In the past decades, models such as cellular automata and agent-based systems, as a discretisation of morphogenetic models coming from the fields of biology and chemistry, have been applied to the study of urban form, in order to describe, especially a posteriori, the stochastic geometry underlying cities in different steps of their development. These techniques are at the basis of more recent studies, also involving artificial intelligence in the process. This turns out to have, at least to some extent, a predictive nature: we cannot foretell the exact shape of a city in the future, but we can observe how the variation of some parameters might affect its development, giving rise to a range of different scenarios. As the shape of urban landscapes continues to evolve in response to socio-economic, environmental, and technological factors, the need for effective city planning tools, with their geometrical and morphological implications, becomes increasingly critical. This review explores a part of the spectrum of graphic tools employed in simulating city growth, ranging from traditional cellular automata models to more complex applica-tions also involving artificial intelligence (AI). By examining recent experiences across these methodologies, we aim to provide insights into the advancements, challenges, and potential future directions in urban simulation. We assume, as the starting point of this development, the morphogenetic model by Alan Turing, published in his 1952 seminal paper [1] to account for the development of living beings and for particular features of them, for instance the appearance of pigmentation patterns on their surface. Such processes were explained by Turing as the result of the interplay of two forces, represented by two chemical substances, which give rise to particular and unstable concentration patterns. In the case of living beings, this corresponds to positional information leading to the generation of particular parts of the body or features such as spots and stripes on the fur of some animals. The process is mathematically described by means of a system of two differential equations, explaining the variation in the concentration of the two "adversarial" substances over time.
Graphic Tools to Simulate the Evolution of Cities: A Review of Recent Experiences from Cellular Automata to Artificial Intelligence
Cazzaro, Irene
;Gay, Fabrizio
2024-01-01
Abstract
In the past decades, models such as cellular automata and agent-based systems, as a discretisation of morphogenetic models coming from the fields of biology and chemistry, have been applied to the study of urban form, in order to describe, especially a posteriori, the stochastic geometry underlying cities in different steps of their development. These techniques are at the basis of more recent studies, also involving artificial intelligence in the process. This turns out to have, at least to some extent, a predictive nature: we cannot foretell the exact shape of a city in the future, but we can observe how the variation of some parameters might affect its development, giving rise to a range of different scenarios. As the shape of urban landscapes continues to evolve in response to socio-economic, environmental, and technological factors, the need for effective city planning tools, with their geometrical and morphological implications, becomes increasingly critical. This review explores a part of the spectrum of graphic tools employed in simulating city growth, ranging from traditional cellular automata models to more complex applica-tions also involving artificial intelligence (AI). By examining recent experiences across these methodologies, we aim to provide insights into the advancements, challenges, and potential future directions in urban simulation. We assume, as the starting point of this development, the morphogenetic model by Alan Turing, published in his 1952 seminal paper [1] to account for the development of living beings and for particular features of them, for instance the appearance of pigmentation patterns on their surface. Such processes were explained by Turing as the result of the interplay of two forces, represented by two chemical substances, which give rise to particular and unstable concentration patterns. In the case of living beings, this corresponds to positional information leading to the generation of particular parts of the body or features such as spots and stripes on the fur of some animals. The process is mathematically described by means of a system of two differential equations, explaining the variation in the concentration of the two "adversarial" substances over time.
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