机器学习分析了COVID-19对移民模式的影响
Farzona Mukhamedova1, Ivan Tyukin2
1King's College London, London, WC2 R2LS, UK. farzona.mukhamedova@kcl.ac.uk.
Scientific reports
|November 30, 2024
概括
COVID-19大流行使欧洲旅游业从多样化的模式转变为全球统一的模式. 国内生产总值 (GDP) 和文化等社会经济因素影响旅游流动走廊,突出了对弹性基础设施和中小企业支持的需求.
科学领域:
- 社会经济学 社会经济学
- 机器学习 机器学习
- 流动性研究 流动性研究
背景情况:
- "COVID-19"大流行严重扰乱了全球旅游业和旅游业.
- 了解旅游流动的变化对于经济性和区域发展至关重要.
研究的目的:
- 分析COVID-19大流行对2019-2021年欧洲旅游流动模式的影响.
- 在可变条件下建模和评估社会经济走廊的稳定性.
主要方法:
- 将国家概念化为发射辐射的单体,用于模式分析.
- 应用扰乱集群,主要组件分析 (PCA) 和树状图.
- 整合社会经济数据 (GDP,文化,语言相似性) 与机器学习.
主要成果:
- 由于疫情限制,旅游流动从2019年的异质 (双模) 转变为2020-2021年的统一 (单模).
- 旅游者偏好与GDP,文化和语言相似性相关,解释了走廊的凝聚力和脆弱性.
- 新兴的走廊 (例如,红色章鱼) 显示脆弱性,而已建立的走廊 (例如,蓝色香) 显示了弹性.
结论:
- 该研究为评估全球危机中的流动性模式提供了强有力的框架.
- 有针对性的政策干预,包括运输基础设施和中小企业支持,对于减轻干扰至关重要.
- 提高经济性需要预测旅游行为的变化和加强社会经济走廊.
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