https://doi.org/10.1140/epjds/s13688-025-00546-w
Research
Detection of anomalous spatio-temporal patterns of app traffic in response to catastrophic events
Mathematical Institute, University of Oxford, Oxford, UK
a
sofia.medina@maths.ox.ac.uk
b
renaud.lambiotte@maths.ox.ac.uk
c
nicola.pedreschi@uniba.it
Received:
2
September
2024
Accepted:
27
March
2025
Published online:
6
May
2025
In this work, we uncover patterns of usage mobile phone applications and information spread in response to perturbations caused by unprecedented events. We focus on categorizing patterns of response in both space and time, tracking their relaxation over time. To this end, we use the NetMob2023 Data Challenge dataset, which provides mobile phone applications traffic volume data for several cities in France at a spatial resolution of 100 
and a time resolution of 15 minutes for a time period ranging from March to May 2019. We analyze the spread of information before, during, and after the catastrophic Notre-Dame fire on April 15th and a bombing that took place in the city centre of Lyon on May 24th using volume of data uploaded and downloaded to different mobile applications as a proxy of information transfer dynamics. We identify different clusters of information transfer dynamics in response to the Notre-Dame fire within the city of Paris as well as in other major French cities. We find a clear pattern of significantly above-baseline usage of the application Twitter (currently known as X) in Paris that radially spreads from the area surrounding the Notre-Dame cathedral to the rest of the city. We detect a similar pattern in the city of Lyon in response to the bombing. Further, we present a null model of radial information spread and develop methods of tracking radial patterns over time. Overall, we illustrate novel analytical methods we devise, showing how they enable a new perspective on mobile phone user response to unplanned catastrophic events and giving insight into how information spreads during a catastrophe in both time and space.
Key words: Data visualization / Mobile applications / Event detection / Disaster response / Urban mobility / Computational social science / Spatiotemporal phenomena / Patterns in data
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-025-00546-w.
© The Author(s) 2025
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