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，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”第一阶段项目的核心目标是对来自美国人口普查（以及美国社区调查和州和地方政府数据）的数据进行结构化和预处理，以最大限度地利用网络科学、机器学习和人工智能等研究技术，这些技术目前处于复杂系统科学研究的前沿。该项目的更广泛影响和社会效益将来自于利用大量、详尽和昂贵的普查数据公共资源进行研究，并在政策、规划、公共卫生和其他主题方面具有强大的应用。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的能力——人口、地理和社会经济信息的混合，排列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，提供快照和趋势线，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接口向学者、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（来自组合学、概率论、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，并以社会科学领域有意义的跨学科合作为基础。该项目旨在从图和网络两方面理解人口普查地理学，开发新的多层网络结构以及用于聚类、分区和特征识别的高效算法。该团队计划在第一阶段扩大学术界、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图分区上离散马尔可夫链技术的进一步发展上，在这个领域，项目团队已经有了重要的专业知识，并期望在混合时间和平稳分布的严格和启发式结果上取得进展。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。

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.