Urban growth processes joining cellular automata and multiagent systems. Part 2: computer simulations

Vancheri, Alberto; Giordano, Paolo; Andrey, Denise; Albeverio, Sergio

doi:10.1068/b31080b

Cited by 15 publications

(9 citation statements)

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“…Neural networks were coupled with CA to calibrate modelling parameters (Almeida et al, 2008;Li and Yeh, 2001;Wu, 2002). The integration of CA with multiagent simulation is at the origin of the concept of geographic automata systems proposed in Benenson and Torrens (2004) and is currently a major subject of research (Diappi and Bolchi, 2006;Vancheri et al, 2008).…”

Section: Literature Overviewmentioning

confidence: 99%

A cellular automata model based on irregular cells: application to small urban areas

Pinto

Antunes

2010

Environ. Plann. B

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We present in this paper the development and application of a cellular automata model that uses irregular cells for simulating land-use change instead of the common regular cells based on the pixels of remote sensing images. The use of irregular cells combines urban form with reliable demographic, socioeconomic, and building data that are usually available for irregular census blocks. The calibration of the model is made through a procedure based on a particle swarm algorithm that optimises a measure of agreement derived from the kappa index for contingency matrices. The model was applied for simulating urban change in a small municipality in Portugal that registered high growth rates during the 1990s. Simulation results indicate the model's ability to capture land-use change in small urban areas.

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Section: Literature Overviewmentioning

confidence: 99%

A cellular automata model based on irregular cells: application to small urban areas

Pinto

Antunes

2010

Environ. Plann. B

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“…Modelling methods that have been used in the urban planning field include agent-based models (Gero and Sarkar, 2005 Waddell, 2002) as well as combinations of the two (Batty, 2005;Torrens and Benenson, 2005). Other combined models of urban growth processes include CA with fuzzy functions (Vancheri et al, 2008) and CA with logistic functions (Poelmans and Van Rompaey, 2010). Mathe matical programming has been used effectively (eg, Janssen et al, 2008) but has not been applied widely.…”

Section: Selection Of the Modelling Methodsmentioning

confidence: 99%

Achieving Residential Connectivity and Density Goals with Computer-Generated Plans in a Greenfield Area

Sun

Qiu

Taplin

2014

Environ Plann B Plann Des

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An algorithm has been developed to generate, without external intervention, a road and land use plan for a regular or irregular site. It starts from an 'embryo' and grows a plan rather than trying to modify an initial solution. The basic modules are universal building blocks which change and adapt in a guided search with random selection of branching points followed by operations to add links or make connections. Deletion operators guide development by removing branches which do not improve the outcome. A hypothetical application, maximizing combined everyoneto-everyone connectivity and dwelling density, has evolved a highly interconnected street plan. However no step is specific to the example; the operators will grow a road and land use network under various specifications and constraints guided by an objective function. Making the process applicable to an actual development might require more constraints and certainly an enlarged objective function. Cost and other goals can be included so long as each goal is in some way functionally related to every change in the plan made by the search procedure. seemingly contradictory and imperfect urban form as suburbia can, and, indeed, does compose an integral part of contemporary capitalism.' Lovejoy et al (2010) note that a suburb often lacks the grid-like street pattern, mix of residential and commercial land uses and distinct centres usually associated with 'traditional' neighborhoods. Nevertheless, their rigorous quantitative assessment leads to the conclusion that '...some of the main differences between suburban and traditional neighborhood designs do not make much of a difference for how satisfied residents are with their neighborhoods, in either neighborhood type.' When comparing suburban neighborhoods in Houston, Rogers et al (2009) found that '...residents in ecologically designed neighborhoods have an enhanced sense of community, at

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“…In this paper we will give a general exposition of the model, leaving to a second paper (Vancheri et al, 2008) the application to a case study. In section 2 we first define the CA components of the model (cellular decomposition of the space, time step, neighbourhoods, state space of a cell, and evolution rules) and subsequently introduce the probability distributions associated with the stochastic rules.…”

Section: Structure Of the Papermentioning

confidence: 99%

“…In sections 4 and 5 we describe the asynchronous evolution algorithm and derive a system of approximated ordinary differential equations associated with the model. Cellular decomposition and time step: The set of cells partitioning the urban space will be indicated with G. In the case study presented in the second paper (Vancheri et al, 2008) the cells are cadastral parcels. Time is discrete, but the duration Dt of the time step is explicitly represented in the model and this enables us to obtain a differential equation in the limit Dt 3 0.…”

Section: Structure Of the Papermentioning

confidence: 99%

Urban Growth Processes Joining Cellular Automata and Multiagent Systems. Part 1: Theory and Models

Vancheri¹,

Giordano²,

Andrey³

et al. 2008

Environ Plann B Plann Des

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A new modelling framework for the study of the dynamics of urban systems is proposed. We generalize the notion of cellular automaton, using continuous variables to describe the state of a cell. The time evolution is given by Poisson-distributed stochastic jumps corresponding to urban interactions, with intensities depending on the configuration of the system in a suitable set of neighbourhoods. These interactions result from decision processes of populations of cognitive agents modelled in the framework of fuzzy decision theory. The behaviour of agents is driven by goals and constraints, partially defined using a fuzzy logic formalization of a real estate appraisal method based on the evaluation of the attractiveness of a terrain for different land uses. Randomness of the dynamics is a consequence of the fuzziness of these decisions. Hence the model can be seen as a multiagent system with sound mathematical foundation. For example, the use of a continuum-valued state space enables us to prove that the expectation of state variables verify a system of differential equations, and thus to study the system from a dynamical systems theory perspective. In this paper we present the theoretical setting, leaving the applications to a second paper.

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Urban growth processes joining cellular automata and multiagent systems. Part 2: computer simulations

Cited by 15 publications

References 0 publications

A cellular automata model based on irregular cells: application to small urban areas

A cellular automata model based on irregular cells: application to small urban areas

Achieving Residential Connectivity and Density Goals with Computer-Generated Plans in a Greenfield Area

Urban Growth Processes Joining Cellular Automata and Multiagent Systems. Part 1: Theory and Models

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