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新冠疫情期间欧洲地区的社区流动性：基于时空自回归模型的带噪声的类中心划分聚类法

Community mobility in the European regions during COVID-19 pandemic: A partitioning around medoids with noise cluster based on space-time autoregressive models.

作者信息

D'Urso Pierpaolo, Mucciardi Massimo, Otranto Edoardo, Vitale Vincenzina

机构信息

Department of Social and Economic Sciences, Sapienza University of Rome, Italy.

Department of Cognitive Science, Education and Cultural Studies, University of Messina, Italy.

出版信息

Spat Stat. 2022 Jun;49:100531. doi: 10.1016/j.spasta.2021.100531. Epub 2021 Jul 17.

DOI:10.1016/j.spasta.2021.100531
PMID:35722170
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9193889/
Abstract

In this paper we propose a robust fuzzy clustering model, the STAR-based Fuzzy C-Medoids Clustering model with Noise Cluster, to define territorial partitions of the European regions (NUTS2) according to the workplaces mobility trends for places of work provided by Google with reference to the whole COVID-19 pandemic period. The clustering model takes into account both temporal and spatial information by means of the autoregressive temporal and spatial coefficients of the STAR model. The proposed clustering model through the noise cluster is capable of neutralizing the negative effects of noisy data. The main empirical results regard the expected direct relationship between the Community mobility trend and the lockdown periods, and a clear spatial interaction effect among neighboring regions.

摘要

在本文中，我们提出了一种稳健的模糊聚类模型，即基于时空自回归（STAR）的带噪声聚类模糊C-中心点聚类模型，以根据谷歌提供的整个新冠疫情期间工作场所的流动趋势，定义欧洲地区（NUTS2）的地域划分。该聚类模型通过STAR模型的自回归时间和空间系数，兼顾了时间和空间信息。所提出的带噪声聚类的聚类模型能够消除噪声数据的负面影响。主要实证结果涉及社区流动趋势与封锁期之间预期的直接关系，以及相邻地区之间明显的空间交互效应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/7778aa824400/gr19_lrg.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/e2a0bdcd29fd/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/1a626e920039/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/e627ca6527df/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/94f4dfcf2439/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/ce8e99bf4b90/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/01832383a009/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/d7c28c433754/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/6d3eb9c316d7/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/e10b5ed4ce96/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/8bf03d7d08b1/gr11_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/57afb28fee29/gr12_lrg.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/81ccae6b60b5/gr14_lrg.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/4043fa8741d5/gr17_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/667bef549158/gr18_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/7778aa824400/gr19_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/7a8f38d7b7c7/fx1001_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/d9601b3a7807/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/e2a0bdcd29fd/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/1a626e920039/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/e627ca6527df/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/94f4dfcf2439/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/ce8e99bf4b90/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/01832383a009/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/d7c28c433754/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/6d3eb9c316d7/gr9_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/e10b5ed4ce96/gr10_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/8bf03d7d08b1/gr11_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/57afb28fee29/gr12_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/72c5544e3c17/gr13_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/81ccae6b60b5/gr14_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/708b2df06400/gr15_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/e87243888c60/gr16_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/4043fa8741d5/gr17_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/667bef549158/gr18_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de29/9193889/7778aa824400/gr19_lrg.jpg

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本文引用的文献

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Corona and coffee on your commute: a spatial analysis of COVID-19 mortality and commuting flows in England in 2020.通勤途中的新冠与咖啡：2020 年英格兰 COVID-19 死亡率与通勤流的空间分析。
Eur J Public Health. 2021 Oct 11;31(4):901-907. doi: 10.1093/eurpub/ckab072.
3
The Framing of COVID-19 in Italian Media and Its Relationship with Community Mobility: A Mixed-Method Approach.
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Spat Stat. 2022 Jun;49:100543. doi: 10.1016/j.spasta.2021.100543. Epub 2021 Oct 6.
意大利媒体对 COVID-19 的报道框架及其与社区流动性的关系：混合方法研究。
J Health Commun. 2021 Mar 4;26(3):161-173. doi: 10.1080/10810730.2021.1899344. Epub 2021 Mar 31.
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Determinants of the community mobility during the COVID-19 epidemic: The role of government regulations and information.新冠疫情期间社区流动性的决定因素：政府法规与信息的作用
J Econ Behav Organ. 2021 Apr;184:199-231. doi: 10.1016/j.jebo.2021.01.023. Epub 2021 Feb 1.
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Community movement and COVID-19: a global study using Google's Community Mobility Reports.社区活动与 COVID-19：利用谷歌的社区流动性报告进行的全球研究。
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Lockdown for COVID-19 and its impact on community mobility in India: An analysis of the COVID-19 Community Mobility Reports, 2020.印度因新冠疫情实施的封锁及其对社区流动性的影响：对《2020年新冠疫情社区流动性报告》的分析
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How mobility habits influenced the spread of the COVID-19 pandemic: Results from the Italian case study.流动性习惯如何影响 COVID-19 大流行的传播：来自意大利案例研究的结果。
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