Internet access and connectivity has become a crucial issue of public policy across the globe. During the COVID-19 pandemic, as individuals and households transitioned to remote work and learning, usage of and strain on home networks increased dramatically. The ability to interact with the internet is quickly becoming recognized worldwide as a determinant of social, economic, and even physiological well-being. With the continuing increase in usage of telehealth, remote work & learning, and distance collaboration tools, the importance of internet access is underwritten by assumptions regarding the internet’s stability of connection. A broadly accepted metric to ascertain two-way video & audio stability is known as latency. Being able to empirically and visually describe the geographic distribution of latency across spatial units is of critical importance to understanding where potential policy interventions or government assistance programs are most needed. Similarly, understanding the spatial landscape of latency reveals inequities between socioeconomic, racial, and regional populations. In order to create the most nuanced, empirically sound predictive models to understand factors that influence latency, local regression techniques must be brought to bear. In this paper, I combine a rigorous exploration of the literature with a variety of empirical tools to solve these challenging issues by examining latency across all census tracts in the United States. Quantitative techniques included in this examination are: traditional univariate, bivariate, and multivariable statistical methods, cartographic transformations, exploratory spatial data analysis, autocorrelation analyses, spatial demographic methods, local regression modeling, geographic interpolation, and kriging. I find that rural census tracts, and tracts with higher poverty rates, particularly those with populations other than non-Hispanic White, experience poorer internet stability. I provide identifiable visualizations for where latency is at its best and worst. I classify and specifically identify typologies of neighborhoods to explicitly show discrete groups of census tracts where policymakers can plan interventions. Finally, I present kriging as a methodological tool to predict previously unknown values of latency in order to better fill in the gaps of coverage areas and stability measurements.