# Tourist Behaviour Analysis Based on Digital Pattern of Life--An Approach and Case Study
## 2 Related Work  
An exploration of the tourist behaviour analysis systems was made in 19 articles over the past 5 years. Most attention was paid to articles that described (1) the existing tourist behaviour analysis frameworks and systems; (2) methods of analysis and approaches that can be used in the system of tourists' behaviour. The authors of Reference [15] propose a Big Data analytic method for destination management organization's decision support. The method analyses unstructured social data for supporting strategic decision-making in tourism. The method includes the following techniques: text processing, geographical data clustering, visual content processing and time series modelling. The P-DBSCAN algorithm was used for clustering purposes. The time series decomposition technique was used for seasonal patterns retrieving. In Reference [16], the authors show a framework of forecasting tourist arrivals to a destination. Google Trends is used as the main source for a multivariate time series data analysis. The selected data were filtrated by using an information processing approach and the theory of planned behaviour. The ARIMAX, VAR and ADRL models are used to evaluate potential tourist behaviour and predict their arrival patterns in the end. The authors of Reference [17] use the neural network autoregressive algorithm for the monthly tourism demand forecasting of ten European countries. The denoising algorithms are used for prediction accuracy improvement. The proposed algorithm is compared with autoregressive integrated moving average and exponential smoothing models and results show that the proposed algorithm is more accurate. Reference [18] presents a tourist travel analysing framework based on the location-based data from social media. Data from 67,000 Twitter users in Florida was gathered. The authors have used the following clustering methods: K-Means, DBSCAN and Mean-Shift and several classification approaches for the data analysing. In Reference [19] the authors describe the process of tourist behaviour mining by using visual content analysis. Around 35,000 tourists' photos from Flickr in Beijing were identified into 103 scenes by computer deep learning technology. The ResNet-101 Deep learning model was used for the photo analysis. Tourists' cognitive maps with different perceptual themes were visualized according to the geographical information from photos. The authors of Reference [20] use travel diaries as a source of popular tourist route extraction. The Foursquare venue check-in data and Twitter data were used for travel diary formation. The high-utility pattern mining technique was applied for the effective discovery of highly valuable travel patterns. In Reference [21], the authors analysed a sequential pattern in tourist behaviour by using combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) neural networks. The proposed artificial neural network (ANN) predicts the possible location visiting. The tourist opinion and reviews were used as main source of data. The the authors of Reference [22] present a method for tourist arrival and tourist demand forecasting. The method includes a novel paired neural network model. The tourist arrival data are decomposed by two low-pass filters into a long-term trend and short-term seasonal components. Two separated three-layer autoregressive neural networks were then estimated by the trend and seasonal component data respectively: paired Neural Network with HP filter, paired Neural network with Wavelet Transformation, and Neural network with Moving Average. The authors of Reference [23] work with self-organizing maps (SOM) for clustering the popular POI visitors profiles and 2500 tourist interviews were used as the main data source and four main tourist profiles types were identified. In Reference [24], the authors describe a framework that capitalizes on the large-scale opportunistic mobile sensing approach for tourist behaviour analysis.