small town england: population change among small to medium sized urban areas, 1971–81

Small Town England: Population Change Among

Small to Medium Sized Urban Areas, 1971-81

JOHNSHEPHERD*~~~PETER CoNGD0N-t

*Department of Geography, Birkbeck College, University of London, 7-1.5 Gresse Street, London WlP IPA. U.K.

tLondon Research Centre, Parliament House, 81 Black Prince Road, London SE1 7SZ, U.K.

PERGAMON PRESS OXFORD. NEWYORK 3 BEIJING. FRANKFURT SAOPAULO + SYDNEY. TOKYO. TORONTO

Progress in Planning, Vol. 33, pp. l-1 1 I, 1990. Printed in Great Britain. All rights reserved.

Contents

Abstract

1. A Study of Small to Medium Sized Urban Areas 1. I. Origins and Aims of the Research 1.2. The Research: Process and Structure 1.3. The Report

2. An Assessment of Definitions and Data 2.1. Introduction 2.2. Approaches to the Definition of Urbanised Areas 2.3. The Need for a Definition of Urban Areas 2.4. The 1981 Definition of Urban Areas 2.5. Small and Medium Sized Urban Areas 2.6. The Importance of Area Base 2.7. Statistical Assessment of Urban Area Indices 2.8. Summary Notes: Chapter 2

0305-9006/90 $O.OO+SO

0 1989 Pergamon Press plc

3. Characteristics and Significance of SAMS Urban Areas 3.1. Introduction 3.2. The Significance of SAMS Urban Areas 3.3. The Significance of SAMS Urban Areas: Population and

Employment Growth 3.3. I. Population 3.3.2. Employment

3.4. SAMS Urban Areas: Selected Census Profiles 3.4.1. Population density 3.4.2. Demographic indicators (I): Persons aged 15 and under 3.4.3. Demographic indicators (2): Persons aged 65 and over 3.4.4. Demographic indicators (3): Average household size 3.4.5. Housing tenure 3.4.6. Employment composition and unemployment 3.4.7. Unemployment

3

7

9 9

10 11

13 13 14 15 16 17 19 23 24 25

26 26 26

30 30 33 34 35 35 36 36 37 38 38

4 Progress in Planning

3.4.8. Social and migration groups 39 3.4.9. Car ownership and commuting mode 39

3.5. Summary 40 Notes: Chapter 3 40

4. Patterns and Processes in SAMS Urban Areas Growth 41 4.1. Introduction 41 4.2. SAMS Urban Area Growth, the Main Trends 42

4.3. Counter-urbanisation: Decentralisation versus Deconcentration 43 4.4. A Functional Region Framework 44

4.5. SAMS Urban Growth in Functional Regions 45

4.6. SAMS Urban Area Growth, Policy Status and Policy Areas 52

4.7. Policy Status 54

4.7.1. New and Expanded Towns 54

4.8. Policy Areas 56

4.8. I. Regional development 56

4.82. Green Belts 57

4.8.3. Motorway access 58

4.9. Combination of Policy Effects 59

4. IO. Summary 60

5. A Classification of SAMS Urban Areas 5.1. Introduction 5.2. Znput Variables 5.3. Cluster Analysis 5.4. The Results

5.4. I. Cluster 1 - Service based employment centres 5.4.2, Cluster 2 - High status commuter and service towns 5.4.3. Cluster 3 -Resort and retirement towns 5.4.4. Cluster 4 - Growth commuter areas with mixed employment 5.4.5. Cluster 5 - Rural andfree-standing towns 5.4.6. Cluster 6 - Milton Keynes 5.4.7. Cluster 7 - Growth through manufacturing prosperity 5.4.8. Cluster 8 - Manufacturing stability 5.4.9. Cluster 9 - Planned decentralisation 5.4. IO. Cluster 10 - Manufacturing towns with ethnic

communities 5.4.11. Cluster 11 - Manufacturing areas with high

unemployment 5.4.12. Cluster 12 - Mining towns

5.5. Summary

62 62 63 64 65 65 72 72 73 74 75 76 76 77

78

78 79 79

6. Modelling Growth and Change 80

6.1. Introduction 80 6.2. The Components of Change 80

Small Town England 5

82 84 87 89 90 90 90 91 91 91 92 92 92 93 93 95 95 95 96 97 97

4.3. 6.4.

6.5. 6.6.

6.7. 6.8.

Components of Change, 1971-1981 Positional Change, 1971-81 6.4.1. Changes in owner occupancy 6.4.2. Changes in municipal renting 6.4.3. Changes in car ownership (one or more) 6.4.4. Changes in two-car ownership 6.4.5. Changes in percentages of children 6.4.6. Changes in retirement age groups 6.4.7. Changes in unemployment 6.4.8. Changes in female economic activity 6.4.9. Overcrowded households 6.4.10. Amenity sharing Modelling Growth Among SAMS Urban Areas Growth Processes and Urban Area Types 6.6.1. Car ownership and commuting 6.6.2. Housing tenure 6.6.3. Employment growth 6.6.4. Employment structure 6.6.5. Policy effects Multivariate Control in Small Town Growth Summary

7. Conclusion 98 7.1. Policy Areas and Policy Status 100 7.2. Technical Issues 101 Notes: Chapter 7 102

Appendix 1. The Definition of Urban Areas Appendix 2. Indices, Normality and Transformation Appendix 3. A Model Of Change Among SAMS Urban Areas Appendix 4. Regression Modelling Of Urban Areas Growth

Bibliography

103 104 106 108

109

Abstract

This research has two aims. First it is an evaluation of a new and valuable set of census data for urban Britain. Secondly, it is a study of recent urbanisation processes in England. The data concerned are for census enumeration districts and tracts/parishes which, according to well-defined and consistently applied criteria, are ‘irreversibly urban’ in character. Over 2,000 such urban areas were defined for Britain in 1981.

The research describes the origins of OPCYDOE urban areas and compares them with previous attempts to describe urban settlements on the basis of land use and discusses the value of treating them as a complement to definitions of urbanism based on functionality. For those who will use the new urban areas data for research and planning, it makes a detailed assessment of census statistics derived from the enumeration district (1981) and change-file (1971-81) constitutions of urban areas.

As an investigation into urbanisation processes, this study is a contribution to the debate on ‘counter-urbanisation’ in Britain. The focus is on that part of the urban hierarchy which, in the recent past, has been the main destination for population moving out of the larger cities: urban areas with between 5,000 to 100,000 population. In 1981 no less than half (47%) of the population of England lived in these small to medium sized (SAMS) urban areas and between 1971 to 1981 their population increased by over 1.2 millions.

CHAPTER 1

A Study of Small to Medium Sized Urban Areas

1.1. ORIGINS AND AIMS OF THE RESEARCH

In 1984 the Department of the Environment (DOE) commissioned Halcrow Fox and Associates in collaboration with the Department of Geography at Birkbeck College to carry out a programme of research into demographic and associated changes in small and medium sized urban areas in England in the period 1971 to 1981. The urban areas used in the study were defined, in a joint exercise carried out by the DOE, the Office of Population Censuses and Surveys (OPCS) and the Ordnance Survey (OS), on the basis of land-use which was ‘irreversibly urban in character’ at the time of the 1981 census (OPCS, 1984). Small and medium sized (SAMS) urban areas were selected by the DOE as those with populations of between 5,000 and 100,000 in 1981.

The main data sets for conducting this research were derived from the Census of Population. One set of data, that for 1981, consists of 100% and 10% ‘Small Area Statistics’ or ‘SAS’. The other is taken from the 1971-1981 ‘Change File’ and consists, for reasons of sampling validity, of the 1981 100% data and their 1971 equivalents. As described in Chapter 2, the relationship that the census reporting units (enumeration districts and census tracts/parishes) bear to the urban area definition is slightly different in each case.

Bearing in mind the wider policy interest of the DOE the aims of the research, as set out by the Department, were threefold:

(i) to assess and describe the main data sets provided by the DOE upon which the research would be based;

(ii) to identify separately those urban areas which, in terms of population and related phenomena had remained static, those which had grown and those which had declined between 1971 and 1981; and

(iii) to identify and investigate the factors causing or influencing these changes in structure.

More specifically, four sets of identifiable and measureable characteristics were seen as relevant to a description and understanding of recent changes in SAMS urban areas:

(i) the demographic structure of the population including age structure, total population change 1971-1981 and the components of change;

9


(ii) the economic characteristics of the population involving employment, unemployment and skill structure;

(iii) the policy characteristics of SAMS urban areas which denoted a context either of constraint (green belt, areas of outstanding natural beauty, etc.) or of growth (assisted areas, new and expanded town status. etc.) and;

(iv) location relative to various elements of infrastructure (larger settlements, the transport network, etc.) and in relation to other relevant areas (i.e., standard regions).

In addition, HFA/Birkbeck were asked by the DOE to identify and carry out a number of case studies of specific urban areas to aid in the evaluation of the aggregate (census) data and to investigate the nature and causes of change in typical or exceptional urban areas in more detail. The case studies are not included in this report. The project is innovatory in another respect. It is the first in Britain, for example, to make use of the ESRI ARC/INFO software to build and apply an Urban Areas Geographical Information System (Shepherd and Green, 1987), for practical research purposes.

In carrying out this research both Halcrow Fox Associates and Birkbeck College received valuable advice and help from a number of organisations and individuals. In particular we would like to express our gratitude to Mike Coombes and David Owen of the Centre for Urban and Regional Development Studies at the University of Newcastle upon Tyne who provided data and advice on the functional urban region scheme for England and also to officers of the Department of Environment and the Department of Transport in London who provided various boundary data in digital format. We also benefitted considerably from the direction and support given by the Advisory Group assembled by the DOE to guide the broad progress of the research. It is stressed, however, that the opinions expressed in this report are those of the authors alone and do not necessarily reflect the policies of any department of central government.

Finally, the present authors would also like to extend their thanks to Peter Daly of Halcrow Fox Associates who managed the project as a whole with efficiency, skill and tact and to the secretarial and production staff at HFA who turned such

difficult manuscripts into professional reports.

1.2. THE RESEARCH: PROCESS AND STRUCTURE

The research strategy adopted by HFA/Birkbeck reflected the tripartite nature of the study, first, as an evaluation of new and untried census data-sets based on urban areas, secondly, as an investigation into the nature of, and influences underlying, change among SAMS urban areas, and thirdly, as a set of case studies

related to the main themes of the work. The research programme was therefore broken down into five essentially self-explanatory stages: l data assessment; l proto-description and analysis; 0 data reduction and classification;


l case studies; and l modelling growth and change.

Taken in sequence these stages represent a fairly standard approach to the exploration and analysis of a large and previously unanalysed set of social data.

1.3. THE REPORT

The form of the present monograph largely follows this research strategy although it concentrates, necessarily, on the major trends and findings derived from the urban areas data. More detailed results, including statistical summaries and listing of indices for urban areas on all variables used in the study, are to be found in the various interim reports and their associated appendices lodged in the DOE library.

In Chapter 2 the study is introduced in more detail by describing, first, the antecedents and origins of the urban area definition, the nature of the definition itself and its advantages compared with previous similar approaches to urban definition. It then moves on to describe and assess the way in which 1981 census and 1971-81 ‘change-file’ data were allocated to urban areas, and to assess the data that have been used in the study. Also included is a consideration of the population size criterion for the definition of small and medium sized towns and of the nature of urban area subdivisions in relation to objectives of the project.

Chapter 3 then discusses the significance of SAMS urban areas in the national urban system in relation to their number, distribution and population and their recent overall performance in terms of population growth and growth of employed residents. The major part of this chapter, however, is given over to a statistical

analysis of selected census variable ‘profiles’ for SAMS urban areas. These profiles were valuable both as a means of assessing the outcome of the urban areas definition in terms of a range of census data (both cross-sectional and change variables), and in selecting groups of variables for subsequent application in more sophisticated analyses.

In Chapter 4 we do two things: set the recent high rate of population growth among SAMS urban areas as a whole in its national and regional context and examine this growth against the background of central and local government policies for encouraging or constraining growth at the regional and urban levels. As part of the first of these objectives we examine SAMS urban area growth within both a Standard Region and a Functional Urban Region framework so as to distinguish between growth in SAMS urban areas due to the well-established processes of metropolitan ‘decentralisation’ compared with more recent processes of ‘deconcentration’ in the urban system (Robert and Randolph, 1983).

Chapter 5 presents a description of the results of a classification of 952 SAMS urban areas using a representative sub-set of both cross-sectional (1981) and change (1971-81) data from the census. Compared with other studies of urban change in Britain, which have been based either on administrative, or on functional definitions of urbanism, this reveals for the first time the remarkable


variety of urban types and local/regional contexts in which recent growth has taken place.

In Chapter 6 we address two main questions: to what extent has change among SAMS urban areas in the period 1971-81 been a consequence of structural shifts in the urban system as a whole, as opposed to localised change, and how is urban rype (including the typology discussed in Chapter 5) related to the growth experienced by SAMS urban areas. A statistical model for decomposing the elements of change is a central feature of this chapter.

The final chapter, Chapter 7, then draws out the main conclusions of the study in relation both to some of the policy concerns of the DOE, which were the

main reasons for conducting the research, and discusses the possibilities for future research into the phenomenon of rapid change among SAMS urban areas in England.

