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Convergence Accelerator Phase I (RAISE): Network Science of Census Data

融合加速器第一阶段（RAISE）：人口普查数据的网络科学

基本信息

批准号：
1937095
负责人：
Moon Duchin
金额：
$ 96.22万
依托单位：
Tufts 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=1937095&HistoricalAwards=false
关键词：
Convergence Accelerator Phase RAISE Network

项目摘要

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. This Convergence Accelerator Phase I project is focused on a central goal of structuring and pre-processing data from the U.S. Census (as well as the American Community Survey and state and local government data) to make it maximally accessible to the research techniques of network science, machine learning, and artificial intelligence that are currently at the forefront of scientific inquiry into complex systems. The broader impacts and social benefit of this project will emerge from leveraging the large, elaborate, and expensively collected public resource of census data for research, with strong applications to policy, planning, public health, and other topics. The ability to apply sophisticated artificial intelligence and complex networks techniques to Census data - a blend of demographic, geographic, and socioeconomic information arranged in an integrated hierarchy - will serve the national interest by providing opportunities to better understand the nation's population and communities, with both snapshots and trendlines. Phase I of the project also includes tool development to provide the processed demographic and civic data to scholars, legislators, public-sector officials, and the general public through open-source apps and other interfaces. The project is a convergence research effort, bringing theoretical mathematicians and computer scientists (from combinatorics, probability, geometry, dynamics, and algorithms) together with applied mathematicians and network scientists, buttressed by meaningful interdisciplinary collaboration in the social sciences. The project seeks to understand census geography in both graph and network terms, developing new multi-layer network structures as well as efficient algorithms for clustering, partitioning, and feature identification. The team plans in phase I to also expand collaborative relationships between academia, industry, government, and public-sector organizations. A particular emphasis will be placed on the further development of techniques for discrete Markov chains on graph partitions, an area in which the project team already has significant expertise and anticipates progress on both rigorous and heuristic results about mixing times and stationary distributions.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融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。这一融合加速器第一阶段项目的核心目标是组织和预处理来自美国人口普查的数据(以及美国社区调查以及州和地方政府数据)，使其最大限度地为网络科学、机器学习和人工智能等研究技术所用，这些研究技术目前处于复杂系统科学研究的前沿。该项目的更广泛的影响和社会效益将来自于利用大量、详细和昂贵的公共资源收集的人口普查数据进行研究，并将其有力地应用于政策、规划、公共卫生和其他主题。将复杂的人工智能和复杂的网络技术应用于人口普查数据-人口、地理和社会经济信息在一个综合的层次结构中排列的混合-的能力将通过快照和趋势线提供更好地了解国家人口和社区的机会，从而服务于国家利益。该项目的第一阶段还包括开发工具，通过开源应用程序和其他界面向学者、立法者、公共部门官员和普通公众提供处理后的人口和公民数据。该项目是一项融合研究工作，将理论数学家和计算机科学家(来自组合学、概率、几何、动力学和算法)与应用数学家和网络科学家聚集在一起，并得到社会科学中有意义的跨学科合作的支持。该项目寻求从图形和网络两个方面了解人口普查地理，开发新的多层网络结构以及用于集群、分区和特征识别的高效算法。该团队计划在第一阶段还扩大学术界、行业、政府和公共部门组织之间的合作关系。一个特别的重点将放在图分区上离散马尔可夫链技术的进一步发展上，在这一领域，项目团队已经拥有了丰富的专业知识，并预期在关于混合时间和平稳分布的严格和启发式结果方面取得进展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Geometry of Graph Partitions via Optimal Transport

通过最佳传输的图分区的几何形状

DOI：
10.1137/19m1295258
发表时间：
2020
期刊：
SIAM Journal on Scientific Computing
影响因子：
3.1
作者：
Abrishami, Tara;Guillen, Nestor;Rule, Parker;Schutzman, Zachary;Solomon, Justin;Weighill, Thomas;Wu, Si
通讯作者：
Wu, Si

Mathematics of Nested Districts: The Case of Alaska

嵌套区域的数学：阿拉斯加的案例