فصلنامه مطالعات توسعه پایدار شهری و منطقه ای

11.کاربرد هوش مصنوعی در برنامه ریزی شهری مشارکتی:با تاکید بر هوش مصنوعی پردازش زبان طبیعی NLP

نوع مقاله : مقاله علمی مروری

نویسندگان

محمد ابراهیمی 1
نیلوفر احسان بخش 2

1 استادیار گروه شهرسازی،مجتمع آموزش عالی لارستان،لار،ایران

2 گروه شهرسازی،مجتمع آموزش عالی لارستان،لار،ایران

چکیده
زمینه و هدف: برنامه‌ریزی شهری مشارکتی با هدف افزایش مشارکت شهروندان در تصمیم‌گیری‌های شهری، نیازمند ابزارهایی برای تحلیل حجم عظیمی از داده‌های متنی است. این پژوهش با هدف بررسی کاربرد پردازش زبان طبیعی در تحلیل نظرات شهروندان در مورد طرح‌های توسعه شهری انجام شده است.
روش بررسی: با استفاده از روش تحلیل محتوا و الگوریتم‌های یادگیری ماشین، نظرات جمع‌آوری شده از پلتفرم‌های اجتماعی تحلیل شدند.
یافته ها و نتیجه گیری: نتایج نشان داد که پردازش زبان طبیعی می‌تواند با دقت قابل قبولی، احساسات، موضوعات اصلی و الگوهای موجود در نظرات شهروندان را شناسایی کند. این یافته‌ها حاکی از آن است که پردازش زبان طبیعی می‌تواند به عنوان یک ابزار قدرتمند برای بهبود فرآیند تصمیم‌گیری در برنامه‌ریزی شهری مورد استفاده قرار گیرد. با این حال، محدودیت‌هایی همچون زبان محاوره‌ای و وجود اصطلاحات تخصصی در نظرات، نیاز به توسعه بیشتر مدل‌های پردازش زبان طبیعی را نشان می‌دهد.                           

کلیدواژه‌ها

برنامه ریزی شهری مشارکتی
پردازش زبان طبیعی
تصمیم گیری
یادگیری ماشین
مشارکت
شهروندان

موضوعات

عنوان مقاله English

The Application of Artificial Intelligence in Participatory Urban Planning: Emphasizing Natural Language Processing (NLP)

نویسندگان English

Mohammad Ebrahimi 1
Niloofar Ehsan Bakhsh 2
1 Assistant Professor of Urban Planning Department, Larestan Higher Education Complex, Lar, Iran
2 .Department of Urban Planning, Larestan Higher Education Complex, Lar, Iran
چکیده English

Background and Objective: Participatory urban planning aims to increase citizen involvement in urban decision-making and requires tools for analyzing vast amounts of textual data. This study investigates the application of Natural Language Processing (NLP) in analyzing citizens' opinions regarding urban development plans.
Methodology: Using content analysis methods and machine learning algorithms, opinions collected from social media platforms were analyzed. The results indicated that NLP can accurately identify sentiments, main topics, and patterns present in citizens' comments.
Findings and Conclusion: These findings suggest that NLP can serve as a powerful tool to enhance the decision-making process in urban planning. However, limitations such as informal language and the presence of specialized terminology in comments indicate a need for further development of NLP models. 

کلیدواژه‌ها English

Participatory Urban Planning
Natural Language Processing
Decision Making
Machine Learning
Participation
Citizens

  1. دویران، اسماعیل،۱۳۸۷،توسعه محله ای در شهر زنجان (نمونه موردی:محله مسکونی اسلام آباد)پایان نامه کارشناسی ارشد،دانشکده علوم زمین،دانشگاه شهید بهشتی

