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Agent-Based Simulation of Household Residential Relocation and Decision-Making Support of Downtown Revitalization

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Strategic Spatial Planning Support System for Sustainable Development

Part of the book series: Advances in Geographic Information Science ((AGIS))

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Abstract

Residential relocation is a traditional research concern in urban study area, in this chapter we will focus on introducing how to develop an Agent-Based Model (ABM) to simulate household residential relocation affected by a local residential policy regarding to downtown revitalization in Japanese local city. Different from conventional residential relocation model developed on utility theory, in this work the agents’ behaviors on residential location choices are supposed to be affected by argued planning policy. The policy effects will be simulated through reflecting households’ policy attitude sharing in whole city and neighborhoods. We define it as an interaction process between agents during they facing to relocation choices. It is necessary for policy analysis and decision-making support by using ABMs. It means when we develop an ABM for policy analysis or decision-making support, the interactions between agents need to be linked to the policy, such as policy information sharing or policy attitude sharing. As a planning support tool to local residential policy, the parameters set for the simulation model should be consistent to reality. In this work we gain them from questionnaire survey and statistical analysis. While in the future this process may be supported by big data analysis.

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References

  • Berger T., Schreinemachers P., Woelcke J., 2006. Multi-agent simulation for the targeting of development policies in less-favored areas. Agricultural Systems, 88(1), pp. 28–43.

    Article  Google Scholar 

  • Benenson, I., 1998. Multi-agent simulations of residential dynamics in the city. Computers, Environment and Urban Systems, vol. 22, pp. 25–42.

    Article  Google Scholar 

  • Building Standards Act., 1998. Ministry of Land, Infrastructure, Transport and Tourism, Japan. Japanese website Archived at: http://www.mlit.go.jp/

  • Babak Mahdavi Ardestani, David O’Sullivan, Peter Davis, 2018. A multi-scaled agent-based model of residential segregation applied to a real metropolitan area, Computers, Environment and Urban Systems, vol. 69, pp. 1–16.

    Article  Google Scholar 

  • Bruch, E. E., 2014. How population structure shapes neighborhood segregation. American Journal of Sociology, 119(5), pp. 1221–1278.

    Article  Google Scholar 

  • Crull S. R., 1979. Residential satisfaction, propensity to move and residential mobility: A causal model.

    Google Scholar 

  • Cao, X., Mokhtarian, P.L., Handy, S.L., 2009. The relationship between the built environment and non-work travel: a case study of Northern California. Transportation Research Part A: Policy Practice, 43, pp. 548–559.

    Google Scholar 

  • Chen C., Gong H.M., Paaswell R., 2008. Role of the built environment on mode choice decisions: additional evidence on the impact of density. Transportation, 35, pp. 285–299.

    Article  Google Scholar 

  • Dawn C. P. and Filatova T., 2008. A conceptual design for a bilateral agent-based land market with heterogeneous economic agents. Computers, Environment and Urban Systems, 32, pp. 454–463.

    Article  Google Scholar 

  • Diappi L. and Bolchi P., 2006. Gentrification Waves in the Inner-City of Milan - A multi agent / cellular automata model based on Smith's Rent Gap theory, Innovations in Design & Decision Support Systems in Architecture and Urban Planning, pp. 187–201.

    Google Scholar 

  • Ettema D., 2011. A multi-agent model of urban processes: Modelling relocation processes and price setting in housing markets. Computers, Environment and Urban Systems, 35, pp. 1–11.

    Article  Google Scholar 

  • Fontaine C.M., Rounsevell M.D.A., 2009. An agent-based approach to model future residential pressure on a regional landscape. Landscape Ecological, 24, pp. 1237–1254.

    Article  Google Scholar 

  • Fatmi M. R., Habib M. A., 2018. Microsimulation of life-stage transitions and residential location transitions within a life-oriented integrated urban modeling system, Computers, Environment and Urban Systems, vol., 69, pp. 87–103.

    Google Scholar 

  • Fossett, M., 2015. Generative models of segregation: Investigating model-generated patterns of residential segregation by ethnicity and socioeconomic status. The Journal of Mathematical Sociology, 35(1–3), pp. 114–145.

    Google Scholar 

  • Gaube, V., Remesch, A., 2013. Impact of urban planning on household's residential decisions: An agent-based simulation model for Vienna. Environmental Modelling & Software, 45, pp. 92–103.

    Article  Google Scholar 

  • Gerber P., Ma T.-Y., Klein O., Schiebel J., Samuel C.-P., 2017. Cross-border residential mobility, quality of life and modal shift: A Luxembourg case study. Transportation Research Part A: Policy Practice, 104, pp. 238–254.

    Google Scholar 

  • Haase D., Lautenbach S., Seppelt R., 2010. Modeling and simulating residential mobility in a shrinking city using an agent-based approach. Environmental Modelling & Software, 4, pp. 1225–1240.

    Article  Google Scholar 

  • Jordana R., Birkin M., Evans A., 2014. An agent-based model of residential mobility Assessing the impacts of urban regeneration policy in the EASEL district. Computers, Environment and Urban Systems, 48, pp. 49–63.

    Article  Google Scholar 

  • Kawakami M., Takama C., 1978. Investigation of household residential relocation desire in Kanazawa City (in Japanese). Architecture Institute of Japan, 13, pp. 67–72.

    Google Scholar 

  • Kikuchi Y., Nojima S., 2007. Resident’s Mind about Residence Selection in Suburban Housing Estates: Case study of 4 suburban estates in Fukui City. City planning review: Special issue on city planning, 42(3), pp. 217–222.

