Image Processing Reference
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clustering patterns (Residential 1, 2, 4 and 5). As expected, given the compact
architecture of terraced (row) housing in the UK, the most clustered neighborhoods
are the linear patterns of Residential-1 and Residential-4. However, although their
nearest-neighbor values may be very similar, it is plainly apparent that Residential-1
exhibits a far denser concentration of postal points. The same situation applies between
Residential-2 (an inner-city local government-owned estate) and Residential-5 (a
more affluent 1980s peripheral estate). Conversely, Residential-3 and Residential-4
have very similar densities yet somewhat dissimilar nearest-neighbor values. The
remaining pattern, Residential-6, is a highly affluent low-density area of Bristol with
large dwellings, and is the only one demonstrating a uniform tendency. Overall, what
is clear is that if nearest-neighbor and postal point densities are taken together they
are valuable measures for identifying and characterizing different residential types.
The same can also be applied to commercial postal points. Again, a strong clustering
pattern is indicative of linear developments in Commercial-1, but the city center
(Commercial-2) and peripheral estates (Commercial-4) exhibit explicit signs of
uniformity. As with the residential values if both nearest-neighbor and density
values are taken in combination then unequivocal differences can be revealed. Both
Commercial-1 and Commercial-3 (commercial development within residential
areas), and Commercial-2 and Commercial-4 have similar nearest-neighbor values
but contrasting densities.
The conventional nearest-neighbor statistic is effective for measuring clustering
patterns but it lacks the capacity to identify spatial arrangement. A variant of two-
dimensional nearest-neighbor analysis is the linear readjustment ( LR ) devised by
Pinder and Witherick ( 1975 ). Instead of measuring all observed distances between
neighboring points, D OBS is determined from a linear sequence ( L ) of consecutive
points ( LN ) in all directions (essentially linear subsets), while,
 
=  
What constitutes a linear arrangement is of course dependent on scale. Any group-
ing of buildings regardless of configuration can be considered linear if the scale is
fine enough. As a consequence, a minimum of 20 buildings were identified manu-
ally but considered representative of a linear formation of both residential and com-
mercial postal points. Although at present this is a subjective process, the intention
is to automate future selections and calculate indices that are more representative
of each sample type.
Values for both R and LR are documented in Table 8.2 . On the whole, they are
very similar for both residential and commercial postal point distributions.
However, LR values are usually lower for linear patterns of points (Residential-1
through 5, and Commercial-1, 2 and 4) and higher for inherently random or uni-
form distributions (Residential-6 and Commercial-3). Another difficulty with the
linear nearest neighbor is the low numbers of points that are used for calculations.
This tends to reduce the significance of LR to levels of randomness demonstrated
by Residential-3 and Commercial-2 through 4.
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