A First Look at the Spatial and Temporal Variability of Internet Performance Data in Hyperlocal Geographies

Abstract

Measuring Internet access network performance has been a persistent challenge for researchers and policymakers alike. Unfortunately, existing "speed test" datasets typically lack comprehensive data across both space and time. Specifically, our past work has highlighted that tools like Ookla's Speed Test and Measurement Lab's NDT rely heavily on convenience samples (user-initiated tests from self-selected participants), resulting in a sample that may not generalize across either time or geography. Our ongoing research seeks to address these issues by developing innovative sampling methods and statistical models to provide a more holistic view of Internet performance. Initial findings, focusing on end-to-end latency across hyper-local regions within a single large city in the United States (Chicago, Illinois), reveal that spatial proximity often does not correlate with simultaneous performance anomalies. These insights underscore the need for advanced methods to generalize Internet performance data across time and space. Improved methods can ultimately enable a better understanding of the effects of infrastructure investments on the evolution of Internet performance.

Cite

@inproceedings{sharma2023first,
  author = {Sharma, Taveesh and Marques, Jonatas and Feamster, Nick and Marwell, Nicole P.},
  title = {A First Look at the Spatial and Temporal Variability of Internet Performance Data in Hyperlocal Geographies},
  booktitle = {Proceedings of the 51st Research Conference on Communications, Information and Internet Policy (TPRC)},
  year = {2023},
  url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4568668}
}