Kentsel Tasarımda Hesaplamalı Tasarım Yaklaşımların Kullanılması Hücresel Otomata Tabanlı Model Çerçevesi

Abstract

Teknolojik gelişmeler Bilgisayar Destekli Tasarımdan Hesaplamalı Tasarıma doğru bir yönelimin hız kazanmasına olanak sağlamıştır. Bu yönelimde Hesaplamalı Tasarımın sahip olduğu üretken yapının, geleneksel tasarım düşüncesini oluşturan mekanizmaları anlama ihtiyacını ortaya çıkarmada önemli bir yeri vardır. Geleneksel tasarım düşüncesinin sahip olduğu örtük yapı, tasarım sürecinin nasıl gerçekleştiği, tasarım sürecinde hangi mekanizmaların yer aldığı, tasarım bilgisinin ve temsilin nasıl oluştuğu anlamamıza ve formüle etmemize izin vermediğinden üretken ve değerlendirici bir bilgiden söz etmek mümkün olmamaktadır. Hesaplamalı tasarım yaklaşımı geleneksel tasarım düşüncesindeki örtük bilginin keşfedilmesini sağlayarak üretken ve değerlendirici bir bilgi oluşturur. Bu çalışmanın amacı Hesaplamalı tasarım yaklaşımının keşfetmemizi sağladığı üretken ve değerlendirici bilginin kentsel tasarımda kullanımına yönelik bir öğrenme çerçevesini oluşturma düşüncesinden ortaya çıkmıştır. Çalışma kapsamında Hesaplamalı tasarım düşüncesinin yansıması olan ve oldukça sık kullanılan üretken sistemlerden Hücresel Otomatların (Cellular Automata) kentsel tasarım bağlamında nasıl ele alınabileceği tartışılacaktır. Hücresel Otomata çalışmalarında hesaplama düşüncesi ve yaklaşım yöntemleri tartışıldıktan sonra kentsel tasarım bağlamında bu üretken yöntemin katkıları incelenecektir. Hücresel Otomata yaklaşımları, parçalardan bütünü görme, parçalar arasındaki ilişkileri ve örüntüleri fark etme ve bunları üretken süreç sırasında ve sonrasında yeniden keşfetme yeteneğini geliştiren keşifsel süreçler olduğundan, özellikle kentsel gelişim senaryolarının bir parçası olmak açısından önemli bir role sahip olmaktadırlar.

Keywords

• Kentsel Tasarım
• Hesaplamalı Tasarım
• Hücresel Otomata
• Üretken Sistemler
• Coğrafi Bilgi Sistemleri

Computational Design Approaches in Urban Design: Cellular Automata-Based Model Framework

Abstract

Technological developments have enabled the acceleration of a shift from Computer Aided Design to Computational Design. The productive structure of Computational Design plays an important role in revealing the mechanisms that make up traditional design thinking. The traditional design thinking approach lacks an explicit structure for understanding and formulating the design process, the mechanisms involved, and the formation of design knowledge and representation. As a result, it is difficult to develop productive and evaluative knowledge. In contrast, the computational design approach facilitates the discovery of tacit knowledge, leading to the creation of productive and evaluative knowledge. The aim of this study is to create a learning framework for using the productive and evaluative knowledge discovered through the computational design approach in urban design. The study will discuss how to handle Cellular Automata, one of the most frequently used generative systems that reflects computational design thinking, in the context of urban design. This section will analyse the contributions of the generative method in the context of urban design, after discussing the computational thinking and approach methods used in Cellular Automata studies. Cellular Automata approaches are useful in urban development scenarios as they allow for the recognition of relationships and patterns between parts, and rediscovery of them during and after the generative process. They develop the ability to see the whole from the parts. It is important to maintain objectivity and avoid biased language.

Keywords

• Urban Design
• Computational Design
• Cellular Automata
• Generative Systems
• Geographic Information Systems

