Journals / Ege Mimarlık / 2024 / Sayı: 124

Yapay Zekâ ile Mahalle Tasarımı Deneyimi: Karaburun Örneği

Gizem Erdoğan Aydın(İZMİR DEMOKRASİ ÜNİVERSİTESİ)0000-0002-1376-6457
Pages
44–53
Publication Date
2024-10-23
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Gelecek çalışmalar, YZ teknolojisi içeren akıllı tasarım sistemlerine katkı sağlayacaktır.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Kapsayıcı ve yenilikçi kentsel çevrelerin planlanması veri odaklı hale gelmiş, Yapay Zekâ (YZ) kentsel tasarımın merkezine yerleşmiştir.
📊
Key Findings & Data:Bu çalışma, YZ'nin mahalle ölçeğindeki kentsel tasarım süreçlerine entegrasyonunu araştırmayı ve veri analizi, modelleme ve optimizasyon olanaklarını kullanarak işlevsel tasarım çözümleri üretmeyi amaçlamaktadır.
💡
Conclusion & Impact:Gelecek çalışmalar, YZ teknolojisi içeren akıllı tasarım sistemlerine katkı sağlayacaktır.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kentsel Tasarım · Yapay Zekâ · Mahalle Tasarımı

Özet

Kapsayıcı ve yenilikçi kentsel çevrelerin planlanması veri odaklı hale gelmiş, Yapay Zekâ (YZ) kentsel tasarımın merkezine yerleşmiştir. Bu çalışma, YZ'nin mahalle ölçeğindeki kentsel tasarım süreçlerine entegrasyonunu araştırmayı ve veri analizi, modelleme ve optimizasyon olanaklarını kullanarak işlevsel tasarım çözümleri üretmeyi amaçlamaktadır. İzmir Karaburun atölye çalışmasında, geleneksel yöntemlerle birlikte, tasarım sürecinde YZ teknolojisi kullanılmış ve tasarımlar mahalle tasarım kriterleri çerçevesinde karşılaştırılmıştır. Çalışma, YZ'nin süreçleri hızlandırdığını ve mahalle tasarım kriterlerine uygun çözümler sunduğunu göstermiştir. Gelecek çalışmalar, YZ teknolojisi içeren akıllı tasarım sistemlerine katkı sağlayacaktır.

🔖

Cite This Article

Aydın, G. E. (2024). Yapay Zekâ ile Mahalle Tasarımı Deneyimi: Karaburun Örneği. Ege Mimarlık, Sayı: 124, 44–53. https://ajindex.com/articles/1644985

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References (59)

