Dataset about Port Research

After detemining that there is no direct connection to the port in the network diagram, get the direct connection distance between ports through the port.sol.com.cn、SeaRates.com and McDistance shipping calculation tool. If there is a big difference between the three query data, the average value method is used for optimization, get the table Port Distance. Using the Floyd algorithm, the path between two ports in the port network graph is solved on the basis of the table Port Distance, there maybe multiple shortest paths between two ports, but this situation is not considered here, the only result will be the result of Python simulation, get the table Port Shortest Path. After get the Port Shortest Path, calculate the value of the shortest path between two ports, get the table Port Shortest Path Value. According to the shortest path between two ports, count the number of routes for each port, then use the K-Medoids, construting the model of strategic importance of ports, get the table The number of ports is crossed by the shortest path. According to the principle of the Betweenness Centrality model, the Betweenness Centrality of each port in the whole network is obtained by the table Port Shortest Path, and then use the K-Medoids, get the table Port Betweenness Centrality. The values and contents of the table The number of ports is crossed by the shortest path and the table Betweenness Centrality Group are combined together to get the table Total Group to facilitate data search.

首先在确认网络图中端口间不存在直接连接关系后，通过port.sol.com.cn、SeaRates.com以及McDistance航运距离计算工具获取各端口间的直接连接距离。若三组查询数据差异较大，则采用平均值法进行优化，最终得到端口距离表（Port Distance）。基于该端口距离表，采用Floyd算法（Floyd）求解端口网络图中任意两端口间的通路；尽管两端口间可能存在多条最短路径，但本研究暂不考虑该情况，仅保留Python仿真得到的唯一结果，最终得到端口最短路径表（Port Shortest Path）。获取该端口最短路径表后，计算任意两端口间最短路径的总权值，得到端口最短路径权值表（Port Shortest Path Value）。基于任意两端口间的最短路径，统计各端口所途经的航线数量；随后采用K-中心点聚类算法（K-Medoids）构建港口战略重要性评估模型，得到最短路径途经港口数量表（The number of ports is crossed by the shortest path）。基于介数中心性（Betweenness Centrality）模型的原理，通过端口最短路径表计算整个网络中各端口的介数中心性；随后再次采用K-中心点聚类算法，得到港口介数中心性表（Port Betweenness Centrality）。将最短路径途经港口数量表与介数中心性分组表（Betweenness Centrality Group）的数值及内容进行合并，得到总分组表（Total Group），以方便后续数据检索。

搜集汇总

背景与挑战

背景概述

该数据集包含港口网络研究相关的多个表格，通过Floyd算法和K-Medoids聚类等方法计算港口距离、最短路径及其战略重要性。数据集由两所大学的研究人员发布，采用CC0许可，包含6个Excel文件。