CHAPTER 2

An Assessment of Definitions and Data

2.1. INTRODUCTION

A basic and long-standing problem for planners and others concerned with understanding the changing geographical distribution of the population and its policy implications has been that of devising an acceptable definition of urbanised areas (United Nations, 1960). One simple and obvious way to define towns has been in terms of administrative areas and this has indeed been the practice in various census reports down the decades, including those for 1981 (OPCS, 1982). Thus, in England and Wales it has been conventional to make a rough and ready division between urban and rural areas by treating administrative rural districts as ‘rural’ and other relevant administrative areas - county boroughs, London boroughs, municipal boroughs and urban districts - as ‘urban’.

There are, however, serious disadvantages in using administrative areas as a proxy for an urban area or town. In all likelihood, the urban area will have physically outgrown its administrative boundaries in one, many or perhaps all directions, giving rise to the phenomenon of underbounding of the actual urbanised area (Davis, 1959). Overspill of urban developments into adjoining rural districts was, for example, particularly apparent in England before the reform of local government in 1974 (Royal Commission on Local Government in England, 1969, pp. 11-38). Administrative boundaries can also ‘underbound’ ‘true’ urban areas in a number of other ways: the administrative area may be part of a larger continuously built-up zone, it may be subsidiary to, and contiguous with a larger town, or it may contain two or more distinct towns or urban centres (OPCS, 1982

P. 2). Conversely, administrative boundaries can overbound the built-up areas of one

or more towns. The 1974 local government reorganisation in England subsumed many urban administrative areas (including many UDs, MBs and CBS), within much larger county districts, thereby combining urbanised and non-urbanised land into a single administrative entity. As a result, post-1974 local authority boundaries no longer provide even a crude definition of towns or an approximate urban/rural division. OPCS therefore saw its 1981 Preliminary Report for Towns based on pre-1974 boundaries, as an interim approach to estimating the urban population until a more accurate and comprehensive exercise in urban area definition could be completed (OPCS, 1982, p. 2).

13


It is, however, in relation to the provision and calculation of population data and other indices that the more serious consequences of relying on administrative areas as a proxy for the urban/rural division make themselves felt. Hence the importance for planning purposes of obtaining an accurate and consistent definition of urban areas. Actual urban populations may, for example, be considerably understated for underbounded areas, whilst population density figures will be overstated for underbounded areas and understated for overbounded areas (OPCS, 1982, pp. 5-6). By extension, these errors may distort the relationship between, say, population change and population density, particularly if the extent of over or underbounding is in some way systematically related to the population size of urban areas.

2.2. APPROACHES TO THE DEFINITION OF URBANISED AREAS

Prior to the joint DOE/OPCS/OS exercise to define a new set of urban areas for England and Wales on which the present study is based, there had been two important attempts to delineate urbanised areas in Britain and to produce a range of useful data based upon such a delineation. In 1956 the General Register Office published its Report on Greater London and Five Other Conurbations which defined six metropolitan areas using a combination of three factors: the extent of the continuously built-up area, attachment of a local area to the centre for work, shopping, educational and other services and population density (General Register Office. 1956, p. xv). This definition was useful and, to some extent, path-breaking in its time, but by focusing solely on the major urban areas of Britain its value in comparative urban analysis was severely limited.

A more geographically comprehensive exercise in urban area definition was undertaken by the Regional Plans Directorate of DOE in the early 1970s using a ward/parish population density of 0.6 persons per acre gross as the primary criterion for distinguishing urbanised land. In addition a minimum population size limit of 2,000 for contiguous combinations of census wards or parishes meeting the density criterion was imposed.1

Applying these criteria resulted in the delineation of 1,333 physically separate areas in England and Wales (1,173 in England alone). As the authors of De Facto Urban Areas in England and Wales, 1966 were at pains to point out, however, this exercise did not result in a set of areas based on local urban development but could, depending on the precise configuration of wards and parishes and the incidence of various ‘urban attributes’, lead to some ‘overbounding’ of purely physically defined settlements (DOE, 1974, p. 7).

These examples of urban area definition demonstrate the two main kinds of criteria that are used in such exercises. On the one hand there are the physical criteria which focus on land-uses that are more or less obviously ‘urban’ in character. This type of definition usually rests on the notion of land occupied by permanent structures or completely or partially surrounded by permanent structures. It has the advantages of being simple and consistent in application, is


implementable from easily available and accurately mapped sources of data (i.e., Ordnance Survey maps), and gives a clear-cut distinction between urban and non-urban land.

On the other hand the functional approach to the definition of urbanised areas (represented in the GRO’s ‘conurbations’ by the ‘attachment’ of local areas to the conurbation centre for work, shopping, etc.), recognises the links that exist between a built-up ‘core’ and the less obviously urbanised tracts of land surrounding that core. Links between core and periphery are measured as indices of interaction based on journey to work, to shop and to obtain other services. This approach recognises that urbanisation is as much a social and cultural process as it is a physical phenomenon.

It is important to note, however, that neither the physical nor the functional approach to the identification of urban areas is completely devoid of ambiguity in definition or of problems in application. The physical approach turns on the notion of land use that is urban in character. But while this may be easily recognisable in the case of land occupied by permanent structures (although the word ‘permanent’ is itself open to a range of interpretations), other uses - parks, golf courses, airfields, etc. - even though they are contiguous with land completely built-over, may not be so clearly interpretable as ‘urban’. Furthermore, the addition of the further criterion that such open land should be ‘surrounded’ by built-up land also creates the need for some arbitrary definition of ‘surroundedness’.

Functional definitions of urbanised areas also require arbitrary definitions. These include the selection of the proxy measure for interaction between core and periphery, the setting of some level of the index of interaction and the determination of the core areas that are to be the foci of functional attraction. Furthermore, and unlike the physical definition, this approach is dependent on some underlying (and also arbitrary) definition of census or administrative areas (enumeration districts, wards, etc.) for the application of the necessary measures of population interaction. Finally, in certain circumstances, the implementation of both approaches involves arbitrary decisions in drawing boundaries because towns tend to merge physically and functionally with neighbouring towns and their hinterlands. However, whereas the definition based on built-up areas leaves a reasonably clear-cut ‘rural’ remainder, the functional approach divides rural areas between competing cores.

2.3. THE NEED FOR A DEFINITION OF URBAN AREAS

In recent years much time and attention has been given to devising systems of functional urban regions for Britain, which to a large extent has shifted attention away from some important applications of, and issues surrounding, the different but equally relevant physical notion of urbanism. As we have seen, the two approaches focus on quite different elements of the urban system and must, if we are to arrive at a more broadly based understanding of the urban system and the changes taking place in it, be viewed as complementing one another.


The relevance of functionally defined urban regions has been persuasively argued elsewhere (Coombes et al., 1978, 1982). Among the more important reasons for analysing the urban system based on a physical definition of urban

areas are the following:

0)

(ii)

(iii)

(iv)

(v)

a clear distinction in type and intensity of land use is what, for most people, still distinguishes a town from its surrounding rural hinterland, however closely interconnected the two may be in functional terms; most of the population of the country as a whole still lives in densely occupied urban areas, though the trend has been toward a more scattered distribution of urban population growth; even in areas otherwise termed ‘rural’ most people live in highly

concentrated settlements; physically defined urban areas or ‘cores’ necessarily lie at the heart of any functional definition of urbanisation and knowledge of what is happening to the ‘cores’ is vital to understanding change in the hinterland; and knowledge of the extent and spread of physically defined urban areas has a direct policy relevance for the delivery and ‘mix’ of local authority services and for the evaluation of the impact of measures of planning restraint such as Green Belts and Areas of Outstanding Natural Beauty.

2.4. THE 1981 DEFINITION OF URBAN AREAS

The DOE/OPCS/OS definition of urban areas for England and Wales represents the first consistent and comprehensive definition of the urbanised area of the country from the land-use point of view. On this basis a wide range of 1981 census data, using aggregates of ‘best-fit’ enumeration districts lying within

urban area boundaries, have been produced and made generally available (OPCS, 1984). In a separate operation DOE has prepared (unpublished) 1971-81 census ‘change-file’ data using a similar ‘best-fit’ method of constituent areas (census tracts and parishes) aggregation. The boundaries of the urban areas themselves

and the data based upon them thus represent a unique and valuable resource for the analysis of the changing national settlement pattern.

The starting point for the definition of urban areas is the identification of land use which is ‘irreversibly urban’ in character. A more detailed description of the elements of the urban areas definition can be found in Appendix 1 but, in broad terms, it consists of the following items: a list of land uses that are regarded as ‘urban’ and the application of simple tests to ascertain (a) whether a continuous area of urban land extended for 20 hectares or more and (b) whether that area of land had attained a population of 1,000 persons in 1981. Urban areas less than 200 metres apart are joined to form a continuous urban area. In addition, large urban agglomerations such as metropolitan counties and multi-centre areas are divided into two or more subdivisions. Wherever possible, subdivisions are based on pre-1974 local authority boundaries or New Town boundaries or, where urban


areas have two or more well-defined centres, on linear physical features such as rivers, roads, railways or narrow necks of built-up land (OPCS, 1984, p. 9).

Census enumeration district ‘constitutions’ of urban areas were then produced by means of overlays superimposed on 1981 enumeration district maps, and a similar procedure was adopted to produce a second constitution of urban areas in terms of 1971-81 census tracts and civil parishes. The latter procedure therefore raises the question of the degree of conformity between the two definitions and its likely impact on analyses using change data (Denham, 1984, p. 17).

A total of 1,780 separate urban areas (i.e., not including subdivisions) were delineated for England and Wales using this method. In aggregate, these urban areas contained a total population present in 1981 of 44.4 millions or just over 90% of the entire population. For England alone 1,612 urban areas were defined with a population of 42.2 million or just under 90% of the total. (Denham, 1984, p. 12). The proportion of the population defined as ‘urban’ based on the identification of urban land is thus revealed to be considerably higher than that arrived at using previous, less accurate, definitions. The Preliminary Report for Towns (OPCS, 1982), on the basis of pre-1974 administrative boundaries (with some up-dating for major inter-censal developments), gives the population in English towns as 35.8 millions. The new urban areas definition thus gives an urban population for England which is 17.8% higher than that arrived at using the definition of towns based on administrative areas.

2.5. SMALL AND MEDIUM SIZED URBAN AREAS

The present study focuses on a subset of the total of urban areas in England, namely those with populations in the range 5,000 to 100,000 hereafter called ‘small and medium sized’ (SAMS) urban areas. Large urban areas without subdivisions such as Plymouth or Oxford, and subdivisions of agglomerations with over 100,000 people are excluded from this study. This gives a total of 957 urban areas - 600 unsubdivided urban areas and 357 urban area subdivisions - for analysis (Fig. 1).

The population cut-off figures defining SAMS urban areas, though to some extent arbitrary, do have broad credibility. The lower population limit appears to be a reasonable threshold for a ‘town’ as opposed to a village or small rural settlement. As Best and Rogers (1973) have demonstrated, there is a distinct change in land-use patterns between settlements averaging 1,000 people (with 80% of land for housing), and larger settlements of 5,000 to 7,000 people with less land for housing and more for services, commerce and industry. Furthermore, towns with a population as low as 5,000 appear to act as independent growth centres developing their own commuting hinterland (Hodge, 1982; Moseley, 1984; Wardwell, 1977). The upper limit of 100,000 is also significant insofar as urban areas above that level grew markedly less rapidly (in fact many were in population decline) in the period 1971-1981, compared with those below that limit.

The inclusion of the 357 urban area subdivisions which fall within the population limits raises the question of the extent to which they differ in role and function

JPP 33:1-B


Q Urban agglomerations wth over 500,000 populatm

SAMS Urban Areas:

. Non- subdwsm

A Subdwsmn

FIG. 1. SAMS urban areas : Sub-divisions and non-subdivisions.

from the other ‘free-standing’ members of the SAMS urban area classification. Inspection of OS 1:50,000 urban areas maps combined with evidence from census data suggests that, on balance, subdivisions should be included in the study. However, this issue underlines the need when using the new urban areas data for careful assessment of the form of urban areas ‘on the ground’ and hence, by implication, of their functional relationship with neighbouring areas.


In this respect, therefore, a number of different physical and functional contexts for urban area subdivisions need to be examined. The functional approach to

urban definition, for example, distinguishes between major or ‘dominant’ centres and relatively minor or ‘subdominant’ centres which have substantial local employment but also commuting dependence on the dominant centre (Coombes et al., 1982). The relationship of Hemel Hempstead to London and of Stretford to Manchester illustrate this point well.

However, although the functional role of ‘subdominants’ such as these is similar, some are excluded as urban areas in their own right by the rigorous application of the rules of urban area definition. Macclesfield and Southend, for example, qualify as separate urban areas because of narrow belts of open land separating them from the dominant centre, while Keighley and Rochdale, each with a similar subdominant role, are merged with an agglomeration on the basis of narrow stretches of ribbon development. The latter are, therefore, designated as subdivisions rather than as urban areas in their own right.

The mechanistic application of the rules of urban area definition also produce discrepancies in the treatment of dormitory areas where local employment is much lower than the resident employed population and where there is substantial out-commuting to nearby employment cores. Some dormitory towns qualify as separate urban areas while others are physically linked (albeit tenuously), to an agglomeration. Thus the functional roles of Epping and Banstead as dormitory towns for London are similar, but only Epping qualifies as a distinct urban area. Similarly, the linking of subdivisions to form a single physical agglomeration may also be anomalous when several smaller urban areas (or, on other criteria, ‘towns’) are ‘chained’ together on the basis of more or less tenuous physical links.

