Transcript The population dynamics of England`s small towns, 1991-2006

The Population Dynamics of
England’s Small Towns, 1991-2006
Tony Champion
CURDS, Newcastle University
Paul Norman
School of Geography, University of Leeds
Acknowledgements
• Mike Coombes, Simon Raybould (Newcastle) for comments & data
• John Shepherd, Brian Linneker, Sam Waples (Birkbeck) for data & map
• Peter Bibby (Sheffield) for data
• ONS for census and population estimates data © Crown copyright
Paper presented at British Society for Population Studies Annual Conference 2008
Context
• The main foci of academic, policy and media
attention on the settlement system is on ‘cities’
(the major ones) and ‘countryside’ (rural
districts)
• Much less attention has been given to smaller
cities and towns, least to Small Towns
• So: How far is this lack of interest justified by
their occupying a small and static position in the
settlement system?
• What makes them tick in terms of demographic
dynamics and their drivers?
Approach
• Exploratory work looking at the range of recent
experience in population growth and the
patterning of this variation
• Small Towns (STs) are defined on the Census
‘urban area’ basis, starting with all such areas
with a 2001 population of 1,500-40,000
• Their numbers of usual residents are estimated
for 1991, 2001 and 2006 on a consistent ‘midyear estimates’ basis
• Population change rates are then analysed for
types of STs and through statistical analysis of
their individual characteristics
Footnote: the Census definition of ‘urban areas’
• ‘an extent of at least 20 hectares and at least 1,500 residents at
the time of the 2001 Census’ (based on the Output Areas which
best fit to the boundary of the urban land)
• The starting point is the identification by OS of areas with land use
which is irreversibly urban in character. This comprises:
· permanent structures and the land on which they are situated,
including land enclosed by or closely associated with such
structures;
· transportation corridors such as roads, railways and canals which
have built up land on one or both sides, or which link built-up sites
which are less than 200 metres apart;
· transportation features such as airports and operational airfields,
railway yards, motorway service areas and car parks;
· mine buildings, excluding mineral workings and quarries; and
· any area completely surrounded by built-up sites
• Areas such as playing fields and golf courses are excluded unless
completely surrounded by built-up sites.
Estimating the population of the ‘urban areas’ (i)
• Ward data 1991-2001
– 1991 & 2001 age-sex estimates by CAS wards
– Estimated during UPTAP project including revisions to
original Estimating with Confidence populations (Norman
et al., 2008)
– 1990s births & deaths allocated to CAS wards
• Ward data 2002-2006
– 2006 age-sex estimates for CAS wards achieved by
constraining 2005 estimates to 2006 district data (to
be recalculated using now-released 2006 ward data)
– 2000s births & deaths allocated to CAS wards
Estimating the population of the ‘urban areas’ (ii)
• Ward data apportioned to urban areas using weights
derived from addresses per postcode
(Norman et al., 2003, Simpson, 2002)
Source geography: wards
Target geography: urban areas
Steps in the analysis
• Q1: What share do the STs make up of national
population and population change 1991-2006?
• Q2: How far does their population growth vary
by ST type based on population size, region,
DEFRA district type, socio-demographic cluster?
• Q3: What characteristics are most strongly
correlated with population growth rate?
• Q4a: How much of the variance in growth rate
be ‘explained’ by regression-based models?
• Q4b: Which seem to be the key ‘drivers’ of ST
population change differentials?
Q1: What is the Small Towns share of national
population and population change 1991-2006?
Urban area
size
(2001)
1991
population
share
2006
population
share
1991-2006
pop change
share
% pop
change rate
for period
London
15.5
16.1
25.5
+9.64
Other 1m+
12.1
11.5
1.7
+0.82
200k-500k
20.7
19.8
5.3
+1.51
40k-200k
19.5
19.5
20.6
+6.21
21.8
22.2
30.0
+8.09
10.5
10.9
17.0
+9.53
100.0
100.0
100.0
+5.88
1.5k-40k
non-UA
England &
Wales
A1: punching over their weight but by not as much
as London and the non-UA parts of England
Q2: Focusing on England’s 1,628 Small Towns,
how far does population change vary by:
• Population size within the 1.5k-40k range?
• Government Office Region excluding
London’s 3 STs?
• DEFRA 6-fold urban/rural district typology
from Major Urban to Rural-80?
• Socio-demographic type based on cluster
analysis of (mainly 2001 Census) variables
for the 1,587 STs without a substantial
institutional presence (e.g. military, prisons,
universities, boarding schools)?
Population size?
