# Tourist Behaviour Analysis Based on Digital Pattern of Life--An Approach and Case Study
## 2 Related Work
The tourist smartphones' GPS data, number of trips and transport type tracking are treated as the basis for the behaviour construction. The DBSCAN method is used for tourist stops clustering and random forest classification is used for determining tourist stop type. The authors of Reference [25] use the SOM approach to clustering in the recommendation systems. The users' ratings clustering is achieved by using this model. The SOM model is trained on TripAdvisor datasets. The authors of Reference [26] describe tourist destinations and web-queries relations. The auto-regression vector model was used to determine weather, temperatures, weekends and public holidays impact on the tourist behaviour. In Reference [27], the authors analyse sentiment from tourist POI reviews. The analysis is based on a bidirectional recurrent neural network model. The model consists of a topic model (Ida2 vec) and an attention mechanism. The Ida2 vec is used for discovering the main topics of the review for the word vector. The attention mechanism is supposed to determine different weights of the words in the text. The authors of Reference [28] describe the convolutional neural network (CNN) model, which labels a food by photo for tourists. CNN was chosen for its reliable and fast process of classification of complex and detailed objects. Indonesian food photos were used as the main source of the data. In Reference [29], the authors use an LSTM neural model to predict tourism flow. They stated that LSTM-based models describe the stochastic and non-linear nature of tourism flow more accurately than the Auto Regressive Integrated Moving Average models. The authors of Reference [30] present the monthly tourist arrival forecasting framework. The authors introduce the deep network architecture with historical time-series tourism demand data. The attention mechanism support was added to the LSTM model. The attention mechanism makes the presented model more interpretive. In Reference [31] the authors describe a neural network's composition for time series forecasting. Back-propagation neural network, dynamic architecture for artificial neural network, Elman artificial neural network and echo state network are selected as component forecasting models by the authors. An input-hidden selection heuristic is designed to determine the input-hidden neuron combination for each component neural network. An in-sample training-validation pair-based neural network weighting mechanism is studied to generate the associated combination weights. The authors of Reference [32] aim to integrate multiple data sources to analyze tourists' spatial-temporal behaviour patterns on micro scale distances. Information about tourists' temporal-spatial behaviour was gathered using handheld GPS tracking devices, and questionnaires were distributed to assess tourists' socio-psychological characteristics. Three spatial-temporal behaviour clusters were identified via density center clustering, consisting of four factors: path length, tour time, coverage area, and oval circumference. The authors of Reference [33] use the LSTM networks that can incorporate multivariate time series data including historical tourism volume data, search engine data and weather data. The model is proposed for forecasting the daily tourism volume of tourist attractions. The research notes that search engine data and weather data have a huge influence on tourism volume forecasting. The following common requirements and similarities were identified after the analysing the related work:  
1. Collecting a large amount of information about a tourist is typical for Big Data [15; 16]. Tourist behaviour analysis systems collect data about certain visits to attractions, and ratings and reviews from social networks. It is recommended to use heterogeneous information from various sources to ensure an accurate representation of the tourist. A large amount of different information will improve the predictive ability of models based on neural networks, which require a large amount of input data for effective training. 2.