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

NSF融合加速器支持以团队为基础的多学科努力，以应对国家重要性的挑战，并在不久的将来展示可交付成果的潜力。 这个融合加速器第一阶段项目的核心目标是对来自美国人口普查（以及美国社区调查和州及地方政府数据）的数据进行结构化和预处理，使其最大限度地可用于网络科学，机器学习和人工智能的研究技术，这些技术目前处于复杂系统科学研究的最前沿。该项目的更广泛的影响和社会效益将来自于利用人口普查数据的大型，精细和昂贵的公共资源进行研究，并在政策，规划，公共卫生和其他主题中具有强大的应用。将复杂的人工智能和复杂网络技术应用于人口普查数据的能力-人口，地理和社会经济信息的混合安排在一个综合的层次结构中-将通过提供更好地了解国家人口和社区的机会来服务于国家利益，包括快照和趋势线。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、公共部门官员和公众提供经过处理的人口和公民数据。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（来自组合数学，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，并得到社会科学领域有意义的跨学科合作的支持。该项目旨在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聚类，分区和特征识别的有效算法。该小组计划在第一阶段还扩大学术界、工业界、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进一步发展的技术，离散马尔可夫链的图形分区，在这个领域，项目团队已经拥有了重要的专业知识，并预计在混合时间和稳态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，并通过利用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

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.