Worthing and Littlehampton, for example, together with Brighton form part of a single agglomeration (total population 431,000) along the coast of Sussex by way of the open land of the River Adur estuary which is ‘bridged’ by Shoreham airport, an urban land-use feature. Other examples of the ‘chaining’ of urban areas creating subdivisions of agglomerations are Whitstable and Herne Bay, Bexhill and Hastings, the Dearne Valley and Mansfield.

There appear, therefore, to be several grounds on which certain subdivisions of urban area agglomerations as well as free-standing towns should be combined together in a group of SAMS urban areas. By their nature, many urban area subdivisions are more or less ‘dominated’ by neighbouring employment centres. It is therefore necessary to distinguish the functional role of such centres compared with that of their free-standing counterparts. This consideration confirms the strategy adopted later in this study of placing physically-defined urban areas within the analytical perspective of functional urban regions.

2.6. THE IMPORTANCE OF AREA BASE

As explained above, change indices for urban areas are derived by aggregating data from the 1971 and 1981 Census Small Area Statistics (SAS) into census tracts


for SAS cells which are comparable in definition between the two censuses. In view of possible sampling problems, such aggregations are confined to 490 cells in the 100% census counts (Morgan and Denham, 1982). Typically, though not universally, this aggregation of census tracts leads to ‘overbounding’ with the census tract area exceeding the urban area in geographical extent. The census tract area may thus include open land and outlying villages and may not match the stricter land-use concept of an urban area. On average, the comparable census tract areas have 2.0% more population than the corresponding urban areas. Denham (1984, p. 17) has argued that this excess of population is likely to

become relatively less as the size of the urban area increases, with the result that the ‘change file’ definition may, by comparison, understate population growth in smaller towns. Because of its potential impact on subsequent analyses, empirical validity of this assertion has been examined for the 952 urban areas which are on both the change file and the urban area file.

But population change is not the only variable that might be biased by the two definitions of an urban area. Tract area definitions may also differ in structure from their urban area counterparts. For example. they may include outlying agricultural villages, as in the example of Newmarket discussed by Denham (1984, p. 16), or they may embrace newly developed pcriphcral estates not included in the urban area. The structure of urban areas in terms of population by age, socio-economic status or households by tenure may, of course, be represented by percentage indices, which to some extent standardise for differences in the population base between census tracts and urban areas.

In order to assess the impact of the tract/area definition on demographic and other indices, ratios of the 1981 census tract population to the 1981 urban area population are plotted against the 1981 urban area population. These are displayed in Fig. 2. It can be seen that the ratios are indeed higher for small urban areas reaching a maximum of 2.10 for Brighton, near Sheffield (with a 1981 urban area

population of 5,588)) and 1.72 for Shevington, a high status area near Wigan (population: 5,906). Overall, however, there is a remarkably good fit between urban areas and tract/parish equivalents. There is a correlation of only -0.10 between the excess of census tract over urban area population and the 1981 urban area population. and of the 12 urban areas with population ratios over 1.4, only two, Yateley (part of Aldershot UA) and Chapeltown (Sheffield UA), have populations in 1981 over 10,000. A noticeable feature of Fig. 2, however, is the greater dispersion of ratios for small areas, so that most very low ratios are also found in small urban areas. Of 11 towns with 1981 population ratios under

0.7 all but three have populations under 10,000, although Hazlemere (near High Wycombe), with a ratio of 0.331 and 1981 population of 19,796 stands out as an exception. However, although such disparities are considerable they are relatively infrequent. In the majority of areas (776 out of 952), the census tract population is within 10.0% of the urban area population.

One way of mitigating disparities in population counts for 1981 is to concentrate on 1981 percentage based structures and on changes in structures (as defined by percentage point differences). Table 1 presents ratios of 1981 census tract


I I I I 1

0 20,000 40,000 60,000 00.000 100,000

Urban Area Population

FIG. 2. Census tract : urban area population ratios and urban area population.

indices to 1981 urban area indices for selected indices. There is no evidence from the ratios of percentage indices on the two area bases that census tracts are consistently more ‘suburban’ or more ‘rural’ than the generally smaller urban areas. The averages of these ratios are close to unity for all eight indices and show no interpretable features, except perhaps for slightly worse housing conditions in the census tract areas, and slightly lower unemployment.

Finally, it is also apparent that discrepancies for structural indices are less than for population counts. Of the eight structural indices, six have standard deviations lower than the 0.12 figure recorded for the ratio of population counts. The averages for all the indices are, therefore, close to one. However, there are, in a few instances, some considerable discrepancies. The most pronounced of these are generally confined to a few urban areas which either have a special function such as Catterick Garrison; those in which the population counts are discrepant because of large differences between the urban area and census tract population count such as Oakley, near Bournemouth; or where special local factors concerned with the timing of the 1971 and 1981 censuses were in operation such as Minehead, Somerset.

The foregoing assessment suggests that pronounced discrepancies between the two urban area definitions (at least among SAMS urban areas), are rare and are restricted to a few urban areas. Moreover, their impact on analysis can be mitigated by considering indices of structure rather than original census counts. Provided local anomalies (which are related to some extent to size of urban area, but more usually to specific functional attributes of urban areas), are borne in mind, these area discrepancies will have little impact on large-scale studies of urban areas. It is, however, necessary to make a final point with regard to

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the measurement of change using 1981 urban areas. The urban area definition does, as has been shown, give a more valid view of variation in social indices than the administrative definition. There are, nevertheless, conceptual and empirical problems associated with measuring change in the population living on urban land. The physical boundaries of urban areas typically expand over time but, using present data, direct comparison between the last two censuses is not possible because urban areas were delineated for 1981 and ‘carried back’ for 1971 enumeration district constitutions. A truly ‘dynamic’ representation of urban areas based on land use would compare the actual urban area and its actual enumeration district constitution at each census date. This aspect of the urban areas definition is worthy of further study particularly if the exercise in urban areas definition is to be repeated for the 1991 census.

2.7. STATISTICAL ASSESSMENT OF URBAN AREA INDICES

A total of 47 census variables or indices have been used in this study: 32 of them are cross-sectional percentage measures from the 1981 census and 15 are change measures from the 1971-1981 change file. 2 Cross-sectional indices were selected in groups to reflect the major processes underlying growth differentials among urban areas. Groups of indices thus represent:

Measures of the age and ethnic composition of the total population; Measures of the socio-economic, manufacturing/service composition and travel to work characteristics of the employed (economically active population); Measures of housing tenure and quality of the housing stock.

Migration measures from the 1981 census and change indices for 1971-1981 are here considered jointly as measures of dynamic processes. Each subject group of cross-sectional indices (e.g., age structure, socio-economic group affiliation), is complemented by indices of migration and change in that subject group. Categories of migration/change indices therefore consist of:

Changes between 1971 and 1981 in total population and its age/birthplace structure, as well as age-specific migration; Indices of migration specific for socio-economic group together with measures of change in employed residents, unemployment and economic activity; Changes in household tenure and composition.

Throughout this study change indices are generally measured as a difference comparison of 1971 and 1981 percentage indices. The exception is population change which is a ratio of 1981 to 1971 population.

The first objective in the research strategy was to ascertain the statistical distributional characteristics of the selected indices such as maximum and minimum values, ranges, mean and measures of skewness and peakedness (Evans, 1983). These were calculated for two reasons: to understand the nature of SAMS urban areas (especially those with social and economic characteristics which departed markedly from the average), and to assess whether, and what type of,


numerical transformations of the data were necessary for subsequent statistical analyses.

Certain indices were, in fact, found to have extremely high positive skewness reflecting very high values in a few urban areas. Cross sectional indices of this type include measures of migration which are distorted by high values in garrison towns (i.e., Gatterick and Lakenheath, Norfolk), and in recent New or Expanded Towns (Milton Keynes and Risley, near Warrington). Measures of ethnic minority status and use of the train to work also exhibited considerable skewness. Less pronounced positive skewness is apparent in such indices as population of retirement age and unemployment, while some indices such as female

economic activity exhibit moderate negative skewness with a few exceptionally low values in mainly retirement towns (i.e., Frinton, Essex, and Sidmouth, Devon). Percentage point differences in census indices between 1971 and 1981 generally exhibit little skewness although change in unemployment and owner occupancy

are skewed by a few exceptionally high values: in the case of the former for urban areas such as Consett and Corby and, for the latter, New Rossington, near Doncaster, and Cotgrave, near Nottingham. The most marked distortions are in growth rates of total population and total employment where there are a few extremely high rates for certain New Towns (Milton Keynes, Telford/Dawley in Shropshire). Discontiguous recent suburban developments, such as Oakley, near Bournemouth, and Haxby near York, also impart considerable positive skewness to the distribution of certain variables.

Most of the 1981 indices and percentage point differences used in this study in fact exhibit relatively little skewness and can be considered to meet the normality assumptions of regression-based statistical techniques. However, several 1981 indices require logit or angular transforms (Evans, 1983), and some with extreme skewness require power transformations. All growth rates (i.e., population. employment and households), with high positive skewness are best treated for statistical purposes in logarithmic form, whereas several percentage point differences require power or logarithmic transformation. Appendix 2 lists all indices used in this study and indicates what, if any, mathematical transformation

is required.

2.8. SUMMARY

The DOE/OPCS/OS definition of urban areas, based on consistently applied land-use criteria with comprehensive geographical coverage, represents a major step forward in the analysis of urbanisation within Britain. By creating a clear-cut distinction between urban and rural land it produces a more accurate definition of the population living in densely occupied areas and constitutes a necessary complement to any definition of urbanism based on functional criteria. In relation to this particular study, urban areas in the population range 5,000 to 100,000 have broad substantive credence as a group of settlements for further analysis, that is, in terms of land use and population structure and recent growth characteristics.


Nevertheless, when urban area subdivisions are included in an analysis, care must be taken to examine the cartographic definition of urban areas and, depending on the precise aims of the research, to assess the nature of the functional relationship between neighbouring urban areas. Finally, the enumeration districts/census tract constitutions of urban areas appear to create no serious problems for research although care should be exercised when using both 1981 and 1971-1981 change data for smaller urban areas. Because of the nature of the urban areas definition some census variables of both the cross-sectional and the change type may display extreme outliers in their distribution of values.

NOTES: CHAPTER 2

1. In reality the criteria for the inclusion of settlements as de facto urban areas were more complicated than this. A population density of 0.6 ppa at the ward or parish level and a population of 3,000 or more, identified an urban area ‘outright’, but a population minimum of 2,000 was permitted if the urban area had at least one ‘urban attribute’. Among the features regarded as ‘urban attributes’ were industrial works, collieries, trading estates, mines. race courses, golf courses, etc.

2. In the original study over 100 variables (64 cross-sectional, 36 change file), were assessed for their statistical properties. Summary statistics and listings of all indices are found in the Appendix volumes for Stage 1 and Stage 2 (Extended Data Set), Reports (Halcrow Fox and Associates/Birkbeck College, 1986a).

CHAPTER 3

Characteristics and Significance of SAMS Urban Areas

3.1. INTRODUCTION

The purpose of this chapter is to introduce the general characteristics and wider significance of SAMS urban areas. There are two elements to the chapter. The first examines the numbers, area, aggregate populations and population growth of SAMS urban areas within the national (England) context of such areas and for Standard Regions within England. The second identifies a number of key variables describing structure and change among SAMS urban areas and compares these with national aggregate figures.

3.2. THE SIGNIFICANCE OF SAMS URBAN AREAS

In 1981 the 2,047 urban areas identified for England (i.e., including subdivisions), contained 42.2 million people on an enumerated basis or almost 90% of the total population. Urban areas with more than 100,000 people had a population of 17.7 million or 38.2% of the total, whilst those with fewer than 5,000 had a population of 2.6 million, some 6% of the total. The 957 SAMS urban areas with between 5,000 and 100,000 people had an aggregate population of 21.8 million people. Given that they contain almost half (47%) of the total population of England, SAMS settlements merit much greater attention than they have so far been given by urban researchers.

In certain regions the significance of SAMS urban areas in terms of population is even more noticeable. As Table 2 shows, SAMS urban areas contain the majority of the population in three of the eight Standard Regions - reaching two-thirds of the regional total in the North and North West - and nowhere do they account for less than one-third of the total. It should be noted, however, that in the North and North West regions many SAMS urban areas are subdivisions of larger agglomerations rather than free-standing towns.

The small to medium sized settlement standing in its own functional hinterland is best represented in the East Midlands, the South West, East Anglia and in the South East outside the Greater London agglomeration (though note that even in the latter area there are significant numbers of urban area subdivisions). In each of these regions there is a considerable proportion of people in SAMS urban

26

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areas, though note that in East Anglia, where there has been very considerable SAMS urban area growth, they do not overwhelmingly dominate the distribution of population.

Of the 957 urban areas in the study, the majority (678 or just over two-thirds) have populations under 25,000 and these, in aggregate, account for roughly a quarter of the SAMS urban area population (Table 3). There are 242 urban areas with between 25,000 and 75,000 people - a quarter of the total - and these together contain a further 25% of the total population. Larger SAMS urban areas, those with over 75,000 people, although only 37 in number, therefore contain over half the population being considered in this study. The relative importance of SAMS urban areas by population size within and between Standard Regions is shown in Table 4 which reveals considerable variation in SAMS urban structure across the country. In aggregate population terms, larger SAMS settlements are more important in the West Midlands, the South East and the North West where the urban structure is dominated by metropolitan agglomerations. All regions, however, have significant proportions of their SAMS urban area populations in settlements of medium size - i.e., lO,OOO-50,000 population.

