The International Archives of the Photogramme try, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008
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matching of end point need infer form the node matching. The
strategy of matching is shown in figure 4.
For 1:N route matching, the buffers around every edge need to
be created firstly. Secondly according to the matching for end
points of an edge, the edges corresponding are joined within
given buffer, and then the candidate route matching is found.
Lastly, the 1:1 route match will be identified by comparing
measures including length, Huausdorff distance and semantic
attributes of the routes. Accomplished 1:1 route matching, the
M:N route matching is processed in succession. The unmatched
edges on the data at small scale are to be joined into a route at
every node based on the principle of perceptual organization,
which describes two edges with the same direction at the
intersection. So M:N route matching will transform into the
matching of 1 :N. The method of 1 :N route matching mentioned
above can be carried out again.
Figure 4. Strategy of matches of objects
Accomplished route matching, the unmatched edges on the data
at large scale are filtered. The matching between node and edge
can be easily inferred according to its definition mentioned
above.
4. SELECTIVE OMISSION BASED ON MESH AND
MESH DENSITY
For the selective omission of roads, various techniques already
exist. For example, the graph theory principles (Mackaness &
Beard, 1993), space syntax (Mackaness, 1995; Jiang &
Claramunt, 2004) and self-organizing maps (Jiang & Harrie,
2004) are employed to support generalization of road network.
Unfortunately, all these methods failed to consider the
distribution density of roads which is an important constraint.
Hu (2007) reveals that selective omission of roads should be
retained the density difference besides topological, geometric
and semantic properties of the road network. Two distinctive
types of density variations are considered, with one across
different regions within the same map (e.g. urban and rural
areas) and the other across different map scales of the same
region. The density conventionally defined as the ratio of total
length of roads in a given region to the area of the region.
However, this measure is not sufficient for the purpose of map
generalization because the local variations of road density over
the space are not indicated. So a novel approach of selective
omission for roads is used in this study, which is based on mesh
and mesh density in a network.
4.1 Concepts of mesh and mesh density
A mesh is defined as a naturally closed region that does not
contain any other region in a road network. Naturally, one
could consider using area of an arbitrary mesh in the new
measure for road density. It is called mesh density. In fact,
mesh density is a special case of the road density expressed
conventionally and can be described as follows:
D = P/A
(1)
Where P is the perimeter of a mesh, A is the area of the mesh,
and D represents the mesh density. Form this equation we can
find that the road stubbles within the mesh boundary are all
ignored. The road segments in a network can be classified into
two types, i.e. the segments that constitute mesh boundaries and
the stubbles lying within the boundaries. Road stubbles can be
relatively easily handled according to their geometric and
semantic properties. The elimination of any road stubble will
not influence the connectivity of network. Moreover, if a mesh
needs to be simplified, the road stubbles within the mesh will be
likely omitted prior to boundary segments. For these reasons,
this simplification is adopted.
The meshes can be classified based on the types of roads on its
boundary. A mesh may be bordered by roads of different
classes such as main streets and secondary streets. Then meshes
are classified based on the bounding road segments. In this
method, the class of a mesh is assigned with bounding segment
of the lowest ranking. For road generalization based on the
mesh density, a mesh can be regarded as the basic unit of
selection. With a given set of roads, the mesh density can be
mapped out. The mesh density is then used as a constraint to
determine which meshes should be treated. Usually, a threshold
is given beforehand or computed from the given set of data.
Moreover different thresholds may be applied to different
classes of meshes in order to preserve the density variations
across different regions of a road network. If the density of a
mesh goes beyond the threshold, then there is a need to
eliminate one or more road segments on its boundary.
4.2 Determination of thresholds for mesh density
There are two different ways to determine a density threshold
for each class of meshes. They are respectively based on
theoretical analysis, map specifications and empirical study.
For maps at a certain scale, there must be a minimum size for a
mesh unit below which the mesh cannot be perceived anymore.
Correspondingly, there must be a threshold of mesh density
beyond which one or more segments on the boundary of the
mesh must be eliminated so that two or more adjoining meshes
could be merged to form a larger mesh. Such a threshold is
regarded as the pennissible largest density (PLD), which
implies the longest possible length of roads in the smallest
visible area. The minimum mesh size could be set to the SVO
(Smallest Visible Object) in “natural principle” proposed by Li
and Openshaw (1992, 1993). SVO could be a small circle, a
raster cell or any other geometric entity. So the PLD i.e. density
threshold can be expressed in terms of the ground size and the
principles of the SVO as follows:
D,h = 4/(5,-i„ (l-SJS,)) (2)