Simulación del crecimiento urbano con AMEBAModelo Basado en Agentes para la ocupación residencial

  1. Cantergiani, Carolina 1
  2. Gómez Delgado, Montserrat 2
  1. 1 TECNALIA-Basque Research and Technology Alliance (BRTA)
  2. 2 Universidad de Alcalá
    info

    Universidad de Alcalá

    Alcalá de Henares, España

    ROR https://ror.org/04pmn0e78

Revista:
BAGE. Boletín de la Asociación Española de Geografía

ISSN: 0212-9426 2605-3322

Año de publicación: 2020

Número: 86

Tipo: Artículo

DOI: 10.21138/BAGE.2910 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: BAGE. Boletín de la Asociación Española de Geografía

Resumen

La utilización de Modelos Basados en Agentes (MBA) abre nuevas posibilidades para simular, entender y analizar los resultados del proceso de crecimiento y ocupación urbana teniendo en cuenta diferentes actores implicados en el mismo. El prototipo AMEBA (Agent-based Model for the Evolution of urBan Areas) pretende simular este fenómeno, donde interactúan planificadores, promotores inmobiliarios y la población, a partir del desarrollo de tres submodelos independientes. En el presente trabajo se describe la estructura y funcionamiento del submodelo de ocupación residencial por parte de la población y su integración final con los otros dos submodelos que representan la acción de los demás agentes. Los resultados muestran que es posible desarrollar una arquitectura integrada que permita simular de manera más completa este tipo de sistemas complejos y con la suficiente flexibilidad para ser utilizado en distintas áreas de estudio y simular diferentes escenarios de dinámica urbana a futuro.

