Agent-Based Simulation Modelling of Housing Choice and Urban Regeneration Policy

Jordan, René; Birkin, Mark; Evans, Andrew

doi:10.1007/978-3-642-18345-4_11

Cited by 10 publications

(3 citation statements)

References 29 publications

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“…Thus, a household takes a decision and looks for a dwelling that corresponds to its expectations (pull factors). These models, generally called ''two stages'' (decision to move and choice of dwelling) or ''three stages'' (decision, search, and choice) consider the push-pull factors in a consecutive and independent fashion (Brown & Robinson, 2006;Fernandez, Brown, Marans, & Nassauer, 2005;Jordan, Birkin, & Evans, 2011;Raju, Sikdar, & Dhingra, 1998). Models that consider push pull factor interactions, most commonly through the idea of economic utility, are rarer (Benenson, 2004;Ettema et al, 2007).…”

Section: A Model Of Complex Processesmentioning

confidence: 98%

Conception and use of an individual-based model of residential choice in a planning decision process. Feedback from an experimental trial in the city of Besançon, France

Tannier

Hirtzel

Stephenson

et al. 2016

Progress in Planning

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International audienceIn this paper, we present an experimental trial involving the use of a complex spatial simulation platform to support a planning decision process in the city of Besançon (eastern France). In medium-sized towns across France, households with children are leaving to settle in periurban areas, and Besançon is no exception. Under those circumstances, the political objective of the Besançon City authority is to keep middle- and high-income households with children within the city. The simulation platform MobiSim was used in this context to explore the possible outcomes of spatial planning policies, set by both the Greater Besançon authority (Besançon and the surrounding periurban communities) and the city of Besançon, on residential migrations over a 20 year period. MobiSim integrates several models representing demographic, social, economic, and spatial processes. Modelled entities are individuals and dwellings. Rules determine how individuals form households. Other rules locate dwellings in buildings

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Section: A Model Of Complex Processesmentioning

confidence: 98%

Conception and use of an individual-based model of residential choice in a planning decision process. Feedback from an experimental trial in the city of Besançon, France

Tannier

Hirtzel

Stephenson

et al. 2016

Progress in Planning

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show abstract

“…Belief in and preferences for spatial objects from multiple actors are simulated in a hypothetical planning scenario in the Netherlands to support decision making of spatial planners (Ligtenberg et al, 2001;. And an urban regeneration policy that intends to encourage social mixing in the UK is simulated in an agent-based housing-choice model to evaluate its effects on the vitality of the housing market and availability of jobs (Jordan et al, 2011;.…”

Section: Different Stages Of the Urbanization Processmentioning

confidence: 99%

A Review of Urban Residential Choice Models Using Agent-Based Modeling

Huang

Parker

Filatova

et al. 2014

Environ Plann B Plann Des

108

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Urban land-use modeling methods have experienced substantial improvements in the last several decades. With the advancement of urban land-use change theories and modeling techniques, a considerable number of models have been developed. The relatively young approach, agent-based modeling, provides urban land-use models with some new features and can help address the challenges faced by traditional models. Applications of agent-based models to study urban dynamics have increased steadily over the last twenty years. To offer a retrospective on the developments in agent-based models (ABMs) of urban residential choices, we review fifty-one relevant models that fall into three general categories: (i) urban land-use models based on classical theories; (ii) different stages of the urbanization process; and (iii) integrated agent-based and microsimulation models. We summarize and compare the main features of these fifty-one models within each category. This review focuses on three fundamental new features introduced byABMs. The first is agent heterogeneity with particular attention to the method of introducing heterogeneity in agents' attributes and behaviors. The second is the representation of land-market processes, namely preferences, resources constraints, competitive bidding, and endogenous relocation. The third is the method of measuring the extensive model outputs. In addition, we outline accompanying challenges to, and open questions for, incorporating these new features. We conclude that, by modeling agent heterogeneity and land markets, and by exploiting a much broader dimension of output, we will enhance our understanding of urban land-use change and are hopefully able to improve model fitness and robustness.

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“…The model is also referred to as the CHAIRS model; Creating Housing Alternatives In Regenerated Societies. This work builds on the earlier work of Jordan, Birkin & Evans (2011) where a methodological framework is presented.…”

Section: Introductionmentioning

confidence: 99%