Augmenting Intuitive Navigation at Local Scale

or long, internet search has been performed almost exclusively at a distance—from one's own desktop computer. When the search contains geographical information, it has been justifiable to provide the entire map of the environment. Such a map typically allows the user to integrate the represented information into his/her geographical survey knowledge. Most recently however, the increasing number of searches is performed from mobile devices, out in the world, and considers places located at closer proximity, within the spatial context of the searcher. And yet, in mobile-based navigation, the only substantial progress in relation to desktop-based (or traditional paper-based) representations is the presence of a " You-Are-Here " indicator. The traditional approach of providing the map of the entire relevant area in order to aid its integration into the searcher's existing survey knowledge thus presents an unmerited cognitive processing challenge. In addition, the sole action of looking at a map (not to mention its processing) seems superfluous. We argue that human-computer interaction for spatial searches should be (a) based on local, egocentric cues, intuitively preferred in human spatial behavior instead of global, allocentric, representations of the entire environment, and (b) confirming to the ideas of HINTeractions [1] and calm computing [2], where the output communicated by the system and hence intrusion is minimized [3]. The local context of a large proportion of spatial searches results in a typical user-case scenario in which the searched location must be navigated to by foot (" nearest pub, " " open shop, " " bus stop "). This task can be performed without referring to high-level representations of the environment. Knowing the user's position in the environment and his or hers orientation, it is possible to make use of the visual cues typically used in everyday navigation. These include, but are not limited to street width, or line-of-sight lengths of alternative spatial choices. Instead of suggesting the user to " turn right after 150 meters, " pre-computing the map (i.e., using the geoinformatics approach) might allow the system to identify " that smaller street on the right, " or " the main road in front. " This natural language approach, even if consisting of fuzzy definitions, provides the opportunity for much more natural interaction [4–5]. While this method bears the risk of generating mistakes, it does not affect the precision of GPS tracking. As a result, despite the fact the user might take a wrong …