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Received: 2025/02/17 | Accepted: 2025/03/15 | Published: 2025/03/21
Received: 2025/02/17 | Accepted: 2025/03/15 | Published: 2025/03/21
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Manuchehri A, Zabihi H, Zarabadi Z. Role of Artificial Intelligence in Optimizing and Enhancing the Accuracy of Parametric Urban Planning and Design. GeoRes 2025; 40 (1) :41-52
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1- Department of Urban Development, Science and Research Branch, Islamic Azad University, Tehran, Iran
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Background
With the rise of urbanization, the need for smart and sustainable urban design is felt more than ever. Smart cities, through the use of artificial intelligence and parametric design, enable spatial optimization and improvement in quality of life. These technologies, by analyzing data, make urban decision-making faster and more efficient.
Previous Studies
Parametric design and artificial intelligence can play a significant role in optimizing urban design. Ebadi (2019) highlight the benefits of parametric design in engineering and construction processes, while also raising concerns about the potential disruption of the historical fabric of cities. Anejionu et al. (2019), in the context of the United Kingdom, introduced urban spatial data infrastructure as a tool for socio-economic analysis. At the international level, the Smart Nation urban design project in Singapore stands as a successful example of applying artificial intelligence and parametric algorithms to urban planning [Urban Redevelopment Authority, 2006]. Moreover, Dangermond and Goodchild (2020) emphasize the necessity of establishing spatial infrastructures to better leverage big data and artificial intelligence in urban management. These studies collectively demonstrate that integrating data, algorithms, and design can lead to the development of smart, resilient, and sustainable urban environments.
Aim(s)
The aim of this research is to develop a model for refining the outcomes of parametric urban design through the application of artificial intelligence.
Research Type
This study is a descriptive-analytical survey research.
Research Society, Place, and Time
This research was conducted in the year 1403 (2024–2025) in two phases: qualitative and quantitative. The statistical population in both phases consisted of subject-matter experts, including university faculty members specializing in urban development, urban planning, and artificial intelligence.
Sampling Method and Number
In both the qualitative and quantitative phases of the research, purposeful sampling was employed. The study population consisted of 16 subject-matter experts in the fields of urban development, urban planning, and artificial intelligence, all of whom had direct research or practical experience related to the study topic.
Additionally, during the AI model training phase, a dataset of 6,000 randomly selected samples was used. These samples were scored and categorized based on 41 parametric urban design indicators. In this phase, random sampling was applied.
Used Devices & Materials
In this study, data collection tools included semi-structured interviews in the qualitative phase and a questionnaire consisting of 41 items in the quantitative phase. For designing and implementing the artificial intelligence model, the Python programming language was utilized. Specialized libraries such as TensorFlow and Keras were employed to develop an Artificial Neural Network (ANN). Additionally, for data preprocessing and normalization, the sklearn.preprocessing library and the StandardScaler function were used. The neural network model was defined using a Sequential structure, and the ReLU function was applied to activate the layers. Model performance was evaluated using the binary cross-entropy loss function and the Adam optimization algorithm, both of which demonstrated high accuracy.
Findings by Text
In the first step of the analysis, through a literature review and expert interviews (Table 1), 41 components related to parametric urban design were identified and categorized into six main dimensions and five structured levels. These components were then fed into the artificial intelligence model to determine the degree of influence and interdependence of each factor (Table 2). The model’s analysis revealed that in the environmental dimension, indicators such as access to green space and average noise pollution showed the highest sensitivity, indicating that improvements in these areas significantly enhance spatial quality (Table 3; Figure 1). In the economic dimension, economic diversity was identified as the most influential factor (Table 4; Figure 2). In the socio-demographic dimension, the tendency to preserve neighborhood identity and history had the greatest impact on improving spatial quality (Table 5; Figure 3). In the physical-spatial dimension, the presence of commercial and recreational centers emerged as the most influential factor, whereas street surface conditions had the least effect (Table 6; Figure 4). In the institutional-management dimension, efficient and effective governance showed the highest sensitivity, while budget adaptability to residents’ needs had the lowest impact (Table 7; Figure 5). Finally, in the information technology dimension, the existence of statistical databases was identified as a key factor in enhancing parametric design. The final model results were visualized as a five-level framework illustrating the penetration and influence levels of the components (Figure 6).
