Cellular Automata-Based Suitability Analysis for Dense Urban Areas: The Case of Istanbul

Year 2023, Volume: 1 Issue: 1, 38 - 49, 15.06.2023

Emirhan Coşkun

https://doi.org/10.26650/JTADP.01.005

Abstract

Cellular automata (CA) have emerged as one of the most popular approaches used in recent years to evaluate and predict the development and transformation of cities. Cellular automata approaches have made the complex interaction between urban dynamics and urban sustainability effectively understandable. These models provide a deeper understanding of the complex relationship between land-use changes and urban sustainability. This understanding enables governments, planners, and stakeholders to predict and evaluate the potential consequences of future policy alternatives. It is essential to create scenarios in determining urban policies. The fact that cellular automata models create what-if scenarios makes it an approach that can be used frequently for urban transformation. Thus, the study focuses on the urban development paradigm by interpreting the urban transformation concepts in the historical coastal areas of Istanbul with geospatial techniques, a CA-based urban growth model, and land use data. Reliability is vital for using CA models as decision-support tools in this context. Testing the reliability of CA models, one of the study’s aims, is an essential parameter in this respect. For this purpose, the CA model was created by collecting population density, focal points, distance to roads, land uses, and land slope data from different periods (1994 and 2006). The results demonstrated that urban simulation models are effective decision-support tools, promising a more inclusive and explicit planning process.

