{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T13:37:34Z","timestamp":1761917854386,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,21]],"date-time":"2018-04-21T00:00:00Z","timestamp":1524268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2016YFC0803106"],"award-info":[{"award-number":["2016YFC0803106"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41571438"],"award-info":[{"award-number":["41571438"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/ijgi7040158","type":"journal-article","created":{"date-parts":[[2018,4,24]],"date-time":"2018-04-24T04:44:48Z","timestamp":1524545088000},"page":"158","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1133-3295","authenticated-orcid":false,"given":"Shanshan","family":"Han","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}]},{"given":"Fu","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University, Wuhan 430079, China"}]},{"given":"Chao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}]},{"given":"Ying","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4615-2029","authenticated-orcid":false,"given":"Qingyun","family":"Du","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"},{"name":"Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University, Wuhan 430079, China"},{"name":"Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8838-9476","authenticated-orcid":false,"given":"Xinyue","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Geography, Kent State University, Kent, OH 44242, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1080\/14616688.2011.598542","article-title":"First and repeat visitor behaviour: Gps tracking and gis analysis in Hong Kong","volume":"14","author":"McKercher","year":"2012","journal-title":"Tour. Geogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.tourman.2016.08.009","article-title":"Understanding the tourist mobility using gps: Where is the next place?","volume":"59","author":"Zheng","year":"2017","journal-title":"Tour. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.pmcj.2014.07.003","article-title":"Understanding tourist behavior using large-scale mobile sensing approach: A case study of mobile phone users in Japan","volume":"18","author":"Phithakkitnukoon","year":"2015","journal-title":"Pervasive Mob. Comput."},{"key":"ref_4","first-page":"684","article-title":"Analysis of tourist behaviour based on the tracking data collected using a mobile communication instrument","volume":"41","author":"Asakura","year":"2007","journal-title":"Transp. Res. Part A"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cao, J., Hu, Q., and Li, Q. (2014, January 29\u201330). A study of users\u2019 movements based on check-in data in location-based social networks. Proceedings of the International Symposium on Web and Wireless Geographical Information Systems, Seoul, Korea.","DOI":"10.1007\/978-3-642-55334-9_4"},{"key":"ref_6","first-page":"72","article-title":"Study on check-in and related behaviors of location-based social network users","volume":"40","author":"Min","year":"2013","journal-title":"Comput. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, L., Yang, L., Zhu, H., and Dai, R. (2015). Explorative analysis of wuhan intra-urban human mobility using social media check-in data. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0135286"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huang, Q., Cao, G., and Wang, C. (2014, January 4\u20137). From where do tweets originate? A gis approach for user location inference. Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, Dallas\/Fort Worth, TX, USA.","DOI":"10.1145\/2755492.2755494"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wu, L., Zhi, Y., Sui, Z., and Liu, Y. (2014). Intra-urban human mobility and activity transition: Evidence from social media check-in data. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0097010"},{"key":"ref_10","unstructured":"World Tourism Organization (2017, August 23). Annual Report 2015. Available online: http:\/\/cf.cdn.unwto.org\/sites\/all\/files\/pdf\/annual_report_2015_lr.pdf."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Salah, A.A., Lepri, B., Pianesi, F., and Pentland, A.S. (2011, January 16). Human behavior understanding for inducing behavioral change: Application perspectives. Proceedings of the International Workshop on Human Behavior Understanding, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-642-25446-8_1"},{"key":"ref_12","unstructured":"Hartford, J.S., Wright, J.R., and Leyton-Brown, K. (2016). Deep learning for predicting human strategic behaviour. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_13","unstructured":"YouTube (2017, July 04). Match 1\u2014Google Deepmind Challenge Match: Lee Sedol vs. Alphago. Available online: https:\/\/www.youtube.com\/watch?v=vFr3K2DORc8&t=1h57m."},{"key":"ref_14","unstructured":"Ritter, S., Barrett, D.G.T., Santoro, A., and Botvinick, M.M. (arXiv, 2017). Cognitive psychology for deep neural networks: A shape bias case study, arXiv."},{"key":"ref_15","unstructured":"TensorFlow (2017, June 25). Image Recognition. Available online: https:\/\/www.tensorflow.org\/tutorials\/image_recognition."