and mostly as mix land use. The spatial growth of the city has been managed properly by taking up appropriate strategies. Recently, a study using Landsat data and remote sensing techniques has shown that Ahmedabad possess an urban form that has sparse development that are yet compact [17]. There is high edge and patch density with average built up density. This represents a complex city structure where as distance from the city centre increases the complexity and compactness of city increases. 4 SL-METRONAMICA SL-METRONAMICA is a modelling platform developed by the Research Institute of Knowledge Systems (RIKS), The Netherlands to model urban complexities and is based on a cellular automata framework. The model operates at three levels, i.e. local, regional and national. It has two sub-models, i.e. a macro model determining demand of land use for every time step at the regional and national level and a micro-model that simulates land use changes at the local level (Figure 1). Information about projected land use demand served by the macro-model acts as a fuel for the simulation and at every time step the projected Figure 1: Three levels of model: METRONAMICA; Source:   demand is sufficed. The change of land use of any cited in [3] cell is determined by computation of transition potential that considers four factors of neighbourhood, accessibility, suitability and zoning along with randomness perturbation (Figure 2). The transition is calculated by choosing either one of the three pre-defined algorithms or by redefining it. The currently available algorithms allows user to define ranking of the factors, thus to be able to represent realistic behaviour of cities. The rules and parameters here are defined by trial and error method. The model has a well developed user interface that Figure 2: Micro‐model framework: METRONAMICA;  allows users to simultaneously define parameters Source:[1] and rules and see the effects of the same on the outputs. The parameters and rules entered into the model thus can be altered easily during simulation that allows users’ intervention of knowledge related to the city being modelled. This thus allows users to study, understand and explore the behaviour of cities. Outputs of the model at every time step is a new land use map which is based on the algorithm and the new land use map developed in the previous time step. Also the interface of the model allows users to change zoning, suitability and road network maps in the time step when required. The outputs of the simulation can also be retrieved for further analysis in GIS applications. The model is highly flexible in defining the cell sizes, temporal scale, extent of studies and algorithms.

The model has been used both for research purposes and practical applications to study changing land use patterns for different regions like Sri Lanka, The Netherlands, and Australia at different scales. Various models and sub-models are being developed under the same framework to be able to model more complex components of cities- land use transport model, activity base model, etc. 5 MODELLING LAND USE OF AHMEDABAD CITY USING SL-METRONAMICA Ahmedabad city is modelled from the years 1983 to 2001 using the SL-METRONAMICA platform to initially develop a model for the city. METRONAMICA employs a trial and error method and the simulation improves with the continuous intervention of expert’s knowledge thereby resulting in a realistic simulation. Though, to start with arbitrary numbers might take long to adjust all the parameters. It is thus required to have information about the effects of the factors used and their associated parameters in hand by using appropriate mathematical and statistical techniques. The simulation was thus started by using the data driven parameters and improved while adjusting some of them as required. The method thus employs the use of data, knowledge and decision making process of individuals to attain a model representing realistic behaviour of cities. As the city grows through time not all land use do change, nor do all land uses emerge into new land. The existing land use structure of the city is a result of the determining factors that change with time like influenced by the transportation network, zoning, etc. The influence of each factor is determined by explaining the existing dominant land use of the year 2001 with respect to the different factors taken into account in SL-METRONAMICA. The effect of each of the factors can be understood as5.1 Neighbourhood factor The factor represents the attraction or repulsion of each land use to every other land use in its neighbourhood that varies with distance from the cell in question and is determined as the enrichment factor for each land use by every other land use. Enrichment factor defines the occurrence of a land use type in the neighbourhood of a location relative to the occurrence of the land use type in the whole study area [18, 19].The factor thus determines the under or over representation of a land use or an activity in a neighbourhood that may vary with distance. It can be computed by using convolution, spatial filtering, or focal functions. The neighbourhood factor for Ahmedabad can be understood as follows• Residential area is more attracted by availability of a commercial land use and then by the industrial and residential land uses. • A commercial land use is attracted by another commercial in the vicinity but in larger neighbourhood sizes it also searches for an availability of residential land use. 5.2 Accessibility The accessibility parameter here is determined as distance decay effect for every land use function to the nearest available infrastructure that can be ranked as per their relative importance in determining the respective land use function or cell state. Accessibility of Ahmedabad city is computed by studying the number of each land use cell present at varying distance from different transportation network type. The location of the different land uses does not primarly change with the distance form the AMTS bus stops. The probability of every land use to exist at a location decreases significantly with an increasing distance from different transport network types. 5.3 Zoning Zoning is the parameter that defines the authoritative decisions regarding the land use and activities whereas suitability is a combined parameter taking into account physical suitability and other factors that add to the preference for a location for an activity or land use function. A zoning map consist of three periods having a value 0 or 1 for each cell where 0 implies the land use in not allowed in the time period [20]. Zoning plans are prepared for two time periods i.e. first t0 starting from 1983 and another t1 applicable from 1996 (derived from [21] and [22]).

