Abstract
Dissipative structures known from non-equilibrium thermodynamics can form patterns. Cities are regarded as open, dissipative structures due to their self-organisation and thus in theory are also capable of pattern formation. In a first step to understand similarities between nonlinear pattern formation and inter-urban systems, we investigate how inter-urban structures are arranged. We use data from the Global Urban Footprint to identify spatial regularities in seven regions (Argentina, China, Egypt, France, India, Ghana and USA) and to quantitatively describe settlement patterns by number of objects and density. We find that small areas of the examined data sets show a regular arrangement, the density and number of settlements differ widely between the different regions and the portion of regular areas within this regions strongly correlates with these two parameters. The results can be used to develop mathematical models that describe inter-urban pattern formation on the one hand and to investigate to what extent the respective settlement patterns are related to infrastructural, economic or political boundary conditions on the other.
Highlights
Urbanisation coupled with major demographic transformations is, along with climate change, one of the most striking phenomena of the early 21st century [1] and has increasingly moved into the focus of scientific work in recent decades [2]
We examine the nature of the structures formed by inter-urban systems on the basis of different examples in order to examine the applicability of these nonlinear models in future work with the knowledge that such an approach is only an indication of pattern formation mechanism [28]
We focus on agricultural dominated landscapes as different settlement theories like the central place theory (CPT) [13] or others [32,33] are based on regular distribution of non-urban settlements as initial state
Summary
Urbanisation coupled with major demographic transformations is, along with climate change, one of the most striking phenomena of the early 21st century [1] and has increasingly moved into the focus of scientific work in recent decades [2]. This development has led to a “science of cities” [3], where with the help of quantitative mathematical models, various attempts are made to gain access to the different processes that shape and change today’s cities [4,5]. A contrary approach is taken when certain phenomena are mapped with comparatively simple mathematical models to describe core processes of urbanisation
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