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

It is well documented that, in the United States (U.S.), the availability of Internet access is related to several demographic attributes. Data collected through end user network diagnostic tools, such as the one provided by the Measurement Lab (M-Lab) Speed Test, allows the extension of prior work by exploring the relationship between the quality, as opposed to only the availability, of Internet access and demographic attributes of users of the platform. In this study, we use network measurements collected from the users of Speed Test by M-Lab and demographic data to characterize the relationship between the quality-of-service (QoS) metric download speed, and various critical demographic attributes, such as income, education level, and poverty. For brevity, we limit our focus to the state of California. For users of the M-Lab Speed Test, our study has the following key takeaways: (1) geographic type (urban/rural) and income level in an area have the most significant relationship to download speed;(2) average download speed in rural areas is 2.5 times lower than urban areas;(3) the COVID-19 pandemic had a varied impact on download speeds for different demographic attributes;and (4) the U.S. Federal Communication Commission's (FCC's) broadband speed data significantly over-represents the download speed for rural and low-income communities compared to what is recorded through Speed Test. © 2022 Owner/Author.

Tribal Mobility and COVID-19: An Urban-Rural Analysis in New Mexico

Tribal communities have experienced disproportionately high infection and death rates during the COVID-19 pandemic [1, 8, 31]. In this work, we examine COVID-19 case growth in proximity to significant tribal presence by providing a novel quantification of human mobility patterns across tribal boundaries and between urban and rural regions at the geographical resolution of census block groups. We use New Mexico as a case study due to its severe case infection rates;however, our methodologies generalize to other states. Results show that tribal mobility is uniquely high relative to baseline in counties with significant case counts. Furthermore, mobility patterns in tribal regions correlate more highly than any other region with case growth patterns in the surrounding county 13-16 days later. Our initial results present a quantification scheme for the underlying differences in human mobility between tribal/non-tribal and rural/urban regions with the goal of informing public health policy that meets the differing needs of these communities. © 2021 Owner/Author.