},{"key":"ref_16","unstructured":"TensorFlow (2017, June 25). Mnist for ml Beginners. Available online: https:\/\/www.tensorflow.org\/versions\/r0.7\/tutorials\/mnist\/beginners\/index.html."},{"key":"ref_17","unstructured":"Kovalev, V., Kalinovsky, A., and Kovalev, S. (2016, January 3\u20135). Deep learning with theano, torch, caffe, tensorflow, and deeplearning4j: Which one is the best in speed and accuracy?. Proceedings of the XIII International Conference on Pattern Recognition and Information Processing, Minsk, Belarus."},{"key":"ref_18","unstructured":"Park, J.J., Chen, S.-C., and Raymond Choo, K.-K. (2017). Applying tensorflow with convolutional neural networks to train data and recognize national flags. Advanced Multimedia and Ubiquitous Engineering: Mue\/Futuretech 2017, Springer."},{"key":"ref_19","unstructured":"Ferri, A. (2016). Object Tracking in Video with Tensorflow, Universitat Polit\u00e8cnica de Catalunya."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dean, J. (2016, January 22\u201325). Large-scale deep learning for intelligent computer systems. Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2835776.2835844"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., and Baskurt, A. (2011, January 16). Sequential deep learning for human action recognition. Proceedings of the International Workshop on Human Behavior Understanding, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-642-25446-8_4"},{"key":"ref_22","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Li, F.F., and Savarese, S. (July, January 26). Social lstm: Human trajectory prediction in crowded spaces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1177\/135676670401000403","article-title":"Predictors of tourists\u2019 shopping behaviour: Examination of socio-demographic characteristics and trip typologies","volume":"10","author":"Oh","year":"2004","journal-title":"J. Vacat. Mark."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1177\/0047287502040003009","article-title":"Market segmentation and the prediction of tourist behavior: The case of bornholm, denmark","volume":"40","author":"Johns","year":"2002","journal-title":"J. Travel Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1177\/004728759603500209","article-title":"A profile of the casino resort vacationer","volume":"35","author":"Morrison","year":"1996","journal-title":"J. Travel Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.tourman.2014.07.003","article-title":"Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos","volume":"46","author":"Vu","year":"2015","journal-title":"Tour. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1057\/palgrave.thr.6050027","article-title":"Understanding tourist movement patterns in a destination: A GIS approach","volume":"7","author":"Lau","year":"2006","journal-title":"Tour. Hosp. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1080\/14616680802236352","article-title":"Movement patterns of tourists within a destination","volume":"10","author":"Mckercher","year":"2008","journal-title":"Tour. Geogr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1002\/jtr.876","article-title":"A social network analysis of overseas tourist movement patterns in beijing: The impact of the olympic games","volume":"14","author":"Leung","year":"2012","journal-title":"Int. J. Tour. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yuan, Y., and Medel, M. (2016). Characterizing international travel behavior from geotagged photos: A case study of flickr. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0154885"},{"key":"ref_31","first-page":"34","article-title":"Quantifying potential tourist behavior in choice of destination using google trends","volume":"24","author":"Padhi","year":"2017","journal-title":"Tour. Manag. Perspect."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Clements, M., Serdyukov, P., de Vries, A.P., and Reinders, M.J.T. (2010, January 19\u201323). Using flickr geotags to predict user travel behaviour. Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Geneva, Switzerland.","DOI":"10.1145\/1835449.1835648"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.tourman.2015.06.008","article-title":"Understanding tourist space at a historic site through space syntax analysis: The case of Gulangyu, China","volume":"52","author":"Yuan","year":"2016","journal-title":"Tour. Manag."},{"key":"ref_34","unstructured":"Plog, S.C., Ritchie, J.R.B., and Goeldner, C.R. (1987). Understanding psychographics in tourism research. Understanding Psychographics in Tourism Research, CABI."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.tourman.2016.08.013","article-title":"Understanding travelers\u2019 intentions to visit a short versus long-haul emerging vacation destination: The case of Chile","volume":"59","author":"Bianchi","year":"2017","journal-title":"Tour. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, K., Yan, Z.J., and Huo, Q. (2016, January 12\u201313). A context-sensitive-chunk bptt approach to training deep lstm\/blstm recurrent neural networks for offline handwriting recognition. Proceedings of the International Conference on Document Analysis and Recognition, Johannesburg, South Africa.","DOI":"10.1109\/ICDAR.2015.7333794"},{"key":"ref_37","unstructured":"Simonyan, K., and Zisserman, A. (2017, June 25). Very Deep Convolutional Networks for Large-Scale Image Recognition. Available online: https:\/\/arxiv.org\/abs\/1409.1556."