    1. Abali, G., Karaarslan, E., Hurriyetoglu, A., & Dalkilic, F. (2018).Detecting citizen problems and their locations using Twitter data. In: 2018 6th International Istanbul Smart Grids and Cities Congress and Fair (ICSG), 30–33.
    2. Ahmadi, M., & Shakouri, T. (2023). Text data analysis in urban planning: Trends and challenges. Urban Planning Research, 9(2), 40-52.
    3. Aldrich, D. P., & Worrell, J. (2023).Social media and crisis communication: The effectiveness of NLP in emergency management. Crisis Management Review, 9(2), 155-171. https://doi.org/10.1016/j.cmr.2023.02.003
    4. Anvigh, A. A., Khavan, Y., & Pourghebleh, B. (2024).Transforming vehicular networks: How 6G can revolutionize intelligent transportation? Science, Engineering and Technology, 4(1).
    5. Bibri, S. E. (2021).Data-driven smart sustainable cities of the future: Urban computing and intelligence for strategic, short-term, and joined-up planning.Computational Urban Science, 1(1), 8.
    6. Bowden, L. W. (1975). Urban environments: Inventory and analysis. In: Manual of Remote Sensing , 12. American Society of Photogrammetry, pp. 1815–1880.
    7. Breheny, M. J., & Batey, P. W. J. (1981).The history of planning methodology: A preliminary sketch. Built Environment (1978), 7(2), 109–120. JSTOR.
    8. Cambria, E., Poria, S., & Hussain, A. (2021). Sentiment analysis and opinion mining: A comprehensive overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(1), e1390. https://doi.org/10.1002/widm.1390
    9. Carenini, M., Whyte, A., Bertorello, L., & Vanocchi, M. (2007). Improving communication in E-democracy using natural language processing. IEEE Intelligent Systems, 22(1), 20–27.
    10. Chowdhary, K. R. 2020. “Natural Language Processing.” In Fundamentals of Artificial Intelligence, 603–649. New Delhi: Springer. https://doi.org/10.1007/978-81-322-3972-7_19..
    11. Cheng, J., Sun, C., & Zhao, X. (2022). Intelligent transportation systems based on big data: A survey. Journal of Urban Technology, 29(3), 1-17. https://doi.org/10.1080/10630732.2022.2039619
    12. Chowdhury, G. (2003). Natural Language Processing. Annual Review of Information Science and Technology, 37, 51–89. ISSN 0066-4200.
    13. El-Masri, S., Rodriguez, K., & Al-Sharif, A. (2024).  Utilizing NLP for the evaluation of environmental impact assessments. Journal of Environmental Management , 326, 116771. https://doi.org/10.1016/j.jenvman.2023.116771
    14. Estevez-Ortiz, F.-J., García-Jimenez, A., & Glosek € otter, P. (2016).  An application of people’s sentiment from social media to smart cities.  El Prof. Inf. , 25(6), 851.
    15. Eskandari .H, Saadatmand .H, M. Ramzan, and M. Mousapour,"Innovative framework for accurate and transparent forecasting of energy consumption: A fusion of feature selection and interpretable machine learning," Applied Energy, vol. 366, p. 123314, 2024.
    16. Fainstein, S. S. (2014).  The just city.  International Journal of Urban Sciences , 18(1), 1–18. https://doi.org/10.1080/12265934.2013.834643
    17. Feng, Y., & Liu, Y. (2023).  Sentiment analysis in citizen participation: How NLP can enhance public engagement in urban planning.  Sustainable Cities and Society , 94, 104736. https://doi.org/10.1016/j.scs.2023.104736
    18. Friedmann, J. (1987).   Planning in the public domain: From knowledge to action . Princeton University Press.
    19. Feng, Y., & Liu, Y. (2023). Sentiment analysis in citizen participation: How NLP can enhance public engagement in urban planning.  Sustainable Cities and Society , 94, 104736. https://doi.org/10.1016/j.scs.2023.104736
    20. Ghosh, S., Basak, S., & Roy, N. (2023).  Exploring urban economic development through natural language processing: A review and future directions.  City, Culture and Society , 28, 100553. https://doi.org/10.1016/j.ccs.2022.100553
    21. Ghosh, S., Gunning, D., 2019. Natural Language Processing Fundamentals: Build Intelligent Applications that Can Interpret the Human Language to Deliver Impactful Results. Packt Publishing Ltd.
    22. Healey, P. (1992).  Planning through debate: The communicative turn in planning theory.  Town Planning Review , 63(2), 143–162. http://www.jstor.org/stable/40113141
    23. Hernández, L. A., & Chiva, R. (2021). Participatory urban planning and development: Experiences from Latin America. Cities , 109, 102947. https://doi.org/10.1016/j.cities.2020.102947
    24. Hirschberg, J., and C. D. Manning. 2015. “Advances in Natural Language Processing.” Science (New York, N.Y.) 349 (6245): 261–66
    25. Hosseini, R., & Mohammadnejad, F. (2022). The role of chatbots in urban engagement: An analysis of trends .  Digital Society Journal , 4(3), 30-46.
    26. Inch, A. (2015). Ordinary Citizens and the Political Cultures of Planning: In Search of the Subject of a new Democratic Ethos. Planning Theory, 14(4), 404–424. https://doi.org/10.1177/1473095214536172
    27. Jacobs, J. (1961).   The death and life of great American cities . Random House.
    28. Jones, K. S. (1994).  Natural Language Processing: A Historical Review. In  Current Issues in Computational Linguistics: In Honour of Don Walker , 3–16. New York.
    29. Kim, S., & Lee, P. (2024). Smart urban planning: Engaging citizens through modern technologies .  Journal of Sustainable Urban Development , 16(2), 201-216.
    30. Lee, J., & Kim, H. (2022). Natural language processing and machine learning in urban economic analysis: A systematic review. Computers, Environment and Urban Systems , 99, 101909. https://doi.org/10.1016/j.compenvurbsys.2022.101909
    31. Li, Y., Fei, T., Zhang, F., 2019. A regionalization method for clustering and partitioning