References

1. 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
2. Alkan, M., Oruc, M., Yildirim, Y., Seker, D. Z., & Jacobsen, K. (2013). Monitoring Spatial and Temporal Land Use/Cover Changes; a Case Study in Western Black Sea Region of Turkey. Journal of the Indian Society of Remote Sensing, 41(3), 587-596. https://doi.org/10.1007/s12524-012-0227-2
3. Alonso, W. (1960). A THEORY OF THE URBAN LAND MARKET. Papers in Regional Science, 6(1), 149-157. https://doi.org/10.1111/j.1435-5597.1960.tb01710.x
4. Al-shalabi, M., Billa, L., Pradhan, B., Mansor, S., & Al-Sharif, A. A. A. (2013). Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: The case of Sana’a metropolitan city, Yemen. Environmental Earth Sciences, 70(1), 425-437. https://doi.org/10.1007/s12665-012-2137-6
5. Al-sharif, A. A. A., & Pradhan, B. (2014). Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 7(10), 4291-4301. https://doi.org/10.1007/s12517-013-1119-7
6. Barredo, J. I., Demicheli, L., Lavalle, C., Kasanko, M., & McCormick, N. (2004). Modelling Future Urban Scenarios in Developing Countries: An Application Case Study in Lagos, Nigeria. Environment and Planning B: Planning and Design, 31(1), 65-84. https://doi.org/10.1068/b29103
7. Batty, M. (1997). Cellular Automata and Urban Form: A Primer. Journal of the American Planning Association, 63(2), 266-274. https://doi.org/10.1080/01944369708975918
8. Batty, M., & Xie, Y. (1994). Research Article. Modelling inside GIS: Part 1. Model structures, exploratory spatial data analysis and aggregation. International Journal of Geographical Information Systems, 8(3), 291-307. https://doi.org/10.1080/02693799408902001