Population change rate, by size group of urban area, England,
1991-2001 and 2001-2006, per 1000 per year
per 1000 per year
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
25k-40k
2001-2006
1991-2001
15k-25k
5k-15k
1.5k-5k
9.0
Government Office Region?
Population change rate for England's small towns, 1991-2001 and 2001-2006,
by region, per 1000 per year
per 1000 per year
-2.0
North East
North West
Yorks/Humber
East Midlands
West Midlands
East
South East
South West
all 1625
0.0
2.0
4.0
6.0
8.0
2001-2006
1991-2001
10.0
DEFRA district type?
Population change rate for England's small towns, 1991-2001 and 2001-2006,
by DEFRA district type, per 1000 per year
per 1000 per year
0.0
Major Urban
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
2001-2006
1991-2001
Large Urban
Other Urban
Significant Rural
Rural-50
Rural-80
9.0
Socio-demographic cluster?
Population change rate for 1587 of England's small towns,
1991-2001 and 2001-2006, by 8 clusters, per 1000 per year
per 1000 per year
-2.0
0.0
2.0
4.0
6.0
8.0
Pensioners
Skilled service profs
Middle aged
High access, single, flats
Coastal, remote, hotel
Agric, low skill
Deprived
Young, high emprat
all 1587
N=1587, i.e. excluding 41 STs with large institutional presence
10.0
2001-2006
1991-2001
12.0
8 clusters of
1587 Small Towns
1 – pensioner
2 – skilled service
professional
3 – middle aged
4 – high access etc
5 – coastal, remote
6 – agric, low skill
7 – deprived
8 – high employment
rate, young
Italics = below-average growth
in both periods (3, 4 & 7)
Cartography by Brian Linneker
Q3: What characteristics are most strongly
correlated with population growth rate?
To be explained:
Distribution of 1587 Small Towns by 1991-2006 population growth rate
(per 1000 per year), using 2 classifications
600
Main pattern
With finer categories for lower range
500
400
300
200
100
.0
0
99
023
3
20
.0
019
.
99
Annual average change per 1000 people in 1991
15
.0
014
.
.9
9
-9
10
.0
5.
00
.9
9
-4
.4
9
2.
50
-2
1
0.
00
-0
.0
.5
-2
to
.5
-2
to
-1
3.
27
.0
0
99
023
3
20
.0
019
.
99
15
.0
014
.
.9
9
-9
10
.0
.9
9
5.
00
-4
0.
00
de
cl
in
e
0
Exploring the role of 100+ continuous variables
relating to ST characteristics, including:
• Demographic, e.g. population size, age, gender, marital
status, ethnicity, illness
• Household, e.g. average size, household composition,
car availability
• Housing, e.g. dwelling type, tenure, overoccupancy,
facilities, vacancy rate, second homes, mobile homes
• Social/cultural, e.g. NS-SeC, qualifications, IMD overall
and domain scores, religion
• Labour market, e.g. economic activity, student,
employment rate, unemployment, industrial structure,
distance to work, commuting mode
• Contextual, e.g. job accessibility, access to Town
Centres, number of service outlets per 100 people,
population density
The strongest positive and negative correlations
Most positive correlations
Most negative correlations
0.279 Aged 25-44
-0.303 Family with non-dependent child
0.249 Couple
-0.279 Providing unpaid care
0.247 Remarried
-0.272 Aged 45-64
0.235 Aged 0-14
-0.204 Households with no car
0.220 Employment rate
-0.189 Long term limiting illness
0.214 Couple with no kids
-0.185 IMD employment domain
0.190 Detached dwelling
-0.176 Mean age
0.187 Households with 2+ cars
-0.173 Households of 1 person of pension age
0.182 Traveling 20km+ to work
-0.161 Widowed
0.164 Cars per household
-0.158 Aged 15-24
0.142 Mean distance to work
-0.154 IMD overall domain
0.140 IMD geography domain
-0.153 Unemployment rate
0.128 No religion
-0.144 IMD income domain
0.114 Rural-80 LA (ordinal)
-0.139 Job accessibility 1991
0.109 Owner occupier
-0.138 Public transport to work
Q4: How much of the variance in growth rate can
be ‘explained’ using regression-based models?
Which seem to be the key ‘drivers’?
• Multiple regression analysis
• Using a reduced set of variables (excluding those
correlated at r=>0.60, those completing the 100% circle)
but including some extra variables (e.g. region, in Green
Belt)
• Initial models:
- all 1,587 STs (i.e. excluding the 41 ‘institutional’ ones)
- just the 310 STs with 10k residents or more in 2001
- the 1,277 STs with less than 10k residents in 2001
- separate models for England and 4 broad regions using
a fixed set of 15 selected variables
Q4a: How much of the variance in growth rate?
