Collaborative Research: HNDS-I. Mobility Data for Communities (MD4C): Using Cell-Phone Records to Study Societal Dynamics

合作研究：HNDS-I。

• 批准号: 2218791
• 负责人: Daniel O'Brien
• 金额: $ 47.93万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218791&HistoricalAwards=false
• 关键词: Collaborative; Research; HNDS; I.; Mobility

Cell phone location records, called mobility data (MD), are data about where people go. These data are an essential resource to study how people move and interact in society. However, these data are also very complex, so very few scientists have the technical skills to work with them. There are also privacy or data ownership issues that require scientists to have permission to access them. For these reasons, policymakers, practitioners, and the people in the communities that the data describe do not use them despite their importance. This award supports the creation of Mobility Data for Communities (MD4C), an infrastructure project that converts MD into a series of useful measures that describe mobility across a region's neighborhoods and places within those neighborhoods. These measures are accessible to everyone, including researchers, policymakers, and practitioners. MD4C’s tools are designed to resolve biases in the measures that result from not having certain people (for example, the elderly or unhoused who use cell phones less frequently) or having too many other people (for example, younger professionals who use cell phones all day) in the datasets. Bias is measured and corrected by comparing MD to data from a survey of people who voluntarily share their mobility information. Once corrected, deep learning techniques can extract measurements of mobility patterns related to social equity: racial and socioeconomic segregation, economic development, and climate resilience. The MD4C platform makes these bias-adjusted mobility measurements accessible for researchers and others and makes new research possible across the social sciences.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.

手机位置记录，称为移动数据(MD)，是关于人们去哪里的数据。这些数据是研究人们如何在社会中移动和互动的重要资源。然而，这些数据也非常复杂，因此很少有科学家拥有处理这些数据的技术技能。还有隐私或数据所有权问题，需要科学家获得许可才能访问它们。出于这些原因，政策制定者、从业者和数据描述的社区中的人不使用它们，尽管它们很重要。该奖项支持创建社区移动数据(MD4C)，这是一个基础设施项目，将MD转换为一系列有用的措施，描述整个地区的社区和这些社区内的地方的移动性。包括研究人员、政策制定者和实践者在内的每个人都可以使用这些措施。MD4C的工具旨在解决因数据集中没有某些人(例如，使用手机频率较低的老年人或无人居住的人)或其他人太多(例如，整天使用手机的年轻专业人士)而导致的衡量标准中的偏差。通过将MD与自愿分享他们的流动信息的人的调查数据进行比较，可以衡量和纠正偏见。一旦纠正，深度学习技术可以提取与社会公平相关的流动模式的衡量标准：种族和社会经济隔离、经济发展和气候适应能力。MD4C平台使研究人员和其他人能够访问这些经过偏差调整的流动性测量，并使跨社会科学的新研究成为可能。该奖项反映了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。