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

长期以来，美国一直在多个层面上遭受数字不平等的困扰：农村和部落地区拥有高速互联网接入的可能性远远低于城市。社区内的互联网可用性和质量通常可以根据人口统计和社会经济因素进行预测。新冠肺炎大流行使这些不平等现象变得尤为突出；由于就地避难所订单，缺乏高质量的互联网接入已对包括参与远程学习、远程工作和远程医疗的能力产生了重大影响。虽然政府制定了新的计划，试图扩大准入，但一个根本性的问题仍然存在：没有人准确地知道谁拥有和不拥有高质量的准入。有很多互联网测量的数据集，但每个数据集本身都代表着一幅太不完整的图景，无法提供所需的细粒度信息，以辨别哪些社区，或者理想情况下，哪些社区缺乏高质量的互联网接入。然而，当这些数据集合并在一起时，预计将提供一个丰富的、地理上广泛的数据源，通过这些数据源，可能有可能准确地评估互联网连接和性能。此外，这项研究可以让人们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。该项目的目标有三个：(I)收集来自公共和私人来源的数据，以产生迄今为止最精细的分析和详细的地图，包括州内、社区和社区层面，最好是社区层面，关于固定和移动互联网接入存在的地方，没有接入的地方，以及质量太差而无法使用的地方；(Ii)建立统计模型，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的变化；以及(Iii)利用所学知识，在现有来源没有足够的测量数据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，包括告知地方、州和联邦政府必须在哪里进行投资，以确保所有美国人都能接入高质量的移动和/或固定互联网。该项目的网站Digitalacces.cs.ucsb.edu将包含有关研究方法和结果的信息，包括一份关于加利福尼亚州的报告，加利福尼亚州是该奖项的第一个重点州。预测模型也将可用。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。

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.