太阳成8722(中国)有限公司-GREEN NO.1

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

作者： Shanshan Han, Fu Ren, Chao Wu , Ying Chen, Qingyun Du,* and Xinyue Ye*

来源出版物： International Journal of Geo-Information卷：7期：4 DOI：10.3390/ijgi7040158 出版年：21 April 2018

摘要：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.

作者关键词：check-in data; visitor behaviours; deep neural network; TensorFlow; Hong Kong

通讯作者地址：

电子邮件地址：qydu@whu.edu.cn;xye5@kent.edu

地址：

[Shanshan Han, Fu Ren, Chao Wu , Ying Chen, Qingyun Du]School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China;

[Fu Ren, Qingyun Du]Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University,Wuhan 430079, China

[Qingyun Du]Key Laboratory of Digital Mapping and Land Information Application Engineering, National,Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan 430079, China

[Qingyun Du]Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China

[Xinyue Ye]Department of Geography, Kent State University, Kent, OH 44242, USA