Abstract
The paper presents a proposal for a hybrid model - based on cellular automata and agents - that simulates the spatial distribution of population and built area according to real estate market’s dynamics and the risk of flooding due to sea-level rise. Its main differential is the integration of network analysis metrics to the functioning of the cellular automata. This proposal was motivated by the interest in analysing future development scenarios for the coast of Rio Grande do Sul, a state located in southern Brazil. Its demographic dynamics have been generating pressure for urban growth to the detriment of the surrounding natural environment, making cities in the region more susceptible to natural phenomena such as the sea-level rise. The proposed model is presented through the ODD + D description protocol and the results of simulations executed for Imbé and Tramandaí, two municipalities located on the coast of Rio Grande do Sul. The results show that the model represents the effect of current planning policies on long-term urban development. However, some urban dynamics are not yet precisely represented by the proposal at its current stage of development.
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References
Bertê, A., Lemos, B., Testa, G., Zanella, M., Oliveira, S.: Perfil Socioeconômico COREDE Litoral. Department of Planning, Mobility and Regional Development of the State Government of Rio Grande do Sul, Porto Alegre, Brazil (2015). https://planejamento.rs.gov.br/upload/arquivos/201512/15134132-20151117102724perfis-regionais-2015-litoral.pdf. Accessed 18 Mar 2021
Zuanazzi, P., Bartels, M.: Estimativas para a População Flutuante do Litoral Norte do RS. Fundação de Economia e Estatística Siegfried Emanuel Heuser, Porto Alegre, Brazil (2016)
Kluge, I.: A Articulação entre Urbanização, Economia e Mercado Imobiliário em Cidades Litorâneas e a Relação com o Ambiente Construído: o estudo de caso do município de Capão da Canoa – RS. Federal University of Rio Grande do Sul - UFRGS, Porto Alegre, Brazil (2015). https://lume.ufrgs.br/handle/10183/130719. Accessed 18 Mar 2021
Climate Central’s Coastal Risk Screening Tool. https://coastal.climatecentral.org/map. Accessed 18 Mar 2021
Casali, Y., Heinimann, H.R.: A topological characterization of flooding impacts on the Zurich road network. PLOS ONE 14(7), e0220338 (2019). https://doi.org/10.1371/journal.pone.0220338
Kim, Y., Newman, G.: Advancing scenario planning through integrating urban growth prediction with future flood risk models. Comput. Environ. Urban Syst. 82, 101498 (2020). https://doi.org/10.1016/j.compenvurbsys.2020.101498
Li, S., Li, X., Liu, X., Wu, Z., Ai, B., Wang, F.: Simulation of spatial population dynamics based on labor economics and multi-agent systems: a case study on a rapidly developing manufacturing metropolis. Int. J. Geogr. Inf. Sci. 27, 2410–2435 (2013). https://doi.org/10.1080/13658816.2013.826360
Taberna, A., Filatova, T., Roy, D., Noll, B.: Tracing resilience, social dynamics and behavioral change: a review of agent-based flood risk models. Socio-Environ. Syst. Model. 2, 17938 (2020). https://doi.org/10.18174/sesmo.2020a17938
Batty, M. Complexity in city systems: understanding, evolution and design. Centre for Advanced Spatial Analysis (CASA), London (2007). https://discovery.ucl.ac.uk/id/eprint/3473/. Accessed 18 Mar 2021
Portugali, J.: What makes cities complex? In: Portugali, J., Stolk, E. (eds.) Complexity, Cognition, Urban Planning and Design. SPC, pp. 3–19. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32653-5_1
Moroni, S., Cozzolino, S.: Action and the city: emergence, complexity, planning. Cities 90, 42–51 (2019). https://doi.org/10.1016/j.cities.2019.01.039
