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Cities and Regions as Self-Organizing Systems: Models of Complexity

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Abstract

ABSTRACT This book sets out the basic ideas underlying complexity and the qualitative, spatial evolution that it allows. It describes the conceptual framework of modelling, showing the assumptions involved in developing models which are: a) deterministic, non-linear equations, b) self-organizing dynamical systems, c) evolutionary systems. The paper then describes the mathematical modelling of the spatial self-organization that occurs in regional and urban development as a result of the interaction of economic, demographic and environmental factors. There are chapters on interurban evolution and the emergence of urban hierarchies, with several examples described in detail. It then moves on to intra-urban evolution, and again uses several examples to show how the ideas of complexity allow us to look at emergence and structural change in cities as different economic and residential functions generate interdependent patterns of land-use and of transport flows. It is a model of the multiple decision making processes that go on in the system, concerning short term flows of people, goods and services, involved in commuting and in the supply chain of the economic system, and also the longer term processes that drive migration and the spatial pattern of investment. There are at least two levels of description for the system. The first concerns individuals in small zones attempt to pursue their own goals, and those of their job, and a second, more macroscopic scale, of the urban and regional structure that results. This macroscopic structure, however, fashions the costs and benefits that enter into the individual/local decision making processes, giving rise to a "self-organization" of the system, as the micro and macro levels interact. Rather than predicting exact paths of the system the method is more useful in exploring the different kind of alternatives that are possible, and to perform a strategic analysis of different possible qualitative structures which the system might evolve. Several examples of these models will be briefly discussed: the Brussels Model within Belgium, other national models such as Senegal, the changing urban hierarchy of the USA reflecting migration flows, as well as interventions in intra-urban evolution that serve to demonstrate the potential usefulness of such systems.
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... The current understanding of cities as complex adaptive systems (CAS) was already established in the 1990s (Allen 1997, Portugali 1999, Portugal et al. 2012, Batty 2007, deRoo & Silva 2010. Complexity theories form a credible planning paradigm, which can also integrate qualitative and quantitative studies, diverse cultural and social-economic dynamics while considering the spatial and physical urbanity. ...
... It may help the system to renew itself in time of crises (Walker & Salt 2012, Portugali 1999. Planning should acknowledge and allow preferable types of self-organization, hinder the harmful ones (segregation, monofunctional areas) and leave the rest to operate -similarly to the ecosystems in nature (Allen 1997, Partanen 2018). ...
Chapter
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... Second, our model only uses landscape datasets as a priori conditions to reproduce the spatial patterns of population. In contrast, in addition to calibration against the population data itself, alternative realistic models incorporate a wide range of external datasets, including inter alia employment levels, economic growth, birth and death rates, bilateral trade flows income and demographic characteristics 15,16,49 . Nevertheless, the natural landscape itself is not included directly in most models, or plays a very limited role. ...
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... Note that certain quantities might also exhibit what is known as an 'inverse cascade', where a transport process from small to large scales occurs, resulting in self-organized large-scale patterns (in the case of two-dimensional turbulence, for instance, vortices tend to develop in clusters and energy is transferred to large scales (Friedrich 2021)). Concepts and methods developed in the context of self-organizing systems have been applied rather extensively in the urban context for example as means to explain how cities evolve and form characteristic spatial patterns (Haken and Portugali 1995;Portugali 1997;Allen 2012). The second important assumption in the context of latter cascade processes is that interactions are assumed to be merely local, occurring from one scale to another. ...
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... Urban systems inherently show complexity, comprising numerous individual decisions and interactions (Allen 1997;Portugali 2000;Batty 2005;Tan et al. 2021). Although we stimulate two basic spatial interactions of concentration and integration within UAs, there are still numerous other complex spatial relationships between urban entities worthy of further investigation to improve our method. ...
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... Evolutionary thinking has been used for explaining the growth and life of cities for a long time. However, most of this evolutionary analysis has looked at the evolution of systems [25][26][27][28][29][30][31]. This aggregate evolutionary perspective is valid, but it omits to pay sufficient attention to the main mechanism that drives the behaviour of the individuals who build up the system and whose values create the rules for operation of the system. ...
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... The complexity of cities have been addressed in a number of works, including Batty [1], [2], Allen [3], [4], White [5], Portugali [6], Siller [7], etc. For instance, emergent social phenomena have been studied from the perspective of complex systems. ...
Chapter
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Emergence of cities and road networks have characterised human activity and movement over millennia. However, this anthropogenic infrastructure does not develop in isolation, but is deeply embedded in the natural landscape, which strongly influences the resultant spatial patterns. Nevertheless, the precise impact that landscape has on the location, size and connectivity of cities is a long-standing, unresolved problem. To address this issue, we incorporate high-resolution topographic maps into a Turing-like pattern forming system, in which local reinforcement rules result in co-evolving centres of population and transport networks. Using Italy as a case study, we show that the model constrained solely by topography results in an emergent spatial pattern that is consistent with Zipf’s Law and comparable to the census data. Thus, we infer the natural landscape may play a dominant role in establishing the baseline macro-scale population pattern, that is then modified by higher-level historical, socio-economic or cultural factors.
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