Referencias bibliográficas

  • Acosta-Michlik, L., Rounsevell, M.D.A., Bakker, M., Van Doorn, A., Gómez Delgado, M., & Delgado, M. (2014). An agent-based assessment of land use and ecosystem changes in traditional agricultural landscape of Portugal. Intelligent Information Management, 6, 55-80. https://doi.org/10.4236/iim.2014.62008
  • Alghais, N., & Pullar, D. (2018). Modelling future impacts of urban development in Kuwait with the use of ABM and GIS. Transactions in GIS, 22(1), 20-42. https://doi.org/10.1111/tgis.12293
  • Alves, R., Da Silva Lima, R.; de Sena, D.C., de Pinho, A.F., & Holguín-Veras, J. (2019). Agent-based simulation model for evaluation urban freight policy to E-Commerce. Sustainability, 11, 4020. https://doi.org/10.3390/su11154020
  • Batty, M. (2016). 20 years of quantitative geographical thinking. Environment and Planning B: Planning and Design, 43(4), 605-609. https://doi.org/10.1177/0265813516655408
  • Barredo, J.I., Kasanko, M., McCormick, N., & Lavalle, C., (2003). Modelling dynamic spatial processes: simulation or urban future scenarios through cellular automata. Landscape and Urban Planning , 64(3), 145-160. https://doi.org/10.1016/S0169-2046(02)00218-9
  • Barros, J. (2012). Exploring urban dynamics in latin american cities using an agent-based simulation approach. In A. Heppenstall, A.T. Crooks, L.M. See & M. Batty (Eds.), Agent-Based Models of Geographical Systems (pp. 571–589). Dordrecht: Springer.
  • Berberoglu, A., Akim, A., & Clarke, K.C. (2016). Cellular automata modeling approaches to forecast urban growth for adana, Turkey: A comparative approach. Landscape and urban planning, 153, 11-27. https://doi.org/10.1016/j.landurbplan.2016.04.017
  • Bonabeau, E. (2002). Predicting the Unpredictable. Harvard Business Review, March Issue, 109-116.
  • Cantergiani, C.C. (2011). Modelos basados en agentes aplicados a estudios urbanos: una aproximación teórica. Serie Geográfica, 17, 29-43.
  • Cantergiani, C.C., & Gómez Delgado, M. (2016). Diseño de un modelo basado en agentes para simular el crecimiento urbano en el Corredor del Henares (Comunidad de Madrid). Boletín de la Asociación de Geógrafos Españoles, 70, 259-283. https://doi.org/10.21138/bage.2171
  • Cantergiani, C.C., & Gómez-Delgado, M. (2018). Urban land allocation model of territorial expansion by urban planners and housing development. Environments, 5(5). https://doi.org/10.3390/environments5010005
  • Crooks, A.T., Patel, A., & Wise, S. (2014). Multi-agent systems for urban planning. In N.N. Pinto, J.A.Tenedório, A.P. Antunes & J. Roca (Eds.), Technologies for Urban and Spatial Planning: Virtual Cities and Territories; (pp. 29-56). Hershey, PA, USA: IGI Global.
  • Dahal, K.R., & Chow, T.E. (2014). An agent-integrated irregular automata model of urban land-use dynamics. International Journal of Geographical Information Science, 28(11), 2281-2303. https://doi.org/10.1080/13658816.2014.917646
  • De Kok, J-L., Overloope, S., & Engelen, G. (2017). Screening models for integrated environmental planning – A feasibility study for Flanders. Futures, 88, 55-68. https://doi.org/10.1016/j.futures.2017.03.007
  • Fang C., & Yu, D. (2017). Urban agglomeration: An evolving concept of an emerging phenomenon. Landscape and urban planning, 162, 126-136. https://doi.org/10.1016/j.landurbplan.2017.02.014
  • Feitosa, F.F., Le, Q.B., & Vlek, P.L.G. (2011). Multi-agent simulator for urban segregation (MASUS): A tool to explore alternatives for promoting inclusive cities. Computers, Environment and Urban Systems, 35, 104–115. https://doi.org/10.1016/j.compenvurbsys.2010.06.001
  • Fernández Güell, J.M. (2011). Recuperación de los estudios del futuro a través de la prospectiva territorial. Ciudad y Territorio, XLIII(167), 11-32.
  • Filatova, T. (2015). Empirical agent-based land market: Integrating adaptive economic behavior in urban land-use models. Computers, Environment and Urban Systems, 54, 397–413. https://doi.org/10.1016/j.compenvurbsys.2014.06.007
  • Fontaine, C.M., & Rounsevell, M.D.A. (2009). An agent-based approach to model futures residential pressure on a regional landscape. Landscape Ecology, 24, 1237–1254. https://doi.org/10.1007/s10980-009-9378-0
  • Gallardo, M., & Martínez-Vega, J. (2016). Three decades of land-use changes in the region of madrid and how they relate to territorial planning. European Planning Studies, 24, 1016–1033. https://doi.org/10.1080/09654313.2016.1139059
  • Ghavami, S. M., Taleai, M., & Arentze, T. (2016). Socially rational agents in spatial landuse planning: A heuristic proposal based negotiation mechanism. Computers, Environment and Urban Systems, 60, 67-78. https://doi.org/10.1016/j.compenvurbsys.2016.08.004
  • Grimm, V., & Railsback, S.F. (Eds) (2005). Individual-based modeling and ecology. Princeton, NJ: Princeton University Press.
  • Groeneveld, J., Müller, B., Buchmann, C.M., Dressler, G., Guo C., Hase, N., ... Schwarz , N. (2017). Theoretical foundations of human decision-making in agent-based land use models - A review. Environmental modelling and Software, 87, 39-48. https://doi.org/10.1016/j.envsoft.2016.10.008
  • Guo, C., Buchmann, C.M., & Schwarz, N. (2017). Linking urban sprawl and income segregation- Findings from stylized agent-based model. Environment and Planning B: Urban analytics and city science, 46(3), 469-489. https://doi.org/10.1177/2399808317719072
  • Hackl, J., & Dubernet, T. (2019). Epidemic spreading in urban areas using agent-based transportation models. Future internet, 11, 92.
  • Hosseinali, F., Alesheikh, A.A., & Nourian, F. (2013). Agent-based modeling of urban land-use development, case study: Simulating future scenarios of Qazvin city. Cities, 31, 105-113. https://doi.org/10.1016/j.cities.2012.09.002
  • Jordan, R., Birkin, M., & Evans, A. (2012). A. Agent-based modeling of residential mobility, housing choice and regeneration. In A. Heppenstall, A.T. Crooks, L.M. See & M. Batty (Eds.), Agent-Based Models of Geographical Systems (pp. 511-524). Dordrecht: Springer.
