Araştırma Makalesi
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An Exploration of Public Open Spaces with Data Driven Approaches: A Case Study of Beyazıt Square

Yıl 2023, Cilt: 4 Sayı: 2, 295 - 321, 30.09.2023
https://doi.org/10.53710/jcode.1325188

Öz

Data-driven approaches are widely used to gain insight in urban dynamics and support urban decisions with pervasive adoption of information technologies. In the presented study, the students adopt data data-driven approaches to observe, and analyze public spaces, and make conceptual decisions for urban furniture in the context of the workshop. This workshop is developed within the scope of the Environmental Computing course. It is conducted with 27 students in Beyazıt Square as a case study area. In the scope of the study, open public spaces were observed and analyzed using data-driven approaches. Based on the analysis results, the students were expected to develop urban furniture design that would enhance user experience and activities in the area. This study questions how data-driven approaches aid in exploring public spaces and support design decisions. The objective of the study was to explore user-generated urban dynamics using multiple data and make decisions for urban furniture that augments urban dynamics. The conceptual design process of urban furniture is shaped as results of data-driven approach. The students were introduced to the Public Life Tools developed by the Gehl Institute for site observation. They were divided into particular groups and used relevant digital tracking applications to measure user activities, user profiles, and live traffic in the area. They evaluated the quality of place based on predetermined criteria by Gehl Institute. The phases of the study involve (1) the exploration of digital observation methods, (2) mapping observational, data, urban data, and locative media data in Geographic Information System (GIS), and (3) defining the relationships between the parameters affecting urban dynamics. (4) This was followed by making conceptual design decisions and (5) developing the design of urban furniture considering data analysis results. According to the findings, the use of data-driven observation and analysis methods has been effective in developing user scenarios, determining user profiles, identifying needs, and taking functional decisions in urban furniture design. Based on the students’ evaluation, the data-driven decision-making process was effective in identifying needs, problems, and potentials in the area. As the limitations of the study, the students stated that the use of digital observation methods and the learning process of GIS software were challenging. This study contributes to the field of urban computing through its conducted fieldwork.

Teşekkür

I would like to express our deepest gratitude to Istanbul Kültür University and the students involved in this study. Their dedicated efforts, unique insights, and rigorous inquiry significantly enriched this research. I express my profound gratitude to the students Dalya H. I. Abushawısh, Nada M.A. Elgaphry in Group S4, Rand Alnajjar and Seema Alghoranı in Group C4, Shurooq Al-hajj and Sheren Shaker in Group S2, and Özlem Özcan, Özüm Ezgi Yalçın, Asiman Cengiz in Group S1. This study is conducted in the scope of Environment Informatics, in Kultur University.

Kaynakça

  • Ardıç, S. İ., Kırdar, G., Lima, A. B. (2020). An Exploratory Urban Analysis via Big Data Approach: Eindhoven Case. Proceedings of the 38th ECAADe Conference on Education and Research in Computer Aided Architectural Design in Europe, 309–318.
  • Brower, S. (1988). Design in Familiar Places: What Makes Home Environments Look Good. Greenwood Press. Campbell, J. E., & Shin, S. (2011). Essentials of Geographic Information Systems. Textbooks. https://digitalcommons.liberty.edu/textbooks/2
  • Counterpoint. (2023). Counterpoint- Where everyone counts. https://counterpointapp.org/
  • EMBARQ, & Gehl Architects. (2013). Istanbul Public Space Public Life. https://wrirosscities.org/sites/default/files/Istanbul-Public-Spaces-Public-Life-EMBARQ-Turkey-Gehl-Architects-Oct-2013.pdf
  • Fusco, G. (2004). Looking for Sustainable Urban Mobility through Bayesian Networks. European Journal of Geography, 292. https://doi.org/https://doi.org/10.4000/cybergeo.2777
  • Fusco, G. (2008). Spatial dynamics in France. In O. Pourret, P. Naim, & B. Marcot (Eds.), Bayesian Networks: A Practical Guide to Applications. John Wiley and Sons.
  • Gehl Architects. (2015). Public life diversity toolkit: a prototype for measuring social mixing and economic integration in public space.
  • Gehl Institute. (2017). Gehl Public Life Tool. https://gehlinstitute.org/tool/
  • Gehl, J., & Gemzoe, L. (1996). Public Spaces-public Life. Danish Architectural Press and the Royal Danish Academy of Fine Arts, School of Architecture.
  • Gehl, J., & Svarre, B. (2013). How to Study Public Life. Island Press.
  • Han, J., Kamber, M., & J., P. (2012). Data mining concepts and techniques (3rd ed.). Morgan Kaufmann. Heckerman, D. (1997). Bayesian Networks for Data Mining. Data Mining and Knowledge Discovery, 1(1), 79–119. https://doi.org/10.1023/A:1009730122752
  • HUGIN Expert. (2013). HUGIN Expert White Paper [White Paper]. https://hugin.com/wp-content/uploads/2016/05/HUGIN-WHITE-PAPER-NEW-AND-REVISED_2013.pdf
  • Kemperman, A., & Timmermans, H. (2014). Green spaces in the direct living environment and social contacts of the aging population. Landscape and Urban Planning, 129, 44–54. https://doi.org/https://doi.org/10.1016/j.landurbplan.2014.05.003
  • Mehta, V. (2014). Evaluating Public Space. In Journal of Urban Design (Vol. 19, Issue 1, pp. 53–88). Taylor & Francis. https://doi.org/10.1080/13574809.2013.854698
  • Nilsson, N. J. (2010). The Quest for Artificial Intelligence: A History of Ideas and Achievements. In M. (transl. Doğan (Ed.), Cambridge University Press (Vol. 139, Issue 25). Bogazici University Publishing.
  • Skjæveland, O., Gärling, T., & Mæland, J. G. (1996). A multidimensional measure of neighboring. American Journal of Community Psychology, 24(3), 413–435. https://doi.org/https://doi.org/10.1007/BF02512029
  • Whyte, W. H. (1980). The Social Life of Small Urban Spaces. Conservation Foundation.
  • Yanık, S., Aktas, E., & Topcu, Y. I. (2017). Traveler satisfaction in rapid rail systems: The case of Istanbul metro. International Journal of Sustainable Transportation, 11(9), 642–658. https://doi.org/10.1080/15568318.2017.1301602
  • Zamanifard, H., Alizadeh, T., Bosman, C., & Coiacetto, E. (2019). Measuring experiential qualities of urban public spaces: users’ perspective. Journal of Urban Design, 24(3), 340–364. https://doi.org/10.1080/13574809.2018.1484664

