## 4 Data Collection
### Data Clustering Analysis
#### 4.1.2 Clustering Scenario, II (Clusters of Offices Based on Tourist Nationalities)  
This scenario aims to create clusters of tourist agencies based on similarities between the tourist nationalities visiting the country. The option of a single linkage algorithm in Minitab software was used in this scenario to process the collected data for the year 2019. The process starts by selecting the number of clusters that are tourist offices (N = 3), applying the clustering algorithm, and analyzing the content of clusters in terms of their goodness; then, the process is repeated until meaningful logical results are attained. In this scenario, there were 19 (\(\times\)1, \(\times\)2, \(\times\)3,..., \(\times\)19) major tourism agencies under considerations. So, the number of clusters used in this study was from N = 3 to N = 9. For every month through 2019, the procedure described in the previous section was conducted using various clustering numbers (n). At each time, the end result of the clustering process was analyzed in terms of its consequences on economic, health, social, sustainability, and administration aspects. With respect to analyze the content of the clusters, the clusters are named A, B, C, etc. As an example, the content of clusters when N = 8 for February 2019 is presented in Table 2, and the correlated dendrogram is shown in Figure 6. For example, cluster A in February 2019 has four tourist offices which are \(\times\)1, \(\times\)16, \(\times\)17, and \(\times\)18. The dendrogram in Figure 6 shows the hierarchal relationship between objects (travel agencies), that will be used to form clusters on the horizontal axis, and the similarity coefficient used to create clusters between its variables--depending on the similarities of tourist nationalities for which offices organize trips--on the vertical axis. For example, the dendrogram suggests that \(\times\)16 and \(\times\)18 are much closer to each other than \(\times\)16 and \(\times\)17. A cluster content visualization chart (CCVC) was used to display regions of each office in the clusters when N = 8. Figure 7 shows cluster A content visualization using color coding for January when N = 8. As shown cluster A has 10 tourist offices, all of these offices have tourists from America and Europe, 90% of these offices deal with Asian tourists, 80% \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Months** & **Cluster A** & **Cluster B** & **Cluster C** & **Cluster D** & **Cluster E** & **Cluster F** & **Cluster G** & **Cluster H** \\ \hline \multirow{3}{*}{February} & \(\times\)1, \(\times\)16, \(\times\)17, \(\times\)18 & \(\times\)2, \(\times\)6 & \(\times\)3 & \(\times\)4 & \(\times\)5, \(\times\)7 & \(\times\)9 & \(\times\)10, \(\times\)11, \(\times\)13, \(\times\)15 \\  & & & & & & & \(\times\)14, \(\times\)19 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Clusters content when the number of clusters is equal to eight (N = 8). of these offices have tourists from the Middle East and Arabia, and just 20% deal with tourists from Oceania. A cluster content explanation chart was used for all clusters throughout the year to monitor how the nationalities number changed in the same cluster from January to December. For example: Figure 8 shows the cluster B content explanation chart. The y-axis represents the number of tourists for each geographical area in the cluster, and the x-axis represents the offices within cluster B throughout the year. Figure 6: Dendrogram for offices during January2019 using single linkage clustering algorithm when N = 8 (scenario II). Figure 7: Cluster A content visualization using color coding for January when N = 8. Clustering agencies into groups based on the similarities between the nationalities they serve will create a positive effect and will add value to the tourism sector on many ways, such as:  
* Transportation costs will be reduced for each agency because all the tourists in the handled by many agencies will go on the same trips, so the transportation cost will decrease.