Simulating city growth by using the cellular automata algorithm

The objective of this thesis is to develop and implement a Cellular Automata (CA) algorithm to simulate urban growth process. It attempts to satisfy the need to predict the future shape of a city, the way land uses sprawl in the surroundings of that city and its population. Salonica city in Greece is selected as a case study to simulate its urban growth. Cellular automaton (CA) based models are increasingly used to investigate cities and urban systems. Sprawling cities may be considered as complex adaptive systems, and this warrants use of methodology that can accommodate the space-time dynamics of many interacting entities. Automata tools are well-suited for representation of such systems. By means of illustrating this point, the development of a model for simulating the sprawl of land uses such as commercial and residential and calculating the population who will reside in the city is discussed

Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...

There are indications that the current generation of simulation models in practical, operational uses has reached the limits of its usefulness under existing specifications. The relative stasis in operational urban modeling contrasts with simulation efforts in other disciplines, where techniques, theories, and ideas drawn from computation and complexity studies are revitalizing the ways in which we conceptualize, understand, and model real-world phenomena. Many of these concepts and methodologies are applicable to operational urban systems simulation. Indeed, in many cases, ideas from computation and complexity studies—often clustered under the collective term of geocomputation, as they apply to geography—are ideally suited to the simulation of urban dynamics. However, there exist several obstructions to their successful use in operational urban geographic simulation, particularly as regards the capacity of these methodologies to handle top-down dynamics in urban systems. This paper presents a framework for developing a hybrid model for urban geographic simulation and discusses some of the imposing barriers against innovation in this field. The framework infuses approaches derived from geocomputation and complexity with standard techniques that have been tried and tested in operational land-use and transport simulation. Macro-scale dynamics that operate from the topdown are handled by traditional land-use and transport models, while micro-scale dynamics that work from the bottom-up are delegated to agent-based models and cellular automata. The two methodologies are fused in a modular fashion using a system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of residential location has been developed with a view to hybridization. The model mixes cellular automata and multi-agent approaches and is formulated so as to interface with meso-models at a higher scale

A complex network approach to urban growth

The economic geography can be viewed as a large and growing network of interacting activities. This fundamental network structure and the large size of such systems makes complex networks an attractive model for its analysis. In this paper we propose the use of complex networks for geographical modeling and demonstrate how such an application can be combined with a cellular model to produce output that is consistent with large scale regularities such as power laws and fractality. Complex networks can provide a stringent framework for growth dynamic modeling where concepts from e.g. spatial interaction models and multiplicative growth models can be combined with the flexible representation of land and behavior found in cellular automata and agent-based models. In addition, there exists a large body of theory for the analysis of complex networks that have direct applications for urban geographic problems. The intended use of such models is twofold: i) to address the problem of how the empirically observed hierarchical structure of settlements can be explained as a stationary property of a stochastic evolutionary process rather than as equilibrium points in a dynamics, and, ii) to improve the prediction quality of applied urban modeling.evolutionary economics, complex networks, urban growth

Tecnologias da geoinformação em estudos da dinâmica urbana

This paper explores the application of geoinformation techniques in urban dynamics models. The goal is to discuss the state of the art in order to achieve the advantages for integrating Cellular Automata (CA) models and Multi-Agent Systems (MAS) in Urban/Regional and Transportation Planning. Finally, this work concludes that the urban dynamics models needs to aggregate the geographic reference and the temporal scale in its conceptual and operational structure. Therefore, the geoinformation techniques become an essential instrument for reaching this precondition because they assemble spatial and temporal analysis through Geographic Information Systems (GIS) with CA and MAS models

A Conceptual framework for multi-scale cellular Modeling of Spatial Change

The use of cellular models for simulating spatial change phenomena has been a subject of intensive theoretical and applied research for the last two decades. This type of models was developed during the 1940s to devise mathematical rules for the evolution of biological systems. They were introduced in urban geography by Waldo Tobler in the 1970s because of their spatial structure and their ability for describing complex behaviors from sets of simple rules. The research effort made since then led to many different spatial change cellular models, some of which are being applied in practice. However, these models are based on a single-scale approach, since they focus on metropolitan or urban areas. In this paper, we present a conceptual framework for 2 multi-scale cellular modeling of spatial change, involving the regional and the metropolitan/urban level, as well as the interactions between them. Several important issues regarding the formulation of a multi-scale cellular model are discussed in detail, such as cell dimension, neighborhood format, transition potential measures, calibration procedures, and policy testing.Peer Reviewe