Conceptual Aspects: How Landmarks Can Be Described in Data Models - Landmarks: GIScience for Intelligent Services - page 129

Fig. 4.3 The top levels of a landmark taxonomy along routes (from [ 24 ] )

Therefore, Hansen et al. [ 24 ] suggest an extension of XLS to capture the
cognitively meaningful semantics of landmarks. Based on a taxonomy shown in
Fig. 4.3 they develop a hierarchic abstract data type for landmarks. Many of
these distinctions will become clearer in Chap. 5 , where we will be dealing with
selecting landmarks along routes. But the taxonomy shows already that these
characteristics of landmarks are route-dependent and thus not suited to be captured
in the conceptual model above. They can only be determined in an ad-hoc manner
for a particular conversation or route request.
Later this work was extended to enable spatial chunking, the cognitively based
grouping of consecutive decision points to achieve cognitively ergonomic route
descriptions [ 34 ] . Route descriptions are called here cognitively ergonomic if they
adhere to the cognitive principles we have collected so far. In short, they are route
instructions that consider the prior knowledge of the traveller, the structure of
the environment, and also the human cognitive capacities. These criteria provide
the scope for flexibly adapting the granularity of route instructions, and for
chunking. Klippel et al. call their extension an Urban Knowledge Data Structure
(UKDS), adding chunking types to the representation of elementary instructions
with landmarks.

4.4 Summary

In this section we have presented a formal model suitable for developing entities in
spatial databases that can represent some landmarkness of the represented objects
in certain contexts. We have also discussed variations of this model, including
structured context models, vectors characterizing an objects' landmarkness in
multiple dimensions, and the landmarkness of types.
What we have not yet discussed is the computational semantics of landmarkness
values. Formally, they represent fuzzy membership values to a vaguely defined