On Correlation Between Demographic Variables and Movement Behavior

Javanmard, R.; Esmaeili, R.; Karimipour, Farid

doi:10.1007/978-3-319-95165-2_33

Cited by 1 publication

(2 citation statements)

References 32 publications

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“…In other words, it indicates the regionalized dispersion of the places that people visit. Among different forms of activity space representation, here we use the ellipsoidal representation fitted to all the visited points [34]. Compared to the ROG (Sec.…”

Section: ) Od Flow Matrix Constructionmentioning

confidence: 99%

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Country-wide Mobility Changes Observed Using Mobile Phone Data During COVID-19 Pandemic

Heiler

Reisch

Hurt

et al. 2020

2020 IEEE International Conference on Big Data (Big Data)

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In March 2020, the Austrian government introduced a widespread lock-down in response to the COVID-19 pandemic. Based on subjective impressions and anecdotal evidence, Austrian public and private life came to a sudden halt. Here we assess the effect of the lock-down quantitatively for all regions in Austria and present an analysis of daily changes of human mobility throughout Austria using near-real-time anonymized mobile phone data. We describe an efficient d ata a ggregation pipeline and analyze the mobility by quantifying mobile-phone traffic at specific point of interests (POIs), analyzing individual trajectories and investigating the cluster structure of the origin-destination graph. We found a reduction of commuters at Viennese metro stations of over 80% and the number of devices with a radius of gyration of less than 500 m almost doubled. The results of studying crowd-movement behavior highlight considerable changes in the structure of mobility networks, revealed by a higher modularity and an increase from 12 to 20 detected communities. We demonstrate the relevance of mobility data for epidemiological studies by showing a significant correlation of the outflow f rom t he t own o f I schgl ( an e arly COVID-19 hotspot) and the reported COVID-19 cases with an 8-day time lag. This research indicates that mobile phone usage data permits the moment-by-moment quantification of mobility behavior for a whole country. We emphasize the need to improve the availability of such data in anonymized form to empower rapid response to combat COVID-19 and future pandemics.Index Terms-big-data, call-data-records (CDR) Apache-Spark, graph-analysis, mobility This work was funded by the Austrian Research Promotion Agency (FFG) under project 857136, the Austrian Science Fund FWF under project P29252, the WWTF under project COV 20-017 and COV20-035 and the Medizinisch-Wissenschaftlicher Fonds des Buergermeisters der Bundeshauptstadt Wien under project CoVid004.

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Section: ) Od Flow Matrix Constructionmentioning

confidence: 99%

“…III-D3), an ellipse provides a more accurate representation of the locations by computing the major and minor axes as well as an angle. We use the OD matrix to compute the following parameters for the activity space ellipse of each political area [34]:…”

Section: ) Od Flow Matrix Constructionmentioning

confidence: 99%