Augmented reality (AR) overlays real-world views or scenes with virtual, computer-generated objects that appear to visually coexist in the same space. Location-based social networks (LBSNs) are platforms for individuals to be connected through the interdependency derived from their physical locations and their location-tagged social media content. Current research and development in both areas focuses on integrating mobile-based AR and LBSNs. Several applications (e.g., Sekai Camera and Wallame) have been developed and commercialized successfully. However, little research has been done on the potential impacts and successful evaluation methods of AR-integrated LBSNs in the GIScience field. To close this gap, the article outlines the impacts and benefits of AR-integrated LBSNs and highlights the importance of LBSNs in GIScience research. Based on the status quo of AR-integrated LBSNs, this article discusses-from theoretical and application-oriented perspectives-how AR-integrated LBSNs could enrich the GIScience research agenda in three aspects: data conflation, platial GIS, and multimedia storytelling. The article concludes with guidelines on visualization, functionality, and ethics that aim to help users develop and evaluate AR-integrated LBSNs.
| I NTR OD U CTI ONAugmented reality (AR) overlays real-world views or scenes with virtual, computer-generated objects that appear to visually coexist in the same space. It is well established in multiple domains and has achieved consumer market status, mainly due to the prevalence of smartphones equipped with high computational processors, high-resolution displays, and multiple sensors. As a result, AR is integrated into everyday applications, including games, marketing strategies, navigation aids, home design software, personal assistance and general education applications. However, one important aspect has not been discussed sufficiently: the potential application of AR in location-based social networks (LBSNs). LBSN discussions started early in 2010 when developers tried to better understand how to connect user locations with user social networks. LBSNs are tightly coupled with location information acquired from multiple sources,