Escaping from Cities during the COVID-19 Crisis: Using Mobile Phone Data to Trace Mobility in Finland
Escaping from Cities during the COVID-19 Crisis: Using Mobile Phone Data to Trace Mobility in Finland
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DOI：
10.3390/ijgi10020103
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发表时间：
2021-02-01
影响因子：
3.4
通讯作者：
Toivonen, Tuuli
中科院分区：
文献类型：
--
作者：
Willberg, Elias;Jarv, Olle;Toivonen, Tuuli
由于受到限制，2019 年冠状病毒疾病（COVID-19）危机导致人们跨社会的空间流动发生了前所未有的变化。这也导致了意想不到的流动性和人口动态，给危机防备工作带来了挑战，其中包括危机期间因多地居住这一基本现象而造成的城市流动。人们改变居住地会使病毒在地区间传播，并造成卫生和应急服务无法应对人口增长的情况。在此，我们的重点是 2020 年 COVID-19 危机期间芬兰的城乡流动性以及多地居住对人口动态的影响。基于三个手机数据集的研究结果表明，城市间的流动性显著下降，人口的存在发生了变化--城市中心人口减少，农村地区人口增加，这与二手房密切相关。这项研究强调，需要通过以下方式提高危机防范能力(1) 认识到多地方居住的重要性与日俱增，(2) 更好地利用新型数据源监测人口动态和流动性。手机数据产品具有巨大潜力，但应注意不同的方法及其对分析可能产生的影响。
The coronavirus disease 2019 (COVID-19) crisis resulted in unprecedented changes in the spatial mobility of people across societies due to the restrictions imposed. This also resulted in unexpected mobility and population dynamics that created a challenge for crisis preparedness, including the mobility from cities during the crisis due to the underlying phenomenon of multi-local living. People changing their residences can spread the virus between regions and create situations in which health and emergency services are not prepared for the population increase. Here, our focus is on urban-rural mobility and the influence of multi-local living on population dynamics in Finland during the COVID-19 crisis in 2020. Results, based on three mobile phone datasets, showed a significant drop in inter-municipal mobility and a shift in the presence of people-a population decline in urban centres and an increase in rural areas, which is strongly correlated to secondary housing. This study highlights the need to improve crisis preparedness by: (1) acknowledging the growing importance of multi-local living, and (2) improving the use of novel data sources for monitoring population dynamics and mobility. Mobile phone data products have enormous potential, but attention should be paid to the varying methodologies and their possible impact on analysis.