Collaborative Research: RUI: HNDS-I: Data Resources and Analytic Tools to Understand Population Scale Human Mobility for Applications in SBE Research

合作研究：RUI：HNDS-I：了解人口规模人口流动性的数据资源和分析工具，用于 SBE 研究中的应用

• 批准号: 2024233
• 负责人: Owais Gilani
• 金额: $ 26.91万
• 依托单位: Bucknell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024233&HistoricalAwards=false
• 关键词: Collaborative; Research; RUI; HNDS; Data

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.

该项目开发以隐私为中心的方法以及数据和软件资源，通过创建大型数据库和工具来了解人口规模的人员流动及其与社会、行为和经济(SBE)动态的关系。人类的流动性，包括知道人们在哪里，去过哪里，去过哪里，是理解通过物理空间过渡而产生的SBE问题的一个基本特征。通过更好地了解人们的流动行为，研究人员可以探索重要的研究领域，包括经济模式、种族隔离、群体间动态以及一系列其他感兴趣的SBE领域。该项目为推进这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。该项目基于小区设备组通过国家网络的蜂窝塔覆盖区域的聚合移动来开发社区级别的移动性模型。该项目创建了一种以隐私为中心的替代方案，用于绘制不依赖设备级地理位置数据的人类空间移动性地图。具体地说，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库；(2)用于检索、处理、可视化这些数据并准备在标准软件包中进行分析的开放源码软件工具和用户友好的网络应用程序；(3)适合自我指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；以及(4)案例使用示例和说明性研究，以加深我们对社交世界和人类移动之间的关系的理解。使用这个项目提供的工具，来自不同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。这个奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The need to incorporate communities in compartmental models

将社区纳入分区模型的必要性

• DOI: 10.4310/20-sii647
• 发表时间: 2021
• 期刊: Statistics and Its Interface
• 影响因子: 0.8
• 作者: Kane, Michael J.;Gilani, Owais
• 通讯作者: Gilani, Owais