Collaborative Research: HNDS-I. Mobility Data for Communities (MD4C): Uncovering Segregation, Climate Resilience, and Economic Development from Cell-Phone Records

合作研究：HNDS-I。

• 批准号: 2420945
• 负责人: Esteban Moro Egido
• 金额: $ 45.14万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2420945&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），是关于人们去往何处的数据，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。但是，这些数据也非常复杂，因此很少有科学家拥有这些数据。由于这些原因，政策制定者、从业者和数据描述的社区中的人们并不使用它们，尽管它们很重要。该奖项支持创建社区移动数据。 (MD4C)，一个基础设施项目，将 MD 转化为一系列有用的衡量标准，描述一个地区的社区和这些社区内的地点的流动性。这些衡量标准可供所有人使用，包括研究人员、政策制定者和从业者，MD4C 的工具旨在解决这一问题。由于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家可归者）或有太多其他人（例如，整天使用手机的年轻专业人士），导致测量结果出现偏差。被测量并且通过将 MD 与自愿分享其流动性信息的人的调查数据进行比较来进行纠正。一旦纠正，深度学习技术就可以提取与社会公平相关的流动性模式的测量值：种族和社会隔离、经济发展和气候适应力。使这些经过偏差调整的流动性测量结果可供研究人员和其他人使用，并使整个社会科学领域的新研究成为可能。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。