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

该项目开发以隐私为中心的方法，沿着数据和软件资源，通过创建大规模数据库和工具，了解人口规模的人类流动及其与社会，行为和经济（SBE）动态的关系。人的流动性，包括知道人们在哪里，去过哪里，去哪里，是理解通过物理空间过渡出现的SBE问题的一个基本特征。通过更好地了解人们的运动行为，研究人员可以探索重要的调查领域，包括经济模式，隔离，群体间动态，以及一系列其他SBE感兴趣的领域。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，其基础是通过国家网络的蜂窝塔覆盖区域的蜂窝设备组的聚合移动。该项目创建了一个以隐私为中心的替代方案，以绘制不依赖于设备级地理位置数据的人类空间移动性。具体而言，该项目将提供：（1）一个数据储存库，其中包含在不同时间间隔内收集的人类流动网络;（2）开放源码软件工具和用户友好的网络应用程序，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）适合于自我引导学习的交互式教程和教学材料，以使用户熟悉所提供的数据、工具和软件包的使用;（4）通过案例分析和例证研究，加深我们对社会世界与人类运动关系的理解。利用该项目提供的工具，来自广泛学科的研究人员可以回答与社会现实到动态物理空间的投影广泛相关的重要问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.