Table 1. Demographic characteristics of the participants
Table 2. Interpretation of the Impact Level of Indicators Based on Model Outputs
Table 3. Status of Changes in Influential and Affected Environmental Indicators in Parametric Urban Design
Figure 1. The status of changes in influential and affected indicators of the environmental dimension in parametric urban design
Table 4. The Status of Changes in Influential and Affected Indicators of the Economic Dimension in Parametric Urban Design
Figure 2) The Status of Changes in Influential and Affected Indicators of the Economic Dimension in Parametric Urban Design
Table 5) The Status of Changes in Influential and Affected Indicators of the Socio-Demographic Dimension in Parametric Urban Design
Figure 3) The Status of Changes in Influential and Affected Indicators of the Socio-Demographic Dimension in Parametric Urban Design
Table 6) The Status of Changes in Influential and Affected Indicators of the Physical Dimension in Parametric Urban Design
Figure 4. The Status of Changes in Influential and Affected Indicators of the Physical Dimension in Parametric Urban Design
Table 7) The Status of Changes in Influential and Affected Indicators of the Institutional-Managerial Dimension in Parametric Urban Design
Figure 5. The Status of Changes in Influential and Affected Indicators of the Institutional-Managerial Dimension in Parametric Urban Design
Main Comparisons to Similar Studies
This study’s findings align with and expand upon existing research on the integration of artificial intelligence (AI) and parametric design in urban planning. While parametric urban design in Iran is still in its infancy and largely limited to academic projects, countries such as the Netherlands and Singapore have made considerable progress. For example, the Zuidas project in Amsterdam applies parametric algorithms to optimize urban density and energy consumption (Kolarevic, 2015). Similarly, Singapore’s “City of the Future” initiative leverages AI for predictive urban modeling and multifunctional land use (Cheng & Steemers, 2020). In the United States, cities like New York and San Francisco use AI for traffic optimization and smart infrastructure development (Batty, 2018). The environmental, economic, social, and physical indicators addressed in this study are consistent with prior research. Previous studies have shown that parametric modeling enhances urban resilience, improves the equitable distribution of green spaces, and supports energy efficiency (Rahmani & Dadvand, 2021). Social research further suggests that well-designed urban spaces can foster social cohesion, safety, and civic engagement (Gehl, 2010). However, critiques warn that AI-driven design may lead to over-standardization, privacy concerns, and cultural uniformity- issues raised by Ferrara et al. (2019) and Zuboff (2023). Despite current challenges in Iran, such as infrastructure and expertise limitations, the study supports the potential of AI and parametric design as tools for sustainable and culturally responsive urban development.
Suggestions
Future studies would be better to focus on developing AI models that are capable of incorporating social justice and equity into urban design. These models should be able to help reduce social and economic inequalities in cities and create spaces that are accessible and beneficial to all segments of society. Such approaches can contribute to enhancing the efficiency and effectiveness of AI models in urban planning and to the creation of more sustainable and equitable cities.
Conclusion
The integration of artificial intelligence with environmental data-among which the social index holds the greatest influence- assists in achieving other components of parametric urban design using spatial data by establishing a systematic framework.
Acknowledgements: The authors sincerely thank the managers, professors, and experts for their valuable responses to the questionnaire.
Ethical Permission: No ethical issues to report.
Conflict of Interest: This article is derived from the first author's thesis. There are no conflicts of interest regarding the authorship or publication of this article.