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Graves, A., Jaitly, N., and Mohamed, A.R. (2013, January 8\u201312). Hybrid speech recognition with deep bidirectional LSTM. Proceedings of the Automatic Speech Recognition and Understanding, Olomouc, Czech.","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"ref_39","unstructured":"Graves, A., and Jaitly, N. (2014, January 21\u201326). Towards end-to-end speech recognition with recurrent neural networks. Proceedings of the International Conference on Machine Learning, Beijing, China."},{"key":"ref_40","unstructured":"Graves, A. (2017, June 25). Generating Sequences with Recurrent Neural Networks. Available online: https:\/\/arxiv.org\/abs\/1308.0850."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yao, D., Zhang, C., Huang, J., and Bi, J. (2017, January 6\u201310). SERM: A recurrent model for next location prediction in semantic trajectories. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore.","DOI":"10.1145\/3132847.3133056"},{"key":"ref_42","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (arXiv, 2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems, arXiv."},{"key":"ref_43","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (arXiv, 2016). Tensorflow: A system for large-scale machine learning, arXiv."},{"key":"ref_44","unstructured":"Haykin, S.S. (2009). Neural Networks and Learning Machines, Pearson."},{"key":"ref_45","unstructured":"TensorFlow (2017, June 25). Tf.Contrib.Learn Quickstart. Available online: https:\/\/www.tensorflow.org\/get_started\/tflearn."},{"key":"ref_46","unstructured":"Hong Kong Tourism Board PartnerNet (2017, July 05). Visitor Arrival Statistics. Available online: https:\/\/securepartnernet.hktb.com\/china\/sc\/research_statistics\/research_publications\/index.html?id=3631."},{"key":"ref_47","unstructured":"Sina Tech (2017, March 12). Gaode Unites Sina to Launch the Social Network Map Platform. Available online: http:\/\/tech.sina.cn\/?sa=t84v44d21223704&pos=108&vt=4."},{"key":"ref_48","unstructured":"(2017, March 12). Gaode Open Platform, Web Services API and Related Downloads. Available online: http:\/\/lbs.amap.com\/api\/webservice\/download\/."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kuah-Pearce, K.E. (2008). Chinese Women and the Cyberspace, Amsterdam University Press.","DOI":"10.1515\/9789048501403"},{"key":"ref_50","unstructured":"Wikipedia (2017, May 05). Shenzhen-Hong Kong Cross-Boundary Students. Available online: https:\/\/en.wikipedia.org\/wiki\/Shenzhen%E2%80%93Hong_Kong_cross-boundary_students."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Manning, C.D., Raghavan, P., and Sch\u00fctze, H. (2008). Introduction to Information Retrieval, Cambridge University Press.","DOI":"10.1017\/CBO9780511809071"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Li, J., Cheng, J.-H., Shi, J.-Y., and Huang, F. (2012). Brief introduction of back propagation (bp) neural network algorithm and its improvement. Advances in Computer Science and Information Engineering, Springer.","DOI":"10.1007\/978-3-642-30223-7_87"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"233","DOI":"10.3109\/10826089809115863","article-title":"Back propagation neural networks","volume":"33","author":"Buscema","year":"1998","journal-title":"Subst. Misuse"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/0893-6080(88)90469-8","article-title":"Theory of the backpropagation neural network","volume":"1","year":"1988","journal-title":"Neural Netw."},{"key":"ref_55","unstructured":"Powell, M.J.D. (1987). Radial Basis Functions for Multivariable Interpolation: A Review, Clarendon Press."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_57","first-page":"1","article-title":"Support vector machine","volume":"1","author":"Ukil","year":"2002","journal-title":"Comput. Sci."},{"key":"ref_58","first-page":"101","article-title":"The levenberg-marquardt algorithm","volume":"11","author":"Ranganathan","year":"2004","journal-title":"Tutor. Algorithm"},{"key":"ref_59","unstructured":"Hong Kong Tourism Board PartnerNet (2017, July 15). Visitor Profile Report\u20142014. Available online: http:\/\/securepartnernet.hktb.com\/filemanager\/intranet\/ir\/ResearchStatistics\/paper\/Visitor-Pro\/Profile2014\/visitor_profile_2014_0.pdf."},{"key":"ref_60","first-page":"37","article-title":"Efficient, high-quality force-directed graph drawing","volume":"10","author":"Hu","year":"2005","journal-title":"Math. J."},{"key":"ref_61","first-page":"401","article-title":"Event detection in twitter","volume":"11","author":"Weng","year":"2011","journal-title":"ICWSM"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"4038","DOI":"10.1002\/cpe.3780","article-title":"Building knowledge base of urban emergency events based on crowdsourcing of social media","volume":"28","author":"Xu","year":"2016","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_63","unstructured":"Weibo (2017, July 27). Weibo Users Development Report\u20142014. 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