A scalable framework for spatiotemporal analysis of location-based social media data

• Published in: Computers, environment and urban systems environment and urban systems, 2015-05, Vol.51, p.70-82
• Main Authors: Cao, Guofeng, Wang, Shaowen, Hwang, Myunghwa, Padmanabhan, Anand, Zhang, Zhenhua, Soltani, Kiumars
• Format: Article
• Language: English
• Subjects: Big data; CyberGIS; Data cube; OLAP; Social media
• ISSN: 0198-9715; 1873-7587
• DOI: 10.1016/j.compenvurbsys.2015.01.002

•We present a scalable computational framework to harness massive, un-structured location-based social media (LBSM) data for systematic and efficient spatiotemporal analysis.•Within this framework, the concept of space–time trajectories is applied to represent activity profiles of social media users.•A hierarchical spatiotemporal data cube model is developed to represent the collective dynamics of social media users across multiple spatiotemporal scales.•The presented framework is implemented based upon a public data stream of Twitter feeds posted in the continent of North America.•Within in this framework, an interactive flow mapping interface is developed to allow interactive visual exploration of movement dynamics in massive LBSM at multiple scales. In the past several years, social media (e.g., Twitter and Facebook) has experienced a spectacular rise in popularity and has become a ubiquitous location for discourse, content sharing and social networking. With the widespread adoption of mobile devices and location-based services, social media typically allows users to share the whereabouts of daily activities (e.g., check-ins and taking photos), thus strengthening the role of social media as a proxy for understanding human behaviors and complex social dynamics in geographic spaces. Unlike conventional spatiotemporal data, this new modality of data is dynamic, massive, and typically represented in a stream of unstructured media (e.g., texts and photos), which pose fundamental representation, modeling and computational challenges to conventional spatiotemporal analysis and geographic information science. In this paper, we describe a scalable computational framework to harness massive location-based social media data for efficient and systematic spatiotemporal data analysis. Within this framework, the concept of space–time trajectories (or paths) is applied to represent activity profiles of social media users. A hierarchical spatiotemporal data model, namely a spatiotemporal data cube model, is developed based on collections of space–time trajectories to represent the collective dynamics of social media users across aggregation boundaries at multiple spatiotemporal scales. The framework is implemented based upon a public data stream of Twitter feeds posted on the continent of North America. To demonstrate the advantages and performance of this framework, an interactive flow mapping interface (including both single-source and multiple-source flow mapping) is