    based on trajectories from NLP perspective. Int. J. Geogr. Inf. Sci. 33 (12), 2385–2405.

    1. Liddy, E. D. (2001).  Natural Language Processing. In  Encyclopedia of Library and Information Science , 2nd ed., 1–16. NY: Marcel Decker, Inc.
    2. Mancini, M., & D’Aspromonte, E. (2023).  Using machine learning and NLP to enhance urban planning processes throughh citizen engagement.  Urban Planning , 8(2), 84-97. https://doi.org/10.17645/up.v8i2.6468
    3. Nadkarni, P. M., Ohno-Machado, L., & Chapman, W. W. (2011).  Natural Language Processing: An introduction.  Journal of the American Medical Informatics Association , 18(5), 544–551.
    4. P and Deepalakshmi.P, "An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis," International Journal of Imaging Systems and Technology, vol. 32, no. 4, pp. 1373-1396, 2022
    5. Nunes, B., & Geissdoerfer, M. (2022).  Natural language processing in environmental governance: Opportunities and challenges.  Sustainability , 14(15), 9663. https://doi.org/10.3390/su14159663
    6. Pérez-Velázquez, J. A., González-Rojas, C. A., & Morán-Martínez, A. (2023).  The role of social media analysis in urban economic development: A case study using NLP techniques.  Sustainability , 15(2), 530. https://doi.org/10.3390/su150200530
    7. Peters, R., & Teeter, W. (2023).  Harnessing artificial intelligence and citizen feedback for smart urban planning.  Journal of Urban Affairs , 45(3), 1-19. https://doi.org/10.1111/juaf.12948
    8. Philipson, W. R. (1997).  Urban analysis and planning. In:  Manual of Photographic Interpretation , 2. American Society of Photogrammetry and Remote Sensing, pp. 517–554.
    9. Pyramid, K., Fisher, C., & Smith, J. (2024).  Enhancing emergency management with AI: NLP and its applications in crisis planning.  Journal of Emergency Management , 22(1), 15-29. https://doi.org/10.5055/jem.2023.0667
    10. Rad, B., & Jin, K. (2022). Facilitating urban decision-making with artificial intelligence and data analysis .  Journal of Advanced Technologies , 8(1), 23-35.
    11. Rashidi, H., & Ziaei, A. (2023).  Natural language processing for regulatory compliance: Insights into environmental policies.  Journal of Cleaner Production , 380, 135234. https://doi.org/10.1016/j.jclepro.2023.135234
    12. Ratti, C., Ray, K., & Aye, K. (2020). The impact of big data on urban planning. Environment and Planning B: Urban Analytics and City Science , 47(6), 795-800. https://doi.org/10.1177/2399808320929048
    13. Römer, V., Duesberg, K., & Schaefer, A. (2022).  Legal text mining: Natural language processing approaches to environmental law.  Environmental Science & Policy , 128, 232-241. https://doi.org/10.1016/j.envsci.2022.09.004
    14. Schrader, H. (1997).  People's participation in urban and regional planning. (Working Paper / Universität Bielefeld, Fakultät für Soziologie, Forschungsschwerpunkt Entwicklungssoziologie, 270). Bielefeld: Universität Bielefeld, Fak. für Soziologie, Forschungsschwerpunkt Entwicklungssoziologie.
    15. Shi, L., and J. Fitzgerald. 2023. “Introduction to the Special Issue: Planning for Climate Transformations.” Journal of Planning Literature. 08854122231180216