  1. Abdelfattah, L., Deponte, D., Fossa, G. (2022). The 15-minute city: Interpreting the model to bring out urban resiliencies. Transportation research procedia, 60, 330-337.
  2. Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S. A. (2019). Applications of artificial intelligence in transport: An overview. Sustainability, 11(1), 189. https://doi.org/10.3390/su11010189
  3. ArcGIS Pro 3.3. (2024). An overview of the GeoAI toolbox. https://pro.arcgis.com/en/pro-app/latest/tool-reference/geoai/an-overview-of-the-geoai-toolbox.htm adresinden 3 Mayıs 2024 tarihinde erişilmiştir.
  4. Alexander, C., Ishikawa, S., Silverstein, M. (1977). A Pattern Language: Towns, Buildings, Construction. Oxford University Press.
  5. Appleyard, D. (1981). Livable Streets. University of California Press.
  6. Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B.,. Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, 104440.
  7. Balletto, G., Ladu, M., Milesi, A., Borruso, G. (2021). A methodological approach on disused public properties in the 15-minute city perspective. Sustainability, 13(2), 593. https://doi.org/10.3390/su13020593
  8. Barton, H., Grant, M., Guise, R. (2021). Shaping Neighbourhoods: For Local Health and Global Sustainability. Spon Press.
  9. Batty, M. (2013). The New Science of Cities. MIT Press.
  10. Beatley, T. (2000). Green Urbanism: Learning from European Cities. Island Press.
  11. Büyükkarakurt, A. ve Mutluoğlu, Ö. (2020). Altyapı Bilgi Sistemi ve Konya Örneği. Türkiye Coğrafi Bilgi Sistemleri Dergisi 2(1), 10-16.
  12. Calthorpe, P. (1993). The Next American Metropolis: Ecology, Community, and the American Dream. New York: Princeton Architectural Press.
  13. Casali, Y., Aydin, N. Y., Comes, T. (2022). Machine Learning For Spatial Analyses in Urban Areas: A Scoping Review. Sustainable Cities and Society, 85, 104050.
  14. Chaturvedi, V. ve de Vries, W. T. (2021). Machine learning algorithms for urban land use planning: A review. Urban Science, 5(3), 68. https://doi.org/10.3390/urbansci5030068.
  15. Conzen, M. R. G. (1960). Alnwick, Northumberland: A Study in Town-Plan Analysis. London: Institute of British Geographers Publication no.27.
  16. Data@Urban (2021, 21 Ekim). How We Used Machine Learning to Predict Neighborhood Change Medium. https://urban-institute.medium.com/how-we-used-machine-learning-to-predict-neighborhood-change-bf52c9f32fda adresinden 4 Mayıs 2024 tarihinde erişilmiştir.
  17. Duany, A., Plater-Zyberk, E., Speck, J. (2000). Suburban Nation: The Rise of Sprawl and the Decline of the American Dream. North Point Press.
  18. Eryiğit S. (2012). Sürdürülebilir Ulaşımın Sosyal Boyutunda Bisikletin Yeri [Yayınlanmamış Yüksek Lisans tezi]. Selçuk Üniversitesi.
  19. Evans-Cowley, J. S. (2018). Planning Education with and through Technologies. A. Frank, C. Silver (Ed.) Urban Planning Education içinde (ss.293-306). The Urban Book Series. Springer, Cham.
  20. Fagnant, D. J., Kockelman, K. (2015). Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167–181. https://doi.org/10.1016/j.tra.2015.04.003
  21. Gehl, J. (2011). Life Between Buildings: Using Public Space. Island Press.
  22. Ghisleni, C. /2024, 8 Şubat). Artificial Intelligence and Urban Planning: Technology as a Tool for City Design. ArchDaily.https://www.archdaily.com/1012951/artificial-intelligence-and-urban-planning-technology-as-a-tool-for-city-design adresinden 1 Mayıs tarihinde erişilmiştir.
  23. GIS Geography. (2024). The Rise of Machine Learning and AI in GIS. https://gisgeography.com/deep-machine-learning-ml-artificial-intelligence-ai-gis/ adresinden 1 Mayıs 2024 tarihinde erişilmiştir.
  24. Grant, J. (2002). Mixed Use in Theory and Practice: Canadian Experience with Implementing a Planning Principle. Journal of the American Planning Association, 68(1), 71-84.
  25. Guan, C., Keith, M., Hong, A. (2020). Designing walkable cities and neighborhoods in the era of urban big data. Urban Planning International, 34(5), 9-15.
  26. Harnik, P. (2010). Urban Green: Innovative Parks for Resurgent Cities. Island Press.
  27. Harrison, C. ve Donnelly, I. A. (2011). A Theory of Smart Cities. Proceedings of the 55th Annual Meeting of the ISSS - 2011, Hull, UK, 55(1). https://journals.isss.org/index.php/proceedings55th/article/view/1703 adresinden erişilmiştir.
  28. Harvard University Graduate School of Design. (2021). Courses: Artificial Intelligence in Contemporary Design Practice. https://www.gsd.harvard.edu/course/artificial-intelligence-in-contemporary-design-practice-spring-2021/ adresinden 4 Mayıs 2024 tarihinde erişilmiştir.
  29. Howell (2024). AI Designed Neighborhoods: The Future of Urban Planning? https://knowhowell.com/ai-designed-neighborhoods-the-future-of-urban-planning adresinden 1 Mayıs 2024 tarihinde erişilmiştir.