    Google Scholar 

  • Klaassen, L. and Paelinck J., 1979. The feature of the large towns. Environment and Panning A, 11, pp. 1095–1104.

    Article  Google Scholar 

  • Klinger T., 2017. Moving from monomodality to multimodality? Changes in mode choice of new residents. Transportation Research Part A: Policy Practice, 104, pp. 221–237.

    Google Scholar 

  • Lee Q., Park B., Vlek S. J. P.L.G., 2010. Land Use Dynamic Simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human–landscape system: 2. Scenario-based application for impact assessment of land-use policies. Ecological Informatics, 5(3), pp. 203–221.

    Google Scholar 

  • LĂłpez-Ospina H. A., MartĂ­nezb F. J., CortĂ©sb C. E., 2016. Microeconomicmodelofresidentiallocationincorporatinglifecycleand social expectations. Computers, Environment and Urban Systems, 55, pp. 33–43.

    Article  Google Scholar 

  • Li Li, Xiaojian Li, Beibei Hai, Xuefeng Wang, Jiawei Xu, 2020. Evolution of rural settlement in an inland nonmetropolitan region of China at a time of rapid urbanisation: The case of Gongyi, Journal of Rural Studies, Volume 79, pp. 45–56.

    Article  Google Scholar 

  • Li Z., Wu F., 2013. Residential Satisfaction in China's Informal Settlements: A Case Study of Beijing, Shanghai, and Guangzhou. Urban Geography, 34(7), pp. 924–949.

    Article  Google Scholar 

  • Mao, Z., Wang, D., 2020. Residential relocation and life satisfaction change: Is there a di?erence between household couples? Cities. 97: 102565.

    Google Scholar 

  • Molin, E., Timmermans, H., 2003. Accessibility considerations in residential choice decisions: accumulated evidence from the Benelux. TRB 2003 Annual Meeting CD-ROM, Washington, DC.

    Google Scholar 

  • Miller, E. J., Hunt, J. D., Abraham, J. E., & Salvini, P. A., 2004. Microsimulating urban systems. Computers, Environment and Urban Systems, 28, pp. 9–44.

    Article  Google Scholar 

  • Mendonça R., et al., 2020. Assessing economic instruments to steer urban residential sprawl, using a hedonic pricing simulation modelling approach, Land Use Policy, Volume 92, 2020, 104458.

    Google Scholar 

  • Ma Y., Shen Z.J., Kawakami M., 2013. Agent-Based Simulation of Residential Promoting Policy Effects on Downtown Revitalization. the Journal of Artificial Societies and Social Simulation, 16(2) 2.

    Google Scholar 

  • MA Y., SHEN Z. J., Nguyen D. T., 2016. Agent-Based Simulation to Inform Planning Strategies for Welfare Facilities for the Elderly: Day Care Center Development in a Japanese City. Journal of Artificial Societies and Social Simulation, 19(4)5.

    Google Scholar 

  • Moreno A. T., Moeckel R., 2018. Population Synthesis Handling Three Geographical Resolutions. International Journal of Geo-Information, 7, pp. 174.

    Article  Google Scholar 

  • Noth, M., Borning, A., Waddell, P., 2003. An extensible, modular architecture for simulating urban development, transportation, and environmental impacts. Computers, Environment and Urban Systems, 27(2), pp. 181–203.

    Article  Google Scholar 

  • Rieniets T., 2009. Shrinking cities: causes and effects of urban population losses in the twentieth century. Nature and Culture, 4(3), pp. 231–254.

    Article  Google Scholar 

  • Shen Z. J., Yao A. Xiaobai, Kawakami M., Chen P., 2011. Simulating Spatial Market Share Patterns for Impacts Analysis of Large-Scale Shopping Centers on Downtown Revitalization, Environment and planning B: planning and design, vol. 38(1): 142–162.

    Article  Google Scholar 

  • Tannier C., Curab R., Leturcq S., Zadora-Rioc E., 2020. An agent-based model for exploring the combined effects of social and demographic changes on the concentration and hierarchy of rural settlement patterns in North-Western Europe during the Middle Ages (800–1200CE). Journal of Anthropological Archaeology, 59, pp. 101204.

    Google Scholar 

  • Waddell, P., Borning, A., Noth, M., Freier, N., Becke, M., & Ulfarsson, G., 2003. Microsimulation of urban development and location choices: Design and implementation of UrbanSim. Networks and Spatial Economics, 3, pp. 43–67.

    Article  Google Scholar 

  • Wang F. L., Mao Z. D., Wang D. G., 2020. Residential relocation and travel satisfaction change: An empirical study in Beijing, China. Transportation Research Part A: Policy Practice, 135, pp. 341–353.

    Google Scholar 

  • Wang D., He S. J., Webster C., Zhang X. L., 2019. Unravelling residential satisfaction and relocation intention in three urban neighborhood types in Guangzhou, China. Habitat International, 85, PP. 53–62.

    Google Scholar 

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Authors and Affiliations

  1. School of Architecture and urban-Rural Planning, Joint SPSD Lab, Fuzhou University, Fuzhou, China

    Yan Ma

  2. Joint SPSD Lab, Kanazawa University, Kanazawa, Japan

    Zhenjiang Shen

Authors
  1. Yan Ma
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  2. Zhenjiang Shen
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Ma, Y., Shen, Z. (2022). Agent-Based Simulation of Household Residential Relocation and Decision-Making Support of Downtown Revitalization. In: Strategic Spatial Planning Support System for Sustainable Development. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-031-07543-8_4

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