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.<br>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.<br>After get the Port Shortest Path, calculate the value of the shortest path between two ports, get the table Port Shortest Path Value.<br>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.<br>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.<br>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.<br><br>首先确定网络图中各端口间无直接连接关系后，通过port.sol.com.cn、SeaRates.com与McDistance海运计算工具获取端口间的直接连接距离。若三项查询数据存在较大差异，则采用平均值法进行优化，最终得到端口距离表（Port Distance）。 基于端口距离表（Port Distance），采用弗洛伊德算法（Floyd Algorithm）求解端口网络图中任意两端口间的路径。尽管两端口间可能存在多条最短路径，但本研究未考虑该场景，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）。 获取端口最短路径表（Port Shortest Path）后，计算任意两端口间的最短路径数值，得到端口最短路径数值表（Port Shortest Path Value）。 根据两端口间的最短路径，统计每个端口被最短路径穿越的次数，随后采用K中心点聚类算法（K-Medoids）构建港口战略重要性模型，得到最短路径穿越端口数量表（The number of ports is crossed by the shortest path）。 依据介数中心性（Betweenness Centrality）模型的原理，通过端口最短路径表（Port Shortest Path）计算得到全网中每个端口的介数中心性数值，随后采用K-Medoids聚类，得到端口介数中心性表（Port Betweenness Centrality）。 将最短路径穿越端口数量表（The number of ports is crossed by the shortest path）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），以方便数据检索。