Abstract
This chapter presents a new computational tool that enables strategic planners to explore the impact of sequences in development. The hypothesis is that the order in which projects are built as part of an area’s strategic development can significantly influence future outcomes. A custom-built tool—with an underlying computational simulation—enables the incorporation of bottom-up dynamics and self-organisational behaviours within explorations of urban policies and interventions aimed at spatial and infrastructural change. The simulation treats areas as temporal, spatial and programmatic morphologies with identified relational drivers stemming from a statistical analysis of historic transformations. Transition rules in the simulation model based on historic relations link future development to known trajectories and path dependencies. The tool enables an exploration of the longer-term outcomes of major and minor changes within existing dynamics and trajectories of urban transformation. The possibility to test probable outcomes of strategic controls within the model, as well as catalysts within different temporal scenarios, has the potential to contribute significantly to the development of strategic future planning, especially in the rapidly developing urban environments of the Global South.
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References
Aburas MM, Ho YM, Ramli MF, Ash’aari ZH (2017) Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio. Int J Appl Earth Obs Geoinf 59:65–78. https://doi.org/10.1016/j.jag.2017.03.006
Allah H, Khalil EE (2012) Enhancing quality of life through strategic urban planning. Sustain Cities Soc 5:77–86
Arsanjani JJ, Helbich M, Kainz W, Boloorani AD (2012) Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int J Appl Earth Obs Geoinf 21(1):265–275. https://doi.org/10.1016/j.jag.2011.12.014
Batty M (2000) Geocomputation using cellular automata. Geocomputation:95–126
Batty M (2005) Agents, cells, and cities: new representational models for simulating multiscale urban dynamics. Environ Plan A 37(8):1373–1394. https://doi.org/10.1068/a3784
Batty M (2008) The dynamics of complex urban systems: an interdisciplinary approach. In: Albeverio S, Andrey D, Giordano P, Vancher A (eds) The dynamics of complex urban systems: an interdisciplinary approach. Physica-Verlag, pp 1–20
Batty M (2017) Cities in disequilibrium. In: Johnson J, Nowak A, Ormerod P, Rosewell B, Zhang Y-C (eds) Non-equilibrium social science and policy: introduction and essays on new and changing paradigms in socio-economic thinking. Springer, pp 81–96. https://doi.org/10.1007/978-3-319-42424-8_6
Benenson I (1998) Multi-agent simulations of residential dynamics in the city. Comput Environ Urban Syst 22(1):25–42. https://doi.org/10.1016/S0198-9715(98)00017-9
Berberoğlu S, Akin A, Clarke KC (2016) Cellular automata modeling approaches to forecast urban growth for Adana, Turkey: a comparative approach. Landsc Urban Plan 153:11–27. https://doi.org/10.1016/j.landurbplan.2016.04.017
Bettencourt LMA, Lobo J, Helbing D, Kuhnert C, West GB (2007) Growth, innovation, scaling, and the pace of life in cities. Proc Natl Acad Sci 104(17):7301–7306. https://doi.org/10.1073/pnas.0610172104
Chen S-H (2012) Varieties of agents in agent-based computational economics: a historical and an interdisciplinary perspective. J Econ Dyn Control 36(1):1–25. https://doi.org/10.1016/j.jedc.2011.09.003
Couclelis H (1985) Cellular worlds: a framework for modeling micro - macro dynamics. Environ Plan A 17(5):585–596. https://doi.org/10.1068/a170585
Crooks AT, Patel A, Wise S (2014) Multi-agent systems for urban planning. In: Pinto N, Tenedório J, Antunes A, Cladera J (eds) Technologies for urban and spatial planning: virtual cities and territories, 1st edn. IGI Global, pp 29–56. https://doi.org/10.4018/978-1-4666-4349-9.ch003
Gardner M (1970) Fantastic combinations of John H = f. Sci Am:120–123. https://doi.org/10.1038/scientificamerican0271-112
Gunderson L, Holling CS (2002) Panarchy. Island Press. https://doi.org/10.1016/B978-008045405-4.00695-9
Haken H (1980) Synergetics - are cooperative phenomena governed by universal principles? Naturwissenschaften 67(3):121–128. https://doi.org/10.1007/BF01073611
Han Y, Jia H (2017) Simulating the spatial dynamics of urban growth with an integrated modeling approach: a case study of Foshan, China. Ecol Model 353:107–116. https://doi.org/10.1016/j.ecolmodel.2016.04.005
Hein C, Schubert D (2020) Resilience and path dependence: a comparative study of the port cities of London, Hamburg, and Philadelphia. J Urban Hist 47(2):389–419. https://doi.org/10.1177/0096144220925098
Holling CS (1996) Engineering resilience versus ecological resilience. In: Engineering within ecological constraints, pp 31–44. https://doi.org/10.17226/4919
Itami R (1994) Simulating spatial dynamics: cellular automata theory. Landsc Urban Plan 30(94):27–47. https://doi.org/10.1016/0169-2046(94)90065-5
Jat MK, Garg PK, Khare D (2008) Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. Int J Appl Earth Obs Geoinf 10(1):26–43. https://doi.org/10.1016/j.jag.2007.04.002
