Characterizing Human Mobility Patterns During COVID-19 using Cellular Network Data

TL;DR

This study analyzes cellular network data from Rio de Janeiro during COVID-19 to understand mobility changes due to lockdowns, revealing significant reductions in connections and shifts in traffic patterns, and provides an interactive tool for visualization.

Contribution

It offers a detailed analysis of human mobility patterns during COVID-19 using cellular data and introduces an interactive visualization tool for better understanding and policy making.

Findings

Cellular connections dropped to 78% during lockdown

Shift in top traffic antennas from downtown to other areas

40-45% users showed no mobility during lockdown

Abstract

In this paper, our goal is to analyze and compare cellular network usage data from pre-lockdown, during lockdown, and post-lockdown phases surrounding the COVID-19 pandemic to understand and model human mobility patterns during the pandemic, and evaluate the effect of lockdowns on mobility. To this end, we collaborate with one of the main cellular network providers in Brazil, and collect and analyze cellular network connections from 1400 antennas for all users in the city of Rio de Janeiro and its suburbs from March 1, 2020 to July 1, 2020. Our analysis reveals that the total number of cellular connections decreases to 78% during the lockdown phase and then increases to 85% of the pre-COVID era as the lockdown eases. We observe that as more people work remotely, there is a shift in the antennas incurring top 10% of the total traffic, with the number of connections made to antennas in downtown Rio reducing drastically and antennas at other locations taking their place. We also observe that while nearly 40-45% users connected to only 1 antenna each day during the lockdown phase indicating no mobility, there are around 4% users (i.e., 80K users) who connected to more than 10 antennas, indicating very high mobility. Finally, we design an interactive tool that showcases mobility patterns in different granularities that can potentially help people and government officials understand the mobility of individuals and the number of COVID cases in a particular neighborhood. Our analysis, inferences, and interactive showcasing of mobility patterns based on large-scale data can be extrapolated to other cities of the world and has the potential to help in designing more effective pandemic management measures in the future.