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Bristol region; this distinction was based on percentage of commuters traveling

daily towards the core urban area. The core urban area is identified differently for

each of the case cities, on the basis of national classification methods while the first

and second rings (suburb and hinterland) consist of zones where more or less than

40% of commuters' trips are directed towards the core area.

The second zoning systems, illustrated for all six cities in Fig.
2.3
, consist of

sub-zones representing the smallest statistical unit for which consistent and compa-

rable data are available. In the UK context, these sub-zones are based on wards and

parishes and aggregations thereof.

The generalized shift-share method computes for each small sub-zone the growth

rate of each variable (population, employment and commuting distances). In a second

step the deviation of each small sub-zone's growth rate from the regional growth rate

is also computed. In the SCATTER project the shift-share method is used to identify

the role played by the two growth components, the overall growth rate, l
()

a

and a

t

time depending factor g ()

i
representing zonal deviations from the average growth

path, in the actual growth of each small zone.

The analysis is carried out in three steps:

a

1. Estimation of the average growth rate as

a

1

Xt t

(

+

D

)

l

a

( )

t

=

ln

(2.1)

D

t Xt

a

()

where

XtXt D
represent the total volume of the variable over the

entire urban region at times
t
and
+

a

( ),

a

(

+

)

t

D

t

respectively.

2. Estimation of the zonal deviations of the average growth path as:

a

1

Xt t

(

+

D

)

(2.2)

a

i

a

g

()

t

=

ln

−

l

()

t

i

D

t
Xt

a

i

()

3. The estimated parameters

i
g
may exhibit some noisy structure,

due to possible data uncertainties. Therefore appropriate data ilters are applied

to the mean growth rates and the deviations of the growth rates in order to smooth

out such disturbances.

a

and

a

l

()

t

()

Tables
2.3
and
2.4
show the values of the parameters for population and employ-

ment growth rate in the six case cities. The values are smoothed using a Gaussian

moving average procedure.

The quantitative analysis has also applied more traditional spatial statistical

measures, such as the indicators of local and global spatial autocorrelation. For a

value of a particular variable (e.g., population density), indicators of spatial autocor-

relation make it possible to estimate whether a zone
i
is surrounded by zones exhibit-

ing very similar or very dissimilar values, or is surrounded by a heterogeneous,

patchy pattern of similar and dissimilar values. To identify local spatiotemporal pat-

tern of variables the correlations between nearby values of the statistics are derived

and verified by simulations. There are many possibilities to test for the existence of

such pattern. One of the most popular is Moran's I statistic, which is used to test the