2. It is important to know London’s
‘true’ boundaries
Need to compare London’s performance with other
cities on a consistent basis
Need to analyse the forces at work in London’s
economy – this requires a definition based on an
economic, not just an administrative, rationale.
The ‘administrative’ boundary does not cease to be
important – this is where ‘London’ policies operate.
GLA boundary also happens to correspond well to
its ‘economic core’, because of the Green Belt
However it does not include a wider area which
interacts with London on a daily basis, principally
through commuting.
3. A case in point: London and Paris
GLA London
– Population (MYE 2004) 7,420,000
– Area 1,584 Km2
Little Paris (TBA)
Isle de France Paris
– Population 11,362,000
– Area 12,012 Km2
Hence our current activities: a London-Paris
comparison, and a sensitivity testing
– Method will be extended to other cities
– We are working with BAK on sensitivity testing
4. Benchmarking – not just a London
issue
Governments need common standards
– to compare the performance of cities
– to allocate and implement policy resources.
Urban regions are relevant spatial units for the
application of significant policy functions.
An Urban region is an ‘economic unit’
We have to be able to measure and compare cities
on the basis of their economic function
Comparability is paramount
It is a distinct issue from ‘how should cities be
governed’ although it can inform the governance
agenda
5. Why a standard is needed
Estimates of 10year productivity
growth rates from
23 cities and 3
suppliers
3.5
3.0
Suppliers 2 and 3
2.5
2.0
1.5
Estimates from the
different suppliers
would be the same if
they lay on this line
1.0
0.5
Supplier 2
Supplier 3
0.0
-0.5
-0.5
0.5
1.5
Supplier 1
2.5
3.5
6. What kind of standard?
City definition cannot take political or administrative
boundaries as a starting point. It should arise from
socio-economic study of what a city is and does.
We need comparisons across the world and at least
with ‘world cities’ hence US, Europe and ideally
Japan
There are broad continental variations – US cities
evolved historically differently from European cities
leading to different patterns of settlement. This has
to be recognised.
For the GLA, the requirement for a standard
dominates over the requirement of scope for local
variation.
7. Four main existing approaches
US metro system
+ long period of development
+ existing data for comparisons
- different historical course of evolution
GEMACA
+ Sound and robust methodology
+ Already tested and demonstrated
– Not much extended outside Europe
Urban Audit
+ official buy-in and support
- uses administrative unit as core
- permits a wide degree of local variation
- not really a standard
- TWA approach
8. What is in common and what
differs?
TWA is a distinctive approach. We will discuss
separately
Common feature of US, UA, and GEMACA is a ‘corehinterland’ or ‘Functional Urban Region’ (FUR)
Core may be either as an area of high population
density or of high job density (or otherwise eg building
density)
Commuting field: people that regularly communicate
with, or travel to, the core, for economic purposes
principally work.
Both thresholds and criteria vary.
– US system has ‘core’ defined by population, with a relatively
low density (1000/500 per square mile = 4/ha), but relatively
high commuting threshold (25 percent but includes outcommuting)
– GEMACA has ‘core’ defined by employment with 7/ha =
9. Issues
Core defined by population, work density, or other
criterion such as morphology
What are the economic purposes of travel and
communication?
What size units are appropriate to define the core
What is the threshold density for the core
What threshold densities for in- and outcommuting?
What size units to define the hinterland
City-Regions: what criteria lead to the exclusion or
separation of distinct conglomerations which fall
statistically within a metro area eg Reading, Harlow?
10. Some initial results
FUR size highly sensitive to the size of core ‘building
block’
FUR size relatively insensitive to the choice between
population or work density
Core size varies with core threshold densities, but FUR
size varies by small magnitude over large spectrum of
densities
We have not yet investigated the sensitivity of FUR size
to commuting densities or to the inclusion of outcommuting
FUR size sensitive (for London) to whether the hinterland
is composed of NUTS3 or NUTS4 building blocks.
This is a significant problem since statutory Eurostat data
is available only at NUTS3 level, which are relatively large
35. London FUR – Jobs
Thousands of workforce jobs in 2004
487
2,294
477
Inner London
498
Commuter Belt
2,884
446
Outer London
533
217
85 85 56
1,659
Thurrock
Buckinghamshire CC
Hertfordshire
Outer London
Medway Towns
Berkshire
Essex
Inner London
Luton
Surrey
Kent CC
36. Paris FUR – Jobs
Thousands of workforce jobs in 2004
850
428
526
1,656
Paris
530
4,313
498
434
152
201
274
422
Seine-et-Marne
Yvelines
Essonne
Hauts-de-Seine
Seine-Saint-Denis
Val-de-Marne
Val-d'Oise
Oise
Eure
Eure-et-Loir
Paris
37. Some summary indicators
Workforce
Population Employment
2003 (000s 2003 (000s of GVA 2003
of resident
workforce
(€billion
population)
jobs)
current)
Inner London
GLA
Surrounds
FUR
2,892
7,371
6,617
13,988
2,485
4,431
3,358
7,789
160
260
171
431
Paris
Surrounds
FUR
2,166
9,872
12,038
1,656
3,961
5,616
141
277
418
38. Sensitivities and data summary
Employment Density Threshold Level
1000
LAU2 units in total FUR
1,786
Resident population of total FUR
13,310,717
Workplace population of total FUR
6,653,364
Geographic area (sq mi)
5,230
LAU1 (NUTS4) units enclosing FUR
83
Resident population of LAU1 units enclosing FUR 12,645,988
Workplace population of LAU1 units enclosing FUR
Geographic area (sq mi)
4,578
Number of NUTS3 units enclosing FUR
14
Resident population of NUTS3 units enclosing FUR 13,922,024
Workplace population of NUTS3 units enclosing FUR
Geographic area (sq mi)
5,855
1500
1813
2000
1,736
1,676
1,685
13,017,914 12,766,609 12,729,043
6,495,638 6,388,281 6,349,001
4,913
4,757
4,716
85
83
82
12,868,188 12,660,293 12,454,272
Lowest/
Highest
2500 Density
1,613
90%
12,407,213
93%
6,197,473
93%
4,355
83%
80
96%
12,255,906
97%
4,263
4,103
4,019
3,732
14
14
13
12
13,922,024 13,922,024 13,737,653 12,407,935
5,855
5,855
5,838
4,470
82%
86%
89%
76%