# Tourist Behaviour Analysis Based on Digital Pattern of Life--An Approach and Case Study
## 2 Related Work
Open data usage as a source of tourist information. References [18; 19; 20; 21] use social networks such as Twitter, Foursquare, Facebook, and so forth. The authors of References [15; 24; 25] analyze data from open sources, provided by the governments of various countries. The usage of various open sources makes it possible to more accurately construct models of tourist behaviour, which improves the predictive ability of the system for analyzing tourist behaviour as a whole. 3. Heterogeneous tourist information usage to build behaviour analysis models. Researchers use text data [15; 23; 26; 27] (user reviews, attractions ratings, attraction requests and keywords in search engines); tourist photographs [19; 28] for the analysis of meta-information and the image itself; data on movements and attraction visiting [17; 18; 21; 22; 24; 29; 30; 31]. Using heterogeneous information will allow the extraction of different behavioural components, which contributes to an increase in the number of possible behavioural patterns. 4. Using behaviour analysis methods such as classification, clustering and prediction of temporal events. The authors of References [15; 18; 19; 27; 28] use the classification, References [18; 23; 24; 25; 32] work with clustering and the authors of References [16; 17; 21; 26; 29] use the prediction of temporal events to predict the behaviour tourists. 5. Neural networks usage to analyze and predict the behaviour of tourists. The authors of References [19; 28] use convolutional neural networks for image analysis, References [22; 29; 30; 31; 33] work with LSTM networks that allow memorizing previous states, Reference [27] describes recurrent networks for analyzing tourist descriptions of attractions, self-organizing maps [23; 25] provide information clustering, the authors of References [16; 21; 30; 31] use combinations of neural networks to improve prediction results. Neural networks, as a rule, work more accurately than similar models, but they require a large amount of data for correct training and revealing hidden dependencies in the provided data, and for each task the volume depends on the type of the task itself (prediction of time events requires more data than classification ) and the number of certain input parameters (the more parameters, the more data is needed to identify dependencies between them). Among the considered solutions, the following main problems were identified that should be taken into the account when developing a reference model of a tourist behaviour analysis system:  
1. Narrow specialization of the presented systems for analyzing the behaviour of tourists. The reviewed articles describe the solution of one problem using one data source that leads to the fact that it is impossible to adapt or extend the proposed systems. The considered systems do not consider the issues of storing the received data, do not categorize data and data analysis tools. 2. Insufficient use of various device sensors used by tourists when traveling. The extracted data will help to more accurately track visits to attractions, modes of travel, intersections with other tourists, etc. Some of the reviewed articles work with GPS devices, but did not consider in their work the values of the sensors throughout the entire route--only visiting certain attractions. It is recommended to store data in anonymous form to comply with the legislation of various countries. 3. Insufficient use of contextual information of both the tourist and the surrounding region in the analysis of tourist behaviour. Due to the narrow specialization of the considered systems, the considered systems use context parameters in a limited way. A wide variety of contextual parameters will allow you to add additional dependencies between the data and reveal hidden dependencies, which can improve the overall prediction of tourist behaviour.