S&CC-IRG Track 1: Connecting the Smart-City Paradigm with a Sustainable Urban Infrastructure Systems Framework to Advance Equity in Communities

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• 批准号: 1737633
• 负责人: Shashi Shekhar
• 金额: $ 250万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737633&HistoricalAwards=false
• 关键词: S& CC; IRG; Track; Connecting

This project will investigate a smart urban infrastructure systems framework for advancing access and wellbeing in cities. With transformative new infrastructures (e.g., smart electricity grid, urban farms) on the horizon, this research will provide new perspectives on how the future spatial deployment of these new infrastructures in cities will shape wellbeing, health, and the environmental sustainability of outcomes in the different areas of cities. The project advances basic research in multiple disciplines including environmental and civil engineering, computer science, urban planning and public policy. It will create a unique public database, establish citizen science protocols, and advance the science of smart sustainable urban systems through knowledge co-production with cities engaged in infrastructure planning. The project will engage in educational activities through interdisciplinary training for graduate students and professionals in urban planning, policy and sustainability. Furthermore, a strong component of citizen science engagement is involved through K-12 teachers and students, particularly in schools with underrepresented populations.Environmental sustainability, human health and wellbeing outcomes in cities are significantly shaped by key physical infrastructure provisions of water, energy, food, shelter, transportation-communications, sanitation waste management and public spaces, as well as their interactions with the social, environmental and urban form parameters. The investigators will conduct an interdisciplinary, community-engaged research project in the cities of Minneapolis, St Paul, and Tallahassee. The research will engage four themes: (a) Develop the first comprehensive fine-scale intra-urban database of over 100 social-ecological-health and well-being parameters via novel citizen science/crowdsourcing campaigns using low cost sensors; (b) Develop advanced computational algorithms to uncover hotspots and spatial correlations in the data and evaluate data-driven as well as discipline-inspired access and wellbeing hypotheses; (c) Using outcomes from (a) and (b) develop connected multi-infrastructure futures scenario models with new infrastructures through shared scenario visioning exercises, and evaluate policy learning and value of information; (d) Focus on education and workforce development for middle-high schoolers, graduate students and sustainability professionals. Outcomes from this research will be useful for informing citizens and policymakers about smart infrastructure transition being planned in cities.

该项目将研究一个智能城市基础设施系统框架，以促进城市的接入和福祉。 借助变革性的新基础设施（例如，智能电网，城市农场）即将到来，这项研究将为这些新基础设施在城市中的未来空间部署如何塑造城市不同地区的福祉，健康和环境可持续性提供新的视角。 该项目推进了多个学科的基础研究，包括环境和土木工程，计算机科学，城市规划和公共政策。 它将创建一个独特的公共数据库，建立公民科学协议，并通过与参与基础设施规划的城市共同生产知识来推进智能可持续城市系统的科学。 该项目将通过对研究生和专业人员进行城市规划、政策和可持续性方面的跨学科培训，开展教育活动。 此外，公民科学参与的一个重要组成部分是通过K-12教师和学生参与的，特别是在人口代表性不足的学校。城市的环境可持续性、人类健康和福祉成果在很大程度上取决于水、能源、食品、住房、交通-通信、卫生废物管理和公共空间等关键物质基础设施的提供，以及它们与社会、环境和社会的相互作用。环境和城市形态参数。 研究人员将在明尼阿波利斯，圣保罗和塔拉哈西市进行跨学科，社区参与的研究项目。 该研究将涉及四个主题：（a）通过使用低成本传感器的新型公民科学/众包运动，开发首个包含100多个社会-生态-健康和福祉参数的全面精细城市内数据库;（B）开发先进的计算算法，以发现数据中的热点和空间相关性，并评估数据驱动以及学科启发的获取和福祉假设;（c）利用（a）和（B）的成果，通过共同的设想方案活动，开发具有新基础设施的相互关联的多基础设施未来设想方案模型，并评价政策学习和信息价值;（d）侧重于中学生、研究生和可持续性专业人员的教育和劳动力发展。 这项研究的结果将有助于向公民和政策制定者提供有关城市规划中的智能基础设施转型的信息。

项目成果

Revolutionizing Tree Management via Intelligent Spatial Techniques

通过智能空间技术彻底改变树木管理

• DOI: 10.1145/3347146.3359066
• 发表时间: 2019
• 期刊: SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
• 作者: Xie, Yiqun;Shekhar, Shashi;Feiock, Richard;Knight, Joseph
• 通讯作者: Knight, Joseph

SRNet: A spatial-relationship aware point-set classification method for multiplexed pathology images

• 发表时间: 2021
• 作者: Yan Li;lixx;Majid Farhadloo;Farha

Discovering Spatial Mixture Patterns of Interest

发现感兴趣的空间混合模式

• DOI: 10.1145/3397536.3422217
• 发表时间: 2020
• 期刊: 28th International Conference on Advances in Geographic Information Systems (SIGSPATIAL '20
• 作者: Xie, Yiqun;Bao, Han;Li, Yan;Shekhar, Shashi
• 通讯作者: Shekhar, Shashi

Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach

• DOI: 10.1145/3466688
• 发表时间: 2021-11-01
• 期刊: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
• 影响因子: 5
• 作者: Gupta, Jayant;Molnar, Carl;Shekhar, Shashi
• 通讯作者: Shekhar, Shashi

An Alternative Approach for Improving Prediction of Integrated Hydrologic‐Hydraulic Models by Assessing the Impact of Intrinsic Spatial Scales

• DOI: 10.1029/2020wr027702
• 发表时间: 2021-09
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: S. Saksena;V. Merwade;P. Singhofen