Urban Evolution: The Role of Water

The structure, function, and services of urban ecosystems evolve over time scales from seconds to centuries as Earth’s population grows, infrastructure ages, and sociopolitical values alter them. In order to systematically study changes over time, the concept of “urban evolution” was proposed. It allows urban planning, management, and restoration to move beyond reactive management to predictive management based on past observations of consistent patterns. Here, we define and review a glossary of core concepts for studying urban evolution, which includes the mechanisms of urban selective pressure and urban adaptation. Urban selective pressure is an environmental or societal driver contributing to urban adaptation. Urban adaptation is thesequential process by which an urban structure, function, or services becomes more fitted to its changing environment or human choices. The role of water is vital to driving urban evolution as demonstrated by historical changes in drainage, sewage flows, hydrologic pulses, and long-term chemistry. In the current paper, we show how hydrologic traits evolve across successive generations of urban ecosystems via shifts in selective pressures and adaptations over time. We explore multiple empirical examples including evolving: (1) urban drainage from stream burial to stormwater management; (2) sewage flows and water quality in response to wastewater treatment; (3) amplification of hydrologic pulses due to the interaction between urbanization and climate variability; and (4) salinization and alkalinization of fresh water due to human inputs and accelerated weathering. Finally, we propose a new conceptual model for the evolution of urban waters from the Industrial Revolution to the present day based on empirical trends and historical information. Ultimately, we propose that water itself is a critical driver of urban evolution that forces urban adaptation, which transforms the structure, function, and services of urban landscapes, waterways, and civilizations over time

A modular modelling framework for hypotheses testing in the simulation of urbanisation

In this paper, we present a modelling experiment developed to study systems of cities and processes of urbanisation in large territories over long time spans. Building on geographical theories of urban evolution, we rely on agent-based models to 1/ formalise complementary and alternative hypotheses of urbanisation and 2/ explore their ability to simulate observed patterns in a virtual laboratory. The paper is therefore divided into two sections : an overview of the mechanisms implemented to represent competing hypotheses used to simulate urban evolution; and an evaluation of the resulting model structures in their ability to simulate - efficiently and parsimoniously - a system of cities (the Former Soviet Union) over several periods of time (before and after the crash of the USSR). We do so using a modular framework of model-building and evolutionary algorithms for the calibration of several model structures. This project aims at tackling equifinality in systems dynamics by confronting different mechanisms with similar evaluation criteria. It enables the identification of the best-performing models with respect to the chosen criteria by scanning automatically the parameter along with the space of model structures (as combinations of modelled dynamics).Comment: 21 pages, 3 figures, working pape

Employment deconcentration: a new perspective on America's postwar urban evolution.

In this study the authors show that during the postwar era, the United States experienced a decline in the share of urban employment accounted for by the relatively dense metropolitan areas and a corresponding rise in the share of relatively less dense ones. This trend, which the authors call employment deconcentration, is distinct from the other well-known regional trend, namely, the postwar movement of jobs and people from the frostbelt to the sunbelt. The authors also show that deconcentration has been accompanied by a similar trend within metropolitan areas, wherein employment share of the denser sections of MSAs has declined and that of the less dense sections risen. The authors provide a general equilibrium model with density-driven congestion costs to suggest an explanation for employment deconcentration.Employment (Economic theory) ; Cities and towns

Study of the urban evolution of Brasilia with the use of LANDSAT data

The urban growth of Brasilia within the last ten years is analyzed with special emphasis on the utilization of remote sensing orbital data and automatic image processing. The urban spatial structure and the monitoring of its temporal changes were focused in a whole and dynamic way by the utilization of MSS-LANDSAT images for June 1973, 1978 and 1983. In order to aid data interpretation, a registration algorithm implemented at the Interactive Multispectral Image Analysis System (IMAGE-100) was utilized aiming at the overlap of multitemporal images. The utilization of suitable digital filters, combined with the images overlap, allowed a rapid identification of areas of possible urban growth and oriented the field work. The results obtained permitted an evaluation of the urban growth of Brasilia, taking as reference the proposed stated for the construction of the city

Modeling urban evolution by identifying spatiotemporal patterns and applying methods of artificial intelligence.Case study: Athens, Greece.

