Frameworks: Collaborative Proposal: Software Infrastructure for Transformative Urban Sustainability Research

框架：合作提案：变革性城市可持续发展研究的软件基础设施

• 批准号: 1931324
• 负责人: Mikhail Chester
• 金额: $ 41万
• 依托单位: Arizona 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: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931324&HistoricalAwards=false
• 关键词: Frameworks; Collaborative; Proposal; Software; Infrastructure

The United States is highly urbanized with more than 80% of the population residing in cities. Cities draw from and impact natural resources and ecosystems while utilizing vast, expensive infrastructures to meet economic, social, and environmental needs. The National Science Foundation has invested in several strategic research efforts in the area of urban sustainability, all of which generate, collect, and manage large volumes of spatiotemporal data. Voluminous datasets are also made available in domains such as climate, ecology, health, and census. These data can spur exploration of new questions and hypotheses, particularly across traditionally disparate disciplines, and offer unprecedented opportunities for discovery and innovation. However, the data are encoded in diverse formats and managed using a multiplicity of data management frameworks -- all contributing to a break-down of the observational space that inhibits discovery. A scientist must reconcile not only the encoding and storage frameworks, but also negotiate authorizations to access the data. A consequence is that data are locked in institutional silos, each of which represents only a sliver of the observational space. This project, SUSTAIN (Software for Urban Sustainability to Tailor Analyses over Interconnected Networks), facilitates and accelerates discovery by significantly alleviating data-induced inefficiencies. This effort has deep, far-reaching impact. It transforms urban sustainability science by establishing a community of interdisciplinary researchers and catalyzing their collaborative capacity. Hundreds of researchers from over 150 universities are members of our collaborating organizations and will immediately benefit from SUSTAIN. Domains where spatiotemporal phenomena must be analyzed benefit from this innovative research; the partnership with ESRI and Google Earth amplify the impact of SUSTAIN, giving the project a global reach and enabling international collaborative initiatives. The direct engagement with middle school students in computer science and STEM disciplines has well-known benefits and, combined with graduate training, produces a diverse, globally competitive STEM workforce. SUSTAIN targets transformational capabilities for feature space exploration, hypotheses formulation, and model creation and validation over voluminous, high-dimensional spatiotemporal data. These capabilities are deeply aligned with the urban sustainability community's needs, and they address challenges that preclude effective research. SUSTAIN accomplishes these interconnected goals by enabling holistic visibility of the observational space, interactive visualizations of multidimensional information spaces using overlays, fast evaluation of expressive queries tailored to the needs of the discovery process, generation of custom exploratory datasets, and interoperation with diverse analyses software frameworks - all leading to better science. SUSTAIN fosters deep explorations through its transformative visibility of the federated information space. The project reconciles the fragmentation and diversity of siloed data to provide seamless, unprecedented visibility of the information space. A novel aspect of the project's methodology is the innovative use of the Synopsis, a spatiotemporal sketching algorithm that decouples data and information. The methodology extracts and organizes information from the data and uses the information (or sketches of the data) as the basis for explorations. The project also incorporates a novel algorithm for imputations at the sketch level at myriad spatiotemporal scopes. The effort creates a collaborative community of multidisciplinary researchers to build an enduring software infrastructure for urban sustainability.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.

