Computational Aspects: How Landmarks Can Be Observed, Stored, and Analysed - Landmarks: GIScience for Intelligent Services - page 153

Tabl e 5. 6 Landmark scoring system based on categories, after [ 9 ]

Frequency of typical landmarks in category

Typical landmark All Most Many Some Few

Ideal 8 4 2 1 0

Highly suitable 4 4 2 1 0

Suitable 2 2 2 1 0

Somewhat suitable 1 1 1 1 0

Never suitable 0 0 0 0 0

cussed in the next chapter. Landmark integration is based on work by Williams [ 50 ] .
The approach is not well documented, but seems to use the location of a landmark
and the travel direction as parameters to determine whether a landmark is relevant
for describing a given route.
As a final thought in this section, let us step away from looking at each individual
geographic object in determining its relevance for the given context, and consider a
simplified, more general approach. Recently, Duckham et al. [ 9 ] explored categories
of features instead of individual properties to determine suitability as a landmark.
They established a rank order of different categories, such as restaurants, gas
stations, or schools, which is based on nine different aspects (e.g., physical size,
proximity to road, visibility, or permanence). For each aspect default assumptions
are made for every category. The rank order was established by a panel of experts,
who also decided on how many instances of a given category are likely to be typical.
The resulting scoring system for each aspect is shown in Table 5.6 .
The overall suitability (or relevance) score is defined as the linear sum of all
nine suitability aspects. This sum is normalized in the range Œ0; 1, with 1 being
most suitable and 0 being least suitable (Eq. 5.3 ) . Normalizing the weighting makes
the score comparable between different settings and also independent from the
absolute numerical values used in the expert rating. The score value score f .c/ is
the suitability score from Table 5.6 for a landmark category c 2 C with respect to
the suitability aspect f . F is the set of all nine suitability aspects.

P f2F

score f .c/ min. f P f2F score f .c 0 / j c 0 2 C/ g /

max. f P f2F

w .c/ D (5.3)

score f .c 0 / j c 0 2 C g /

Using categories instead of individuals has been implemented in the WhereIs
route planne r 4 , a commercial route planning website in Australia owned by the same
company that produces the yellow pages. Hence, these yellow pages are used as
source for establishing suitable categories, and as data source for the locations of
landmark candidates. The route planning algorithm calculates a shortest path using
some standard shortest path algorithm (Dijkstra or A ) and then finds all landmark

4 http://www.whereis.com.au , last visited 8/1/2014