While during the past decades, urban areas experience constant slow population growth, the spatial patterns they form, by means of their limits and borders, are rapidly changing in a complex way. Furthermore, urban areas continue to expand to the expense of "rural” intensifying urban sprawl. The main aim of this paper is the definition of the evolution of urban areas and more specifically, the specification of an urban model, which deals simultaneously with the modification of population and building use patterns. Classical theories define city geographic border, with the Aristotelian division of 0 or 1 and are called fiat geographic boundaries. But the edge of a city and the urbanization "degree" is something not easily distinguishable. Actually, the line that city ends and rural starts is vague. In this respect a synthetic spatio - temporal methodology is described which, through the adaptation of different computational methods aims to assist planners and decision makers to gain an insight in urban - rural transition. Fuzzy Logic and Neural Networks are recruited to provide a precise image of spatial entities, further exploited in a twofold way. First for analysis and interpretation of up - to - date urban evolution and second, for the formulation of a robust spatial simulation model, the theoretical background of which is that the spatial contiguity between members of the same or different groups is one of the key factors in their evolution. The paper finally presents the results of the model application in the prefecture of Attica in Greece, unveiling the role of the Athens Metropolitan Area to its current and future evolution, by illustrating maps of urban growth dynamics.urban growth; urban dynamics; neural networks; fuzzy logic; Greece; Athens

Defining a geographically weighted regression model of urban evolution. Application to the city of Volos, Greece

The main objective of this paper is the multivariate analysis of urban space and specifically with the use of data that refer to the level of city block. Part of the analysis has been the comparative assessment of multiple linear regression and geographically weighted regression (GWR) analysis as well as the application of the aforementioned methods in the study of the central district of the Volos metropolitan area. The city of Volos is an urban conglomeration of approximately 110.000 inhabitants, located at the middle-east of Greece and is considered to be in the upper extreme in the cities’ urban hierarchy in Greece. The results provide a response to a question raised by spatial scientists during the last decades: is there a way that regression analysis can reveal spatial variations of results and with respect to scale fluctuation? The use of classical multiple regression analysis provides a single result – equation for the entire area. On the other hand, geographically weighted regression analysis stems from the fact that the above result is inadequate to reflect the different relational levels among selected variables characterizing the entire area. New estimations with the use of GWR declare the existence of various sub-areas – divisions of the initial territory – formulating a set of equations that reveal the spatial variations of variable relations. The results of the application have well proved the dominance of the analysis in the local level towards the analysis in the global level, highlighting the existence of intense spatial differentiations of variables that “interpret” the rate of land values in the city. Moreover, the distinct spatial patterns that emerge throughout the entire area, establish an alternative approach of urban spatial phenomena interpretation and a new explanatory basis for the clarification of obscure relations.

Soft Computing Modelling of Urban Evolution: Tehran Metropolis

Exploring computational intelligence, geographic information systems and statistical information, a creative and innovative model for urban evolution is presented in this paper. The proposed model employs fuzzy logic and artificial neural network as forecasting tools for describing the urban growth. This dynamic urban evolution model considers the spatial data of population, as well as its time changes and the building usage patterns. For clustering the spatial features, fuzzy algorithms were implemented to represent different levels of urban growth and development. Then, these fuzzy clusters were modeled by the multi-layer neural networks to estimate the urban growth. Based on this novel intelligent model, the current state of development of Tehran’s population and the future of this urban evolution were evaluated by empirical data and the achieved outcomes were detailed in qualitative charts. The input data-set includes four censuses with five-year intervals. Tehran's demographic evolution model forecasts the next five years with an overall accuracy of 81% and Cohen's kappa coefficient up to 74% beside the qualitative charts. These performance indicators are higher than the previous advanced models. The primary objective of this proposed model is to aid planners and decision makers to predict the development trend of urban population