美国是一个高度城市化的国家，80%以上的人口居住在城市。城市利用并影响自然资源和生态系统，同时利用庞大、昂贵的基础设施来满足经济、社会和环境需求。美国国家科学基金会在城市可持续性领域投入了多项战略研究，所有这些研究都产生、收集和管理大量的时空数据。在气候、生态、健康和人口普查等领域也提供了大量数据集。这些数据可以刺激对新问题和假设的探索，特别是在传统上不同的学科中，并为发现和创新提供前所未有的机会。然而，这些数据以不同的格式编码，并使用多种数据管理框架进行管理-所有这些都有助于打破阻碍发现的观测空间。科学家不仅必须协调编码和存储框架，还必须协商访问数据的授权。其结果是，数据被锁定在机构的筒仓中，每个筒仓只代表观测空间的一小部分。该项目名为SUSTAIN（互联网络上定制分析的城市可持续性软件），通过显著缓解数据导致的低效率来促进和加速发现。这一努力具有深远的影响。它通过建立跨学科研究人员社区并促进他们的合作能力来改变城市可持续发展科学。来自150多所大学的数百名研究人员是我们合作组织的成员，他们将立即从SUSTAIN中受益。必须对时空现象进行分析的领域将从这一创新研究中受益;与ESRI和Google Earth的合作扩大了SUSTAIN的影响，使该项目具有全球影响力，并使国际合作计划成为可能。与计算机科学和STEM学科的中学生直接接触具有众所周知的好处，并与研究生培训相结合，产生了多元化，具有全球竞争力的STEM劳动力。SUSTAIN的目标是在大量高维时空数据上进行特征空间探索、假设制定以及模型创建和验证的转换能力。这些能力与城市可持续发展社区的需求密切相关，它们解决了阻碍有效研究的挑战。SUSTAIN通过实现观察空间的整体可见性、使用叠加对多维信息空间进行交互式可视化、快速评估根据发现过程需求定制的表达性查询、生成自定义探索性数据集以及与不同分析软件的互操作来实现这些相互关联的目标框架--所有这些都有助于更好的科学。SUSTAIN通过其对联邦信息空间的变革性可见性促进了深入的探索。该项目协调了孤立数据的碎片化和多样性，以提供无缝的、前所未有的信息空间可见性。该项目方法的一个新颖方面是创新地使用了Synopsis，这是一种时空草图算法，可以简化数据和信息。该方法从数据中提取和组织信息，并使用这些信息（或数据草图）作为探索的基础。该项目还采用了一种新的算法，在草图级的无数时空范围内的估算。该奖项反映了NSF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

Infrastructure resilience to navigate increasingly uncertain and complex conditions in the Anthropocene

• DOI: 10.1038/s42949-021-00016-y
• 发表时间: 2021-02-23
• 期刊: NPJ URBAN SUSTAINABILITY
• 作者: Chester, Mikhail;Underwood, B. Shane;Miller, Thaddeus R.
• 通讯作者: Miller, Thaddeus R.

Rapid, Progressive Sub-Graph Explorations for Interactive Visual Analytics over Large-Scale Graph Datasets

• DOI: 10.1145/3365109.3368793
• 发表时间: 2019-12
• 期刊: Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
• 作者: Samuel Armstrong;Kevin Bruhwiler;S. Pallickara

ADQuaTe2: A Data Quality Test Approach for Automated Constraint Discovery and Fault Detection

ADQuaTe2：用于自动约束发现和故障检测的数据质量测试方法

• 发表时间: 2019
• 期刊: ACM TAPIA
• 作者: Homayouni, Hajar;Ghosh, Sudipto;Ray, Indrakshi;Kahn, Michael G.
• 通讯作者: Kahn, Michael G.

Centralization and decentralization for resilient infrastructure and complexity

• DOI: 10.1088/2634-4505/ac0a4f
• 发表时间: 2021-09-01
• 期刊: ENVIRONMENTAL RESEARCH: INFRASTRUCTURE AND SUSTAINABILITY
• 作者: Helmrich, Alysha;Markolf, Samuel;Chester, Mikhail
• 通讯作者: Chester, Mikhail

Adaptive transit scheduling to reduce rider vulnerability during heatwaves

自适应交通调度可减少热浪期间乘客的脆弱性

• DOI: 10.1080/23789689.2022.2029324
• 发表时间: 2022
• 期刊: Sustainable and Resilient Infrastructure
• 影响因子: 5.9
• 作者: Rosenthal, Noam;Chester, Mikhail;Fraser, Andrew;Hondula, David M.;Eisenman, David P.
• 通讯作者: Eisenman, David P.