Artificial intelligence enabled participatory planning: a review

感恩 公民新闻 SWOT分析 优势和劣势 过程(计算) 管理科学 建设性的 工作(物理) 社会学 工程伦理学 计算机科学 政治学 心理学 工程类 管理 社会心理学 万维网 机械工程 操作系统 经济
作者
Jiaxin Du,Xinyue Ye,Piotr Jankowski,Thomas W. Sanchez,Gengchen Mai
出处
期刊:The International Journal of Urban Sciences [Informa]
卷期号:28 (2): 183-210 被引量:12
标识
DOI:10.1080/12265934.2023.2262427
摘要

ABSTRACTParticipatory planning is a democratic spatial decision-making process involving multiple stakeholders. The integration of artificial intelligence (AI) methods in participatory planning has the potential to improve the decision-making process. However, there are challenges and limitations that need to be addressed. In this paper, we systematically review the progress of AI-enabled participatory planning, identifying strengths and weaknesses. We used a Strengths, Weaknesses, Opportunities, and Threats (SWOT) framework for our analysis, highlighting the opportunities for advancing AI in participatory planning and the potential threats that may arise. Our study provides valuable insights into the current state of AI-enabled participatory planning, paving the way for future developments and improvements.KEYWORDS: Artificial intelligenceGISparticipatory planningspatial decision support‌AI challenges and limitationsdemocratic decision-making Disclosure statementNo potential conflict of interest was reported by the author(s). We wish to extend our sincere gratitude to the anonymous reviewers for their insightful comments, constructive criticisms, and invaluable suggestions, all of which significantly improved the quality of this paper. We also thank the editor for their guidance and support throughout the review process. Their collective expertise and dedication have greatly enhanced our work. Additionally, we are grateful for the discussions with Dr. Walter Peacock and Dr. Michelle Meyer from Texas A&M University.Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.Additional informationFundingThis work was supported by USA National Science Foundation [grant number 2122054, 2232533].
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