Kamusal Açık Mekanların Veriye Dayalı Yaklaşımlar ile Keşfi: Beyazıt Meydanı Örneği

Yıl 2023, Cilt: 4 Sayı: 2, 295 - 321, 30.09.2023
https://doi.org/10.53710/jcode.1325188

Öz

Bilişim teknolojilerinin yaygınlaşması ile veriye dayalı yaklaşımlar karmaşık kent dinamiklerini anlamak ve kentsel karar alma sürecinde yaygın olarak kullanılmaktadır. Çalışmada veriye dayalı yaklaşımlar kamusal mekanın gözlemlenmesi, analizi ve tasarım kararlarında uygulanması ders kapsamında geliştirilen atölye çalışması ile deneyimlenmiştir. Çalışma kapsamında geliştirilen atölye Çevresel Bilişim dersi kapsamında 27 öğrenci tarafından yürütülmüştür. Çalışma alanı Beyazıt Meydanı’dır. Çalışma kapsamında açık kamusal mekanlar veriye dayalı yaklaşımlar ile analiz edilmiş, analiz sonuçlarına dayanarak öğrencilerden mekandaki kullanıcı deneyim ve aktivitelerini arttıracak kentsel mobilya tasarımı geliştirilmesi beklenmiştir. Araştırma sorusu veriye dayalı yaklaşımların kamusal mekanların dinamiklerini keşfetmede nasıl yardımcı olacağını ve tasarım kararlarını nasıl destekleyebileceğini sorgular. Çalışmanın amacı kamusal alandaki kullanıcı kaynaklı kent dinamiklerinin farklı veri kaynakları keşfedilmesi, ilişkilendirilmesi ve kent dinamiğini arttırabilecek kentsel mobilya tasarım kararları alınmasıdır. Kentsel mobilya tasarımının kavramsal süreci veriye dayalı yaklaşımların sonuçlarına göre şekillenmiştir. Çalışmada öğrencilere alan gözlemi için Gehl Institute tarafından geliştirilmiş Kamusal Yaşam Ölçme Araçları (Public Life Tools) tanıtılmıştır. Öğrenciler belirli gruplara ayrılarak ilgili dijital takip uygulamaları ile alandaki kullanıcı aktivitelerini, kullanıcı profilini, canlı trafiği ölçmüşledir. Alanın kalitesini Gehl Institute tarafından belirlenen kriterlere göre değerlendirmişlerdir. Çalışma aşamalarını kamusal alandaki veriye dayalı ölçme ve gözlemleme yöntemlerinin dijital araçlar ile keşfi, verinin Coğrafi Bilgi Sistemi’nde (Geographic Information Systems: GIS) haritalanması, veri haritalama sonucunda veriler arasındaki ilişkinin tanımlanması oluşturmaktadır. Daha sonra veriye dayalı olarak kentsel mobilya konseptinin kavramsal tasarım kararlarının alınması ve tasarımını geliştirilmesi ile takip etmektedir. Veriye dayalı gözlem ve analiz yöntemlerinin kentsel mobilya tasarımında kullanıcı senaryoları geliştirme, kullanıcı profili belirleme ve ihtiyaçlarını belirleme bu bağlamda işlev kararlarını almada etkili olmuştur. Öğrencilere göre veriye dayalı karar alma süreci alandaki ihtiyaçların, problemlerin ve potansiyellerin belirlenmesinde etkili olmuştur. Öğrenciler çalışmanın kısıtları olarak dijital gözlem yöntemlerinin kullanımı ve GIS programının öğreniminin zor olduğunu belirtmiştir. Çalışma yürütülen alan çalışması üzerinden kentsel bilişim alanına katkı sağlamaktadır.