In the South West and East Anglia the SAMS urban area structure is dominated by small ‘free-standing’ settlements (though note the relative importance in East Anglia of a very small number of SAMS urban areas with over 50,000 population). The Yorkshire/Humberside distribution, on the other hand, indicates the importance of a classic ‘market town’ structure existing alongside a fragmented (and highly subdivided) metropolitan area.

Finally, although urban areas in the SAMS category account for a little more than half (51%) of the population of all urban areas, in aggregate territory they

make up proportionately slightly more (54%) of the physically urbanised area of England. But, as Table 3 also shows, the discrepancy between the proportion of the total population in SAMS categories and the proportion of the urban land area

TABLE 3. SAMS urban areas: Size, area and population, England

SAMS urban areas size range population (000s) No.

Area in hectares Population’ % (000s) % (000s)

Under 10 335 7 (7) 77 6 (6) 2,337 10 under 25 343 14 (21) 157 13 (19) 5,420 25 under 40 137 11 (32) 118 I1 (29) 4,384 40 under 75 105 14 (46) 156 14 (43) 5,754 75 and over 37 7 (54) 83 8 (51) 3,184

Total 957 54 591 51 21,075

All urban areas 2,047 100 1,095 100 41,335

England (inc. rural) 13,044 45,772

‘1981 population usually resident. Figures in brackets denote cumulative percentages.

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accounted for, increases with increasing SAMS urban size indicating, as might be expected, that the proportion of non-residential land uses rises with the increasing size and functional complexity of SAMS urban areas.

3.3. THE SIGNIFICANCE OF SAMS URBAN AREAS: POPULATION AND

EMPLOYMENT GROWTH

3.3.1. Population

It is in relation to recent population growth that the real significance of SAMS urban areas becomes apparent. In the period 1971-1981, the aggregate population of these urban areas grew from 20.6 million to 21.8 million and, at a time when the population of England increased hardly at all (+0.7%), the growth rate for SAMS urban areas as a whole was, at 5.9%, almost nine times higher. This rate of growth stands in even starker contrast with the situation in larger urban areas in England (i.e., those over 100,000 population) where there was an inter-censal decline of almost 7.0%.

Population growth among SAMS urban areas occurred in all Standard Regions but has been a particular feature of East Anglia, the South West, the South East and the West and East Midlands (Table 5 and Fig. 3). SAMS urban areas grew in population in all regions (if only very slowly in the North and North West), even in those where there was overall population decline (the North West, North and South East) and, except for East Anglia where there was strong growth across the range of urban and rural environments, SAMS urban areas grew where larger urban areas were in marked decline. It can also be seen in Table 5 that, except for the Northern region where many very small settlements (those under 5,000) are mining communities, there is evidence of a continuing downward movement of growth into increasingly lower levels of the urban hierarchy and into rural

TABLE 5. Percentage population changes by urban/rural status and 1981 population size 1971-1981

Standard region Rural Under 5,000 5,000-100,000 Over 100,000 Total

South East 3.3 West Midlands 5.0 North West 2.6 Yorkshire/Humberside 5.5 North 1.3 East Midlands 4.6 South West 7.2 East Anglia 1 !O.O

10.0 8.7 -8.9 -0.8 8.8 9.5 -5.5 0.8 8.0 0.6 -10.4 -2.8

14.3 3.6 -6.5 0.1 0.3 0.8 -9.3 -1.2

11.1 7.2 -0.6 5.1 12.0 9.8 -0.3 6.6 21.0 13.0 8.1 12.1

England 5.2 11.0 5.9 -7.0 0.7


(Percent change)

l Less than - 5.0

. -5.0 to + 30.0

A Over 30.0

0 Urban agglomerations with

over 500,000 population

FIG. 3. SAMS urban areas: Population change 1971-1981.

areas. In both the North West and Yorkshire/Humberside, for example, there was stronger growth in very small settlements and rural areas than even in SAMS urban areas and this relationship is apparent in a number of other regions where SAMS urban area growth is stronger.

At this point it is instructive to examine the size and growth distribution for SAMS urban areas in more detail, both for the light it sheds on this apparent inverse size/growth relationship and on the nature of SAMS urban areas themselves. As Table 6 shows, the broad (inverse) relationship between size of


TABLE 6. Urban areas by 1981 size, population and growth

1981 size Number of in 000s urban areas

Total 1981 population

(000s)

Average Average growth population rate of employed growth rate residents

1971-81 1971-81

50-55

5-10 IO-15 15-20 20-25

55-60

25-30 30-35 35-40 40-45 45-50

60-65 14 872 65-70 6 402 70-75 10 733 75-80 9 696 80-85 5 406 85-90 11 959 90-95 9 830 95-100 3 293

Total (or global average) 957 20175 11.4 9.5

335 169 110 64 51 47 39

20

19 19

17

2033 2082

1046

1916 1421 1405 1521

982

1459 816 902

7.3 10.9 5.5 3.8 6.6 2.5

12.3 1.1 4.4 2.1 2.4 7.6 0.6 1.8

-0.3 1.6 0.5 1.7

-5.8

6.6 9.7 4.8 2.6 5.6 1.3 9.7

-0.7 2.9 1.3 1.7 4.9 0.0 0.2

-2.7 -0.6 -0.4

1.2 -11.6

SAMS urban area and rate of recent population growth is apparent to some extent even for a very detailed breakdown of urban areas by size. There are, however, two significant ‘breaks’ in the relationship, the explanation for which requires more specific knowledge of individual SAMS urban areas. The size category 35,000-40,000, for example, with 24.5% growth overall contains both relatively large, attractive settlements in rural locations (i.e., Boston, Yeovil, Banbury) and a number of New or Expanded Towns such as Wellingborough, whilst the category 60,00&65,000, on the other hand, contains some larger New Towns such as Runcorn and Tamworth that grew very rapidly in the 1970s as a result of planned overspill.

Behind this table is a variation in population change among SAMS urban areas that has been extremely wide: ranging from +729.0% in Central Milton Keynes (a subdivision of the Milton Keynes urban area) to -35.0% in Tidworth, a military base in Wiltshire. Seven other SAMS urban areas experienced growth rates above 100%. These were either designated New Towns such as Washington, Telford. and Dawley, or discontiguous suburbs near fast-growing employment centres like Haxby near York and Oakley near Bournemouth. SAMS urban areas experiencing rapid population decline in the 1970s included subdivisions of metropolitan areas (Gateshead, Salford), garrison settlements (Tidworth near Salisbury) and municipal overspill areas such as New Addington near Croydon.

3.3.2. Employment


Census of Population data measure the number and characteristics of employed (and unemployed) residents in an area and not the number of jobs located in that area. Given the nature of the urban areas definition it is therefore expected that there will be a considerable inter-area exchange of workers, both between urban areas and between urban areas and the rural remainder. Nevertheless, to the extent that there are significant differences in the rates of change of population and employed residents among urban areas, and given additional knowledge of the characteristics of individual urban areas, it is possible to draw inferences on the broad nature of employment growth among SAMS settlements.

Trends in the growth of employed residents in SAMS urban areas have, to a considerable extent, matched those in population. Thus employment growth has been prominent among the residents of SAMS urban areas of the South East, South West and East Anglia (Table 7) and, once again, more significant in smaller rather than larger SAMS settlements (Table 6). Differences between standard regions and size of urban area in employment growth are most apparent in the contrast between urban areas over 50,000 population in the North (where there was an average decline of 8.9% in employed residents), and urban areas of between 10,000 and 20,000 population in East Anglia where there was a 26.2% growth. Urban areas with between 20,000 and 50,000 population in the South East and West Midlands also experienced very high rates of growth of employed residents.

There has also been a considerably higher rate of growth of female employed residents in SAMS urban areas (21.4 percentage points),’ compared with male employed residents (3.8 percentage points). The discrepancy between male and female employment growth is most apparent in East Anglia, where female economic activity has traditionally been low and in the North where there have been considerable losses of male jobs in mining, heavy engineering and iron and steel making. Individual urban areas with very high growth in employed residents include New/Expanded Towns such as Milton Keynes, Telford, Dawley and Washington; dormitory settlements for New/Expanded Towns or for expanding

TABLE 7. Percentage employment changes by urban/rural status and 1981 population size, 1971-1981

Standard region Rural Under 5,000 5,000-100,000 Over 100,000 Total

South East 0.9 11.5 12.2 -10.8 -4.6 West Midlands 1.8 7.3 6.7 -15.5 -8.9 North West -0.7 9.3 1.2 -12.2 -9.1 Yorkshire/Humberside 4.1 13.2 1.6 -9.6 -5.6 North -3.7 -2.1 0.9 -11.3 -5.6 East Midlands 3.5 11.5 3.9 -4.3 0.7 South West 3.6 8.4 7.8 -1.4 3.9 East Anglia 6.8 20.2 13.1 3.5 10.3

JPP 33:1-C


employment centres (Oakley near Poole/Bournemouth) and smaller towns with an established local employment base such as St. Ives. Cambridgeshire and Grove, near Swindon. The highest rates of job loss among SAMS urban areas are in metropolitan county subdivisions (Salford, Gateshead, Birkenhead), in housing overspill developments in older manufacturing areas (Kirkby), and in the manufacturing/mining towns of the North and North West like Anfield Plain, Newton-Le-Willows and Consett.

However, it is the discrepancies between population change and change in employed residents that are potentially most revealing in terms of processes of growth among SAMS urban areas. These differences (i.e.. resident population growing more rapidly than resident employment within an urban area and vice versa), may be interpreted in a number of ways and these broad interpretations may be supported by other census variables and more detailed local knowledge. For example:

(9

(ii)

(iii)

The lower average rate of growth of employed residents (9.5%) in SAMS urban areas compared with population growth (11.4%), may imply a slower rate of decentralisdon of jobs away from larger urban areas into their hinterlands and towards free-standing towns. Greater increases in population compared with growth in employed residents may reflect regional or local differences in job uvaifubility

and unemployment. Thus SAMS urban areas in the West Midlands, for example, had a similar average population growth rate to those in the South East (8.7% and 9.5% respectively), but the increase in employed residents was only half as much in the former (8.0%) as in the latter

(15.0%). Greater increases in population compared with employed residents may, on the other hand, reflect retirement migration. In the South East, for example, the average increase in employed residents exceeds that of population while in the South West and East Anglia (recent foci of retirement migration), population increases often substantially exceed

those of employed residents. Subsequent chapters of this report dealing with the classification and modelling

of change among SAMS urban areas return to these possibilities for interpret- ing discrepancies in population and employment change as elements in an explanation of growth in SAMS urban areas.

3.4. SAMS URBAN AREAS: SELECTED CENSUS PROFILES

In the course of making a statistical assessment of census variables in the urban area data files, an initial picture of the main characteristics of SAMS urban areas was developed. This section therefore presents some key findings from this work for indices which emerge later as important in understanding the determinants of SAMS urban area growth.

3.4.1. Population density


This index is influenced by a number of factors including, most critically, the precise way in which the rules of urban area definition are applied in specific instances. Large open areas (airports, golf courses, parade grounds, etc.) on the perimeter of urban areas but surrounded by even narrow bands of building can be included in the urban area definition and hence reduce population density. Bearing these caveats in mind, the overall population density of SAMS urban areas ranges from 6 persons per hectare in Minster/Manston urban area in Kent (which contained 5,150 people in 19Sl), to 82 pph at New Addington, a tightly bounded municipal estate on the southern fringe of Greater London. The population densities of most SAMS urban areas, however, fall in the range 30 pph to 50 pph with an average of 35.7 pph.

There is some geographical variation in average population densities of urban areas. SAMS urban areas in the North and West are noticeably more densely populated, size for size, than those in other regions. These are generally working class areas with much municipal housing (e.g., Newbiggin-by-the-Sea, Northumberland) or with mostly family housing (Droylsden and Denton in Greater Manchester). Low densities are generally found among SAMS urban areas with relatively little recent residential development acting as dormitory areas for high status commuters (e.g., Chorleywood and Virginia Water on the periphery of Greater London).

3.4.2. Demographic indicators (1): Persons aged 15 and under

There were slightly more (23.1%) children aged 15 and under on average in SAMS urban areas in 1981 than in England as a whole (22.2%). High percentages of children under 15 are found in areas of recent growth (the highest is for urban areas of 10,000 to 20,000 population in East Anglia where there was 26.8% overall growth in this age group between 1971 and 1981), in garrison towns, in urban areas with recent owner-occupier development and in New/Expanded Towns. High growth has also taken place in urban areas where the main wave of family immigration took place in the 1960s and where there are now increased numbers of older children. Overspill municipal estates such as New Addington (near Croydon), Partington (near Manchester) are typical of this group as are urban areas which experienced rapid growth of population and employment in the 1960s such as Immingham, on Humberside.

Recent growth among SAMS urban areas is well illustrated by the percentages of children aged under 4 years in 1981 and in changes in the percentage of very young children. The average percentage of population of children under 4 among SAMS urban areas was 6.0% in 1981, but in growth towns this figure exceeds 10%. Examples of urban areas with high levels of young children include New Towns (Washington, Durham and Milton Keynes); those with new owner-occupied developments for middle and upper-income groups (Chinnor in Buckinghamshire


and Linford in Essex) and some overspill municipal developments such as Birchwood near Lincoln.

