Entity-Based Modeling of Urban Residential Dynamics: The Case of Yaffo, Tel Aviv

Benenson, Itzhak; Omer, Itzhak; Hatna, Erez

doi:10.1068/b1287

Cited by 148 publications

(106 citation statements)

References 28 publications

(29 reference statements)

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“…Their behavior has been formulated on the basis of cultural and cognitive dissonance among individual households and the resulting patterns of segregation they influence. The simulations have been used to explore scenarios for the sociospatial segregation of diverse groups in Israeli cities (Benenson, 1998;Benenson et al, 2002), and to examine the properties of complex adaptive systems in those cities (Portugali et al, 1994;. These CA and agent models may be differentiated from GA in a number of ways, considering the definition of cell geography, states, state transition rules, representation of agency, georeferencing, neighborhoods, and movement.…”

Section: The Methodsological Importance Of Geographymentioning

confidence: 99%

A Geographic Automata Model of Residential Mobility

Torrens

2007

Environ Plann B Plann Des

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In this paper is described a model of residential mobility, built to simulate individual households, their perception of and reaction to varying conditions across different scales of interaction, and their movements to occupy housing in a physical, social, and economic environment. The methodology underpinning the model is based on an automata core, which leverages the advantages it offers in terms of representing individual entities and their rule-based interactions. This methodology is extended, however, to incorporate geography-specific functionality, with advantages for the modeling of human systems. The applicability of the methodology is demonstrated through the development of a rich model of residential mobility, in which individual households interact with other households and real-estate infrastructure, dynamically in space and time, to form synthetic communities and artificial property submarkets. Use of the model for what-if experimentation is demonstrated with synthetic economic and sociodemographic simulation scenarios.

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Section: The Methodsological Importance Of Geographymentioning

confidence: 99%

A Geographic Automata Model of Residential Mobility

Torrens

2007

Environ Plann B Plann Des

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“…The "city" simulated with chessboard has gone through a self-organizing process of microscopic interactions, wich has fully reflected the principle of "producing orders from instabilities". In 1998, Benenson simulated self-organizing phenomena of urban spatial evolution, segregation of urban residents and residential division by multi-agent models [25]. In 2001, Ligtenberg proposed a land use planning model based on cellular automata and multi-agent, while governmental planning factors are introduced into this model [26].…”

Section: B Modeling Of Urban Systemmentioning

confidence: 99%

Complexity and Dynamic Modeling of Urban System

Zhou¹,

Li²

2013

IJMLC

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Abstract-Complex system theory has made people recognize and understand urban phenomena from a new perspective. In particular, The research method of the complex system has provided a powerful tool to explore problems of urban system. In this paper, basic theories of research on complexity science and complex system are described, the necessity of dynamic modeling and simulation methods as well as general steps are analyzed for the complex system. Based on this, the complexity of urban system is discussed and analyzed. Besides, the means by which dynamic simulation methods of complex system are applied in studying urban system and their significances are discussed. The result shows that it is necessary to depict, analyze and know about problems on urban complexity and explore deeper rules hidden in dynamical urban development with the theoretical framework of complexity science, so as to better interpret, design and plan cities.Index Terms-Complexity science, CA, MAS, urban system. I. INTRODUCTIONWith the rapid development of cities and constant integration of disciplines, urban planning has increasingly become a discipline not purely focusing on designing physical forms, but has also begun to pay great attention to other non-physical factors (e.g. human and society) and more fundamental rules hidden in dynamic development of cities. Studied by urban planners, urban system is becoming more and more complex in both forms and nature. Thus, how to understand, analyze and depict such a complex system has become an urgent problem to be solved by planners. Traditional research methods based on reductive thinking encounter great challenges in interpreting the formation of urban structure, urban spatial evolution and laws of human activities in cities, etc, which fail to reveal deeper causes and rules of dynamic urban development.By the end of 1980s, the founding of Santa Fe Institute directly facilitated and led to the emergence of complexity science. The complexity science had achieved rapid development within less than 20 years and has become the focus of attention in the scientific field at present. On one hand, the theories of complexity science greatly change people"s ideas of urban system. On the other hand, research methods of complex system particularly make powerful means available to explore urban system. The rest of the paper is organized as follows: The basic theories of research on complexity science and complex system are described in Manuscript received September 6, 2013; revised October 17, 2013. This work was supported by the National Science Foundation of China (No. 51178236).Zhou Wensheng is with School of Architecture, Tsinghua University, Beijing, 100084, China (e-mail: zwsbj@163.com).Li Qiang is with 295847 Department of PLA, Beijing, 100120, China (e-mail: liquider@sina.com).Section II. Section III addresses the complexity of urban system. Section III discusses the modeling and simulation of complex systems and modeling of urban system. The paper is concluded in Section V. II. MAIN THEORIES OF C...

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“…Benenson et al, 2002). Increased computer power and storage capacity has made individual-level modelling more practical in recent times.…”

Section: Operational Modellingmentioning

confidence: 99%