Geotagging has become popular for many multimedia applications. In this chapter, we present an integrated and intuitive system for location-driven tag suggestion, in the form of tag-clouds, for geotagged photos. Potential tags from multiple sources are extracted and weighted. Sources include points of interest (POI) tags from a public Geographic Names Information System (GNIS) database, community tags from Flickr® pictures, and personal tags shared through users’ own, family, and friends’ photo collections. To increase the effectiveness of GNIS POI tags, bags of place-name tags are first retrieved, clustered, and then re-ranked using a combined tf-idf and spatial distance criteria. The community tags from photos taken in the vicinity of the input geotagged photo are ranked according to distance and visual similarity to the input photo. Personal tags from other personally related photos inherently carry a significant weight due more to their high relevance than to both the generic place-name tags and community tags, and are ranked by weights that decay over time and distance differences. Finally, a rich set of the most relevant location-driven tags is presented to the user in the form of individual tag clouds under the three mentioned source categories. The tag clouds act as intuitive suggestions for tagging an input image. We also discuss quantitative and qualitative findings from a user study that we conducted. Evaluation has revealed the respective benefits of the three categories toward the effectiveness of the integrated tag suggestion system.