A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media

ABSTRACT

Location prediction based on spatio-temporal footprints in social media is instrumental to various applications, such as travel behavior studies, crowd detection, traffic control, and location-based service recommendation. In this study, we propose a model that uses geotags of social media to predict the potential area containing users’ next locations. In the model, we utilize HiSpatialCluster algorithm to identify clustering areas (CAs) from check-in points. CA is the basic spatial unit for predicting the potential area containing users’ next locations. Then, we use the LINE (Large-scale Information Network Embedding) to obtain the representation vector of each CA. Finally, we apply BiLSTM-CNN (Bidirectional Long Short-Term Memory-Convolutional Neural Network) for location prediction. The results show that the proposed ensemble model outperforms the single LSTM or CNN model. In the case study that identifies 100 CAs out of Weibo check-ins collected in Wuhan, China, the Top-5 predicted areas containing next locations amount to an 80% accuracy. The high accuracy is of great value for recommendation and prediction on areal unit.

KEYWORDS:

• Location prediction
• social media
• spatial cluster
• graph embedding
• bilstm-CNN

Data and code availability statement

The location prediction model’s source code and data have been released on Github (https://github.com/s3pku/Next_areal_location_predict).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by grants from the National Key Research and Development Program of China (2017YFE0196100), and the National Natural Science Foundation of China (41771425,41830645,41625003). We also appreciate the detailed comments from the Editor and the anonymous reviewers.

Notes on contributors

Yi Bao

Yi Bao received the B.S. degree from China University of Geosciences, Wuhan in 2018. He is currently a PhD candidate in GIScience with the Institute of Remote Sensing and Geographical Information Systems, Peking University. His main research interests include spatial data mining and location-based services.

Zhou Huang

Zhou Huang received the B.Sc. degree in GIS and the Ph.D. degree in cartography and GIS from Peking University, China, in 2004 and 2009, respectively. He is currently an associate professor of GIScience with the Institute of Remote Sensing and Geographical Information Systems, Peking University. His current research interests include big geo-data, high-performance geocomputation, distributed geographic information processing, spatial data mining, and spatial database.

Linna Li

Linna Li is an associate professor in Geography at California State University, Long Beach. She got her B.S. degree in Geographic Information Science from Peking University in China in 2004, her M.S. degree in Geography from the University of South Carolina in 2006, and her Ph.D. in Geography from the University of California, Santa Barbara in 2010. Her teaching and research interests include geographic information science theories and applications in public health, transportation, and urban development. Her current research focuses on big geodata analytics and spatio-temporal data mining using social media.

Yaoli Wang

Yaoli Wang is a postdoc in Geographic Information Science at Peking University. Her research specializes in inferring human behaviors from social and spatial network analysis, and on-demand urban transportation systems.

Yu Liu

Yu Liu is currently the Boya Professor of GIScience at the Institute of Remote Sensing and Geographical Information Systems, Peking University. He received his B.S., M.S., and Ph.D. degrees from Peking University in 1994, 1997, and 2003, respectively. His research interest mainly concentrates in humanities and social science based on big geo-data.