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On an Aggregated Estimate for Human Mobility Regularities through Movement Trends and Population Density
Version 1
: Received: 2 April 2024 / Approved: 2 April 2024 / Online: 3 April 2024 (16:37:39 CEST)
A peer-reviewed article of this Preprint also exists.
Vanni, F.; Lambert, D. On an Aggregated Estimate for Human Mobility Regularities through Movement Trends and Population Density. Entropy 2024, 26, 398. Vanni, F.; Lambert, D. On an Aggregated Estimate for Human Mobility Regularities through Movement Trends and Population Density. Entropy 2024, 26, 398.
Abstract
This article introduces an analytical framework that interprets individual measures of entropy-based mobility derived from mobile phone data. We explore and analyze two widely recognized entropy metrics: random entropy and uncorrelated Shannon entropy. These metrics are estimated through collective variables of human mobility, including movement trends and population density. By employing a collisional model, we establish statistical relationships between entropy measures and mobility variables. Furthermore, our research addresses three primary objectives: firstly, validating the model; secondly, exploring correlations between aggregated mobility and entropy measures in comparison to five economic indicators; and finally, demonstrating the utility of entropy measures. Specifically, we provide an effective population density estimate that offers a more realistic understanding of social interactions. This estimation takes into account both movement regularities and intensity, utilizing real-time data analysis conducted during the peak period of the Covid-19 pandemic.
Keywords
human mobility; collisiona model; informational entropy; population density; economic variables
Subject
Physical Sciences, Applied Physics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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