Analysis of the performance and robustness of methods to detect base locations of individuals with geo-tagged social media data

ABSTRACT

Various methods have been proposed to detect the base locations of individuals, with their geo-tagged social media data. However, a common challenge relating to base-location detection methods (BDMs) is that, the rare availability of ground-truth data impedes the method assessment of accuracy and robustness, thus undermining research validity and reliability. To address this challenge, we collect users’ information from unstructured online content, and evaluate both the performance and robustness of BDMs. The evaluation consists of two tasks: the detection of base locations and also the differentiation between local residents and tourists. The results show BDMs can achieve high accuracies in base-location detection but tend to overestimate the number of tourists. Evaluation conducted in this study, also shows that BDMs’ accuracy is subject to the intensity of user’s activities and number of countries visited by the user but are insensitive to user’s gender. Temporally, BDMs perform better during weekends and summertime than during other periods, but the best performances appear with datasets that cover the whole time periods (whole day, week, and year). To the best of knowledge, this study is the first work to evaluate the performance and robustness of BDMs at individual level.

KEYWORDS:

• Base-location detection
• geo-tagged social media data
• smart tourism

Acknowledgments

We would like to express our gratitude to editor Prof. May Yuan and Dr Michela Bertolotto and the anonymous reviewers, for their insightful comments and feedback, especially during all the chaos caused by Covid-19.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data and codes that support the findings of this study are available with a DOI at https://doi.org/10.6084/m9.figshare.12362567

Additional information

Funding

This work was supported by The Hong Kong Polytechnic University [1-ZVN6]; the Ministry of Science and Technology of the People’s Republic of China [2019YFB2103102].

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 volunteered geographic information, spatial big data and geospatial artificial intelligence.

Anshu Zhang

Anshu Zhang is a Research Assistant Professor 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.

Yepeng Yao

Yepeng Yao is a PhD candidate in the Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University. His research interests are urban morphology, 3D GIS and geovisualization.

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.

Xiao Huang

Xiao Huang received his BS degree from Wuhan University in 2015. He obtained his Master's degree from Georgia Institution of Technology in Geographic Information Science and Technology in 2016 and PhD in Geography from the University of South Carolina in 2020. He is currently an assistant professor in the Department of Geosciences at the University of Arkansas. His research interests cover GeoAI, deep learning, and human-environmental interactions.

Xiaoqi Shen

Xiaoqi Shen is a PhD candidate in School of Environment Science and Spatial Informatics at ChinaUniversity of Mining and Technology. His research interest includes human mobility analysis, geographical data mining, and geospatial artificial intelligence.