Beyond Data Points: Regionalizing Crowdsourced Latency Measurements

Despite significant investments in access network infrastructure, universal access to high-quality Internet connectivity remains a challenge. Policymakers often rely on crowdsourced datasets to assess Internet performance across geographic regions. However, such datasets exhibit non-uniform sampling densities and misalignment with infrastructure boundaries. This work presents a spatial analysis framework to construct stable sampling boundaries for Internet performance metrics using crowdsourced latency data. We apply statistical techniques to aggregate performance over geographic regions, interpolate unsampled locations, and cluster spatial units into contiguous regions with similar characteristics. Our evaluation demonstrates that these methods achieve higher temporal stability compared to traditional approaches that aggregate directly over administrative boundaries. The findings highlight the importance of spatial modeling in optimizing Internet performance distribution for policy and planning.