“What is this” and “where am I” are two of the most common questions that arise when people travel abroad. Recently, landmark image retrieval has shown great promise for the addressed problem, where most common approaches are either visual-based or location-based. However, for city-view image retrieval, there could be a number of buildings in a close proximity. Moreover, it is common that photos are taken indoors. The former may degrade the performance of location-based approaches, while the latter may degrade the performance of both the visual-based and location-based approaches. To remedy the deficiencies, this paper further considers the use of check-in data of photos and presents a simple approach that unifies visual features, geo-tags, and check-in data for city-view image retrieval. Furthermore, sparse coding is applied for a high-performance and memory-efficient retrieval implementation, where a graph-based dictionary learning approach is proposed. Experimental results show the effectiveness of the proposed approaches.