Conceptual Aspects: How Landmarks Can Be Described in Data Models - Landmarks: GIScience for Intelligent Services - page 125

Fig. 4.1 The Voronoi diagram of the set of Melbourne's inner city train stations Southern Cross,
Flagstaff, Melbourne Central, Parliament, and Flinders Street. Map copyright OpenStreetMap
contributors, used under CC BY-SA 2.0
most central station in the public transport network is Flinders Street Station, so
in the process of establishing a next level of the salience hierarchy the only local
maximum, Flinders Street Station, gets lifted. Thus the final salience hierarchy
consists of a root node (Flinders Street Station), and a second level, containing
all stations. This hierarchy reflects that in some communication contexts “the train
station” refers to Flinders Street Station, city-wide. In other communication contexts
the contrast set may consist of all five stations, and a finer distinction has to be made.
These salience hierarchies are also suited to map hierarchical cognitive reasoning
across hierarchies of spatial granularity (Sect. 4.2.5.2 ) . In order to illustrate this, let
us consider a hierarchical verbal place description. “I am at Melbourne Central,
Swanston Street exit” is a description zooming in, using landmarks from different
levels of spatial granularity as relata. In Voronoi Diagrams this zooming behavior
can be reflected. The first relatum, Melbourne Central may be understood in
the context of a train traveller, i.e., applying the contrast set of Fig. 4.1 . The
corresponding Voronoi cell was perceived by the speaker to be too coarse for this
particular communication purpose, and a refinement was sought. Figure 4.2 shows
a local refinement of the original Voronoi diagram built from the salient elements of
Melbourne Central—the new contrast set. Without a commitment to a geometrically
exact position the speaker conveys a location closer to the Swanston Street exit than
to any other salient element of the train station.
Hence, spatial entities bearing landmarkness information and being explicitly or
implicitly organized in hierarchical data structures, can reflect hierarchical cognitive
reasoning.