Collaborative Research:HNDS-I:Data Resources and Analytic Tools to Understand Population Scale Human Mobility for Applications in Social, Behavioral, and Economic Sciences Research

该项目开发了以隐私为中心的方法以及数据和软件资源，通过创建大规模数据库和工具来理解人口规模的人类流动性及其与社会、行为和经济（SBE）动态的关系。人类的移动性，包括了解人们在哪里，去过哪里，去哪里，是理解通过物理空间过渡产生的SBE问题的基本特征。通过更好地理解人们的运动行为，研究人员可以探索重要的研究领域，包括经济模式、隔离、群体间动态以及一系列其他感兴趣的SBE领域。该项目为推进此类研究提供了新的机会。它还将为研究生和本科生提供教育和研究机会。该项目开发了社区级移动模型，该模型基于通过国家网络的蜂窝塔覆盖区域的蜂窝设备组的总体移动。该项目创建了一个以隐私为中心的替代方案，以绘制人类空间移动，而不依赖于设备级地理位置数据。具体而言，该项目将提供(1)包含在不同时间间隔捕获的人类移动网络的数据存储库；(2)开源软件工具和用户友好的web应用程序，用于检索、处理、可视化和准备这些数据，以便在标准软件包中进行分析；(3)适合自学的互动式教程和教学材料，使用户熟悉所提供的数据、工具和软件包的使用；(4)用案例和说明性研究来加深我们对社会世界和人类运动之间关系的理解。利用该项目提供的工具，来自不同学科的研究人员可以回答与将社会现实投射到动态物理空间有关的重要问题。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。

This project develops privacy-centric methods along with data and software resources for understanding population-scale human mobility and its relationship with social, behavioral, and economic (SBE) dynamics through the creation of a large-scale database and tools. Human mobility, which includes knowing where people are, have been, and go to, is an essential feature for understanding SBE issues emerging from transitions through physical space. By better understanding the movement behavior of people, researchers can explore important areas of inquiry including economic patterns, segregation, inter-group dynamics, as well as a range of other SBE areas of interest. The project provides new opportunities for advancing such research. It will also provide educational and research opportunities for graduate and undergraduate students. This project develops community-level mobility models based on the aggregate movement of groups of cell devices through cell tower coverage areas of a national network. The project creates a privacy-centric alternative to mapping human spatial mobility that does not rely on device-level geolocation data. Specifically, the project will provide (1) a data repository containing human mobility network captured over various temporal intervals; (2) open-source software tools and user-friendly web applications for retrieving, processing, visualizing, and preparing these data for analyses in standard software packages; (3) interactive tutorials and instructional materials suitable for self-guided learning to familiarize users with the use of provided data, tools, and software packages; and (4) case use examples and illustrative studies to further our understanding of the relationship between the social world and human movement. Using the tools this project provides, researchers from a wide range of disciplines can answer important questions broadly concerned with the projection of social reality onto dynamic physical space.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.