Point Process Models for Distribution of Cell Phone Antennas

TL;DR

This paper presents a new spatial point process model for urban cell phone antenna distribution, moving beyond randomness assumptions to a Gaussian-based approach that captures local clustering.

Contribution

The paper introduces a Gaussian-based spatial model for cell antenna distribution, enabling synthetic data generation without proprietary information.

Findings

Model accurately fits real antenna distributions

Synthetic data preserves spatial clustering patterns

Model facilitates open research without privacy concerns

Abstract

We introduce a model for the spatial distribution of cell phone antennas in a urban environment. After showing that the complete spatial randomness (homogeneous Poisson distribution) hypothesis does not hold, we propose a model in which each point is distributed according to a bivariate Gaussian variable with mean given by the barycenter of its neighbors in the Delaunay triangulation. We show that this model is suitable, and can be used to generate a synthetic distribution of antennas. The generated distribution contains no sensitive or proprietary information, and can thus be freely shared with research groups, fostering further research on the subject.