Conceptual Aspects: How Landmarks Can Be Described in Data Models - Landmarks: GIScience for Intelligent Services - page 116

With the help of a third data type the model will be enabled to refer to an object
in a flexible, context-dependent manner:
type Ref = [Char]
- a context-specific reference
With these three types we can introduce a complex data type describing the
context-dependent landmarkness property of an entity in a spatial database:
data Ls = Ls cid :: Cid, lns :: Lns, ref :: Ref
- a context-dependent landmarkness property: a triplet
- any entity can have multiple of these properties
Note that any entity in a spatial database can have multiple of these properties,
but only one for each Cid . This means entities have lists of Ls with Cid as unique
key:
data Entity = Entity { (anything) , lnss :: [Ls]}
Entities can have any structure. Typically it will be a type from some database
taxonomy, an identifier, an official name, a geometric description as a point,
polyline, polygon, volume or a set of these, and further thematic descriptors. For
readability we have generalized this structure in the code and concentrated on the
relevant bit. All what we did was adding one property, a list of landmarknesses in
different contexts. Here is an example of what we can represent now:
Entity ... [Ls Pedestrian 0.7 "St Francis",
Ls Motorist 0.9 "the church"]
This database entity is a good landmark for motorists, for whom “the church”
should be an appropriate reference in verbal communication. It will also be a good
landmark, despite its spatial extension, for pedestrians, for whom it is easy to
identify the building in situ as “St Francis”. The database has not yet stored any
information about the suitedness to refer to this building in communication with
Tom personally, but of course Tom can be identified as a pedestrian. Also the model
has not yet been equipped with an ability to store information about the suitedness