Low percentages of children aged 15 and under, on the other hand, occur primarily in sea-side areas with high percentages of elderly people and also in suburban commuter areas with planning constraints on new family housing development and with declining populations, i.e., Alderley Edge south of Manchester. Decline in the size of the under 15 age group has occurred in urban areas where the main phase of family migration was in the 1950s and 1960s such as the older New Towns (Harlow, Stevenage), owner-occupied suburbs in Green Belt

areas (Culcheth near Warrington, Coleshill near Birmingham and Calverton near Nottingham); overspill municipal estates (Partington, Kirkby, New Addington, Longendale) and areas where the main phase of employment related migration has passed such as Havant near Portsmouth and Hythe, Kent.

3.4.3. Demographic indicators (2): Persons aged 65 and over

SAMS urban areas as a whole, with 14.6% of their population in the age range 65 and over, are only a little below the national average of 15.1%. Again, however. there is considerable variation among them on this indicator. An increased proportion of old people in the population may reflect either net out-migration by younger age groups (which particularly affects depressed industrial areas or inner cities), or it may be the result of retirement migration to environmentally favoured locations. Regionally. the highest levels on this index are found in the coastal resorts of the South West (Sidmouth in Devon has 43.0% of its population aged over 65) and East Anglia. Low values for these age groups occur in new suburban developments, metropolitan overspill settlements, New Towns and garrison towns.

Recent immigration by persons over 65 - including local turnover - is similarly biased towards resort towns in the South West and East Anglia. However, the high levels on this index for certain inland centres suggest that limitations on further growth in traditional retirement centres has diverted the flow to nearby urban areas to, for example, such places as Verwood near Bournemouth and Storrington, inland from Worthing. Retirement migration is also the source of large percentage point increases in retired people in small urban areas such as Sherborne and Gillingham in Dorset and in Seaton, Devon, and also of large absolute increases in areas such as Eastbourne and Bognor Regis. It is noticeable that many of the largest percentage point increases on this variable are in inland locations close to traditional retirement areas, a reflection, perhaps, of restrictions on coastal development and the lure of attractive inland scenery.

3.4.4. Demographic indicators (3): Average household size

On this indicator, which reflects the balance between small households, young single people or elderly couples and widows on the one hand, and large


households, married couples with children or extended families on the other, SAMS urban areas recorded 2.76 persons per household in 1981 compared with an average for England of 2.63. The highest levels of average household size are found in garrison towns such as Lakenheath, Norfolk and Carterton in Oxfordshire; in overspill suburbs intended primarily to house families (New Addington, Kirby and Partington); and in some commuter towns with high or middle-income owner occupancy (Hullbridge near Southend and Yately, Aldershot UA). The lowest household sizes are found in retirement and resort urban areas. There are no pronounced regional or population size variations in this indicator.

Between 1971-81 there was a fall in average household size which can be attributed to an increasing proportion of elderly households, lower birth rates, reductions in sharing households, an increased tendency for young people to live away from parents and the reduction in rented tenures. As a result the largest declines in this index have occurred in larger SAMS urban areas, particularly in the North, though urban areas with 20,000 to 50,000 population in the West Midlands and Yorkshire/Humberside have also experienced significant reductions in average household size. Elsewhere large reductions have occurred in areas of municipal housing which have lost population (i.e., Kirkby, New Addington) and in commuter belt areas with ageing populations (South Ockenden, Essex, and Borehamwood, Hertfordshire). Increases in average household size were apparent in fast-growing suburbs (Oakley, Hampshire, Eaton Socon, Bedfordshire, and Whitfield, near Dover) and in areas with large retired populations which have also experienced growth in family or young/adult households (Dartmouth, Ventnor, Isle of Wight).

3.4.5. Housing tenure

SAMS urban areas tend to have noticeably higher rates of owner occupancy, (62.6% on average) than the country as a whole (England: 57.8%), but they have only a slightly lower average (27.0%) of local authority tenures (England: 28.9%), indicating that the discrepancy is accounted for by lower levels of private renting and other tenures. Highest rates of owner occupancy are found in smaller urban areas, particularly in the North West, South East and East Midlands. In the North West rates of owner occupancy of 90% occur in suburban areas near Preston (Higher Walton, Longton), Stockport (High Lane), Lancaster (Bolton-le-Sands) and Blackpool (Poulton-le-Fylde). Local authority renting is also highest in metropolitan areas of the North and North West and lowest in smaller SAMS urban areas of the South East.

Large increases in owner occupancy between 1971 and 1981 - home ownership in SAMS urban areas increased by nearly 8.0 percentage points in the decade - are most pronounced in smaller SAMS urban areas in Yorkshire/Humberside and East Midlands. In Yorkshire/Humberside such areas had relatively low owner-occupancy in 1971 so these increases imply a significant restructuring of tenures. Medium sized (though not larger) urban areas in East Anglia (i.e., Bury


St. Edmunds, Beccles, Huntingdon and Thetford), have also had very significant increases in owner occupancy suggesting that SAMS urban area growth in less urbanised areas is mostly associated with private home ownership.

3.4.6. Employment composition and unemployment

Indices of employment composition (residents employed in manufacturing or service jobs) and of unemployment provide further insights into changes in population and economic activity. There are considerable variations in the employment structure of SAMS urban areas which, in conjunction with their location relative to labour markets and the size of urban area, are important indicators of job availability which in turn can affect population redistribution.

The highest percentages of resident workers in manufacturing jobs in SAMS urban areas are in the North, North West and the East and West Midlands, but there is also a relatively large manufacturing base in the SAMS urban areas of East Anglia. In the West Midlands and the North larger SAMS settlements tend to have relatively more manufacturing while in the East Midlands over- representation is in towns of 20,000 to 50,000. This may reflect decentralisation of manufacturing from larger agglomerations, often under New or Expanded Town schemes. Huntingdon. St. Neots, Thetford and Haverhill have all experienced recent growth based on manufacturing.

Individual urban areas with a pronounced manufacturing bias are primarily smaller towns (e.g., Barnoldswick in the Pennines and Sileby near Leicester), although some larger SAMS urban areas like Nelson and Barrow-In-Furness have over half of their workers in manufacturing.

SAMS urban areas in the South West and South East easily have the highest relative levels of residents in service employment, this being evident in small commuter settlements as well as in larger employment centres. The distribution of finance workers show similar regional distinctions with high levels in urban areas on the London periphery reflecting, probably, both decentralisation of such employment as well as continued commuting to the conurbation centre. In the South West and elsewhere local service employment in distribution and catering is

over-represented in tourist and resort towns.

3.4.7. Unemployment

There is a well-established connection between employment composition and differentials in levels of unemployment (Townsend, 1983). Unemployment

rates among SAMS urban areas varied in 1981 from under 3% in high social status, service-based commuter settlements near London (i.e., Writtle, near Chelmsford, and Chinnor, near High Wycombe), to over 20% in urban areas that have experienced job losses in heavy industry, such as Consett and Corby. A population size gradient in unemployment is also apparent with higher levels in


larger urban areas acting as employment centres, including some New Towns with a manufacturing bias (i.e., Skelmersdale).

The average percentage point increase in unemployment for SAMS urban areas was 3.8 points between 1971 (4.6%) and 1981 (8.4%), a somewhat lower level and rate of change than for England as a whole (4.9% in 1971, 9.7% in 1981). Some urban areas, however, show significant decreases against the trend (i.e., Epsom, Wetherby, Durham, Winchester), while others have increases exceeding 10 percentage points such as Consett, Corby, Telford/Dawley and Scunthorpe. Larger SAMS urban areas have fared worse than average but some small urban areas have not been exempt from increased unemployment.

3.4.8. Social groups and migration

Recent trends in migration by socio-economic group conform partly to the existing distribution of such groups among SAMS urban areas and partly to patterns of overall population redistribution. Compared with England as a whole (15.0% of economically active persons in professional and managerial groups), SAMS urban areas have slightly more (16.3%) high status workers and slightly more (25.8% as opposed to 23.5%) skilled manual workers.

Concentrations of high status workers are highest, on average, in small commuter towns adjacent to larger agglomerations, especially in South East England. However, the highest percentages of all are found in the West Midlands (Hagley, near Dudley 44% of economically active residents), and the North (Ponteland, near Newcastle upon Tyne 44%). Migration by high status workers appears to be noticeably biased towards smaller SAMS urban areas especially in regions like East Anglia which lack a large metropolitan centre. Concentrations of skilled manual workers, on the other hand, do not show a metropolitan area bias. The highest regional average is, in fact, for the East Midlands. Also, in some contrast to high status migration, migration by skilled manual workers has been towards SAMS urban areas of all sizes, including those adjacent to and more distant from the metropolitan centres. There have been significantly high levels of skilled worker migration to New Towns and to urban areas in regions with Development Area status.

3.4.9. Car ownership and commuting mode

Residents of SAMS urban areas own rather more cars (65.9% of households had one or more cars in 1981), than do those in England as a whole (61.4%), and are more disposed to use the car to commute to work (55% compared with 50.4%). In four SAMS urban areas (Ravenshead, near Nottingham; South Woodham Ferrers, Essex; Biggin Hill, Kent; and Saint Leonards, Hampshire) car ownership has penetrated to over 90% of households. The lowest levels of car ownership are to be found in the North and North West: Hebbern, Salford and Gateshead having 35% or less of car-owing households.


The average increase of one-car ownership among SAMS urban areas of 9.2% between 1971-1981 was comparable with that for England as a whole, but, again there have been considerable geographical variations in change. Increases in one-car ownership have been greatest in middle income urban areas such as Killamarsh near Sheffield (21 percentage point increase) and Brotton in Cleveland (21%), and also in certain New Towns (Milton Keynes, Telford, Dawley and Washington). Increases in two-car ownership appear to have been greatest in high status urban areas where one-car ownership was already high in 1971, i.e., Yately and Fleet near Aldershot, Biggin Hill and Hazlemere/Tyler in Surrey.

Levels of car ownership are a major influence on commuting mode (Button, 1982). The highest levels of car commuting to work are therefore found in small urban areas close to major centres of employment, especially in the West Midlands and the South East where, in addition, motorway access is particularly good. By contrast the use of the bus for journeys to work increases with SAMS urban area size and is a particular feature of the North (Felling, 40.5%; Gateshead 40.2%), and Yorkshire/Humberside (Conisbrough, 40.0%).

3.5. SUMMARY

SAMS urban areas form an extremely important element of the English urban system on a number of counts. They contain almost one half of the population of the country but, more significantly, they are in aggregate the fastest growing of all urbanised areas. In some regions, notably the South West, East Anglia, the East Midlands and the South East their growth has been extremely rapid, but even in regions with overall population decline certain types of SAMS urban area have experienced significant growth. SAMS urban areas also have special characteristics connected with both population and employment growth. Depending on location and size they have high and growing percentages of children under 15 and high and growing percentages of older people; they have higher rates of owner occupancy than England as a whole, and high rates of car ownership and car use for the journey to work. The next chapter examines these characteristics and patterns of change within the context of broader changes taking place in the urban system of England and, in particular, in relation to the forces of decentralisation and deconcentration of the population. It also investigates the pattern of SAMS urban area growth in relation to the ‘macro-scale’ planning policies of central and local government.

NOTES: CHAPTER 3

1. This figure is boosted by an eight-fold growth in female employment in Milton Keynes Central urban area.

CHAPTER 4

Patterns and Processes in SAMS Urban Areas Growth

4.1. INTRODUCTION

In the previous two chapters, the investigation into the growth of SAMS urban areas has been led by the requirement to assess the main characteristics of the SAMS urban area data. This chapter examines population change among these areas from perspectives that were designed to draw out the patterns and processes underlying change.

The first of these perspectives is a complement to the physical notion of urbanisation represented by the SAMS urban areas in that it places the latter in their functional region context. More importantly, however, this framework for analysis enables us to examine the whole range of population change among SAMS urban areas, from population decline in subdivisions of urban agglomerations to rapid population growth in certain non-metropolitan and even rural areas. It also enables us to distinguish growth among SAMS urban areas that is probably still linked, via the daily journey to work, to employment in the major centres (decentralisation), from growth that is due to the relocation of people and jobs in non-metropolitan areas (deconcentration). In addition to the basic characteristics of population change a number of other variables indicative of the predisposing conditions or outcomes of population change such as demographic and social structure, type of employment, owner occupation and car ownership, are also examined in this framework.

The second perspective on SAMS urban area growth examines the latter in relation to their policy status or the policy area in which they are located. Policy status or area characteristics include those measures designed to encourage growth such as New Town/Expanded Town designation and Assisted Area status, and those designed to discourage or divert growth such as the designation of land as Green Belt or as Areas of Outstanding National Beauty (AONB). A key element of transport infrastructure - in this case the national motorway network - is included as a policy measure that is hypothesised as promoting growth among SAMS urban areas. Before discussing the results of these analyses, however, we summarise the main trends in SAMS urban area growth and assess them in relation to recent ideas on the processes of population redistribution in England.

41


4.2. SAMS URBAN AREA GROWTH, THE MAIN TRENDS

A number of characteristics of SAMS urban area growth emerged in the two preceding chapters which raise issues for further investigation:

SAMS urban areas have grown markedly less fast in the North, North West and Yorkshire1 Humberside than in the Standard Regions comprising the south of the country.

Within the latter SAMS urban areas have grown fastest of all in the South West and East Anglia, regions which are themselves peripherally located in relation to the London-dominated South East.