Authors' Contributions: Manuchehri A (First Author): Introduction Writer/Discussion Writer/Methodologist (50%); Zabihi H (Second Author): Discussion Writer / Introduction Writer (25%); Zarabadi ZSS (Third Author): Introduction writing / Discussion writing (25%)
Funding: No funding to report.
With the rise of urbanization, the need for smart and sustainable urban design is felt more than ever. Smart cities, through the use of artificial intelligence and parametric design, enable spatial optimization and improvement in quality of life. These technologies, by analyzing data, make urban decision-making faster and more efficient.
Previous Studies
Parametric design and artificial intelligence can play a significant role in optimizing urban design. Ebadi (2019) highlight the benefits of parametric design in engineering and construction processes, while also raising concerns about the potential disruption of the historical fabric of cities. Anejionu et al. (2019), in the context of the United Kingdom, introduced urban spatial data infrastructure as a tool for socio-economic analysis. At the international level, the Smart Nation urban design project in Singapore stands as a successful example of applying artificial intelligence and parametric algorithms to urban planning [Urban Redevelopment Authority, 2006]. Moreover, Dangermond and Goodchild (2020) emphasize the necessity of establishing spatial infrastructures to better leverage big data and artificial intelligence in urban management. These studies collectively demonstrate that integrating data, algorithms, and design can lead to the development of smart, resilient, and sustainable urban environments.
Aim(s)
The aim of this research is to develop a model for refining the outcomes of parametric urban design through the application of artificial intelligence.
Research Type
This study is a descriptive-analytical survey research.
Research Society, Place, and Time
This research was conducted in the year 1403 (2024–2025) in two phases: qualitative and quantitative. The statistical population in both phases consisted of subject-matter experts, including university faculty members specializing in urban development, urban planning, and artificial intelligence.
Sampling Method and Number
In both the qualitative and quantitative phases of the research, purposeful sampling was employed. The study population consisted of 16 subject-matter experts in the fields of urban development, urban planning, and artificial intelligence, all of whom had direct research or practical experience related to the study topic.
Additionally, during the AI model training phase, a dataset of 6,000 randomly selected samples was used. These samples were scored and categorized based on 41 parametric urban design indicators. In this phase, random sampling was applied.
Used Devices & Materials
In this study, data collection tools included semi-structured interviews in the qualitative phase and a questionnaire consisting of 41 items in the quantitative phase. For designing and implementing the artificial intelligence model, the Python programming language was utilized. Specialized libraries such as TensorFlow and Keras were employed to develop an Artificial Neural Network (ANN). Additionally, for data preprocessing and normalization, the sklearn.preprocessing library and the StandardScaler function were used. The neural network model was defined using a Sequential structure, and the ReLU function was applied to activate the layers. Model performance was evaluated using the binary cross-entropy loss function and the Adam optimization algorithm, both of which demonstrated high accuracy.
Findings by Text
In the first step of the analysis, through a literature review and expert interviews (Table 1), 41 components related to parametric urban design were identified and categorized into six main dimensions and five structured levels. These components were then fed into the artificial intelligence model to determine the degree of influence and interdependence of each factor (Table 2). The model’s analysis revealed that in the environmental dimension, indicators such as access to green space and average noise pollution showed the highest sensitivity, indicating that improvements in these areas significantly enhance spatial quality (Table 3; Figure 1). In the economic dimension, economic diversity was identified as the most influential factor (Table 4; Figure 2). In the socio-demographic dimension, the tendency to preserve neighborhood identity and history had the greatest impact on improving spatial quality (Table 5; Figure 3). In the physical-spatial dimension, the presence of commercial and recreational centers emerged as the most influential factor, whereas street surface conditions had the least effect (Table 6; Figure 4). In the institutional-management dimension, efficient and effective governance showed the highest sensitivity, while budget adaptability to residents’ needs had the lowest impact (Table 7; Figure 5). Finally, in the information technology dimension, the existence of statistical databases was identified as a key factor in enhancing parametric design. The final model results were visualized as a five-level framework illustrating the penetration and influence levels of the components (Figure 6).
