# Tourist Behaviour Analysis Based on Digital Pattern of Life--An Approach and Case Study
## 4 Tourist Behaviour Analysis Case Studies
### Clustering  
The case study presents the tourist categorization based on their trips. The chosen model uses a neural network based on the concept of self-organizing maps [43], which is capable of clustering tourists without using a teacher during training. The neural network transforms an array of input parameters into a simplified two-dimensional map. The closer the sets are to each other on the map, the more \"similar\" they are to each other. Self-organizing maps preserve the relationship and structure of the input dataset and can reveal correlations within the data that are difficult to identify with manual data analysis. During the training of models, the process of back propagation of errors doesn't occur, as well as the activation functions are not used due to a different internal structure of the neural network than in the solutions of deep neural networks.  
The input parameters for the clustering neural network are presented in the Table 3. Parameters 1-4 describe the general characteristics of the routes that a tourist usually follows in their trips. Parameters 5-6 describe the contextual parameters of routes that were calculated indirectly. Parameters 7-9 refer to the description of the tourist, parameters 10-25 describe the tourist's preferences for certain attractions.  
Each tourist is characterized on the basis of preferred attractions, travel spending, characteristics of the routes themselves, ratings and reviews of attractions. The obtained clustering results can be used to improve the performance of recommendation services, as an additional criterion for evaluating users. In addition, it is possible to provide recommendations for not yet visited attractions within the formed groups of tourists based on the clustering results. The use of contextual parameters of routes allows to improve the accuracy of clustering tourists.  
Figure 3: Classification neural network training results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \# & **Input Feature** & **Unit Measurement** & **Description** \\ \hline
1 & distance & meters & Average tourist trip distance \\
2 & duration & seconds & Average tourist trip duration \\
3 & attractions count & integer number & Average amount of visited attractions by trip \\
4 & attractions rating & float number & Total rating of all visited attractions, based on experts ratings \\
5 & winding path & float number & Overall tortuosity, 0-straight line, 5-many turns \\
6 & attraction attendance & integer number & Average attraction attendance \\
7 & average speed & m/s & Average tourist speed during trips \\
8 & tourist generated content & integer number & Average amount of user generated content (photo/videos) during the trip \\
9 & budget spending & integer number & Average tourist spending while visiting the route in local currency for the region \\
10...25 & attraction preferences & number & List of tourist attraction preferences \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of the clustering neural network input layer features.  
Around 100 people were selected based on the data set of automobile tourist trips and the input parameters for neural network training were calculated, according to the Table 3. After the data analysing, it was decided to divide the tourists into the 5 cluster groups. The spread of the function that estimates the \"neighborhood\" of neurons is set to 0.5, the learning rate is also set to 0.5, the Gaussian function was used as the method for determining the neighborhood, and the Euclidean distance measuring function was taken for the distance calculation. The model was trained over 500 epochs. The results of clustering tourists are presented in Figure 4--the dots represents the tourists, the colors show the clusters calculated on the basis of the data presented, the black rhombuses represent the centroids of the clusters. For tourists in the same cluster, it is possible to recommend attractions that have not yet been visited, which are typical for tourists visiting this cluster.