Convergence Accelerator Phase I (RAISE): Open Knowledge Network for Spatial Decision Support

融合加速器第一阶段（RAISE）：空间决策支持的开放知识网络

• 批准号: 1937908
• 负责人: Sean Gordon
• 金额: $ 99.97万
• 依托单位: Portland State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937908&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Phase; RAISE; Open

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is improved access to spatial data and related software tools that will support decision making and public participation that are needed to address a wide range of complex problems. Spatial decision support (SDS) tools are used across a diversity of domains, including public health, emergency management, city planning, education, natural resource management, public safety, transportation, utilities, and the delivery of public and private services more generally. Despite many successful applications, the effectiveness of spatial decision support contributions is limited by challenges in integrating information across complex organizational networks and across an array of data and tools developed for narrow (often discipline-specific) applications. This project will develop technologies and participatory methods that will help bridge organizational and disciplinary boundaries, thereby building a foundation for an open knowledge network for spatial decision support (OKN-SDS). The interdisciplinary project team plans to engage stakeholders in three applied case studies: the management of wildland fire, water quality, and biodiversity conservation. These three use-cases will be used to develop and test participatory and automated methods for structuring, tagging and sharing decision-relevant information. These demonstration projects, the accompanying development of the underlying technical architecture for the OKN-SDS, and outreach to broader SDS-related communities will motivate and facilitate the integration of SDS data and tools that is needed to better address a diverse range of decision-making processes that require spatial decision support.The intellectual merit of the project is based on the participatory and open technology development process that the team will use to develop resources for creating use-inspired spatial decision support applications relevant to challenges in many disciplines. The project team will investigate the utility of automated tools for resource discovery, ontology development, and social network analysis, validating the methods in the three proposed use cases (wildland fire, water quality, and biodiversity conservation). Through integration and comparison of these techniques, the project team will deliver insights into efficient and effective methods for OKN development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF融合加速器支持以团队为基础的多学科努力，解决国家重要性的挑战，并在不久的将来显示出可交付成果的潜力。“融合加速器”第一阶段项目的更广泛影响和潜在社会效益是改善对空间数据和相关软件工具的获取，这些工具将支持解决各种复杂问题所需的决策和公众参与。空间决策支持（SDS）工具用于各种领域，包括公共卫生、应急管理、城市规划、教育、自然资源管理、公共安全、交通、公用事业以及更普遍的公共和私人服务的提供。尽管有许多成功的应用，但空间决策支持贡献的有效性受到跨复杂组织网络和跨为狭窄（通常特定于学科）应用开发的一系列数据和工具集成信息的挑战的限制。该项目将开发有助于跨越组织和学科界限的技术和参与性方法，从而为空间决策支持的开放知识网络（OKN-SDS）奠定基础。跨学科项目团队计划让利益相关者参与三个应用案例研究：野火管理、水质和生物多样性保护。这三个用例将用于开发和测试用于结构化、标记和共享决策相关信息的参与性和自动化方法。这些示范项目，伴随OKN-SDS底层技术架构的开发，以及向更广泛的SDS相关社区的延伸，将激励和促进SDS数据和工具的整合，这些数据和工具是更好地解决需要空间决策支持的各种决策过程所必需的。该项目的智力价值基于参与式和开放式技术开发过程，团队将使用该过程开发资源，以创建与许多学科挑战相关的使用启发的空间决策支持应用程序。项目团队将调查自动化工具在资源发现、本体开发和社会网络分析方面的效用，并在三个提议的用例（荒地火灾、水质和生物多样性保护）中验证方法。通过对这些技术的整合和比较，项目团队将为OKN开发提供高效和有效的方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Towards an AI-driven framework for multi-scale urban flood resilience planning and design

• DOI: 10.1007/s43762-021-00011-0
• 发表时间: 2021-07
• 期刊: Computational Urban Science
• 作者: Xinyue Ye;Shaohua Wang;Zhipeng Lu;Yang Song;Siyu Yu

Geospatial and Semantic Mapping Platform for Massive COVID-19 Scientific Publication Search

• DOI: 10.1007/s41651-021-00073-y
• 发表时间: 2021-01-19
• 期刊: Journal of Geovisualization and Spatial Analysis
• 影响因子: 4
• 作者: Ye X;Du J;Gong X;Na S;Li W;Kudva S
• 通讯作者: Kudva S