5.4 Suitability The suitability map is a composite measure computed by using various factors. The value of suitability ranges from 0 to 1 where 1 implies the most suitable location. Ahmedabad has a flat terrain and does not have specific features like a coastline, marshy land etc. that may have influence on the development pattern of the city. To determine suitability factor for different land uses accessibility to services like schools, hospitals and CBDs is determined in terms of travel time to the locations using public transport. As the probability of a residential land use to exist at a location decreases with increasing travel time from CBD, schools and health service centres. Though, commercial land use exists near to schools and industrial areas are better explained by the location of hospitals. 5.5 Randomness factor The randomness factor and its influence can be defined explicitly in the model though it depends very much on the robustness of other defined factors. The more robust and crisp are the other influencing factors the more is the randomness required to be taken into account in the simulation and vice-versa. This therefore, introduces the degree of noise defining the deviation from the processed decisions taken like developing a new land far off from the city periphery. These factors together determine the change of land use for every cell or piece of land in the model. The effect of each of the factors discussed above can be determined and understood individually but the complexity increases when these factors are taken up together. Figure 3: Actual dominant land use map 1983

6

SIMULATION RESULTS

A model and the factors used can be judged by analysing how well the simulation and the outputs retrieved represent the reality of changing urban structure. This takes into account the comparison of simulated maps to the actual maps of the period. However, given the complexity of cities and the uncertainities related to its behavior a modeller has to be careful in judging the robustness of a model based on this. The model is successful in representing the realistic distribution of land uses and changes.

Figure 4: Simulated land use map 2001

Simulated land use 2001

Figure 5: Actual land use map 2001

Actual land use 2001

The simulated result is further compared with the actual land use map of 2001 by computing the Kappa coefficient by using the Map Comparison Kit as developed by RIKS for calibrating the results of METRONAMICA model. Kappa statistics compares cell by cell land use of the actual and the simulated land use to judge the accuracy with which the land use has transformed of the simulated land use as compare to the actual land use that varies from 0 to 1. The simulated result is close to the actual land use map with the Kappa coefficient of 0.58. Table 2 represents the accuracy level with which a cell represents the land use after simulation. Table 2: Contingency table: Simulated and actual land use 2001 (Note: Map 1 is the simulated land use of 2001 and Map 2 is the actual land use map of 2001)

Visually comparing, there are some disparities in the simulated and actual land use pattern. For example, in the North West of the city the simulated land use has shown a development along the SG-Highway, while in Eastern Ahmedabad, the existing industrial areas have developed into dominant residential area and new industrial areas have emerged along the major road networks in the eastern periphery. With the outputs in hand there are numerous questions around about the model like-

• •

Is the model successful in representing a realistic behaviour of the city modelled? Are the factors defined satisfactory, what if the new definition of factors is introduced?

•

How will the model behave if different parameters are used instead?

Due to the uncertainities and complexities related to the system being modelled it is difficult to account for the questions. Therefore, modelling a city does not mean making predictions about the city in future rather it is about developing an understanding of how a city might behave and emerge in future under the known influencial factors and conditions. 7 DISCUSSION Indian cities typically have heterogeneous structures rather than a homogenous structure. Mix land use exists at the parcel level i.e. defined as the existence of more than one land use on a land parcel. This is often seen in Ahmedabad the intensity of the mix varies from location to location. Also the built up density varies parcel by parcel and is not just a function that is predefined by laws. The uppermost limit of the density is set by laws and actual existence of low and high density is determined by the city dynamics and individual preferences. This may be accounted to the fact that the formal land use planning in these cities started to be practiced very late. Though this has resulted in development of good city form, as mostly the areas are compactly developed. SL-METRONAMICA can model a single land use layer and considers the existence of single land at the level of disaggregation chosen for the study. The change in land use is a gradual process where the change in density of each of the land use components on the land determines the change in dominance of the land use. Given the heterogeneity of the city of Ahmedabad and comparing the changes that have actually occurred, with the changes that have been simulated, the current model is not the most appropriate tool to model the city. To model urban form, not just land use per cell, an Activity Based Model should be used, as can be done in METRONMICA as well [23]. This is likely to be the most appropriate way to study and understand the changes occurring in the city and use it for further analysis. Though, if some amendments are incorporated to the current model like redefining land use and state in the model it can be used to achieve the set objectives. 8 CONCLUSION The researchers have tried to develop a model relevant to represent the behaviour of Ahmedabad city. In the process, SL-METRONAMICA has been used to represent the changing land use pattern. Given the development pattern of Ahmedabad city and the limitations of SL-METRONAMICA in handling it development of a more complex model is proposed. The model should be able to deal with other dimensions of city structure- mixed density and building density along with land use at disaggregated level. 9 ACKNOWLEDGEMENT The research presented in this paper is part of the research project ‘Land, urban form and the ecological footprint of transport: application of geo-information to measure transport-related urban sustainability in developing countries with a case study of Ahmedabad, India.’, which received a project grant (SP-2006-09) from Volvo Research and Educational Foundations (VREF). The Research Institute for Knowledge Systems (RIKS) in Maastricht, The Netherlands is acknowledged for the provision of their METRONAMICA SL software and for their continuous support on the use of the software as well as on the concepts and ideas of this research.

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