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

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.