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性数据（MD），是有关人们去向的数据。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，这些数据也很复杂，因此很少有科学家具有与之合作的技术技能。还有一些隐私或数据所有权问题，需要科学家允许访问它们。由于这些原因，数据描述的政策制定者，从业者和社区中的人们不会使用它们的重要性。该奖项支持创建社区移动数据（MD4C），这是一个基础架构项目，将MD转换为一系列有用的措施，这些措施描述了各个社区和这些社区内部的移动性。每个人都可以访问这些措施，包括研究人员，政策制定者和从业人员。 MD4C的工具旨在解决由于没有某些人而导致的测量中的偏见（例如，使用手机频率较低的年龄较大或未遭受的使用）或在数据集中拥有太多其他人（例如，整天使用手机的年轻专业人员）。通过将MD与自愿共享其流动性信息的人的调查的数据进行比较来衡量和纠正偏差。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：种族和社会经济隔离，经济发展和气候弹性。 MD4C平台使这些偏见调整后的移动性测量值可访问研究人员和其他人，并使整个社会科学的新研究成为可能。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来通过评估来获得的支持。