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.

长期以来，美国在多个方面都遭受着数字不平等的困扰：农村和部落地区远不如城市拥有高速互联网接入的可能性。社区内的互联网可用性和质量通常可以根据人口和社会经济因素来预测。2019冠状病毒病大流行使这些不平等现象凸显出来;由于就地安置订单，缺乏高质量的互联网接入产生了巨大影响，包括参与远程学习、远程工作和远程医疗的能力。虽然新的政府计划已经建立，试图扩大访问，一个根本的问题仍然存在：没有人准确地知道谁有和没有高质量的访问。有许多互联网测量数据集，但每一个都代表了太不完整的画面，无法提供识别哪些社区或理想情况下的社区缺乏高质量互联网接入所需的细粒度信息。然而，这些数据集结合在一起，预计将提供一个丰富和地理范围广泛的数据源，通过它可以准确地评估互联网的连接和性能。此外，这项研究可以让人们从这些数据集中了解趋势，以预测目前没有测量数据的地区的互联网可访问性。该项目有三个目标：㈠汇总公共和私人来源的数据，以制作迄今为止各州、社区、最好是邻里一级最精细的分析和详细地图，说明哪里有固定和移动的互联网接入，哪里没有，以及哪里的互联网质量太差而无法使用;（ii）建立统计模型，使用人口和其他社会变量来了解互联网可用性和质量的变化;（iii）使用所学到的知识，在现有来源的测量数据不足的地区建立互联网服务的预测模型。这项工作将产生广泛的影响，包括通知地方、州和联邦政府必须在哪里投资，以确保所有美国人都能获得高质量的移动的和/或固定互联网。该项目的网站digitalaccess.cs.ucsb.edu将包含有关研究方法和成果的信息，包括一份关于加州州的报告，这是该奖项的第一个重点州。 该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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

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.