Keywords

Decision support systems, urban growth, cellular automata, GIS

References

• Aburas, M. M., Ho, Y. M., Ramli, M. F., & Ash’aari, Z. H. (2016). The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review. International Journal of Applied Earth Observation and Geoinformation, 52, 380-389. https://doi.org/10.1016/j.jag.2016.07.007 google scholar
• Batty, M. (2005). Cities and complexity: Understanding cities with cellular automata, agent-based models, andfractals. MIT Press. google scholar
• Chakraborty, A., Sikder, S., Omrani, H., & Teller, J. (2022). Cellular Automata in Modeling and Predicting Urban Densification: Revisiting the Literature since 1971 Land, 11,(7) 1113 https://doi.org/10.3390/land11071113 google scholar
• Hashemi, A. B., & Meybodi, M. R. (2009). A multi-role cellular PSO for dynamic environments. 2009 14th International CSI Computer Conference, pp. 412-417. https://doi.org/10.1109/CSICC.2009.5349615 google scholar
• Iovine, G., D’Ambrosio, D., & Di Gregorio, S. (2005). Applying genetic algorithms for calibrating a hexagonal cellular au- google scholar
• tomata model for the simulation of debris flows characterized by strong inertial effects. Geomorphology, 66(1), 287-303. https://doi.org/10.1016/j.geomorph.2004.09.017 google scholar
• Li, X., Yang, Q., & Liu, X. (2008). Discovering and evaluating urban signatures for simulating compact development using cellular automata. google scholar
• Landscape and Urban Planning, 86, 177- 186 https://doi.org/10.1016/j.landurbplan.2008.02.005 google scholar
• Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., Li, S., Wang, S., & Pei, F. (2017). A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 168, 94-116.https://doi.org/10.1016/j.landurbplan.2017.09.019 google scholar
• Liu, Y., & Feng, Y. (2012). A Logistic Based Cellular Automata Model for Continuous Urban Growth Simulation: A Case Study of the Gold Coast City, Australia. Içinde A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Ed.), Agent-Based Models of Geographical Systems (ss. 643- 662). Springer Netherlands. https://doi.org/10.1007/978-90-481-8927-4_32 google scholar
• Mantelas, L., Prastacos, P., Hatzichristos, T., & Koutsopoulos, K. (2012). Using fuzzy cellular automata to access and simulate urban growth. GeoJournal, 77(1), 13-28. https://doi.org/10.1007/s10708-010-9372-8 google scholar
• Mantelas, L., Prastacos, P., Hatzichristos, T., & Koutsopoulos, K. (2012). Using fuzzy cellular automata to access and simulate urban growth. GeoJournal, 77, 13-28. https://doi.org/10.1007/s10708-010-9372-8 google scholar
• Musa, S. I., Hashim, M., & Reba, M. N. M. (2017). A review of geospatial-based urban growth models and modeling initiatives. Geocarto International, 32(8), 813-833. https://doi.org/10.1080/10106049.2016.1213891 google scholar
• Ning Wu, & Silva, E. A. (2010). Artificial Intelligence Solutions for Urban Land Dynamics: A Review. Journal of Planning Literature, 24(3),246-265. https://doi.org/10.1177/0885412210361571 google scholar
• O’Sullivan, D., & Torrens, P. M. (2001). Cellular Models of Urban Systems. Içinde S. Bandini & T. Worsch (Ed.), Theory and Practical Issues on Cellular Automata (ss. 108-116). Springer. https://doi.org/10.1007/978-1-4471-0709-5_13 google scholar
• Rodnguez Puente, R., Perez Betancourt, Y. G., & Mufeti, K. (2015). Cellular Automata And Its Applications In Modeling And Simulating The Evolution Of Diseases. National Research Symposium, Namibia. google scholar
• https://www.researchgate.net/publication/282249343_Cellular_Automata_And_Its_Applications_In_Modeling_And_Simulating_The_Evolution_Of_D google scholar
• Sante, I., Garcıa, A. M., Miranda, D., & Crecente, R. (2010). Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 96, 108-122. https://doi.org/10.1016/jMandurbplan.2010.03.001 google scholar
• Shi, W., & Pang, M. Y. C. (2000). Development of Voronoi-based cellular automata -an integrated dynamic model for Geographical Information Systems. International Journal of Geographical Information Science, 14(5), 455-474. https://doi.org/10.1080/13658810050057597 google scholar
• Sietchiping, R. (2004). A Geographic Information Systems and cellular automata-based model ofinformal settlement growth [Doctoral, University of Melbourne]. http://hdl.handle.net/11343/38860 google scholar
• Silva, E. A., & Clarke, K. C. (2005). Complexity, emergence, and cellular urban models: Lessons learned from applying SLEUTH to two Portuguese metropolitan areas. European Planning Studies, 13(1), 93-115. https://doi.org/10.1080/0965431042000312424 google scholar
• Sipahioğlu, N., & Çağdaş, G. (2022). Scenario-Based Cellular Automata and Artificial Neural Networks in Urban Growth Modeling. Gazı Unıversıty Journal OfScıence. https://doi.org/10.35378/gujs.998073 google scholar
• Triantakonstantis, D., & Mountrakis, G. (2012). Urban Growth Prediction: A Review of Computational Models and Human Perceptions. Journal of Geographic Information System, 04(06), 555-587. https://doi.org/10.4236/jgis.2012.46060 google scholar
• Tripathy, P., & Kumar, A. (2019). Monitoring and modeling spatio-temporal urban growth of Delhi using Cellular Automata and Geoinformatics.Cities, pp. 90, 52-63. https://doi.org/10.1016/jxities.2019.01.021 google scholar
• Usanmaz Coşkun, B. (2020). Kıyı Alanlarındaki Kentsel Projelerin Bağlamla İlişkilenmesi İçin Arazi Kullanımına Dayalı Bir Yaklşaım Önerisi -İstanbul Örneği [Doctoral]. Mimar Sinan Güzel Sanatlar Üniversitesi. google scholar
• Wallentin, G. (2020). UNIGIS Module: Spatial Simulation. The University of Salzburg. https://unigis- salzburg.github.io/Opt_Spatial-Simulation/cellular-automata.htmlWolfram, S. (2002). A new kind ofscience. Wolfram Media. google scholar
• WorldPop, & Bondarenko, Maksym. (2020). Individual Countries 1km UNAdjusted Population Density (2000-2020) [Data set]. The University of Southampton. https://doi.org/10.5258/SOTON/WP00675 google scholar
• Yeh, A. G. O., Li, X., & Xia, C. (2021). Cellular Automata Modeling for Urban and Regional Planning. Içinde W. Shi, M. F. Goodchild, M. google scholar
• Batty, M.-P. Kwan, & A. Zhang (Ed.), Urban Informatics (ss. 865-883). Springer Singapore. https://doi.org/10.1007/978-981-15-8983-6_45 google scholar