STLP-GSM: a method to predict future locations of individuals based on geotagged social media data. Issue 12 (2nd December 2019)

• Record Type: Journal Article
• Authors: Chen, Pengfei; Shi, Wenzhong; Zhou, Xiaolin; Liu, Zhewei; Fu, Xuandi
• Is Part Of: International journal of geographical information science. Volume 33:Issue 12(2019)
• Page Start: 2337
• Page End: 2362
• Publication Date: 2019-12-02
• Subjects: Spatio-temporal location -- prediction uncertainty -- daily trajectory -- online footprint -- social network; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2019.1630630
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: An increasing number of social media users are becoming used to disseminate activities through geotagged posts. The massive available geotagged posts enable collections of users' footprints over time and offer effective opportunities for mobility prediction. Using geotagged posts for spatio-temporal prediction of future location, however, is challenging. Previous studies either focus on next-place prediction or rely on dense data sources such as GPS data. Introduced in this article is a novel method for future location prediction of individuals based on geotagged social media data. This method employs the hierarchical density-based clustering algorithm with adaptive parameter selection to identify the regions frequently visited by a social media user. A multi-feature weighted Bayesian model is then developed to forecast users' spatio-temporal locations by combining multiple factors affecting human mobility patterns. Further, an updating strategy is designed to efficiently adjust, over time, the proposed model to the dynamics in users' mobility patterns. Based on two real-life datasets, the proposed approach outperforms a state-of-the-art method in prediction accuracy by up to 5.34% and 3.30%. Tests show prediction reliability is high with quality predictions, but low in the identification of erroneous locations.