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The use of Artificial Intelligence (AI) in urban planning and the design of architectural projects is a new step in the development of cities. Currently, researchers report the need for changing a traditional approach to planning cities and buildings because of the continuous growth of the population and their expectations regarding urban life (Bunnell 46; Meijer et al. 648). Much attention should be paid to developing new solutions for citizens to address their needs regarding the effective design of buildings and their location in the streets in the context of reconsidering the basics of urban planning through the application of AI to create smart cities.
AI as the imitation of human intelligence with the help of technologies allows for collecting and analysing corpora of data, making forecasts, and presenting effective solutions within a short period of time (Allam and Dhunny 81). Referring to the complex analysis of urban population dynamics, processes, transportation, building locations and other details, AI-based platforms can provide architects and city planners with efficient decisions to realise them in innovative projects and address citizens’ interests (Geertman 89; Kitchin 2). In the UAE, the government proposed the UAE Strategy for Artificial Intelligence (AI) in the end of 2017 for the purpose of making cities sustainable (“UAE Strategy for Artificial Intelligence”). In other countries, the process of shifting to the application of AI in urban planning and architecture has also started, and it is important to study the related outcomes that are observed today.
Research Problem, Aims and Questions
The problem is that many modern cities are overpopulated, resulting in barriers to using traditional approaches to urban planning and creating the need for applying more innovative methods to make cities comfortable for all citizens. In this context, it is necessary to study how urban forecasting and planning can be empowered and improved with the help of AI to complete data analysis and inform decision making (Coletta et al. 350; Dorr 392). The aim of this research is to examine the relationship between the application of effective AI technologies to enhance urban planning approaches and the development of modern smart and people focused cities. It is necessary to understand what AI technologies and how can be used to meet people’s needs in overcrowded cities.
The following research questions have been formulated:
- RQ1: Is there a relationship between the application of AI in urban planning and architecture and the development of modern smart and people focused cities?
- RQ2: How can AI contribute to building smart, people oriented cities with reference to the effect on architecture and urban planning?
The research problem covered in this study is connected with the research areas studied by specialists of Digital Architecture Research Centre (DARC) at Kent School of Architecture and Planning. Since DARC initiates and supports research on the connection between architecture and digital technologies, the proposed study directly fits this area. The reason is that the potential contribution of this proposed study is the development of the framework for the efficient integration of AI in the spheres of architecture and urban planning with reference to improving data analysis and design, achieving sustainability, and responding to people’s needs.
The set aims and research questions will be addressed with the help of a mixed methods approach, involving the collection and analysis of both qualitative and quantitative data. The sample selected for this study will include 20 experts in urban planning who practice in the UAE, the UK and other developed countries, and who have experience in applying AI technologies in their work. A non-probability purposive sampling approach will be used because it is necessary to recruit only experts working with AI in urban planning (Morse 114; Walliman 212). The participants will be selected depending on their consent to join the study and take part in surveys and interviews.
Quantitative data will be collected with the help of a questionnaire organised according to the principles of a Likert scale to determine the participants’ views regarding the success of using AI in the practice of urban planning and associated benefits or challenges. These data will be analysed with the help of the correlational statistical analysis to indicate any relationship between the application of AI in architecture and urban planning and the creation of people-focused smart cities addressing citizens’ needs (Walliman 167-180). Qualitative data on the participants’ evaluation of AI in urban planning will be collected with the help of semi-structured interview protocols. Participants’ narratives will be analysed according to the principles of thematic analysis to identify major themes prevailing in experts’ views regarding the improvement of urban planning procedures with the help of AI.
Potential Implications for Theory and Practice
The study findings will be used for formulating a framework for the successful application of AI in urban planning and architecture and developing smart cities with reference to the information collected from the experts in the area. Thus, the key implication of this research for theory will be the identification of the correlation between the use of AI and the potential changes in urban planning to address citizens’ needs to add to the existing research on the problem (Allam and Dhunny 81; Kitchin 8). The major implication for practice will be the development of the framework to follow by city developers, architects, engineers and other specialists in the sphere of urban planning to guarantee the effective use of AI to build smart human-oriented cities.
The presented timescale indicates the periods for completing different tasks associated with this project.
|Activity||Year 1||Year 2||Year 3|
|Literature review and analysis||X||X|
|Design of two instruments||X||X|
|Quantitative data collection||X||X|
|Qualitative data collection||X||X|
|Analysis of both types of data||X||X|
|Editing, revising, proofreading||X||X|
Allam, Zaheer, and Zaynah A. Dhunny. “On Big Data, Artificial Intelligence and Smart Cities.” Cities, vol. 89, 2019, pp. 80-91.
Bunnell, Tim. “Smart City Returns.” Dialogues in Human Geography, vol. 5, no. 1, 2015, pp. 45-48.
Coletta, Claudio, et al. “From the Accidental to Articulated Smart City: The Creation and Work of ‘Smart Dublin.’” European Urban and Regional Studies, vol. 26, no. 4, 2019, pp. 349-364.
Dorr, Adam. “Technology Blindness and Temporal Imprecision: Rethinking the Long Term in an Era of Accelerating Technological Change.” Foresight, vol. 18, no. 4, 2016, pp. 391-413.
Geertman, Stan. Computational Urban Planning and Management for Smart Cities. Springer, 2019.
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Kitchin, Rob. “The Ethics of Smart Cities and Urban Science.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2083, 2016, pp. 1-12.
Meijer, Albert J., et al. “Smart City Research: Contextual Conditions, Governance Models, and Public Value Assessment.” Social Science Computer Review, vol. 34, no. 6, 2016, pp. 647-656.
Morse, Janice M. Mixed Method Design: Principles and Procedures. Routledge, 2016.
“UAE Strategy for Artificial Intelligence.” Government.ae, 2019, Web.
Walliman, Nicholas. Research Methods: The Basics. Routledge, 2017.