Kaynakça

  • Ardıç, S. İ., Kırdar, G., Lima, A. B. (2020). An Exploratory Urban Analysis via Big Data Approach: Eindhoven Case. Proceedings of the 38th ECAADe Conference on Education and Research in Computer Aided Architectural Design in Europe, 309–318.
  • Brower, S. (1988). Design in Familiar Places: What Makes Home Environments Look Good. Greenwood Press. Campbell, J. E., & Shin, S. (2011). Essentials of Geographic Information Systems. Textbooks. https://digitalcommons.liberty.edu/textbooks/2
  • Counterpoint. (2023). Counterpoint- Where everyone counts. https://counterpointapp.org/
  • EMBARQ, & Gehl Architects. (2013). Istanbul Public Space Public Life. https://wrirosscities.org/sites/default/files/Istanbul-Public-Spaces-Public-Life-EMBARQ-Turkey-Gehl-Architects-Oct-2013.pdf
  • Fusco, G. (2004). Looking for Sustainable Urban Mobility through Bayesian Networks. European Journal of Geography, 292. https://doi.org/https://doi.org/10.4000/cybergeo.2777
  • Fusco, G. (2008). Spatial dynamics in France. In O. Pourret, P. Naim, & B. Marcot (Eds.), Bayesian Networks: A Practical Guide to Applications. John Wiley and Sons.
  • Gehl Architects. (2015). Public life diversity toolkit: a prototype for measuring social mixing and economic integration in public space.
  • Gehl Institute. (2017). Gehl Public Life Tool. https://gehlinstitute.org/tool/
  • Gehl, J., & Gemzoe, L. (1996). Public Spaces-public Life. Danish Architectural Press and the Royal Danish Academy of Fine Arts, School of Architecture.
  • Gehl, J., & Svarre, B. (2013). How to Study Public Life. Island Press.
  • Han, J., Kamber, M., & J., P. (2012). Data mining concepts and techniques (3rd ed.). Morgan Kaufmann. Heckerman, D. (1997). Bayesian Networks for Data Mining. Data Mining and Knowledge Discovery, 1(1), 79–119. https://doi.org/10.1023/A:1009730122752
  • HUGIN Expert. (2013). HUGIN Expert White Paper [White Paper]. https://hugin.com/wp-content/uploads/2016/05/HUGIN-WHITE-PAPER-NEW-AND-REVISED_2013.pdf
  • Kemperman, A., & Timmermans, H. (2014). Green spaces in the direct living environment and social contacts of the aging population. Landscape and Urban Planning, 129, 44–54. https://doi.org/https://doi.org/10.1016/j.landurbplan.2014.05.003
  • Mehta, V. (2014). Evaluating Public Space. In Journal of Urban Design (Vol. 19, Issue 1, pp. 53–88). Taylor & Francis. https://doi.org/10.1080/13574809.2013.854698
  • Nilsson, N. J. (2010). The Quest for Artificial Intelligence: A History of Ideas and Achievements. In M. (transl. Doğan (Ed.), Cambridge University Press (Vol. 139, Issue 25). Bogazici University Publishing.
  • Skjæveland, O., Gärling, T., & Mæland, J. G. (1996). A multidimensional measure of neighboring. American Journal of Community Psychology, 24(3), 413–435. https://doi.org/https://doi.org/10.1007/BF02512029
  • Whyte, W. H. (1980). The Social Life of Small Urban Spaces. Conservation Foundation.
  • Yanık, S., Aktas, E., & Topcu, Y. I. (2017). Traveler satisfaction in rapid rail systems: The case of Istanbul metro. International Journal of Sustainable Transportation, 11(9), 642–658. https://doi.org/10.1080/15568318.2017.1301602
  • Zamanifard, H., Alizadeh, T., Bosman, C., & Coiacetto, E. (2019). Measuring experiential qualities of urban public spaces: users’ perspective. Journal of Urban Design, 24(3), 340–364. https://doi.org/10.1080/13574809.2018.1484664
Toplam 19 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mimarlık ve Tasarımda Bilgi Teknolojileri, Mimarlık (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Gülce Kırdar 0000-0002-4700-6077

Yayımlanma Tarihi 30 Eylül 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 4 Sayı: 2

Kaynak Göster

APA Kırdar, G. (2023). An Exploration of Public Open Spaces with Data Driven Approaches: A Case Study of Beyazıt Square. Journal of Computational Design, 4(2), 295-321. https://doi.org/10.53710/jcode.1325188

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