Within individual Standard Regions there are a number of .signij?cant deviations from the broad North-South trend.

In the three northern regions the lower overall growth of SAMS urban areas is due in large part to the fact that many such areas are contiguous subdivisions of large agglomerations in population decline. It is only in these regions, for example, that medium-sized urban areas (50-100,000 population) have declined in population. Nevertheless, except in the Northern Region, smaller (5-50,000) SAMS urban areas have, even here, grown at rates close to the average for England as a whole. In marked contrast, smaller SAMS urban areas in East Anglia, the South West and the East Midlands have had very high rates of population growth.

There is some evidence, both for England as a whole and within Standard Regions, of an inverse relationship between SAMS urban areu size and rate of population growth (and again this is especially true of East Anglia, the South West and the East Midlands).

However, this relationship breaks down for fine gradations of SAMS urban areas population size. To some extent this is due to the fact that planned additions to urban growth (New and Expanded Towns) make their impact felt at specific points in the urban size hierarchy. This is especially apparent in the South East and West

Midlands.

The relationship between growth in population and growth in employed residents is different for different regions.

In the South East, the South West and East Anglia there is a consistent pattern

of above average growth in both population and employed residents; whilst in the East Midlands the averages are close to those for the country as a whole but, again, rather similar. In the West Midlands, on the other hand, growth in

employed residents in SAMS urban areas has lagged behind average population growth and in the North and North West growth in both population and employed residents has been below the national average, with losses of employment

out-stripping those of population.

It is apparent that SAMS urban area growth is, in some way? related to the regional or local urban structure of which SAMS urban areas are themselves such an important part.


This is seen partly in the inverse size/growth relationships which, as is shown later, goes beyond the mere statistical artefact that produces higher proportional changes for a given absolute change in smaller rather than larger areas. More importantly, it is seen in the way that SAMS urban area growth has been fastest in those Standard Regions lacking a major urban centre (i.e., the South West and East Anglia) or, in the case of those regions with such centres, in those areas which lie at a distance from the metropolitan core (as in the outer South East and West Midlands and the more rural parts of Yorkshire/Humberside).

Combined with the trends towards spontaneous population distribution are policy measures which in some places reinforce the trend and in others run counter to it.

The Metropolitan and West Midlands green belts are the most obvious examples of policy measures which have deflected growth in widening arcs beyond major cities; whereas in certain locations (especially in the North West), the growth of the so-called Mark II New Towns has, to some extent, compensated for population decline due to unplanned decentralisation.

4.3. COUNTER-URBANISATION: DECENTRALISATION VERSUS

DECONCENTRATION

A number of the broad trends to which the growth of SAMS urban areas conform have been apparent for some time. Studies of population change in the 1960s and 197Os, for example, focused on the North-South shift and the movements of people from the inner areas to the suburbs and outer fringes of the big cities. However, the results of the 1981 census made researchers aware of an additional (some would say quite new) process of evolution in the urban system which, borrowing from terminology originally applied in the United States, has been called ‘counter-urbanisation’ (Berry, 1976; Dean et al., 1984).

In its generally accepted sense counter-urbanisation is more than a simple spatial extension of the long-standing process of urban decentrafisation whereby migrants to the suburbs and the hinterland of a city continue to use that city on a daily basis for work and other facilities. In essence, population movement and activity is, in this way contained within the same city region or ‘daily urban system’. Counter-urbanisation is conceived of as the autonomous growth of population and employment taking place in non-metropolitan and even deeply rural locations (Champion, 1981, 1982; Cloke, 1978, 1985; Fielding, 1982, 1986).

The determinants of this movement are varied and overlapping but include the restructuring of industrial production away from older, urban locations towards less costly ‘greenfield’ sites; improved accessibility and communications for both manufacturing and service industries; the search for more pleasant residential environments and, in some instances, the unintended consequences of government policies (Bourne, 1980).

In England urbanisation processes are more appropriately conceptualised as being divided into two main elements, although because of the compactness and


complexity of the urban system there is likely to be considerable spatial overlap of each (Robert and Randolph, 1983). On the one hand there is decentralisation

of population away from metropolitan areas and large cities towards smaller towns which is occurring without change of workplace. This entails increased distances between new residences and workplaces, especially if the metropolitan/urban core is surrounded by areas of planning restraint such as Green Belt. This form of population movement is closely related to environmental preferences (and ability to pay), and to new housing development (mostly private sector) taking place in small towns and ‘rural’ locations (Hamnett and Randolph, 1982).

Population ‘deconcentration’, on the other hand, is not tied to jobs in the

metropolitan areas and large cities. A number of studies have shown that, in recent years, both population and employment have been moving to outer metropolitan areas or to ‘satellite’ towns well away from the metropolitan core (Fothergill and Gudgin, 1982; Gillespie, 1983). Recent employment relocation has included office and high technology jobs and has been mostly associated with migration by non-manual workers. However, it has also included substantial shifts in manufacturing jobs and hence of manual workers. The recent growth of small free-standing towns and rural areas similarly reflects on urban-rural shifts in manufacturing but it may also be the result of retirement migration independent of employment movements. (Keeble, 1976; Fothergill and Gudgin, 1982; Gould and Keeble, 1984; Warner and Law, 1985).

The trend towards population ‘deconcentration’ is thus reflected in two types

of population growth which may, in certain locations, be inter-related: growth in smaller rather than larger urban areas and growth in non-metropolitan and rural areas rather than close to the big cities (Gordon, 1979). However, although growth has been the predominating theme in counter-urbanisation studies a full explanation of changing population in small and medium sized towns must consider towns with lower than average growth or even with population losses. These may occur, as has been shown above, in a number of situations: in metropolitan urban area subdivisions due either to employment losses or tenure restructuring in older industrial towns (through slum clearance or gentrification), which reduces residential densities in towns where development is restricted, e.g., in the inner commuter ring or green belt; in historic towns with pressures to conserve existing land uses; and in towns in remote rural areas yet untouched by

economic restructuring.

4.4. A FUNCTIONAL REGION FRAMEWORK

In order to investigate the full range of population change among SAMS urban areas at the sub-regional level and to distinguish, so far as is possible, between growth due to decentralisation and that due to deconcentration, it is necessary to place SAMS urban areas in their complementary functional region context. The most up-to-date and comprehensive functional regionalisation of Britain for this purpose is that devised by the Centre for Urban and Regional Development


Studies at the University of Newcastle (Coombes et al., 1982) and it has therefore

been adopted as the functional region framework for this study. The CURDS approach divides England into roughly 190 functional urban

regions each of which consist of an employment centre (defined as an urban core with at least 50,000 population) together with its commuting hinterland. This hinterland is made up of pre-1974 local authority areas (in some cases divided on a ward basis) which send a predefined proportion (using 1971 travel-to-work data) of their residents to work in the employment centre. Within these regions four types of ‘zone’ are identified depending on the strength of commuting attraction to the centre. The core is the main built-up area of the functional region. Most ‘core’ urban areas are urban centres in their own right with considerable local employment. However, some small urban areas are located in CURDS cores but are physically separate from the major centre constituting the core (thus Biggin Hill urban area is part of the London core in the CURDS scheme), and others are subdivisions of CURDS cores (i.e., Cheadle and Gatley urban area is not a metropolitan core in its own right but part of the Manchester core). The remainder of the functional region consists of the inner ring which has over 15% commuting dependence on the core; the outer ring with relatively low integration with the core and the rural area with very little commuting dependence on a core.

In the functional urban region (FUR), scheme employment centres (cores) are differentiated into three types according to their position in the national urban hierarchy. The dominant metropolitan centres of which there are 15 in England (e.g., London, Birmingham, Newcastle), are the principal cities which also define metropolitan regions, subdominant or ‘satellite’ cities, though important employment centres in their own right, are dependent on the dominant cities for both jobs and services, whilst free-standing towns, as their name suggests, are relatively separate from dominant and subdominant centres and from each other in commuting terms.

SAMS urban areas can bear one of several relationships to this system. If they have over 50,000 residents they may constitute employment cores themselves or, if smaller, they may lie in either of the commuter rings of a core or in the less integrated (in travel-to-work terms) rural areas. SAMS urban areas in commuter rings can also be distinguished by their dependence on the core: those in the inner ring have over 15% commuting dependence while those in the outer ring have relatively low levels of integration with the core. Urban areas in the rural remainders may constitute small employment centres in their own right even though they do not meet the functional region threshold.

4.5. SAMS URBAN AREA GROWTH IN FUNCTIONAL REGIONS

In Table 8 the functional status of SAMS urban areas (as derived from their location within the CURDS functional regions) is cross-tabulated with 1981 population size. It can be seen that 97 larger SAMS urban area (those with more than 50,000 population) either form employment cores in their own right or are


TABLE 8. Urban areas - size by functional status

1981 - population size (thousands) Functional status Under 10 10-25 25-50 50-75 75 All sizes

Metro core 7 17 23 16 2 65 Metro inner ring 46 42 24 3 1 116 Metro outer ring 3 11 0 0 0 14 Metro rural area 4 2 0 0 0 6

Satellite core 7 38 37 26 21 129 Satellite inner ring 72 71 20 1 1 165 Satellite outer ring 10 18 4 1 0 33 Satellite rural area 4 1 0 0 0 5

Free-standing core 8 29 52 20 12 121 Free-standing inner ring 116 54 7 0 0 177 Free-standing outer ring 34 41 4 0 0 79 Free-standing rural areas 24 19 4 0 0 47

Total 335 343 175 67 37 957

subdivisions of employment cores: 18 metropolitan (i.e., Solihull); 47 satellite or subdominant (i.e., Chelmsford, Hartlepool, Southport); 32 free-standing (i.e.. Worcester, Stevenage, Crewe); whilst a further 458 are located in the inner rings of each of the three types of centre. Just over half (234) of these inner-ring areas are also very small. i.e.. under 10,000 population. A significant number (over 180) of SAMS urban areas constitute relatively autonomous centres in outer rings (126) or rural areas (58). almost all of them in the high growth size category of under 25,000 population.

In Tables 9 to 14 SAMS urban area growth and certain concomitants of growth are examined simultaneously in two ways: by functional region location and by size of urban area. As we have seen these two dimensions of growth are key indicators of the relationship between the processes of decentralisation and deconcentration in the urban system. However, in many of the tables the Standard Region context for growth by size of SAMS urban area is also given, a procedure which enables us to make certain generalisations about the environmental and planning context of growth.

In addition. the following analyses focus on variations in growth rates of the total population (or of household or employment totals). It is assumed that these variations primarily reflect the balance of net migration and that the spatial variation of natural change is a less influential element of population redistribution (Robert and Randolph, 1983). Districts with the most rapid net migration increase also tend to have rapid natural increase because their population inflows are composed disproportionately of young married couples. However, in some retirement areas, positive net migration may compensate for natural decreases in population.

The pattern of total population change among SAMS urban areas by functional region location and size is shown in Table 9. There is evidence here of a


TABLE 9. Population change by location and size

Location 5-10 Urban area population size, 1981 (thousands)

IO-25 25-50 50-100 All sizes

Average level of population change, % growth 1971-81 Standard Region

South East 14.4 11.9 22.6 West Midlands 11.4 10.5 30.6 North West 13.0 4.8 3.6 Yorkshire/Humberside 11.4 10.4 1.0 North 4.1 5.2 7.5 East Midlands 17.1 8.7 5.6 South West 19.6 14.0 9.8 East Anglia 25.2 22.9 17.6

7.7 10.9 -1.2 -0.8 -4.7

2.1 5.0 1.5

14.1 13.0 5.2 8.9 4.6

11.7 15.6 22.5

England 14.6 10.3 11.8 4.2 11.4

Functional Region Metropolitan Satellite Free-standing Rural

Functional Region Zone Core Inner ring Outer ring

10.8 10.5 5.4 -3.8 7.1 11.1 7.7 7.0 8.2 8.6 17.6 12.3 21.8 3.2 15.3 15.8 13.9 7.9 - 14.6

18.9 7.2 11.8 4.6 8.9 13.2 10.7 10.3 -3.3 11.8 19.1 11.8 22.5 7.2 15.0

considerable deconcentration trend in the growth of SAMS urban areas with a 15.3% increase in the population of free-standing (all sizes) centres and a 14.6% increase in those located in rural areas. The trend towards deconcentration also appears to be size-related with stronger growth among smaller SAMS urban areas in ‘free-standing’ and rural locations. Note, however, that the fastest growth rate of all in a functional region context (21.8%) is for SAMS urban areas in the 25-50,000 population category. Here the FUR classification has singled out a number of New and Expanded Towns which were planned as self-contained settlements well away from the main centres of population. This is also reflected in the very high growth rates for this category of SAMS urban areas in the South East, West Midlands and East Anglia Standard Regions.

However, alongside this trend towards deconcentration there is still a very significant ‘metropolis-centred’ tide of decentralisation. This is reflected in the comparatively high rates of growth among SAMS urban areas in metropolitan and satellite FURS, though this is now affecting mainly the smaller SAMS urban areas, i.e., those under 25,000 population. The continuing commuter function of many small SAMS urban areas is also indicated by the high rates of population growth for those areas located in the core and inner ring FUR zones, whilst those in outer ring zones show more consistent growth across the whole size range.

