Mapping the beach beneath the street:digital cartography for the playable city

Maps are an important component within many of the playful and gameful experiences designed to turn cities into a playable infrastructures. They take advantage of the fact that the technology used for obtaining accurate spatial information, such as GPS receivers and magnetometers (digital compasses), are now so wide-spread that they are considered as ‘standard’ sensors on mobile phones, which are themselves ubiquitous. Interactive digital maps, therefore, are are widely used by the general public for a variety of purposes. However, despite the rich design history of cartography digital maps typically exhibit a dominant aesthetic that has been de-signed to serve the usability and utility requirements of turn-by-turn urban navigation, which is itself driven by the proliferation of in-car and personal navigation services. The navigation aesthetic is now widespread across almost all spatial applications, even where a be-spoke cartographic product would be better suited. In this chapter we seek to challenge this by exploring novel neo-cartographic ap-proaches to making maps for use within playful and gameful experi-ences designed for the cities. We will examine the potential of de-sign approaches that can producte not only more aesthetically pleasing maps, but also offer the potential for influencing user be-haviour, which can be used to promote emotional engagement and exploration in playable city experiences

Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty

IA 2-3-0

Interactive visual cluster detection in large geospatial datasets based on dynamic density volume visualization

The emerging spatial big data (e.g. detailed spatial trajectories, geo-referenced social media data) provide tremendous opportunities for GIScientists and geographers. However, their large volume also poses challenges to existing spatial data analytical techniques (including visual analytical techniques). This article presents an interactive visual approach to detect clusters from those emerging data sets based on dynamic density volume visualization in a three-dimensional space (two spatial dimensions plus a third temporal or thematic dimension of interest). Cluster can be visually discovered through dynamic adjustment of density to colour/opacity mapping and extracted through flexible selection tools. The approach was tested on a large simulated data-set and a spatial trajectory data-set. The results show that the approach can overcome the visual clotting problem in traditional visualization tools caused by large data volume and facilitate the involvement of domain knowledge in analysis. It can effectively support visual cluster detection in the emerging large geospatial data sets