AF: Small: Collaborative Research:Geometric and topological algorithms for analyzing road network data

AF：小型：协作研究：用于分析道路网络数据的几何和拓扑算法

• 批准号: 1618247
• 负责人: Yusu Wang
• 金额: $ 18.91万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618247&HistoricalAwards=false
• 关键词: AF; Small; Collaborative; Research; Geometric

The project aims to develop theoretically grounded, effective methods for analyzing data associated with road networks -- using graphs that represent road networks as a framework for analyzing network data. Thanks to the spread of GPS-enabled devices, trajectory data has become ubiquitous. Many other sources, including census data and crime statistics, have addresses or geographic locations that link to an underlying road network. Algorithms with mathematical guarantees will be developed to align trajectories to the network under natural and realistic properties of true trajectories, to reconstruct road networks from trajectory and density data. It will also provide two frameworks for comparing data-endowed networks at different levels. While the problems of trajectory alignment, map reconstruction, and map comparison have attracted a lot of attention in the GIS community, most approaches are ad-hoc, provide no quality guarantees, and are limited to post-hoc analysis. This project will provide novel theoretical foundations combining approaches from computational topology and geometry, and will further advance the state-of-the-art of the field of topological / geometric data analysis. The PIs will continue to combine educational and research activities through this project. Students will be tightly integrated into the research and practical implementation of this project, and will be trained in integrating geometric thinking, algorithms development, and (trajectory) data analysis. The combination of such skills is increasingly important in data science. Topics involved in this project will enrich the course material and curriculum development at each of the three institutions.

该项目旨在开发有理论基础的有效方法，用于分析与道路网络相关的数据-使用代表道路网络的图形作为分析网络数据的框架。由于GPS设备的普及，轨迹数据变得无处不在。 许多其他来源，包括人口普查数据和犯罪统计数据，其地址或地理位置都与基础道路网相关联。将开发具有数学保证的算法，以在真实轨迹的自然和现实属性下将轨迹与网络对齐，从轨迹和密度数据重建道路网络。它还将提供两个框架，用于比较不同级别的数据网络。虽然轨迹对齐，地图重建和地图比较的问题已经引起了GIS社区的大量关注，大多数方法是临时的，没有提供质量保证，并仅限于事后分析。该项目将提供新的理论基础，结合计算拓扑学和几何学的方法，并将进一步推进拓扑/几何数据分析领域的最新技术。PI将继续通过该项目将联合收割机的教育和研究活动相结合。学生将紧密融入该项目的研究和实际实施，并将接受几何思维，算法开发和（轨迹）数据分析的培训。这些技能的结合在数据科学中越来越重要。该项目涉及的专题将丰富这三个机构各自的教材和课程编制。