RAPID: Neighborhood-level U.S. Internet Accessibility Assessment through Dataset Aggregation and Statistical and Predictive Modeling

RAPID：通过数据集聚合以及统计和预测建模进行美国社区级互联网可访问性评估

• 批准号: 2033946
• 负责人: Elizabeth Belding
• 金额: $ 14.94万
• 依托单位: University of California-Santa Barbara
• 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: 2020-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033946&HistoricalAwards=false
• 关键词: RAPID; Neighborhood; level; U.S.; Internet

The U.S. has long suffered from digital inequities in multiple dimensions: rural and tribal regions are far less likely than urban cities to have high speed Internet access. Internet availability and quality within communities can often be predicted based on demographic and socioeconomic factors. The COVID-19 pandemic has brought to the forefront these inequalities; due to shelter-in-place orders, the lack of high quality Internet access has had dramatic impacts, including on the ability to participate in remote learning, remote work, and telehealth. While new government programs have been created to try to broaden access, a fundamental problem persists: no one accurately knows who does and does not have high quality access. There are many datasets of Internet measurements, but each on its own represents too incomplete a picture to provide the fine-grained information needed to discern which communities, or, ideally, neighborhoods lack quality Internet access. However, these datasets, when combined, is expected to provide a rich and geographically broad data source through which it may be possible to accurately assess Internet connectivity and performance. Furthermore, this study can let one learn trends from these datasets to predict Internet accessibility in regions for which no measurement data is currently available. The goal of this project is threefold: (i) to aggregate data from public and private sources to produce the most fine-grained analysis and detailed maps, to date, within states, at the community and, ideally, neighborhood level, of where fixed and mobile Internet access exists, where it does not, and where it is of too poor quality to be usable; (ii) to build statistical models that use demographic and other social variables to understand variation in Internet availability and quality; and (iii) to use what is learned to build predictive models of Internet service in areas for which there exist insufficient measurement data from available sources. This work will have broad impacts, including the informing of local, state and federal governments about where investments must be made to ensure all Americans have access to high quality mobile and/or fixed Internet. The project website, digitalaccess.cs.ucsb.edu, will contain information about research methodology and outcomes, including a report on what is learned about the state of California, the first state of focus for this award. Prediction models will also be made available.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.

美国长期以来在多个维度上遭受数字不平等的困扰：农村和部落地区的可能性远低于城市城市高速互联网访问的可能性。通常可以根据人口统计学和社会经济因素来预测社区内的互联网可用性和质量。共同的19个大流行使这些不平等达到了最前沿。由于现场订单，缺乏高质量的互联网访问会产生巨大的影响，包括参与远程学习，远程工作和远程医疗的能力。尽管已经创建了新的政府计划来试图扩大访问权限，但一个基本问题仍然存在：没有人准确地知道谁做和没有高质量的访问权限。有许多互联网测量数据集，但是每个数据集都代表图片太完整，无法提供识别哪些社区所需的细粒度信息，或者理想情况下，社区缺乏优质的Internet访问。但是，这些数据集合结合在一起，预计将提供丰富且地理上广泛的数据源，可以通过这些数据来准确评估Internet的连接性和性能。此外，这项研究可以让一个人从这些数据集中学习趋势，以预测目前没有测量数据的地区的互联网可访问性。该项目的目的是三倍：（i）从公共和私人来源汇总数据，以制作最细粒度的分析和详细的地图，迄今为止，在州内，社区，理想情况下，固定的互联网访问和移动互联网访问的位置，以及在哪里不存在的地方，以及它的质量太差，无法使用； （ii）建立使用人口统计和其他社会变量来了解互联网可用性和质量变化的统计模型； （iii）使用所学的知识来建立Internet服务的预测模型，而这些领域的测量数据不足。这项工作将产生广泛的影响，包括向当地，州和联邦政府告知必须在何处进行投资，以确保所有美国人都能获得高质量的移动和/或固定的互联网。项目网站digitalAccess.cs.ucsb.edu将包含有关研究方法和结果的信息，包括有关该奖项的第一个重点关注的加利福尼亚州学到的报告的报告。 该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响标准通过评估来支持的。

项目成果

Characterizing Internet Access and Quality Inequities in California M-Lab Measurements

加州 M-Lab 测量中互联网接入和质量不平等的特征

• DOI: 10.1145/3530190.3534813
• 发表时间: 2022
• 期刊: ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS
• 作者: Paul, Udit;Liu, Jiamo;Farias-llerenas, David;Adarsh, Vivek;Gupta, Arpit;Belding, Elizabeth
• 通讯作者: Belding, Elizabeth

Characterizing Performance Inequity Across U.S. Ookla Speedtest Users

描述美国 Ookla Speedtest 用户的性能不平等

• 发表时间: 2021
• 期刊: ArXivorg
• 作者: Paul, U.;Liu, J.;Adarsh, V.;Gu, M.;Gupta, A.;Belding, E.
• 通讯作者: Belding, E.