Recommending attractive thematic regions by semantic community detection with multi-sourced VGI data

ABSTRACT

Attractive regions can be detected and recommended by investigating users’ online footprints. However, social media data suffers from short noisy text and lack of a-priori knowledge, impeding the usefulness of traditional semantic modelling methods. Another challenge is the need for an effective strategy for the selection/recommendation of candidate regions. To address these challenges, we propose a comprehensive workflow which combines semantic and location information of social media data to recommend thematic urban regions to users with specific interests. This workflow is novel in: (1) developing a data-driven geographic topic modelling method which utilizes the co-occurrence patterns of self-explanatory semantic information to detect semantic communities; (2) proposing a new recommendation strategy with the consideration of region’s spatial scale. The workflow was implemented using a real-world dataset and evaluation conducted at three different levels: semantic representativeness, topic identification and recommendation desirability. The evaluation showed that the semantic communities detected were internally consistent and externally differentiable and that the recommended regions had a high degree of desirability. The work has demonstrated the effectiveness of self-explanatory semantic information for geographic topic modelling and highlighted the importance of including region spatial scale into the model for an effective region recommending strategy.

KEYWORDS:

• Location recommendation
• geographic topic modelling
• community detection
• multi-sourced VGI

Acknowledgments

We would like to thank the editor Dr Shawn Laffan and the anonymous reviewers for their insightful comments and substantial help on improving this article. We also thank Jittin Chaitamart for providing the valuable sample data and Dr Zhang Xiaokang for helpful suggestions for improvement.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by Ministry of Science and Technology of the People's Republic of China (2017YFB0503604) and the Hong Kong Polytechnic University [1-ZVF2, 1-ZEAB, 4-ZZFZ].

Notes on contributors

Zhewei Liu

Zhewei Liu is a PHD candidate in the Department of Land Surveying and Geo-informatics at the Hong Kong Polytechnic University, with a bachelor degree in remote sensing from Wuhan University. His research interest includes location-based social networks, volunteered geographic information.

Xiaolin Zhou

Xiaolin Zhou is a PhD candidate in the Department of Land Surveying and Geo-informatics at the Hong Kong Polytechnic University. Her research interests include GIScience, location-based social networks, and commercial site selection.

Wenzhong Shi

Wenzhong Shi is the Head and Chair Professor of Geographical Information Science and Remote Sensing in the Department of Land Surveying and Geo-informatics at the Hong Kong Polytechnic University. His research interests include GIScience and remote sensing, focusing on uncertainties and quality control of spatial data, satellite images and LiDAR data, 3D modeling, and human dynamics.

Anshu Zhang

Anshu Zhang is a Postdoctoral Fellow in the Department of Land Surveying and Geo-informatics at the Hong Kong Polytechnic University. Her research interests include spatial data mining, human dynamics, and machine learning for human geography.