  30. IBM. (2019). Planning and management solutions. Smarter Cities, New cognitive approaches to long-standing challenges. https://www.ibm.com/smarterplanet/us/en/smarter_cities/solutions/planning_mgt_solutions/ adresinden 5 Mayıs 2024 tarihinde erişilmiştir.
  31. Jacobs, J. (1961). The Death and Life of Great American Cities. Random House.
  32. Jiang, F., Ma, J., Webster, C. J., Chiaradia, A. J., Zhou, Y., Zhao, Z., Zhang, X. (2024). Generative urban design: A systematic review on problem formulation, design generation, and decision-making. Progress in Planning 180, 100795. https://doi.org/10.1016/j.progress.2023.100795.
  33. Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. SAGE Publications.
  34. Koca, T. ve Çolpan Erkan, N. (2022). Kentsel Güvenliğin Sağlanmasında Tasarım Rehberleri. Planlama, 32(1), 162-173.
  35. Koutra, S. ve Ioakimidis, C. S. (2023). Unveiling the potential of machine learning applications in urban planning challenges. Land, 12(1), 83. https://doi.org/10.3390/land12010083
  36. Lehmann, S. (2016). Sustainable urbanism: towards a framework for quality and optimal density? Future Cities and Environment, 2, 8, 1-13.
  37. Litman, T. (2004). Economic Value of Walkability. Transportation Research Record: Journal of the Transportation Research Board, 1828(1), 3-11.
  38. Moral-Carcedo, J. (2024). Predicting traffic intensity in the urban area of Madrid: Integrating route network topology into a machine-learning model. Engineering Applications of Artificial Intelligence, 137, Part B, 109154.
  39. Newman, P. ve Jennings, I. (2008). Cities as Sustainable Ecosystems: Principles and Practices. Island Press.
  40. Pucher, J. ve Buehler, R. (Ed.) (2012). City Cycling. MIT Press.
  41. Roumyeh, M. L. ve Badenko, V. L. (2022). Integration between BIM and GIS for decision-making. BIM Modeling for Construction and Architecture içinde (ss.20-27). Saint Petersburg, Russia. https://doi.org/10.23968/BIMAC.2022.003
  42. Rowley, A. (1996). Mixed-use Development: Ambiguous Concept, Simplistic Analysis and Wishful Thinking? Planning Practice & Research, 11(1), 85-97.
  43. Sanchez, T. W. (2023). Planning on the Verge of AI, or AI on the Verge of Planning. Urban Science, 7(3), 70.
  44. Southworth, M. ve Ben-Joseph, E. (1997). Streets and the Shaping of Towns and Cities. Island Press.
  45. Shah S. (2021). Neighborhood GIS: A Tool for Community Participation in Planning. https://proceedings.esri.com/library/userconf/proc02/pap0448/p0448.htm adresinden 06 Temmuz 2024 tarihinde erişilmiştir.
  46. Talen, E. (2008). Design for Diversity: Exploring Socially Mixed Neighborhoods. Architectural Press.
  47. Talen, E. ve Shah, S. (2007). Neighborhood evaluation using GIS: An exploratory study. Environment and Behavior, 39(5), 583-615.
  48. Tan, Y., Huang, Z., Wu, X., Wang, J. (2021). Artificial intelligence based methods for smart and sustainable urban development. Sustainability, 13(14), 7935. https://doi.org/10.3390/su13147935
  49. TDK (Türk Dil Kurumu) (2015). Genel Açıklamalı Sözlük. Ankara: TDK Yayınları.
  50. Tekouabou, S. C. K., Diop, E. B., Azmi, R., Jaligot, R., Chenal, J. (2022). Reviewing the application of machine learning methods to model urban form indicators in planning decision support systems: Potential, issues and challenges. Journal of King Saud University-Computer and Information Sciences, 34(8), 5943-5967.
  51. Townsend, A. M. (2013). Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia. W. W. Norton & Company.
  52. Tatian, P. A., Kingsley, G.T., Parilla, J., Pendall, R. (2012). Building Successful Neighborhoods. Urban Institute, What Works Collaborative. https://www.urban.org/sites/default/files/publication/25346/412557-building-successful-neighborhoods.pdf
  53. Wang, J., Xiang, X., Sun, J. (2021). Generative design in building information modelling (BIM): Approaches and requirements. Sensors, 21(16), 5439. https://doi.org/10.3390/s21165439
  54. Wheeler, S. M. (2013). Planning for Sustainability: Creating Livable, Equitable, and Ecological Communities. Routledge.
  55. Wei, Y., Li, J., Zhao, D., Dong, J. (2021). Leveraging machine learning to understand urban change with net construction. Remote Sensing, 13(5), 965. https://doi.org/10.3390/rs13050965
  56. Whyte, W. H. (1980). The Social Life of Small Urban Spaces. New York: Project for Public Spaces.
  57. Zhang, Y., Li, X., Liu, Y. (2020). Enhancing urban sustainability with AI and big data analytics: A review of applications and trends. Sustainable Cities and Society, 63, 102444.
  58. Zheng, Y., Lin, Y., Zhao, L. et al. (2023). Spatial planning of urban communities via deep reinforcement learning. Nature Computational Science 3, 748–762. https://doi.org/10.1038/s43588-023-00503-5
  59. Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B.,. Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, 104440.