Employment change as measured by change in employed residents (Table lo),


TABLE 10. Change in employed residents by location and size


lo-25 25-50 50- 100 All sizes

Standard Region Average level of employment change, % growth 1971-81 South East 15.3 13.3 21.5 8.2 14.8 West Midlands 6.9 6.0 20.9 5.0 7.7 North West 13.7 0.4 0.1 -7.0 1.9 YorkshireIHumberside 10.7 9.3 -3.0 -6.0 7.3 North 0.1 2.2 5.0 -9.0 1.2 East Midlands 15.3 6.4 0.4 -2.3 9.0 South West 17.2 11.7 7.0 4.5 13.3 East Anglia 22.2 21.2 13.4 -3.2 19.7

England 13.2 8.8 8.7 1.3 9.5


11.2 9.8 3.6 -5.4 6.9 11.3 6.9 5.1 5.7 7.6 15.0 9.8 16.1 -1.1 12.2 13.0 10.6 3.1 - 11.6


15.2 4.6 7.7 1.8 5.5 13.1 10.7 9.3 -8.3 11.6 13.1 8.7 21.9 3.8 11.1

reveals a broadly similar geographical pattern to that of population change but at generally lower levels of growth or decline and with some significant deviations from trend. It has been fastest in smaller SAMS urban areas located away from

the major centres of population (note again, however, the impact of planned employment growth in New and Expanded Towns in SAMS urban areas with 2%50,000 population in both ‘free-standing’ and ‘satellite’ locations), and in inner and outer ring zones rather than in SAMS urban areas constituting ‘cores’ or parts of cores. Again, smaller urban areas continued to be favoured with more employment growth rather than larger ones, with actual declines experienced in SAMS urban areas over 50,000 population in metropolitan and (curiously) free-standing regions and in inner ring locations.

Once again, therefore, the decentralisation versus deconcentration dichotomy is

apparent with a surplus of growth in employed residents over population growth in small (5-10,000 population) SAMS urban areas in metropolitan functional regions, representing a growing commuter function for such areas, and simultaneous rapid growth of employed residents in smaller (under 25,000 population) SAMS urban areas in free-standing and rural regions. This type of size-related deficit is especially apparent in the South East. In other regions the local growth in employed residents has lagged behind population for most size categories of SAMS urban areas, except that in which New and Expanded Towns are represented.


Turning to the type of jobs performed by employed residents of SAMS urban areas - whether service or manufacturing - we can see in Table 11 that the former are well represented in the ‘classic’ deconcentrating Standard Regions of the South West and East Anglia and also in the South East, though not in the East Midlands. Moreover, although the sensitivity of these measures is open to question, service employment is to some extent a feature of smaller rather than larger SAMS urban areas.

TABLE 11. Service employment by location and size


IO-25 25-50 50-100 All sizes

Standard Region South East West Midlands North West YorkshireIHumberside North East Midlands South West East Anglia

37.2 29.6 30.1 30.3 28.7 28.1 37.8 34.2

Average level of service employment, 198 I 36.9 37.1 37.3 30.2 31.4 29.3 29.8 31.1 31.6 28.7 26.7 32.0 32.0 30.3 28.4 24.0 23.7 29.1 35.1 35.5 37.5 28.3 31.0 32.6

37.1 29.9 30.5 29.2 30.1 26.0 36.6 32.1

England 32.8 32.0 31.9 33.5 32.4


33.4 35.0 32.4 35.1 33.9 31.7 30.7 30.8 33.1 31.4 32.1 31.0 32.4 33.0 31.8 38.1 34.7 37.2 - 36.8


32.6 31.0 31.1 33.7 32.0 32.1 32.3 33.1 31.9 32.3 32.3 31.5 35.0 - 31.9

In terms of functional region type, however, the picture is much clearer with noticeably high levels of service employment in rural and free-standing locations and also in the growing smaller SAMS settlements in metropolitan regions. Service employment is also a significant feature of SAMS urban areas in the 25-50,000 population category in the outer rings of functional regions which probably represents an in situ growth of service jobs in the sort of residential environment to which ‘footloose’ non-manual workers are attracted.

The disposition of manufacturing jobs in SAMS urban areas, on the other hand, is rather more even and therefore more difficult to interpret (Table 12). However, it does demonstrate the rather different economic basis for population deconcentration in the East Midlands and, to some extent, Yorkshire/Humberside. Also, despite evidence of a recent dispersal of manufacturing industry, SAMS

JPP 33:1-D


TABLE 12. Manufacturing jobs by location and size


lo-25 25-50 50-100 All sizes

Standard Region Average level of manufacturing (% employed residents) 1981 South East 24.7 24.2 24.7 26.4 24.7 West Midlands 31.5 35.3 35.4 36.7 34.3 North West 31.8 32.8 30.4 30.5 31.5 Yorkshire/Humberside 29.8 29.5 35.0 33.9 30.6 North 29.9 28.7 31.0 30.2 29.8 East Midlands 32.2 33.6 37.2 30.6 33.6 South West 24.5 24.8 26.7 22.4 24.8 East Anglia 23.9 34.3 28.1 24.9 27.3

England 28.1 28.8 29.8 29.4 28.8


21.6 26.0 30.4 29.2 27.9 28.3 29.9 29.9 30.7 29.6 28.6 29.9 29.5 21.7 29.1 25.2 26.3 26.8 25.7


30.6 31.6 30.2 29.3 30.3 28.7 28.3 29.5 32.2 28.7 25.5 27.8 21.5 31.0 27.0

urban areas in rural regions and outer rings are still characterised by lower than average levels of employees in manufacturing.

The trends towards decentralisation into smaller SAMS urban areas and deconcentration into a wider range of SAMS urban area sizes has been facilitated by high rates of private sector housing development in SAMS urban areas (Table 13). The importance of owner occupation in the growth of smaller SAMS urban areas is seen in all Standard Regions and especially in metropolitan commuter areas (9.4%). In subdominant or satellite areas, where local employment and, probably, land are likely to be more easily available, the growth in owner occupancy has occurred strongly across all sizes of SAMS urban areas. The 8.0% and 8.6% growth in private sector housing in rural and outer ring areas, respectively, marks an important element in the changing tenure structure of areas which are the foci of the ‘deconcentration’ trend.

Along with the trend towards increased owner occupancy in less metropolitan and smaller SAMS urban areas there are also signs of a concomitant migration of professional and managerial workers (Table 14). In the South East and East Anglia this is not confined to the areas nearer to Greater London and it is also clearly apparent in smaller rural and outer ring areas.

Finally, we examine one aspect of the link between decentralisation of people and the deconcentration of both people and jobs, namely, travel to work by car


TABLE 13. Change in owner occupancy by location and size


lo-25 25-50 50-100 All sizes

Standard Region South East West Midlands North West Yorkshire/Humberside North East Midlands South West East Anglia

England



Average level of change in owner occupancy, percent point difference 1971-81

8.3 6.6 6.0 8.7 7.3 7.5 9.0 4.9 6.9 7.6 6.6 6.1 4.6 6.4 5.8

11.4 10.7 6.7 7.1 10.2 4.9 7.1 6.4 5.2 6.0

10.8 8.2 8.2 7.1 9.4 7.0 6.1 6.8 6.8 6.6 8.0 10.3 11.2 4.4 8.6

8.3 7.6 6.1 7.3 7.5

9.4 6.6 5.8 5.6 7.1 7.8 7.5 6.3 8.0 7.4 8.3 8.0 6.2 7.3 7.8 8.0 8.9 6.1 - 8.3

7.9 6.2 6.0 7.3 6.5 8.3 8.2 6.4 7.6 8.1 8.6 7.4 6.6 11.5 7.8

TABLE 14. Professional/managerial migration by location and size


lo-25 25-50 50-100 All sizes


England



Average level of migration by professional/managerial households (1980-S 1) 2.9 2.6 2.4 2.3 2.6 1.8 1.7 1.6 I.7 1.7 1.8 I.6 1.5 I.3 1.5 I.4 1.3 1.5 1.3 1.4 I.1 1.3 1.1 0.9 1.1 2.0 1.5 1.2 1.7 1.7 2.1 1.9 1.7 2.0 1.9 2.2 1.7 2.3 2.0 2.1

2.0 I.9 1.7 1.8 1.9

1.9 2.1 1.7 1.7 1.9 2.2 1.9 1.7 1.9 1.9 2.0 I.7 1.7 I.6 1.8 2.0 1.8 1.5 - 1.9

1.8 1.7 1.6 1.8 1.7 2.0 1.9 I.9 1.5 2.0 2.3 1.9 2.3 1.4 2.1


(Table 15). There are clearly higher rates of such commuting from smaller SAMS urban areas in metropolitan and satellite FUR locations and the inner rings of FUR zones, where it is likely to be related to employment in the metropolitan core and periphery. However, car commuting is also very high in certain areas of deconcentration, that is, among employed residents of SAMS urban areas with between 25-50,000 population in rural functional regions (56.7%) and in the outer rings of functional region zones. Amongst the Standard Regions car commuting is highest in the smaller SAMS urban areas of Yorkshire/Humberside, the East Midlands and East Anglia and generally low in the North and North West.

TABLE 15. Car commuting by location and size


IO-25 25-50 50-100 All sizes

Standard Region Average level of car to work (% employed residents) 198 1 South East 59.9 56.9 54.9 54.1 57.1 West Midlands 62.2 59.6 59.8 58.3 60.3 North West 58.0 56.2 53.7 53.0 55.4 Yorkshire/Humberside 50.2 48.4 52.0 47.4 49.5 North 51.2 49.7 49.3 45.5 49.8 East Midlands 57.8 52.9 51.7 51.0 54.9 South West 54.6 56.7 57.3 55.1 55.7 East Anglia 57.9 51.9 52.9 44.5 54.9

England 56.6 54.6 53.7 53.3 55.0



57.2 55.8 54.3 53.9 55.6 58.2 55.6 53.6 54.9 55.8 56.0 53.1 53.1 50.6 54.3 53.9 50.1 56.7 52.6

56.6 53.3 52.3 53.3 53.2 57.7 56.9 56.2 53.0 57.2 53.1 51.7 56.6 62.2 52.6

4.6. SAMS URBAN AREA GROWTH, POLICY STATUS AND POLICY AREAS

The functional region location of SAMS urban areas represents one important territorial breakdown against which to examine patterns of population growth among SAMS urban areas. A second is the policy status of certain SAMS urban areas and their policy area location which. combined with the functional region location of individual SAMS settlements, constitute an initial approach to an examination of the impact of central and local government planning policies on SAMS urban area growth.


A total of four policy area/status variables with their concomitant subdivisions were considered to be appropriate to the broad intentions and scale of analysis adopted in the study. They, along with all other data for the study, were stored and manipulated using the ESRI ARC/INFO software in an integrated Urban Areas Geographical Information System (UAGIS). Use of ARC/INFO permitted maximum flexibility in manipulating the various data types (numerical, point, line and area) that were central to the study. whilst the use of MAP LIBRARIAN functions enabled us to produce a number of novel spatial combinations of policy areas (Shepherd and Green, 1987).

The four policy variables, their source of information and the data types generated were as follows:

Policy

1. Regional Development

Variable

1.1. Special Development Area 1.2. Development Area 1.3. Intermediate Area 1.4. Not Assisted

2. Planning Constraint

2.1. Approved Green Belt 2.2. Interim/Submitted 2.3. Not Green Belt 2.4. Area Of Outstanding

Natural Beauty

3. New or 3.1. New Town Expanded 3.2. Expanded Town Town 3.3. Neither

4. Motorway Access

4.1. Within 5 miles 4.2. Not within 5 miles

Source

D.E., 1974

Data type

Polygon

DOE, 1976 Polygon

DOE, 1975 Point

DTp (Present year network file) 1981

Line

It is important to note the way in which these policy variables have been used insofar as their inferred ‘impact’ on SAMS urban area growth is concerned. The study of policy impacts in a rigorous sense is an extremely complex undertaking involving not only the careful selection of the ‘objects’ (population groups, towns, employment catagories etc) which are ‘impacted’ in order to ensure that they are initially similar, but also requiring careful control for the effect of other extraneous impacts (i.e., the unintended effects of other policies). The present research was not intended as an impact study in this sense, although the case studies and the modelling approach to SAMS urban area growth adopted in a later chapter do provide more detail on the local and controlled effects of a range of planning policies on population growth. Here, the intention is to ‘sieve’ growth patterns among SAMS urban areas for the concomitant effects of policy (and combinations of policies) as indicated by location within/or outside a policy area or category.


Thus given certain characteristics of a SAMS urban area, i.e., that it is of a population size predisposed to rapid growth in approved Green Belt, in an outer ring, near a motorway and with a high proportion of professional and managerial workers - it is possible to arrive at an understanding of whether recent growth is in line with, above or below that for SAMS areas in one or more of those classes. However, it is not possible to say which policy determined the particular outcome or particular level of impact.

4.7. POLICY STATUS

4.7.1. New and Expanded Towns

New and Expanded Town schemes have been seen both as a way of channelling growth beyond the green belts surrounding major urban areas and of redistributing growth towards less prosperous regions. In the West Midlands and South East pressures for planned decentralisation in the 1960s and the growing housing needs of the conurbations led to the creation of several large New Towns (Redditch, Telford, Milton Keynes) and the considerable expansion of some existing towns. The main impact of these second generation New and Expanded Town Schemes was felt in terms of population growth during 1971-81. Several first generation New Towns such as Crawley and Bracknell and various Expanded Towns under the 1952 Town Development Act also maintained high growth during 197181. However, some of the first generation New Town schemes located in the metropolitan ‘penumbra’ like Hatfield and Welwyn had low growth in the 1970s compared with other SAMS urban areas.