De Roo, G.: Spatial planning, complexity and a world ‘out of equilibrium’: outline of a non-linear approach to planning. In: de Roo, G., Hillier, J., van Wezemael, J. (eds.) Complexity and Spatial Planning: Systems, Assemblages and Simulations. Ashgate Publishing, Farnham (2012)
Zellner, M., Campbell, S.D.: Planning for deep-rooted problems: what can we learn from aligning complex systems and wicked problems? Plan. Theor. Pract. 16, 457–478 (2015). https://doi.org/10.1080/14649357.2015.1084360
White, R., Engelen, G., Uljee, I.: Modeling Cities and Regions as Complex Systems: From Theory to Planning Applications. The MIT Press, Cambridge, Massachusetts (2015).978-0-262-02956-8
Torrens, P.M.: How Cellular Models of Urban Systems Work (1. Theory). Centre for Advanced Spatial Analysis (CASA), London (2000). https://discovery.ucl.ac.uk/id/eprint/1371/. Accessed 18 Mar 2021
Torrens, P.M.: Automata-based models of urban systems. In: Longley, P., Batty, M. (eds.) Advanced Spatial Analysis, pp. 61–79. ESRI Press, Redlands (2003)
Liu, Y., Batty, M., Wang, S., Corcoran, J.: Modelling urban changes with cellular automata: contemporary issues and future research directions. Prog. Hum. Geogr. 45, 3–24 (2021). https://doi.org/10.1177/0309132519895305
Crooks, A.T., Patel, A., Wise, S.: Multi-agent systems for urban planning. In: Pinto, N., Tenedório, J.A., Antunes, A.P., Caldera, J.R. (eds.) Technologies for Urban and Spatial Planning: Virtual Cities and Territories, pp. 29–56. IGI Global (2014)
Dahal, K.R., Chow, T.E.: An agent-integrated irregular automata model of urban land-use dynamics. Int. J. Geogr. Inf. Sci. 28, 2281–2303 (2014). https://doi.org/10.1080/13658816.2014.917646
Batty, M., Torrens, P.M.: Modelling and prediction in a complex world. Futures 37, 745–766 (2005). https://doi.org/10.1016/j.futures.2004.11.003
Batty, M.: Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. 1. Paperback edn. MIT Press, Cambridge, Massachusetts (2007). ISBN 978-0-262-52479-7
Ngo, T.A., See, L.: Calibration and validation of agent-based models of land cover change. In: Heppenstall, A.J., Crooks, A.T., See, L.M., Batty, M. (eds.) Agent-Based Models of Geographical Systems, pp. 181–197. Springer Netherlands, Dordrecht (2012). https://doi.org/10.1007/978-90-481-8927-4_10
Müller, B., et al.: Describing human decisions in agent-based models – ODD + D, an extension of the ODD protocol. Environ. Model. Softw. 48, 37–48 (2013). https://doi.org/10.1016/j.envsoft.2013.06.003
Krafta, R.: Spatial self-organization and the production of the city. Cybergeo: Eur. J. Geogr. (1999). Document 350. https://doi.org/10.4000/cybergeo.4985
Krafta, R.: Urban convergence: morphology and attraction. Environ. Plan. B Plan. Des. 23, 37–48 (1996). https://doi.org/10.1068/b230037
Filatova, T.: Empirical agent-based land market: integrating adaptive economic behavior in urban land-use models. Comput. Environ. Urban Syst. 54, 397–413 (2015). https://doi.org/10.1016/j.compenvurbsys.2014.06.007
Taillandier, P., et al.: Building, composing and experimenting complex spatial models with the GAMA platform. GeoInformatica 23(2), 299–322 (2018). https://doi.org/10.1007/s10707-018-00339-6
OpenStreetMap. https://www.openstreetmap.org/search?query=imb%C3%A9#map=12/-29.9355/-50.1156. Accessed 19 Mar 2021
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Dalcin, G.K., Krafta, R. (2021). Hybrid Urban Model (CA + Agents) for the Simulation of Real Estate Market Dynamics and Sea-Level Rise Impacts. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12950. Springer, Cham. https://doi.org/10.1007/978-3-030-86960-1_52
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