Using the tensor flow deep neural network to classify mainland China visitor behaviours in Hong Kong from check-in data

Shanshan Han; Fu Ren; Chao Wu; Ying Chen; Qingyun Du; Xinyue Ye

doi:https://doi.org/10.3390/ijgi7040158

Using the tensor flow deep neural network to classify mainland China visitor behaviours in Hong Kong from check-in data

Shanshan Han, Fu Ren, Chao Wu, Ying Chen, Qingyun Du, Xinyue Ye

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

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.

Original language	American English
Article number	158
Journal	ISPRS International Journal of Geo-Information
Volume	7
Issue number	4
DOIs	https://doi.org/10.3390/ijgi7040158
State	Published - Apr 2018
Externally published	Yes

ASJC Scopus subject areas

Geography, Planning and Development
Computers in Earth Sciences
Earth and Planetary Sciences (miscellaneous)

Keywords

Check-in data
Deep neural network
Hong Kong
TensorFlow
Visitor behaviours

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https://doi.org/10.3390/ijgi7040158

Cite this

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title = "Using the tensor flow deep neural network to classify mainland China visitor behaviours in Hong Kong from check-in data",

abstract = "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.",

keywords = "Check-in data, Deep neural network, Hong Kong, TensorFlow, Visitor behaviours",

author = "Shanshan Han and Fu Ren and Chao Wu and Ying Chen and Qingyun Du and Xinyue Ye",

note = "Funding Information: Acknowledgments: We thank Dawei Gui, Pengfei Ning, Yuan Wei and Zekun Li for their help. In addition, this study was supported by the National Key Research and Development Program of China (2016YFC0803106) and the National Natural Science Foundation of China (Project No. 41571438). Publisher Copyright: {\textcopyright} 2018 by the authors.",

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AU - Chen, Ying

AU - Du, Qingyun

AU - Ye, Xinyue

N1 - Funding Information: Acknowledgments: We thank Dawei Gui, Pengfei Ning, Yuan Wei and Zekun Li for their help. In addition, this study was supported by the National Key Research and Development Program of China (2016YFC0803106) and the National Natural Science Foundation of China (Project No. 41571438). Publisher Copyright: © 2018 by the authors.

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N2 - 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.

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Using the tensor flow deep neural network to classify mainland China visitor behaviours in Hong Kong from check-in data

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ASJC Scopus subject areas

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