Population estimation from mobile network traffic metadata

TL;DR

This paper introduces a novel method for estimating urban population densities using aggregated mobile network traffic metadata, providing real-time, accurate, and scalable population mapping that surpasses traditional survey-based methods.

Contribution

It presents a new approach leveraging mobile traffic data to accurately estimate both static and dynamic populations at urban scales, improving upon existing solutions.

Findings

Significant accuracy improvement over state-of-the-art methods

Effective estimation of both static and dynamic populations

Validated across multiple city scenarios

Abstract

Smartphones and other mobile devices are today pervasive across the globe. As an interesting side effect of the surge in mobile communications, mobile network operators can now easily collect a wealth of high-resolution data on the habits of large user populations. The information extracted from mobile network traffic data is very relevant in the context of population mapping: it provides a tool for the automatic and live estimation of population densities, overcoming the limitations of traditional data sources such as censuses and surveys. In this paper, we propose a new approach to infer population densities at urban scales, based on aggregated mobile network traffic metadata. Our approach allows estimating both static and dynamic populations, achieves a significant improvement in terms of accuracy with respect to state-of-the-art solutions in the literature, and is validated on different city scenarios.