    16. Skrodelis, H. K., A. Romanovs, N. Zenina, and H. Gorskis. 2023, April. “The Latest in Natural Language Generation: Trends, Tools and Applications in Industry.” In 2023 IEEE 10th Jubilee Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE), 1–5. IEEE
    17. Smarter Cities Challenge. (2022). Transportation and mobility: A guide to smarter cities . IBM.
    18. Smith, R. W. (1973). A theoretical basis for participatory planning.
    19. Soleimani, H. (2022). Natural language processing in urban decision-making: Opportunities and threats .  Information Technology Studies , 7(1), 15-28.
    20. Sui, D. Z., Zhuang, Y., & Wei, J. (2022). Data-intensive urban studies: A review of the state of the art. Journal of Urban Technology , 29(1), 1-15. https://doi.org/10.1080/10630732.2022.2031602
    21. Tinbergen, J. 1964: Grundlagen der Entwicklungsplanung . Hannover: Verlag für Literatur und Zeitgeschehen.12-14
    22. (2022). World Population Prospects 2022 . United Nations. https://www.un.org/development/desa/pd/content/world-population-prospects-2022
    23. Youssefi, N., & Mirzaei, R. (2021). Smart cities and social interactions: A role for artificial intelligence .  Journal of Urban Sociology , 14(3), 88-100.   
    24. 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.
    25. Vargas-Calderon, V., Camargo, J.E., 2019. Characterization of citizens using word2vec and latent topic analysis in a large set of tweets. Cities 92, 187–196
    26. Wang, H., Li, J., & Chen, S. (2022). The role of social media in crisis management: A review and future directions. Telecommunications Policy , 46(5), 102400. https://doi.org/10.1016/j.telpol.2022.102400
    27. Wankhade, M., A. C. S. Rao, and C. Kulkarni. 2022. “A Survey on Sentiment Analysis Methods, Applications, and Challenges.” Artificial Intelligence Review 55 (7): 5731–80
    28. Zhang, Y., Zhao, L., & Zhang, J. (2023). Integrating big data and AI in urban planning: Advances and challenges. Sustainable Cities and Society , 81, 104048. https://doi.org/10.1016/j.scs.2022.104048
    29. Zhang, Z., Al Hamadi, H., Damiani, E., Yeun, C. Y., & Taher, F. (2022). Explainable artificial intelligence applications in cyber security: State-of-the-art in research. IEEE Access, 10, 93104–93139.
    30. Zhou, L., Chen, J., & Rainey, B. (2024). Leveraging natural language processing to inform urban economic policies: Opportunities and challenges. Urban Studies , 61(1), 203-220. https://doi.org/10.1177/00420980211055748
    31. Zhou, L., Wang, Y., & Zhang, X. (2023).Leveraging natural language processing for effective disaster response. International Journal of Disaster Risk Reduction , 75, 102853. https://doi.org/10.1016/j.ijdrr.2022.102853
فصلنامه مطالعات توسعه پایدار شهری و منطقه ای
دوره 6، شماره 2 - شماره پیاپی 20
تابستان 1404
صفحه 152-166

  • تاریخ دریافت 18 تیر 1403
  • تاریخ بازنگری 28 مهر 1403
  • تاریخ پذیرش 10 آذر 1403
  • تاریخ اولین انتشار 05 دی 1403
  • تاریخ انتشار 01 شهریور 1404

صفحه اصلی

راهنمای نویسندگان

ارسال مقاله

تماس با ما