• Stepwise regression of all 1,587 STs:
R2=0.330, with 19 variables
• Stepwise regression of 310 STs with 10k+ residents:
R2=0.665, with 17 variables
• Stepwise regression of 1,277 STs with <10k
residents: R2=0.300, with 15 variables
• Separate models for England and 4 broad regions
using the same 15 selected variables:
- England: R2=0.265 (11 variables significant @ 5%)
- North: R2=0.415 (6 variables significant @ 5%)
- Midlands: R2=0.295 (7 variables significant @ 5%)
- Southwest: R2=0.294 (5 variables significant @ 5%)
- Southeast: R2=0.215 (5 variables significant @ 5%)
Modelling 1991-2006 change rate by broad region
Variable name
North
Mids
SW
SE
All
(Constant)
-69.8
-113.8
-88.7
-119.6
-93.2
%pop who are aged 25-44
1.371
2.139
1.876
2.350
1.912
%pop who are aged 75+
1.059
1.262
0.830
1.506
1.278
% households that are Couple/no-child
0.485
-0.122
-0.356
0.311
0.139
% classified persons Lower Manag/Prof
0.214
0.848
0.304
0.139
0.303
% 16-74 who have no qualifications
-0.023
0.478
0.297
0.534
0.174
% active women who work part-time
0.127
-0.075
0.258
0.206
0.090
% employed who work in farming etc
0.687
0.205
0.385
-0.758
0.177
% employed who work in trade etc
0.337
0.370
0.537
0.178
0.460
% employed who travel 20km+ to work
-0.064
0.190
0.046
0.265
0.094
Job accessibility
-0.039
-0.205
-0.179
-0.108
-0.184
Number of services per 100 people
0.073
0.570
0.495
-0.156
0.285
% housing that is detached
0.111
0.305
0.214
0.313
0.233
% household spaces in caravan etc
0.091
0.427
0.941
0.358
0.476
In Area of Outstanding Natural Beauty
1.254
-1.647
-1.443
-1.248
-1.704
-2.646
-3.440
2.026
-0.361
-1.579
0.415
0.295
0.294
0.215
0.265
In Green Belt zone
Adjusted R2
Q4b: Which seem to be the key ‘drivers’?
•
•
•
•
•
Age structure: % 25-44, but also % 75+
Detached housing, but also caravans/mobile homes
Managerial and Professional, but also No qualifications
% commuting 20km+, and also Low access to jobs
Work in Trade (wholesale, retail, motors, etc), and also in
Hotels etc and Primary sector
• Located outside AONBs and Green Belts, also in SW
• Number of service outlets per 100 residents (weak)
Verdict:
• All these are operating independently, suggesting
several growth components for any individual place
• Results suggest diversity in drivers between places, too,
as also reflected in the analysis by socio-demog cluster
Main findings
• Small Towns (urban areas with 1.5k-40k in 2001) make
up a substantial and growing share of population, with
growth accelerating most rapidly in the 1.5k-5k range
• There is great diversity not just in individual ST growth
rates (especially among the smaller ones) but also in
terms of the different types of places growing fastest
(e.g. young/high-employment-rate, agriculture/low-skill,
coastal/remote/hotel types)
• Not surprisingly, therefore, there is no simple story
behind variations in growth rate across England’s Small
Towns, though the regression model for the largest 310
places reached 66% ‘explanation’ based on 17 out of the
71 (cross-sectional) variables in the reduced dataset
Next steps: your comments/advice please!
• Replace the 2006 populations with the final estimates
• Recalculate the population growth rates on basis of
average population or compound rates, so as to reduce
extreme growth values
• Possibly weight the correlation and regression analyses
by some function of ST size, so as to reduce the effect of
the large number of small places
• Analyse separately the natural-change and migrationresidual components of change (nb: natural change is
highly correlated with mean age, so focus on migration)
• Analyse separately the two periods 1991-2001 and
2001-2006, so as to detect any alteration in ‘drivers’
• Develop more sophisticated variables representing
geographical context; also, consider including measures
of change as ‘real’ drivers in a fully dynamic model
References on estimating the population of
urban areas
Norman P, Rees P, Boyle P (2003) Achieving data compatibility over
space and time: creating consistent geographical zones.
International Journal of Population Geography. 9(5): 365-386
Norman P, Simpson L & Sabater A (2008) ‘Estimating with Confidence’
and hindsight: new UK small area population estimates for 1991.
Population, Space & Place (in press, due out 11/09/08)
Simpson L (2002) Geography conversion tables: a framework for
conversion of data between geographical units. International Journal
of Population Geography 8: 69-82