Artificial intelligence in urban science: why does it matter?
Urban science aims to explain, discover, understand, and generalize (EDUG) complex, human-centric systems, emphasizing societal context and sustainability. However, integrating artificial intelligence (AI) into urban science presents challenges, including data availability, ethical considerations, a...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-04-01
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| Series: | Annals of GIS |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475683.2025.2469110 |
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| author | Xinyue Ye Tan Yigitcanlar Michael Goodchild Xiao Huang Wenwen Li Shih-Lung Shaw Yanjie Fu Wenjing Gong Galen Newman |
| author_facet | Xinyue Ye Tan Yigitcanlar Michael Goodchild Xiao Huang Wenwen Li Shih-Lung Shaw Yanjie Fu Wenjing Gong Galen Newman |
| author_sort | Xinyue Ye |
| collection | DOAJ |
| description | Urban science aims to explain, discover, understand, and generalize (EDUG) complex, human-centric systems, emphasizing societal context and sustainability. However, integrating artificial intelligence (AI) into urban science presents challenges, including data availability, ethical considerations, and the ‘black-box’ nature of many AI models. Despite these limitations, AI offers significant opportunities for urban management and planning by leveraging vast, multimodal datasets to optimize infrastructure, predict trends, and enhance resilience. Techniques such as explainable AI and knowledge-driven approaches have begun addressing transparency concerns, aligning AI outputs with urban science’s emphasis on interpretability. Urban science reciprocally contributes to AI development by embedding contextual awareness and human-centric insights, enhancing AI’s ability to navigate urban complexities. Examples include digital twins for real-time urban analysis and generative AI for inclusive urban modelling. This opinion piece advocates for fostering a symbiotic relationship between AI and urban science, emphasizing co-learning and ethical collaboration. By integrating technical innovation with societal needs, the convergence of AI and urban science – termed the ‘New Urban Science’ – promises smarter, equitable, and sustainable cities. This paradigm underscores the transformative potential of aligning AI advancements with urban science’s foundational goals. |
| format | Article |
| id | doaj-art-d678ed0065494c4e9eea37faf7b951db |
| institution | Kabale University |
| issn | 1947-5683 1947-5691 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of GIS |
| spelling | doaj-art-d678ed0065494c4e9eea37faf7b951db2025-08-20T03:48:14ZengTaylor & Francis GroupAnnals of GIS1947-56831947-56912025-04-0131218118910.1080/19475683.2025.2469110Artificial intelligence in urban science: why does it matter?Xinyue Ye0Tan Yigitcanlar1Michael Goodchild2Xiao Huang3Wenwen Li4Shih-Lung Shaw5Yanjie Fu6Wenjing Gong7Galen Newman8Department of Landscape Architecture and Urban Planning & Center for Geospatial Sciences, Applications, and Technology, Texas A&M University, College Station, Texas, USACity 4.0 Lab, School of Architecture and Built Environment, Faculty of Engineering, Queensland University of Technology, Brisbane, Queensland, AustraliaDepartment of Geography, University of California, Santa Barbara, California, USADepartment of Environmental Sciences, Emory University, Atlanta, Georgia, USASchool of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona, USADepartment of Geography and Sustainability, University of Tennessee, Knoxville, Tennessee, USASchool of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, USADepartment of Landscape Architecture and Urban Planning & Center for Geospatial Sciences, Applications, and Technology, Texas A&M University, College Station, Texas, USADepartment of Landscape Architecture and Urban Planning & Center for Geospatial Sciences, Applications, and Technology, Texas A&M University, College Station, Texas, USAUrban science aims to explain, discover, understand, and generalize (EDUG) complex, human-centric systems, emphasizing societal context and sustainability. However, integrating artificial intelligence (AI) into urban science presents challenges, including data availability, ethical considerations, and the ‘black-box’ nature of many AI models. Despite these limitations, AI offers significant opportunities for urban management and planning by leveraging vast, multimodal datasets to optimize infrastructure, predict trends, and enhance resilience. Techniques such as explainable AI and knowledge-driven approaches have begun addressing transparency concerns, aligning AI outputs with urban science’s emphasis on interpretability. Urban science reciprocally contributes to AI development by embedding contextual awareness and human-centric insights, enhancing AI’s ability to navigate urban complexities. Examples include digital twins for real-time urban analysis and generative AI for inclusive urban modelling. This opinion piece advocates for fostering a symbiotic relationship between AI and urban science, emphasizing co-learning and ethical collaboration. By integrating technical innovation with societal needs, the convergence of AI and urban science – termed the ‘New Urban Science’ – promises smarter, equitable, and sustainable cities. This paradigm underscores the transformative potential of aligning AI advancements with urban science’s foundational goals.https://www.tandfonline.com/doi/10.1080/19475683.2025.2469110Artificial intelligenceurban scienceexplainable AIdigital twinshuman dynamics |
| spellingShingle | Xinyue Ye Tan Yigitcanlar Michael Goodchild Xiao Huang Wenwen Li Shih-Lung Shaw Yanjie Fu Wenjing Gong Galen Newman Artificial intelligence in urban science: why does it matter? Annals of GIS Artificial intelligence urban science explainable AI digital twins human dynamics |
| title | Artificial intelligence in urban science: why does it matter? |
| title_full | Artificial intelligence in urban science: why does it matter? |
| title_fullStr | Artificial intelligence in urban science: why does it matter? |
| title_full_unstemmed | Artificial intelligence in urban science: why does it matter? |
| title_short | Artificial intelligence in urban science: why does it matter? |
| title_sort | artificial intelligence in urban science why does it matter |
| topic | Artificial intelligence urban science explainable AI digital twins human dynamics |
| url | https://www.tandfonline.com/doi/10.1080/19475683.2025.2469110 |
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