# Tourist Behaviour Analysis Based on Digital Pattern of Life--An Approach and Case Study
## 3 Tourist Behaviour Analysis System, Based on Digital Pattern of Life Concept  
The developed reference model of the tourist behaviour analysis system is presented on the Figure 1. All tourist data from sensors, social networks, smart city [34] services and polls are supposed to be stored in a data lake storage. This approach allows to store a large amount of the information without changing the internal data structure. A large amount of data sources increases chances to find internal dependencies in the behaviour of tourists. Storing data without changing the internal structure is well suited for machine learning tools, because researchers need to select and format data separately for each task. If the data is stored in a predetermined structure (as recommended when using the data warehouse approach), then the retrieved data will need to be changed again, which can potentially slow down the analysis of tourist behavior. The extracted information based on the tourist digital pattern of life can be processed for analyzing tourist behaviour. Travel data and activities can be classified, grouped and predicted by machine learning models. The results obtained can be used to improve the work of existing tourist support services (recommendation systems, services for building a tourist route, etc.). For behaviour analysis three main instruments for analyzing tourist behaviour were chosen: classification, clustering and time series prediction. The presented instruments are most often used in the research of tourist behaviour based on the analysis of scientific works. For this system, the neural network based approach is well suited due to capability of storing the big amount of data from different resources. The enhanced digital pattern of life ontological model from [35] is shown in the Figure 2. Circles represent ontology classes, rectangles represent data properties, and arrows with labels between classes show relationships between classes as predicates. The main class of the proposed ontology is _Tourist_, which represents a traveling person. The tourist is characterized by his behaviour--_behaviour_. Tourist behaviour can be analyzed using _Analysis Instruments_. These tools use information from _Route_, _Sensors_, attraction visiting plan (_Plan_) and Tourist Context (_Context_) and can be divided into three methods: _Classification_, _Clustering_ and _Prediction_. Travel _Preferences_ affect travel planning and travel itinerary style. During the trip a tourist uses a mobile device with different characteristics that is designated in the ontology as _Device_. The device can contain sensors (_Sensors_ class) that can track changes in a person's characteristics over time. The tourist can be identified by the spatial coordinates from the GPS navigator (class _GPS_ and child class _Coordinate_); using a gyroscope (_Gyroscope_) allows you to track the turns of people; accelerometer (_Accelerometer_ sensor) can determine the speed and acceleration of a person and predict the type of travel (on foot or using any type of transport); _Light sensor_ can indirectly determine whether the tourist is on the street or indoors; magnetometer (_Magnetometer_) allows you to determine the direction of sight of the tourist. During the trip the tourist moves along a specific route (_Route_), which can be built on the basis of sensor data. The route includes information about the tourist sequential movement between places, tourist stops and other characteristics. The route passes in a specific city (_City_) that has contextual information (_City context_). The context is divided into information about the weather (_Weather_--data about the current temperature, season and wind) and traffic situation (_Traffic_). Each tourist route contains information about the context of the city and provides some information about the tourist context at a given time. The tourist sets a travel plan (_Plan_), which includes a set of places (_Place_) to visit.