Knowledge co-creation during urban simulation computation to enable broader participation

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2024-11-17 DOI:10.1016/j.scs.2024.105994

Zaiyang Ma , Hengyue Li , Kai Zhang , Jin Wang , Songshan Yue , Yongning Wen , Guonian Lü , Min Chen

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引用次数: 0

Abstract

Preparing knowledge on urban simulation computation is necessary to help participants build consensus, reduce expertise gaps, and guide participatory sustainable urban planning. Knowledge co-creation is an effective way to prepare the needed knowledge related to urban simulation computation. However, the procedural and operational information that can help instruct the implementation of urban simulation is extensively hidden in the implementation processes of urban simulation in various forms (e.g., dialog records, configuration parameters, and model operations). Difficulties remain in extracting this implicit information and synthesizing the related knowledge. Therefore, a strategy is proposed to support the co-creation of knowledge during the urban simulation computation. In this strategy, the structural knowledge expression methods are first designed to support information extraction and knowledge synthesis. Based on interaction tracking and natural language understanding techniques, the related information can be obtained from simulation computation processes. Using this information, four main types of knowledge can be generated, optimized and visualized to assist collaborative urban simulation practices. This strategy was implemented in an online collaboration prototype system and verified with two sustainable urban case studies involving the simulation of urban noise environments and solar radiation assessment of photovoltaic noise barriers in cities. The results show that the knowledge co-creation can be effectively implemented by using the information extracted from simulation computation processes, which can benefit broader collaboration in urban simulation and sustainable urban planning.

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在城市仿真计算过程中共同创造知识，实现更广泛的参与

为帮助参与者达成共识、减少专业知识差距并指导参与式可持续城市规划，有必要准备有关城市仿真计算的知识。知识共创是准备城市仿真计算相关所需知识的有效途径。然而，有助于指导城市仿真实施的程序和操作信息以各种形式（如对话记录、配置参数和模型操作）广泛隐藏在城市仿真的实施过程中。提取这些隐含信息并综合相关知识仍然存在困难。因此，我们提出了一种支持在城市仿真计算过程中共同创造知识的策略。在这一策略中，首先设计了结构性知识表达方法，以支持信息提取和知识合成。基于交互跟踪和自然语言理解技术，可以从模拟计算过程中获取相关信息。利用这些信息，可以生成、优化和可视化四种主要类型的知识，以协助城市仿真协作实践。这一策略已在一个在线协作原型系统中实施，并通过两个可持续城市案例研究进行了验证，涉及城市噪声环境模拟和城市光伏隔音屏障的太阳辐射评估。结果表明，通过使用从模拟计算过程中提取的信息，可以有效地实现知识共创，从而有利于城市模拟和可持续城市规划领域的更广泛合作。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

期刊介绍： Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;