Ruas, 1998b), multi-variate clustering (Ormbsy and
Mackanness, 1999).
For this purpose, we decided to use buffering technique and
voronoi diagrams (polygons) Using the semi-size of
minimum distance between two building symbols (10 m -
1:50K) according to visual graphic resolution (cartographic
minimum sizes) as buffer size, we can find buildings in
conflict for target scale. As can be guessed, combined buffers
(building clusters) are created after individual building
generalization otherwise no conflict will occur. Later,
vertices of blocks and buildings are derived and using them,
voronoi polygons are created and partitioned according to
blocks, and combined according to the clusters (Figure 1).
TRE C TEE al
D MERO AT
Kd ERV CHI
ATR GS (Ban
[177 NE a
XS M^ i
OU SC T Tcl
z
aT i!
DC FT [ENSE
E
gc
sd x SN s TA
Se OA
Figure 1. Combined voronoi polygons of building clusters
(pink: settlement areas, light green: combined voronoi
polygons, gray: empty areas)
3.2 Road Generalization
Road generalization mainly consists of three steps:
simplification, smoothing and selection. While simplifying
the roads in order to maintain geometric accuracy, parameters
must be selected carefully. For this purpose, Douglas-
Peucker algorithm is used with 0.2 mm (10 m — 1:50K)
tolerance value (band with) according to visual graphic
resolution. Thus, road geometry will be within a 10 meter-
tolerance band, namely accurate within the scale limits.
Besides, angle tolerance and vertex separation is controlled.
Later, smoothing is applied but this can create a deviation
more than the tolerance. This can sometimes be useful to
prevent road symbols from self- or inter-overlapping
however this will not work in every case. Sinuosity of a line,
the ratio of the distance between first and last points of the
line to the line length, can be used here to decide smoothing
parameter. But this will also not give good results every time.
Segmentation strategy and more advanced measures and
algorithms are needed here. Besides, local enlargement or
caricature (Plazanet et al, 1998) for emphasizing shape
characteristics can be necessary. These are beyond scope of
this paper. Consequently, some small corrections are made
interactively.
Generalization, i.e. selection of subset, of road networks 1s
another important issue. Due to the increase in symbol sizes
of roads and buildings, namely in density, while scale
decreasing, it will not possible to show every road at target
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
190
scale. So, we have to generalize road networks. According to
Thomson and Richardson (1999) “good continuation"
perceptual grouping principle can serve as the basis for
analysing a road network into a set of linear elements, i.e.
‘strokes’. Further analysis allows the strokes to be ordered, to
reflect their relative importance in the network. The deletion
of the elements according to this sequence provides a simple
and effective method of generalizing (attenuating) the
network. Jiang and Claramunt (2002) proposes a novel
generalization model which retains the central structure of a
street network, it relies on a structural representation of a
street network using graph principles where vertices
represent named streets and links represent street
intersections. In our study, only computer-assisted techniques
are used.
To tackle with this problem, four criteria are used in
removing the road segments interactively: road type,
connectivity to main roads, continuity with same orientation
and the area of blocks created using the buffers of
surrounding roads at their symbol sizes. Important roads are
always retained. At this scale range (from 1:25K to 1:50K),
inner-city roads usually needs generalizing because of their
density. By means of a simple code, we select a few blocks
interactively until their total area is about 1 ha (= 10 000 sq
m) regarding first three criteria. When the size criteria are
met, blocks are combined and the road segments intersecting
these new blocks are removed. After finishing this operation,
roads are displaced if they have conflicts with cach other.
Finally settlement blocks surrounded by roads are created.
3.3 Building and Settlement Area Generalization
Steps for building and settlement area generalization are
given below:
- Select complex shaped buildings and enlarge 50%. The
criteria are corner number >= 6, compactness > 1.05,
rectangularity < 0.75, convexity < 0.9 and 625 <= area
<= 2 000 sq m. Only these buildings are enlarged before
simplification to increase the possibility of preserve
their shape characteristics.
- Square (the edges of) buildings if rectangularity <>1.
- Simplify buildings if area > 416 sq m.
- . Collapse and symbolise buildings (as minimum sized
square polygon — 625 sq m at 1:50K) if area <= 416 sq
m, enlarge 50% if 416 <= area <= 2 000 sq m and
granularity >= 10 m and shape is not complex (see first
step).
- Enlarge or diminish the size of building if the ratio of its
first and last areas is different from 1.5 and not square
(compactness <> 1.27) (and granularity >= 22.5 m — in
case of diminishing).
- Change the elongation of buildings by preserving their
area if rectangularity = | and compactness > 1.27.
- Create single and combined buffers (clusters) of
buildings with 0.5*minimum separation value. The
buildings in clusters are in conflict at target scale.
- Create settlement blocks among surrounded roads (see
previous section).
. Vol XXXV, Part B4. Istanbul 2004
Interne
