Until recently crowdsourcing has been primarily conceived as an online activity to harness resources for problem solving. However, the emergence of Opportunistic Networking (ON) has opened up crowdsourcing to the spatial domain. In this article, we bring the ON model for potential crowdsourcing in the smart city environment. We introduce cognitive features of the ON that allow users’ mobile devices to become aware of the surrounding physical environment. Specifically, we exploit cognitive psychology studies on dynamic memory structures and cognitive heuristics—mental models that describe how the human brain handles decision making among complex and real-time stimuli. Combined with ON, these cognitive features allow devices to act as proxies in their users’ cyberworlds and exchange knowledge to deliver awareness of places in an urban environment. This is done through tags associated with locations. They represent features that are perceived by humans about a place. We consider the extent to which this knowledge becomes available to participants using interactions with locations and other nodes. This is assessed taking into account a wide range of cognitive parameters. Outcomes are important because this functionality could support a new type of recommendation system that is independent of the traditional forms of networking.

Gazetteers play an essential role in GIS in translating between place name and coordinate-based descriptions of location. The proliferation of location-aware social media applications has led to new sources of gazetteer data, many of which are crowd-sourced. They complement the conventional authoritative resources that are typically linked to published map products. We illustrate the variation in performance of several, mostly social media based, gazetteer resources for a reverse-geocoding photo captioning task and demonstrate the advantage of a meta-gazetteer service that integrates multiple individual gazetteer resources and employs several toponym ranking methods.

Social media sources such as Flickr and Twitter continuously generate large amounts of textual information (tags on Flickr and short messages on Twitter). This textual information is increasingly linked to geographical coordinates, which makes it possible to learn how people refer to places by identifying correlations between the occurrence of terms and the locations of the corresponding social media objects. Recent work has focused on how this potentially rich source of geographic information can be used to estimate geographic coordinates for previously unseen Flickr photos or Twitter messages. In this article, we extend this work by analysing to what extent probabilistic language models trained on Flickr and Twitter can be used to assign coordinates to Wikipedia articles. Our results show that exploiting these language models substantially outperforms both (i) classical gazetteer-based methods (in particular, using Yahoo! Placemaker and Geonames) and (ii) language modelling approaches trained on Wikipedia alone. This supports the hypothesis that social media are important sources of geographic information, which are valuable beyond the scope of individual applications.