9. Bosque-Sendra, J. (2004). COMPARISON OF MULTI-CRITERIA EVALUATION METHODS INTEGRATED IN GEOGRAPHICAL INFORMATION SYSTEMS TO ALLOCATE URBAN AREAS. https://www.semanticscholar.org/paper/COMPARISON-OF-MULTI-CRITERIA-EVALUATION-METHODS-IN-Bosque-Sendra/d024625bc7c8aa1ad0ae6a4f25a19da979711b51
10. Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24(2), 247-261. https://doi.org/10.1068/b240247
11. Crooks, A. T., Patel, A., & Wise, S. (2014). Multi-Agent Systems for Urban Planning. İçinde Technologies for Urban and Spatial Planning: Virtual Cities and Territories. IGI Global. DOI: 10.4018/978-1-4666-4349-9
12. Çalışır Adem, P., & Çağdaş, G. (2020). Computational Design Thinking through Cellular Automata: Reflections from Design Studios. Journal of Design Studio, 71-83. https://doi.org/10.46474/jds.816833
13. Gero, J. S., & Kazakov, V. A. (1998). Evolving design genes in space layout planning problems. Artificial Intelligence in Engineering, 12(3), 163-176. https://doi.org/10.1016/S0954-1810(97)00022-8
14. Gu, N., Singh, V., & Merrick, K. (2010). A framework to integrate generative design techniques for enhancing design automation. 127-136.
15. Hashemi, A. B., & Meybodi, M. R. (2009). A multi-role cellular PSO for dynamic environments. 2009 14th International CSI Computer Conference, 412-417. https://doi.org/10.1109/CSICC.2009.5349615
16. Huang, C.-Y., Sun, C.-T., Hsieh, J.-L., & Lin, H. (2004). Simulating SARS: Small-World Epidemiological Modeling and Public Health Policy Assessments. ournal of Artificial Societies and Social Simulation, 7(4), 100-131.
17. Jensen, M. B., & Foged, I. W. (2014). Cellular Automata as a learning process in Architecture and Urban design. 297-302. https://doi.org/10.52842/conf.ecaade.2014.1.297
18. Jiang, F., Ma, J., Webster, C. J., Chiaradia, A. J. F., Zhou, Y., Zhao, Z., & Zhang, X. (2023). Generative urban design: A systematic review on problem formulation, design generation, and decision-making. Progress in Planning, 100795. https://doi.org/10.1016/j.progress.2023.100795
19. Knight, T. W. (1999). Shape grammars: Six types. Environment and Planning B: Planning and Design, 26(1), 15-31. https://doi.org/10.1068/b260015
20. 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
21. 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
22. McCormick, N., Lavalle, C., Kasanko, M., Demicheli, L., & Barredo, J. (2003). Mapping and modelling the impact of land use planning and management practices on urban and peri-urban landscapes in Europe: The MOLAND project. 22nd Digital Avionics Systems Conference. Proceedings (Cat. No.03CH37449), 243-244. https://doi.org/10.1109/DFUA.2003.1219996
23. Menges, A., & Ahlquist, S. (Ed.). (2011). Computational design thinking. John Wiley & Sons.
24. Mohammadi, M., Sahebgharani, A., & Malekipour, E. (2013). Urban growth simulation through cellular automata (CA), analytic hierarchy process (AHP) and GIS; case study of 8th and 12th municipal districts of Isfahan. Geographia Technica, 8(2), 57-70.
25. Musa, S. I., Hashim, M., & Reba, M. N. M. (2017). A review of geospatial-based urban growth models and modelling initiatives. Geocarto International, 32(8), 813-833. https://doi.org/10.1080/10106049.2016.1213891
26. 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
27. 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
28. Oxman, R. (2008). Digital architecture as a challenge for design pedagogy: Theory, knowledge, models and medium. Design Studies, 29(2), 99-120. https://doi.org/10.1016/j.destud.2007.12.003
29. Pijanowski, B. C., Tayyebi, A., Doucette, J., Pekin, B. K., Braun, D., & Plourde, J. (2014). A big data urban growth simulation at a national scale: Configuring the GIS and neural network based Land Transformation Model to run in a High Performance Computing (HPC) environment. Environmental Modelling & Software, 51, 250-268. https://doi.org/10.1016/j.envsoft.2013.09.015
30. Poelmans, L., & Van Rompaey, A. (2009). Detecting and modelling spatial patterns of urban sprawl in highly fragmented areas: A case study in the Flanders–Brussels region. Landscape and Urban Planning, 93(1), 10-19. https://doi.org/10.1016/j.landurbplan.2009.05.018
31. Popov, N. (2011). Generative sub-division morphogenesis with Cellular Automata and Agent-Based Modelling. 166-174. https://doi.org/10.52842/conf.ecaade.2011.166
32. Santé, I., García, A. M., Miranda, D., & Crecente, R. (2010a). Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 96(2), 108-122. https://doi.org/10.1016/j.landurbplan.2010.03.001
33. Santé, I., García, A. M., Miranda, D., & Crecente, R. (2010b). Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 96(2), 108-122. https://doi.org/10.1016/j.landurbplan.2010.03.001
34. 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
35. Sietchiping, R. (2004). A Geographic Information Systems and cellular automata-based model of informal settlement growth [Doctoral, University of Melbourne]. http://hdl.handle.net/11343/38860
36. 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
37. Singh, V., & Gu, N. (2012). Towards an integrated generative design framework. Design Studies, 33(2), 185-207. https://doi.org/10.1016/j.destud.2011.06.001
38. Sipahioğlu, N., & Çağdaş, G. (2022). Scenario-Based Cellular Automata and Artificial Neural Networks in Urban Growth Modeling. GAZI UNIVERSITY JOURNAL OF SCIENCE. https://doi.org/10.35378/gujs.998073
39. Sudhira, H. S., Ramachandra, T. V., & Jagadish, K. S. (2004). Urban sprawl: Metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation, 5(1), 29-39. https://doi.org/10.1016/j.jag.2003.08.002
40. Terzidis, K. (2003). Expressive form: A conceptual approach to computational design. Spon Press.
41. Wagner, D. F. (1997). Cellular automata and geographic information systems. Environment and Planning B: Planning and Design, 24(2), 219-234. https://doi.org/10.1068/b240219
42. Wahyudi, A., & Liu, Y. (2016). Cellular Automata for Urban Growth Modelling: A Review on Factors defining Transition Rules. International Review for Spatial Planning and Sustainable Development, 4(2), 60-75. https://doi.org/10.14246/irspsd.4.2_60
43. Wolfram, S. (2002). A new kind of science. Wolfram Media.
44. Wu, F., & Webster, C. J. (2000). Simulating artificial cities in a GIS environment: Urban growth under alternative regulation regimes. International Journal of Geographical Information Science, 14(7), 625-648. https://doi.org/10.1080/136588100424945
45. Yeh, A. G. O., Li, X., & Xia, C. (2021). Cellular Automata Modeling for Urban and Regional Planning. Içinde W. Shi, M. F. Goodchild, M. 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
46. Yigitcanlar, T., Corchado, J. M., Mehmood, R., Li, R. Y. M., Mossberger, K., & Desouza, K. (2021). Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 71. https://doi.org/10.3390/joitmc7010071
47. Zhang, Q., Ban, Y., Liu, J., & Hu, Y. (2011). Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China. Computers, Environment and Urban Systems, 35(2), 126-139. https://doi.org/10.1016/j.compenvurbsys.2010.12.002