Kent Araştırmalarında Dijital Dönüşü Anlamak: Çağdaş Küresel Kentsel Literatürün Bibliyometrik Analizi

Abstract

Son birkaç on yılda gerçekleşen teknolojik ilerlemeler, kentlerin karmaşık yapılarını keşfetmek, güncel kentsel sorunları anlamak ve bunlara yönelik çözümler üretmek ve gelecekteki potansiyel gelişmeleri tahmin etmek için benzersiz olanaklar yaratmıştır. Bu durum, başta şehir coğrafyası olmak üzere kent çalışmalarını içeren tüm akademik disiplinlerde önemli değişikliklere yol açmıştır. Bu çalışma, bahse konu değişikliklerin akademik bilgi üretimine nasıl yansıdığına dair kapsamlı bir anlayış sağlamayı amaçlamaktadır. Bu çalışma için Web of Science Core Collection'da bulunan kent çalışmaları alanındaki akademik literatürde büyük veri, yapay zeka (AI) ve nesnelerin interneti (IoT) gibi kavramların nasıl ele alındığına dair kapsamlı ve güncel bir inceleme yapılmıştır. Tarama sonucunda belirlenen kriterleri karşılayan toplam 2055 akademik çalışma tespit edilmiş ve Bibliometrix adlı bibliyometrik analiz yazılımı kullanılarak analiz edilmiştir. Araştırmanın sonuçları bilimsel makalelerin zamansal, mekânsal ve disiplinler arası dağılımını, yeni araştırma alanlarını ve hem sıklıkla çalışılan hem de henüz keşfedilmemiş konuları ortaya koyarak teknolojik gelişmelerin kentsel araştırmalara entegrasyonunu incelemeye yardımcı olmaktadır.

Keywords

• Kent Araştırmaları
• Şehir Coğrafyası
• Büyük Veri
• Yapay Zeka
• Nesnelerin İnterneti

Understanding Digital Turn in Urban Research: A Bibliometric Analysis of Contemporary Global Urban Literature

Abstract

This paper aims to examine the effects of digital technologies on academic knowledge production in the field of urban research. It provides a comprehensive overview of the evolution of technology-based urban research literature, delineates the knowledge structure, and investigates prevalent trends. The study employs bibliometric analysis to analyze bibliographical and textual data extracted from scientific documents, which allows for the discovery of the existing epistemological structure of the discipline or research domain. The research was conducted by conducting a comprehensive and up-to-date review of the literature available in the Web of Science Core Collection regarding how concepts such as big data, artificial intelligence (AI), and the Internet of Things (IoT) are discussed in academic papers in the field of urban studies. A total of 2055 academic papers that met the established criteria were identified and analyzed using bibliometric analysis software called Bibliometrix. The results of the research aid in examining the integration of technological advancements into urban research, and reveal the temporal, spatial, and disciplinary distribution of scientific articles, as well as their reflections on new research areas and both frequently studied and yet unexplored topics.

Keywords

• Urban Research
• Urban Geography
• Big Data
• Artificial Intelligence
• Internet of Things

References

1. Agbo, F. J., Oyelere, S. S., Suhonen, J., & Tukiainen, M. (2021). Scientific production and thematic breakthroughs in smart learning environments: a bibliometric analysis. Smart Learning Environments, 8(1). https://doi.org/10.1186/s40561-020-00145-4
2. Alberti, M. (2017). Grand Challenges in Urban Science. Frontiers in Built Environment, 3. https://doi.org/10.3389/fbuil.2017.00006
3. Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80–91. https://doi.org/10.1016/j.cities.2019.01.032
4. Aria, M., & Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
5. Bircan, T., & Salah, A. A. A. (2022). A Bibliometric Analysis of the Use of Artificial Intelligence Technologies for Social Sciences. Mathematics, 10(4398). https://doi.org/10.3390/math10234398
6. Brenner, N. (2018). Debating planetary urbanization: For an engaged pluralism. Environment and Planning D: Society and Space, 36(3), 570–590. https://doi.org/10.1177/0263775818757510
7. Bunnell, T. (2019). Inclusiveness in Urban Theory and Urban-Centred International Development Policy. Journal of Regional and City Planning, 30(2), 89. https://doi.org/10.5614/jpwk.2019.30.2.1
8. Büyükkıdık, S. (2022). A Bibliometric Analysis: A Tutorial for the Bibliometrix Package in R Using IRT Literature. Eğitimde Ve Psikolojide Ölçme Ve Değerlendirme Dergisi, 13(3), 164–193. https://doi.org/10.21031/epod.1069307