  • Langlois, P. (2013). Simulation of complex systems in GIS. Londres, John Wiley & Sons
  • Ligmann-Zielinska, A., & Jankowski, P. (2010). Exploring normative scenarios of land use development decisions with an agent-based simulation laboratory. Computers, Environment and Urban Systems, 34, 409–423. https://doi.org/10.1016/j.cities.2012.09.002
  • Long Y., & Zhang, Y. (2015). Land-use pattern scenario analysis using planner agents. Environment and planning B: Planning and Design, 42, 615-637. https://doi.org/10.1068/b130012p
  • Magliocca, N., Safirova, E., Mcconnell, V., & Walls, M. (2011). An economic agent-based model of coupled housing and land markets (CHALMS). Computers, Environment and Urban Systems, 35, 83-191. https://doi.org/10.1016/j.compenvurbsys.2011.01.002
  • Malik, A., & Abdalla, R. (2017). Agent-based modelling for urban sprawl in the region of Waterloo, Ontario, Canada. Modeling Earth Systems and Environment, 3(7). https://doi.org/10.1007/s40808-017-0271-6
  • Mansury, Y. (2015). Bootom-up computational models or urban systems: In search of micro-fundations. Computers, Environment and Urban Systems, 54, 385-387. https://doi.org/10.1016/j.compenvurbsys.2015.10.006
  • Matthews, R.B., Gilbert, N.G., Roach, A., Polhill, J.G., & Gotts, N.M. (2007). Agent-based land-use models: a review of applications. Landscape Ecology, 22, 1447-1459. https://doi.org/10.1007/s10980-007-9135-1
  • Motieyan, H., & Mesgari, M.S. (2018). An agent-based modeling approach for sustainable urban planning from land use and public transit perspectives. Cities, 81, 91-100. https://doi.org/10.1016/j.cities.2018.03.018
  • Mustafa, A., Cools, M., Saadi, I., & Teller, J. (2017). Coupling agent-based, cellular automata and logistic regression into a hybrid urban expansion model (HUEM). Land Use Policy, 69, 529-540. https://doi.org/10.1016/j.landusepol.2017.10.009
  • Parker, D.C, Manson, S.M., Janssen, M.A., Hoffmann, M.J., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: a review. Annals of the Association of American Geographers, 93(2), 314-337. https://doi.org/10.1111/1467-8306.9302004
  • Plata Rocha, W., Gómez Delgado, M., & Bosque Sendra, J. (2011). Simulation urban growth scenarios using gis and multicriteria evaluation techniques. Case study: Madrid region, Spain. Environment and Planning B: Planning and Design, 38, 1012–1031. https://doi.org/10.1068/b37061
  • Pumain, D., & Sanders, L. (2013). Theoretical principles in interurban simulation models: a comparison. Environment and Planning A: Economy and Space, 45, 2243-2260. https://doi.org/10.1068/a45620
  • Soria-Lara, J.A., Ariza-Álvarez, M.A., Aguilera-Benavente, F., Cascajo, R., Arce, R., López-García de Lenaiz, C., & Gómez-Delgado, M. (Final review). Participatory visioning for building disruptive future scenarios for transport and land use planning. Journal of Transport Geography.
  • Souza, L.C.G.D. (2005). O ensino da psicologia social e suas representações. A formação do saber o saber em formação. PHd, UFRJ. Retrieved from http://livros01.livrosgratis.com.br/cp023945.pdf
  • Sun, Z., Lorscheid, I., Millington, J.D., Lauf, S., Magliocca, N.R., Groeneveld, J., ... Buchmann, C.M. (2016). Simple or complicated agent-based models? A complicated issue. Environmental modelling and software, 86, 56-67. https://doi.org/10.1016/j.envsoft.2016.09.006
  • Tabernero, C., Hernández, B., Cuadrado, E., Luque, B., & Pereira, C.R. (2015). A multilevel perspective to explain recycling behaviour in communities. Journal of Environmental Management, 159, 192-201. https://doi.org/10.1016/j.jenvman.2015.05.024
  • Tan, R., Liu, Y., Zhou, K., Jiao, L. & Tang, W. (2015). A game-theory based agent-cellular model for use in urban growth simulation: A case study of the rapidly urbanizing Wuhan area of central China. Computers, environment and urban systems, 49, 15-29. https://doi.org/10.1016/j.compenvurbsys.2014.09.001
  • Tayyebi, A., Pijanowski, B. C., & Pekin, B. (2011). Two rule-based urban growth boundary models applied to the Tehran metropolitan area, Iran. Applied Geography, 31(3), 908–918. https://doi.org/10.1016/j.apgeog.2011.01.018
  • Tesfatsion, L., Rehmann, C.R., Cardoso, D.S., Jie, Y., & Gutowski, W.J. (2017). An agent-based platform for the study of watersheds as coupled natural and human systems. Environmental Modelling & Software, 89, 40-60. https://doi.org/10.1016/j.envsoft.2016.11.021
  • Torrens, P.M., & O’Sullivan, D. (2001). Cellular automata and urban simulation: where do we go from here? Environment and Planning B: Planning and Design, 28(2), 163-168. https://doi.org/10.1068/b2802ed
  • Torrens, P.M. (2012). Moving agent-pedestrians through space and time. Annals of the Association of American Geographers, 102(1), 35-66. https://doi.org/10.1080/00045608.2011.595658
  • Triantakonstantis, D., & Mountrakis, G. (2012). Urban growth prediction: A review of computational models and human perceptions. Journal of Geographical Information Systems, 4, 555-587. https://doi.org/10.4236/jgis.2012.46060
  • UN-HABITAT (2016). Urbanization and development: emerging futures. Nairobi, Kenia: World Cities report 2016.
  • Valenzuela Rubio, M. (2010). La planificación territorial de la región metropolitana de Madrid. Una asignatura pendiente. Cuadernos geográficos, 47(2), 95-129.
  • Valera, S., & Pol, E. (1994). El concepto de identidad social urbana: una aproximación entre la Psicología Social y la Psicología Ambiental. Anuario de Psicología, 62, 5-24.
  • Yang, Y., Mao, L., & Metcalf, S.S. (2019). Diffusion of hurricane evacuation behavior through a home-workplace social network: A spatially explicit agent-based simulation model. Computers, environment and urban systems, 74, 13-22. https://doi.org/10.1016/j.compenvurbsys.2018.11.010
  • Zhuge, C., Shao, C., Gao, J., Dong, C., & Zhang, H. (2016). Agent-based joint model of residential location choice and real estate price for land use and transport model. Computers, environment and urban systems, 57, 93-105. https://doi.org/10.1016/j.compenvurbsys.2016.02.001