Beyond Voronoi: Plain Probabilistic Spatial Coverage Inference from Base Station Deployments

Abstract

Mapping information collected at the level of individual base stations onto the geographical space is a required operation for many works relying on mobile network metadata. The common practice is to represent base station coverage as Voronoi cells, and assume that users are uniformly distributed therein. In this paper, we leverage a large-scale dataset of realistic spatial association probabilities to over 5,000 operational base stations, and quantify the substantial problems of such a simplistic mapping approach. To address the limitations of legacy Voronoi representations, we develop VoronoiBoost, a data-driven model that scales Voronoi cells to match the probabilistic distribution of users associated to each base station. VoronoiBoost relies on the same input as traditional Voronoi decompositions, but provides a richer and more accurate rendering of where users are located: hence, it can be readily used by researchers to substantially improve the spatial representation of mobile network metadata. Our experiments demonstrate that VoronoiBoost improves the quality of mapping by 44% on average over standard Voronoi cells. We also showcase the utility of our model in a practical Edge network planning use case, where the information produced by VoronoiBoost drives a deployment up to 28% more accurate than that obtained with Voronoi cells.