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ISPRS Int. J. Geo-Inf. 2018, 7(4), 158;

Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data

1,2,3,4,* and 5,*
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University, Wuhan 430079, China
Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan 430079, China
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
Department of Geography, Kent State University, Kent, OH 44242, USA
Authors to whom correspondence should be addressed.
Received: 26 February 2018 / Revised: 29 March 2018 / Accepted: 19 April 2018 / Published: 21 April 2018
(This article belongs to the Special Issue Web and Mobile GIS)
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Over the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field. View Full-Text
Keywords: check-in data; visitor behaviours; deep neural network; TensorFlow; Hong Kong check-in data; visitor behaviours; deep neural network; TensorFlow; Hong Kong

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Han, S.; Ren, F.; Wu, C.; Chen, Y.; Du, Q.; Ye, X. Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data. ISPRS Int. J. Geo-Inf. 2018, 7, 158.

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