Detection of anomalous spatio-temporal patterns of app traffic in response to catastrophic events.

Sofia Medina, Shazia'Ayn Babul, Timothy LaRock, Rohit Sahasrabuddhe, Renaud Lambiotte, Nicola Pedreschi

Abstract read

In one paragraph

Article in EPJ data science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sofia MedinaMathematical Institute, University of Oxford, Oxford, UK.

Shazia'Ayn BabulMathematical Institute, University of Oxford, Oxford, UK.

Timothy LaRockMathematical Institute, University of Oxford, Oxford, UK.

Rohit SahasrabuddheMathematical Institute, University of Oxford, Oxford, UK.

Renaud LambiotteMathematical Institute, University of Oxford, Oxford, UK.

Nicola PedreschiMathematical Institute, University of Oxford, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 Supplementary Information: The online version contains supplementary material available at 10.1140/epjds/s13688-025-00546-w.

Indexed as

Computational social scienceData visualizationDisaster responseEvent detectionMobile applicationsPatterns in dataSpatiotemporal phenomenaUrban mobility

Identifiers

PMID40342613

DOI10.1140/epjds/s13688-025-00546-w

PMCPMC12055615

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.