Even though places are immensely useful referents for geocoding and interlinking other information, e.g. in terms of gazetteers, place related information often still needs to be generated, linked and curated in a manual and time consuming fashion. This problem has become increasingly pressing in the age of Big Data, where the generation, provenance, curation and quality of place related data becomes uncontrollable and does not scale with the growth of other data in need of georeferencing. This workshop aims at computational models of place which can be used to automate the process of place information inference. Computational models of place have to deal with a variety of conceptual as well as computational challenges. From a conceptual viewpoint, places are not point-like, they are vaguely determined by physical, cultural, and experiential processes, in particular by human activities. Furthermore, the location occupied by places, such as city centers, can change over time. This renders simplistic place models, which are mainly based on static name-coordinate pairs, insufficient. Gazetteer relations should be temporally indexed, but how should this be done? How to address changes of place identity, such as disappearances and merging or splits? How can we compute a snapshot representing the region occupied by a place at a certain time? From a computational viewpoint, the challenge lies in finding tractable procedures to infer places as well as their relations to other kinds of information. We are especially interested in research that demonstrates this in the context of Linked Data. One way to create computational place models is by extracting knowledge from user-generated data and streams, such as tags, texts, activity streams, trajectories and POI mapping. Recent research focused on the discovery of places and user activities by mining (semantic) trajectories. Research also investigated how to derive the region occupied by a place, or to extract place-related activities from free text. An alternative option is to compute places based on social tagging. Here, one challenge lies in identifying social relations which allow associating tags of the same place, as well as distinguishing tags of different places. Another approach consists in identifying observation procedures and observable proxies in an environment. Such proxies may be perception-action cycles that can be traced by observed or recorded actions. One promising direction of further research are affordance-based approaches towards place. Finally, one more option is to use robots, sensors and actuators, as well as spatial reasoning in order to explore place extents. Novel research is also needed in order to design place-based GIS operations. Considering place as a fundamental element for organizing space may require a very different set of spatial operators. Places are hierarchically organized objects. Nearness may be computed as the situation-dependent reachability between two places. The challenge lies in transforming traditional GIS operations, such as spatial queries (buffers, region-based queries), in order to deal with place.