9. Cai, M. (2021). Natural language processing for urban research: A systematic review. Heliyon, 7(3), e06322. https://doi.org/10.1016/j.heliyon.2021.e06322
10. Cioffi‐Revilla, C. (2010). Computational social science. WIREs Computational Statistics, 2(3), 259–271. https://doi.org/10.1002/wics.95
11. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
12. Edelmann, A., Wolff, T., Montagne, D., & Bail, C. A. (2020). Computational Social Science and Sociology. Annual Review of Sociology, 46(1), 61–81. https://doi.org/10.1146/annurev-soc-121919-054621
13. Garrigós-Simón, F., Sanz-Blas, S., Narangajavana, Y., & Buzova, D. (2021). The Nexus between Big Data and Sustainability: An Analysis of Current Trends and Developments. Sustainability, 13(12), 6632. https://doi.org/10.3390/su13126632
14. Glänzel, W. (2012). Bibliometric methods for detecting and analysing emerging research topics. El Profesional De La Informacion, 21(2), 194–201. https://doi.org/10.3145/epi.2012.mar.11
15. Guo, Y.‑M., Huang, Z.‑L., Guo, J., Li, H., Guo, X.‑R., & Nkeli, M. J. (2019). Bibliometric Analysis on Smart Cities Research. Sustainability, 11(13), 3606. https://doi.org/10.3390/su11133606
16. Hao, J., Zhu, J., & Zhong, R. (2015). The rise of big data on urban studies and planning practices in China: Review and open research issues. Journal of Urban Management, 4(2), 92–124. https://doi.org/10.1016/j.jum.2015.11.002
17. Herath, H., & Mittal, M. (2022). Adoption of artificial intelligence in smart cities: A comprehensive review. International Journal of Information Management Data Insights, 2(1), 100076. https://doi.org/10.1016/j.jjimei.2022.100076
18. Hérubel, J.‑P. V. M. (2020). Disciplinary Permeability, Academic Specializations, and University Presses. Publishing Research Quarterly, 36(1), 17–31. https://doi.org/10.1007/s12109-019-09707-y
19. Ibrahim, M. R., Haworth, J., & Cheng, T. (2020). Understanding cities with machine eyes: A review of deep computer vision in urban analytics. Cities, 96, 102481. https://doi.org/10.1016/j.cities.2019.102481
20. Jakobsen, K., Mikalsen, M., & Lilleng, G. (2023). A literature review of smart technology domains with implications for research on smart rural communities. Technology in Society, 75, 102397. https://doi.org/10.1016/j.techsoc.2023.102397
21. Kamrowska-Załuska, D. (2021). Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities. Land, 10(11), 1209. https://doi.org/10.3390/land10111209
22. Kandt, J., & Batty, M. (2021). Smart cities, big data and urban policy: Towards urban analytics for the long run. Cities, 109, 102992. https://doi.org/10.1016/j.cities.2020.102992
23. Kitchin, R. (2016). The ethics of smart cities and urban science. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 374(2083). https://doi.org/10.1098/rsta.2016.0115
24. Kong, L., Liu, Z., & Wu, J [Jianguo] (2020). A systematic review of big data-based urban sustainability research: State-of-the-science and future directions. Journal of Cleaner Production, 273, 123142. https://doi.org/10.1016/j.jclepro.2020.123142
25. Lobo, J., Alberti, M., Allen-Dumas, M., Arcaute, E., Barthelemy, M., Bojorquez Tapia, L. A., Brail, S., Bettencourt, L., Beukes, A., Chen, W.‑Q., Florida, R., Gonzalez, M., Grimm, N., Hamilton, M., Kempes, C., Kontokosta, C. E., Mellander, C., Neal, Z. P., Ortman, S., . . . Youn, H. (2020). Urban Science: Integrated Theory from the First Cities to Sustainable Metropolises. SSRN Electronic Journal. Advance online publication. https://doi.org/10.2139/ssrn.3526940
26. Marasinghe, R., Yigitcanlar, T., Mayere, S., Washington, T., & Limb, M. (2024). Computer vision applications for urban planning: A systematic review of opportunities and constraints. Sustainable Cities and Society, 100, 105047. https://doi.org/10.1016/j.scs.2023.105047
27. Niu, J., Tang, W., Xu, F., Zhou, X., & Song, Y. (2016). Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis. ISPRS International Journal of Geo-Information, 5(5), 66. https://doi.org/10.3390/ijgi5050066
28. Rashid, S., Rehman, S. U., Ashiq, M., & Khattak, A. (2021). A Scientometric Analysis of Forty-Three Years of Research in Social Support in Education (1977–2020). Education Sciences, 11(4), 149. https://doi.org/10.3390/educsci11040149
29. Robinson, J. (2022). Introduction: Generating concepts of ‘the urban’ through comparative practice. Urban Studies, 59(8), 1521–1535. https://doi.org/10.1177/00420980221092561
30. Robinson, J. (2023). Comparative urbanism: Tactics for global urban studies. IJURR studies in urban and social change book series. John Wiley & Sons Ltd.
31. Roy, A. (2016). What is urban about critical urban theory? Urban Geography, 37(6), 810–823. https://doi.org/10.1080/02723638.2015.1105485
32. Sayın, Ö., Hoyler, M., & Harrison, J. (2022). Doing comparative urbanism differently: Conjunctural cities and the stress-testing of urban theory. Urban Studies, 59(2), 263–280. https://doi.org/10.1177/0042098020957499
33. Sharifi, A., Khavarian-Garmsir, A. R., Allam, Z., & Asadzadeh, A. (2023). Progress and prospects in planning: A bibliometric review of literature in Urban Studies and Regional and Urban Planning, 1956–2022. Progress in Planning, 173, 100740. https://doi.org/10.1016/j.progress.2023.100740
34. Sheppard, E., Leitner, H., & Maringanti, A. (2013). Provincializing Global Urbanism: A Manifesto. Urban Geography, 34(7), 893–900. https://doi.org/10.1080/02723638.2013.807977
35. van Meeteren, M., Bassens, D., & Derudder, B. (2016). Doing global urban studies. Dialogues in Human Geography, 6(3), 296–301. https://doi.org/10.1177/2043820616676653
36. Yu, D., & Fang, C. (2023). Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent Decades. Remote Sensing, 15(5), 1307. https://doi.org/10.3390/rs15051307