Table 1. Demographic characteristics of the participants
Table 2. Interpretation of the Impact Level of Indicators Based on Model Outputs
Table 3. Status of Changes in Influential and Affected Environmental Indicators in Parametric Urban Design
Figure 1. The status of changes in influential and affected indicators of the environmental dimension in parametric urban design
Table 4. The Status of Changes in Influential and Affected Indicators of the Economic Dimension in Parametric Urban Design
Figure 2) The Status of Changes in Influential and Affected Indicators of the Economic Dimension in Parametric Urban Design
Table 5) The Status of Changes in Influential and Affected Indicators of the Socio-Demographic Dimension in Parametric Urban Design
Figure 3) The Status of Changes in Influential and Affected Indicators of the Socio-Demographic Dimension in Parametric Urban Design
Table 6) The Status of Changes in Influential and Affected Indicators of the Physical Dimension in Parametric Urban Design
Figure 4. The Status of Changes in Influential and Affected Indicators of the Physical Dimension in Parametric Urban Design
Table 7) The Status of Changes in Influential and Affected Indicators of the Institutional-Managerial Dimension in Parametric Urban Design
Figure 5. The Status of Changes in Influential and Affected Indicators of the Institutional-Managerial Dimension in Parametric Urban Design
Main Comparisons to Similar Studies
This study’s findings align with and expand upon existing research on the integration of artificial intelligence (AI) and parametric design in urban planning. While parametric urban design in Iran is still in its infancy and largely limited to academic projects, countries such as the Netherlands and Singapore have made considerable progress. For example, the Zuidas project in Amsterdam applies parametric algorithms to optimize urban density and energy consumption (Kolarevic, 2015). Similarly, Singapore’s “City of the Future” initiative leverages AI for predictive urban modeling and multifunctional land use (Cheng & Steemers, 2020). In the United States, cities like New York and San Francisco use AI for traffic optimization and smart infrastructure development (Batty, 2018). The environmental, economic, social, and physical indicators addressed in this study are consistent with prior research. Previous studies have shown that parametric modeling enhances urban resilience, improves the equitable distribution of green spaces, and supports energy efficiency (Rahmani & Dadvand, 2021). Social research further suggests that well-designed urban spaces can foster social cohesion, safety, and civic engagement (Gehl, 2010). However, critiques warn that AI-driven design may lead to over-standardization, privacy concerns, and cultural uniformity- issues raised by Ferrara et al. (2019) and Zuboff (2023). Despite current challenges in Iran, such as infrastructure and expertise limitations, the study supports the potential of AI and parametric design as tools for sustainable and culturally responsive urban development.
Suggestions
Future studies would be better to focus on developing AI models that are capable of incorporating social justice and equity into urban design. These models should be able to help reduce social and economic inequalities in cities and create spaces that are accessible and beneficial to all segments of society. Such approaches can contribute to enhancing the efficiency and effectiveness of AI models in urban planning and to the creation of more sustainable and equitable cities.
Conclusion
The integration of artificial intelligence with environmental data-among which the social index holds the greatest influence- assists in achieving other components of parametric urban design using spatial data by establishing a systematic framework.
Acknowledgements: The authors sincerely thank the managers, professors, and experts for their valuable responses to the questionnaire.
Ethical Permission: No ethical issues to report.
Conflict of Interest: This article is derived from the first author's thesis. There are no conflicts of interest regarding the authorship or publication of this article.
Authors' Contributions: Manuchehri A (First Author): Introduction Writer/Discussion Writer/Methodologist (50%); Zabihi H (Second Author): Discussion Writer / Introduction Writer (25%); Zarabadi ZSS (Third Author): Introduction writing / Discussion writing (25%)
Funding: No funding to report.
Keywords:
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