全球海港国家根据其DEA和FDEA网络效率分数进行了新的集群化
Dineswary Nadarajan1,2, Elayaraja Aruchunan3,4, Noor Fadiya Mohd Noor2,4
1Faculty of Business, Accountancy & Law, SEGi University, Petaling Jaya, Malaysia.
PloS one
|July 30, 2024
概括
本研究介绍 hkmeans 集群分析全球海港网络效率,将133个国家分为四个连接组. 这些发现为海上经济和运输政策提供了实际见解.
科学领域:
- 海洋经济与物流 海洋经济与物流
- 数据科学和分析数据科学和分析
- 网络分析 网络分析
背景情况:
- 全球海港网络的效率对于国际贸易和经济发展至关重要.
- 现有的研究缺乏对海港网络效率的具体聚类.
- 航线航运连接指数 (LSCI) 和国内生产总值 (GDP) 是关键指标.
研究的目的:
- 通过先进的数据分析技术,根据海港网络效率对133个国家进行集群.
- 为这个特定的应用程序引入和验证 hkmeans 集群算法.
- 根据其海港连接水平,确定不同的国家组.
主要方法:
- 使用数据包围分析 (DEA) 和模糊数据包围分析 (FDEA) 进行效率评分.
- 应用了k-means和等级集群策略,重点关注 hkmeans.
- 他们将133个国家分为四组:低连接 (LC),中等连接 (MC),高连接 (HC) 和非常高连接 (VHC).
主要成果:
- 通过使用 hkmeans 成功将国家分为LC (24),MC (47),HC (40),和VHC (22) 的集群.
- 证明了 hkmeans 的一致性和实用性,有或没有模糊数据集分布.
- 确定了四个新的集群,代表不同级别的海港网络效率.
结论:
- Hkmeans集群是分析全球海港网络效率的有效策略.
- 已建立的集群 (LC,MC,HC,VHC) 为了解海上连接提供了一个新的框架.
- 调查结果支持研究人员,政策制定者和海事和运输部门的投资者.
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