Urban growth Prediction: Historical and current perspectives in Urban growth modelling
Most of us in the general population perceive cities as systems, places that cradle with human population, combination of thoughts, accumulation of jobs and economic stimulus. Moreover, for more than half of the population in the planet , it is the place where we live.
Since, the start of the industrial revolution the growth of cities started rapidly. Most of the economic centers started flourishing around the cities or urban areas. The shift in prodigy from agricultural society to industrial societies accompanied by growth in population nourished the growth of urban centers. The modern cities of today , somewhere have followed this path and stand as achievements of modern civilization.
As, cities grew, the need to plan cities to cater for services also grew. This led planners to use concepts and theories, to predict the amount of growth in a future time. This indeed, would help in planning for infrastructure and services in an efficient way for the future.
The difficulty however, lied, to what tools and techniques could be used to predict the rate growth and and what kind of growth need to be predicted. Once of the simple answers to the problem is, we can predict what can be seen in terms of geographical boundary changes and also changes in conversion of land use type like change from agricultural to residential, agricultural to industrial and so on.
The simplest way was to look at morphology of cities or geographical forms of cities, theorize trends in the urban form using historical data, and use the patterns of morphological change to predict future. This kind of prediction relied on the fact that rate of growth can be estimated by a function whose changes are determined by changes in related variable with respect to time or any other variable. This prompted that general system based theories could be applicable to determining the future urban form of the city. Solutions , thus, were the solutions of ordinary differential equations of theories used for description of the urban form. Theories like central place theories, entropy theory and Newtons Theory of gravitation were used as base theories to derive model theories of cities. This concept lasted until the end of 1950s.
During the 1950s — 1980s, concept that cities not only grow in a central way concentric to central zone, but various factors do influence growth was highly notional. This was based on the observation of growth due to expansion of highways in America and Western Europe. Thus new concepts were needed where movement of people, trips they make, transport infrastructure played a vital role. Early Land Use base transportation theories were based on this fact and served as models of planning during the time. Also, Concepts that economy and social indicators help cities grow and change came into light and annexed with the LUT theories or early theories to develop various theories and models like Land use rent theories. During this period of time, computers were also advancing , thus theories were turned into computer models to simulate and predict the nature of growth.
Towards the 1980s, it was being realized that growth of cities is a dynamic and complex process. Simple mathematical formulations and theories would be unable to describe the nature of complexity of cities. Thus, new tools and techniques required to address the complexity in terms of dynamic nature of growth and variables that influence growth, show a emergence from interactions of local factors and elements, show network connections between processes and sub systems of cities.
Complexity theory was at a time growing field highly used in cellular biology to describe the emergence pattens of cells, micro organisms; used in chemistry to describe the forms of molecule formation and crystallization; and in other fields as well. This theory was a combination of dynamic systems theory, chaos theory, network theory and cybernetics. Researchers in mid 1980s realized that urban forms of cities can be described using these theories or complexity theory itself. However, it was important to visualize the growth from in terms of computer models. Popular and simple models to model complexity at the time included the Cellular automata and the agent based models.
The application of these models brought forward a new revolution in terms of modelling of urban growth or growth of cities. These model's are highly efficient in terms of representation and simulation of growth. Over time, various calibration techniques have been done to improve the efficiency, capability to input data, represent various elements, variables and process of the growth including the growth types like urban agglomeration, diffusion and negative growth, etc. Cellular automata remain the frequently used models to predict the urban growth and have been integrated with different techniques like Artificial neural networks, agent based models, heuristic algorithms based on need to represent different aspects of growth.
The models have thus grown complicated but efficient in terms of visualizing growth. However, the models lack capability to derive into a theory that can be used to describe the growth. The whole idea of urban growth prediction started with theories, now we have computer models, however, lack theories to backup the models.
Another, point of urban growth prediction is to facilitate the planning of cities. However, current models of cellular automata, agent based models, neural network models, fail to incorporate planning scenarios in the modelling process. This stands out to be one of the challenges in the field of study today. As, long as predictive modelling cannot incorporate or simulate planning scenarios, the adaptability will be hard for city planners and policy makers.
The modern era faces challenges in terms of changing global climate, sustainability issues in urbanizations and various other sectors. As, predicted by UN, cities will accommodate around 70% of World’s population by 2050. Thus, it becomes utmost necessary that the models of urban growth incorporate and address these issues through new concepts in modelling, exploration of new areas of modelling and theories that can represent the spatial and temporal dimensions of growth to address the policy and planning scenarios.