Jokar Arsanjani J, Helbich M, de Noronha Vaz E (2013) Spatiotemporal simulation of urban growth patterns using agent-based modeling: the case of Tehran. Cities 32:33–42. https://doi.org/10.1016/j.cities.2013.01.005
Liao J, Tang L, Shao G, Su X, Chen D, Xu T (2016) Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations. Environ Model Softw 75:163–175. https://doi.org/10.1016/j.envsoft.2015.10.014
Manchester City Council (2007) East Manchester Strategic Regeneration Framework 2008 - 2018. https://www.manchester.gov.uk/downloads/download/5719/strategic_regeneration_framework_east
Portugali J (2006) Complexity theory as a link between space and place. Environ Plan A 38(4):647–664. https://doi.org/10.1068/a37260
Portugali J (2018) Cognition and the city: an introduction. Built Environ 44(2):133–135. https://doi.org/10.2148/benv.44.2.133
Portugali J, Haken H (2018) Movement, cognition and the city. Built Environ 44(2):136–161. https://doi.org/10.2148/benv.44.2.136
Rauws W, De Roo G (2016) Adaptive planning: generating conditions for urban adaptability. Lessons from Dutch organic development strategies. Environ Plann B Plann Design 43(6):1052–1074. https://doi.org/10.1177/0265813516658886
Rienow A, Goetzke R (2015) Supporting SLEUTH - enhancing a cellular automaton with support vector machines for urban growth modeling. Comput Environ Urban Syst 49:66–81. https://doi.org/10.1016/j.compenvurbsys.2014.05.001
Roy Chowdhury PK, Maithani S (2014) Modelling urban growth in the Indo-Gangetic plain using nighttime OLS data and cellular automata. Int J Appl Earth Obs Geoinf 33(1):155–165. https://doi.org/10.1016/j.jag.2014.04.009
Russell SJ, Norvig P (1995) Artificial intelligence: A modern approach. Englewood Cliffs, N.J: Prentice Hall
Ruttan VW (1997) Induced innovation, evolutionary theory and path dependence: sources of technical change. Econ J 107:1520–1529
Santé I, García AM, Miranda D, Crecente R (2010) Cellular automata models for the simulation of real-world urban processes: a review and analysis. Landsc Urban Plan 96(2):108–122. https://doi.org/10.1016/j.landurbplan.2010.03.001
Sensing R, Sciences SI (2013) Complex urban simulations and sustainable urban planning with spatial and social implications. II(November):27–29
Shafizadeh-Moghadam H, Asghari A, Tayyebi A, Taleai M (2017) Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth. Comput Environ Urban Syst 64:297–308. https://doi.org/10.1016/j.compenvurbsys.2017.04.002
Silva EA, Clarke KC (2002) Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Comput Environ Urban Syst 26(6):525–552. https://doi.org/10.1016/S0198-9715(01)00014-X
Sorensen A (2014) Taking Path Dependence Seriously: a historical institutionalist research agenda in planning history. Plan Perspect. https://doi.org/10.1080/02665433.2013.874299
Tan M, Li X, Xie H, Lu C (2005) Urban land expansion and arable land loss in China - a case study of Beijing-Tianjin-Hebei region. Land Use Policy 22(3):187–196. https://doi.org/10.1016/j.landusepol.2004.03.003
Teerarojanarat S, Fairbairn D, Chunithipaisan S (2004) Urban growth simulation with UrbanSim. In: Proceedings of the FOSS/GRASS users conference, pp 12–14
Tian G, Ma B, Xu X, Liu X, Xu L, Liu X, Xiao L, Kong L (2016) Simulation of urban expansion and encroachment using cellular automata and multi-agent system model—a case study of Tianjin metropolitan region, China. Ecol Indic 70:439–450. https://doi.org/10.1016/j.ecolind.2016.06.021
Tiwari A (2016) Urban infrastructure research: a review of Ethiopian cities, 1st edn. Springer. https://doi.org/10.1007/978-3-319-30403-8
Tobler WR (1979) Cellular geography. In: Gale S, Olsson G (eds) Philosophy in geography. Springer. https://doi.org/10.1007/978-94-009-9394-5
Torrens PM, O’Sullivan D (2001) Cellular automata and urban simulation: where do we go from here? Environ Plann B Plann Design 28(2):163–168. https://doi.org/10.1068/b2802ed
White R, Engelen G (1997) Cellular automata as the basis of integrated dynamic regional modelling. Environ Plann B Plann Design 24(2):235–246. https://doi.org/10.1068/b240235
Wolfram S (1994) Cellular automata and complexity. CRC Press, Taylor & Francis Group
Zhang J, Pham TTH, Kalacska M, Turner S (2014) Using Landsat Thematic Mapper records to map land cover change and the impacts of reforestation programmes in the borderlands of Southeast Yunnan, China: 1990-2010. Int J Appl Earth Obs Geoinf 31(1):25–36. https://doi.org/10.1016/j.jag.2014.01.006
Acknowledgements
Special thanks to all Complexity Planning and Urbanism (CPU) Lab staff at the Manchester School of Architecture for their help and guidance with all computational aspects of the research as well as their support.
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Solomou, S., Sengupta, U., Cheung, E., Oredein, O. (2022). A Strategic Planning Problem: The Relationship Between Urban Transformation Outcomes and the Temporal Order of Planned Projects. In: Iossifova, D., Gasparatos, A., Zavos, S., Gamal, Y., Long, Y. (eds) Urban Infrastructuring. Sustainable Development Goals Series. Springer, Singapore. https://doi.org/10.1007/978-981-16-8352-7_15
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