The high average growth of SAMS urban areas which are designated New Towns in the South East and West Midlands (Table 16) particularly reflects the inclusion of a few extreme population increases (such as central Milton Keynes with over 700% population growth and Telford Dawley with nearly 200% growth). Population increases among SAMS urban areas that are Expanded Towns have been more modest, but still generally in excess of non-designated towns. In the North and North West high growth has occurred in the more recently designated New Towns such as Washington and Leyland, both of which were designated in the late 1960s and Skelmersdale (designated in 1961). However, growth has been lower even in more recent New Towns such as Warrington and in some first generation New Towns like Peterlee. The exceptional growth of Expanded Towns in the North reflects, to a large extent, the mixed private/public sector development at Cramlington.

On the other hand, in less heavily urbanised Standard Regions such as the South West, East Anglia and the East Midlands, Expanded rather than New Town schemes have been the major policy instrument of growth and their growth advantage is less marked (especially in East Anglia where spontaneous growth has been so rapid). Expanded Towns in these regions have gained from extended


TABLE 16. Population growth by New/Expanded Town status and location/size

Not New/ Expanded

Status Expanded New

Town Town All

Standard Region South East West Midlands North West YorkshireIHumberside North East Midlands South West East Anglia

10.5 8.6 4.1 8.7 3.0

11.6 15.5 22.5

24.5 23.8

3.0 -

93.4 18.0 23.3 22.5

England 9.6 23.2



198 1 Population 5-1o;ooo

10-25.000 25-SO;000 so-100,000

6.6 48.5 101.6 7.7 1.7 19.6 21.5 8.7

12.4 20.1 152.0 15.3 13.4 26.9 - 14.1

4.1 17.5 70.5 8.8 11.6 46.6 35.1 11.9 13.6 25.8 43.7 15.0

14.5 41.6 6.1 14.6 9.6 24.3 47.6 10.3 4.5 20.9 103.0 11.8 0.5 19.8 28.2 4.2

80.0 14.1 128.3 12.7 31.1 5.1 - 8.7 39.2 5.2 -0.8 11.7 - 15.6 - 22.5

64.7 11.4

decentralisation from the larger urban areas (especially Greater London) as well as from movements within the Standard Region. King’s Lynn for example, has been a major recipient of London overspill population and employment.

Finally, the range of location contexts for New/Expanded Town growth is apparent at the Functional Urban Region level, with the growth of designated towns taking place in a range of contexts from metropolitan to rural labour markets. Thus New/Expanded Town policies have some affinities with Assisted Area schemes (see below), in raising growth rates in both metropolitan and satellite regions, and in medium sized as well as small urban areas where growth rates exceeding 25% in New/Expanded Towns are apparent for SAMS urban areas over 50,000 in size in 1981. However, this growth stimulus also extends to non-metropolitan locations, where Assisted Area schemes have a less apparent effect.


4.8. POLICY AREAS

4.8.1. Regional development

Assisted Area schemes are intended to reduce inter-regional disparities in unemployment, and Green Belt and New/Expanded Town schemes have been directed primarily towards controlling population and employment growth intra-regionally. It is apparent, however, that these policies have different impacts on SAMS urban area growth in different parts of the country and in different combinations.

Among Standard Regions, only three (the East Midlands, North West and South West) contain a mix of Assisted and non-Assisted areas (Table 17), and in these regions - as in England as a whole - growth rates among SAMS urban areas are higher in non-Assisted areas. The exceptional pattern in South West

TABLE 17. Population growth by Assisted Area status and location/size

Not Assisted

Status Special

Intermediate Development Development Area Area Area

All statuses


14.1 - 13.0 -

7.2 5.0 - 8.8 - -

13.2 8.0 15.1 38.9 22.5 -

14.1 12.7

- 5.2 5.1 7.5 - 8.7 5.8 5.0 5.2 - - 11.7

13.2 15.6 - - 22.5

England 14.6 7.4 8.3 4.9 11.4


7.3 9.3 7.9 6.4 7.7 10.9 6.2 10.9 5.6 8.7 19.3 8.2 8.6 0.7 15.3 18.2 -2.4 7.2 - 14.1


14.4 2.3 3.9 0.3 8.8 13.2 11.0 10.8 7.7 11.9 17.3 5.8 14.1 II.2 15.0

1981 Population s-10,000

IO-25,000 25-50,000 50-100,000

17.1 12.5 18.5 7.6 14.6 4.2 2.1 12.8 -0.8 10.3 9.1 12.4 -0.3 -0.1 11.8

13.3 8.0 1.8 -2.6 4.2


England reflects a particular component of regional policy aimed at employment diversification in peripheral rural areas. Here, high growth of inner urban areas near Plymouth, i.e., Ivybridge and Saltash - with Intermediate Area status - and Development Area designation of growth urban areas such as Bideford and Bodmin, probably accounts for the Assisted Area growth advantage since Assisted Areas have benefited from designation in comparison with adjacent ‘grey’ areas (Manners, 1980).

Nevertheless, the most significant growth advantage associated with Assisted Area status is at functional region rather than Standard Region level. Assisted Area schemes appear to have alleviated population losses - or contributed to growth - in metropolitan regions in the North, North West and Yorkshire/ Humberside. Such schemes contrast with controls on industrial expansion and more rigorous Green Belt constraints in metropolitan towns in the prosperous regions of the South East and West Midlands (though by the late 1970s Assisted Area status had been extended to much of the latter). Thus metropolitan urban areas with Intermediate Area status average 9.3% population growth compared to 7.7% in all metropolitan urban areas between 5,000 and 100,000 population. In decentralised satellite labour markets, Development Area status is also associated with growth differentials: 10.9% compared to 8.7% in all urban areas in satellite labour markets.

Assisted Area status has not, it appears, contributed markedly to deconcentration, that is, growth in free-standing and rural labour markets, except perhaps in the South West. Non-Assisted SAMS urban areas in free-standing and rural regions show higher growth. Nor is growth in Assisted urban areas a primary contributor to counter-urbanisation (in the sense of small town growth). These findings run counter to arguments that Assisted Area policies have contributed largely to non-metropolitan growth (Fielding, 1982; Goddard, 1984).

4.8.2. Green Belts

In contrast, Green Belt and New Town policies on the other hand have been directed towards constraining growth in metropolitan regions and directing decentralisation towards satellite or free-standing towns, primarily within Standard Regions. Nevertheless, both policies have inter-regional implications and New/Expanded Town policies have been partly regarded as an instrument of inter-regional policy. Table 18 shows that the most pronounced growth deficit in Green Belt urban areas occur in the South East and West Midlands, where Green Belt policies have been most consistently applied to prevent conurbation sprawl. In regions of the North, Green Belt policies appear to have a less consistent effect (particularly in Yorkshire/Humberside).

In terms of functional regions the constraining effects of Green Belt designation is most apparent in the metropolitan and satellite regions since there are few such designations in non-metropolitan locations. Green Belt policy has particularly

5% Progress in Planning

TABLE 18. Population growth by Green Belt status and location/size

Not Green Belt

Status Interim/ Approved All

submitted Green Belt areas




1981 Population 5-10,000

lo-25,000 25-50,000 50-100,000

20.3 11.7 15.7 5.5 4.7 6.1 7.3 12.3 5.4 -

12.5 8.8 16.4 -

23.5 17.6

13.0 9.5

10.1 10.3 15.4 14.1

10.6 5.2 -1.9 8.8 13.5 11.8 3.4 11.9 16.5 4.5 3.2 15.0

16.6 11.1 14.5 4.6

1.6 1.7 8.3 1.6

16.4 5.2

12.5 10.9 3.7 5.4

0.1 5.4

11.9 2.4

2.6 -7.8

1.9

1.0 4.0 0.0

-1.0

14.1 12.7 5.1 8.7 5.2

11.7 15.6 22.5

11.4

7.7 8.7

15.3 14.1

14.6 10.3 11.8 4.2

inhibited the growth of smaller SAMS urban areas - there is only 1.0% growth in urban areas under 10,000 population in Approved Green Belt. Green Belt policy explains much, though not all, of the growth deficit in metropolitan regions - metropolitan towns outside Green Be6 have a growth rate of lo%, only five points

below that in free standing towns.

4.8.3. Motorway access

Motorway developments have been seen as an element of inter-regional policy in improving the infrastructure of less prosperous regions and reducing the comparative accessibility advantage of more prosperous regions such as the South East and West Midlands. However, motorways are best seen as a necessary or permissive preliminary to development rather than a sufficient condition for growth.


Moreover, many motorways have coincided with Green Belt zones (see below) or with areas of high unemployment and below average growth. Thus Table 19 shows that in England as a whole, urban areas within 5 miles of a motorway have lower growth (10.7%) compared with urban areas further away (11.9%). This growth deficit is particularly apparent in metropolitan and satellite labour markets, and in several Standard Regions. Only in the South East and West Midlands is there a noticeable growth advantage in motorway towns, though this is partly due to very high growth among New Towns adjacent to motorways. These concomitant effects nicely illustrate the complexities of unravelling sometimes conflicting policy impacts at this level of analysis but nevertheless point the way to significant possibilities for research.

TABLE 19. SAMS urban area growth and policy status (average growth rates 1971-81)

Policy 5-10 Population size, 1981 (thousands)

lo-25 25-50 Over 50 All sizes

Not Green Belt, not near m/way Not Green Belt, near m/way Green Belt, not near m/way Green Belt, near m/way

Percent No. Percent No. Percent No. Percent No. Percent 17.4 (183) 10.6 (156) 8.6 (79) 5.8 (35) 12.7 14.4 (71) 12.2 (84) 22.2 (56) 3.1 (44) 13.5 7.5 (38) 10.7 (49) 5.6 (15) 3.0 (9) 8.3 9.0 (39) 6.1 (53) 2.4 (25) 4.4 (16) 6.1

Not Green Belt, not Assisted Area 21.4 (150) 14.5 (131) 25.4 (64) 8.8 (44) 18.3 Not Green Belt, Assisted Area 9.6 (104) 7.2 (109) 4.2 (71) -1.4 (35) 6.4 Green Belt, not Assisted Area 5.2 (54) 8.1 (58) 3.1 (29) 5.2 (22) 5.9 Green Belt, Assisted Area 15.4 (23) 8.6 (44) 4.7 (11) -5.9 (3) 9.5

Not Assisted Area, not near m/way 19.4 (146) 11.6 (133) 10.2 (55) 7.8 (33) 14.1 Not Assisted, near m/way 11.3 (58) 14.7 (56) 30.4 (38) 7.4 (33) 15.6 Assisted Area, not near m/way 8.5 (75) 8.9 (72) 5.2 (39) -2.5 (11) 7.4 Assisted Area, near m/way 13.8 (52) 6.5 (81) 3.4 (43) -1.4 (27) 6.7

4.9. COMBINATION OF POLICY EFFECTS

In a preliminary attempt to unravel some of these complexities we have, in Table 19, cross-tabulated the size of SAMS urban areas (indicative of one aspect of the deconcentration trend in population redistribution) with combinations of two types of policy area (Green Belts and Assisted Areas) in their on/off states and accessibility to the motorway network (as measured by location within/outside a five mile zone on either side of the road). The expectation is that, other things being equal, certain types of policy area combination (i.e., not Green Belt/near motorway) will encourage growth, whilst others (i.e., Green Belt/not near motorway) will retard it.

Overall in this table the inverse relationship between size of SAMS urban area is still apparent - with the significant exception of very high growth in the 2550,000 population category containing New/Expanded Towns - though not so clearly as in other tables. Furthermore, behind the results in this table is the substantial effect of the North-South divide in rates of SAMS urban area


growth. This is brought out by the very high overall rate of growth (18.3%) for SAMS urban areas which are located neither in Green Belts nor in Assisted Areas, i.e., mainly in the southern part of England away from the major urban agglomerations.

In the first part of the table which combines Green Belt/non-Green Belt location with near/not near a motorway, the impact of Green Belt designation, as noted above, is clear. Growth rates are significantly lower for SAMS urban areas lying in Green Belt and either near or not near a motorway. In this, as in other parts of the table, though, the effect of high growth in SAMS urban areas that are New/Expanded Towns in the 25-50,000 population category makes itself felt since many of these, especially in the South East and North West are located close to motorways. However, it would appear that, for SAMS urban areas lying in Green Belt, controls on land-use have been less effective (or ‘in-filling’ has been more widespread), among smaller (under 10,000) and larger (over 50,000) urban areas than among those in the 10-50,000 range.

The combination of Green Belt controls and Assisted Area status also appears to have had an interesting effect with growth taking place almost three times faster among very small SAMS urban areas in Green Belt and with Assisted Area status (15.4% growth) compared with their Green Belt counterpart areas without inducement to growth (5.2%). There is a similar pattern of increased growth in these prima facie situations of policy contradiction for SAMS urban areas with between 10-50,000 population. In marked contrast, however, the larger SAMS urban areas located in both Green Belt and Assisted Areas have declined in population (-5.9%) compared with the growth (5.2%) of their non-Assisted Area counterparts. In such circumstances it may be that Green Belt controls are applied more strictly than for smaller settlements or that there is less suitable land for in-filling or, simply, lack of demand for new housing.

The third part of the table sets out growth rates among SAMS urban areas for combinations of Assisted Area status and motorway proximity. Again, the results are by no means clear-cut and the impact of New/Expanded Town growth is making itself felt in the (expected) higher overall rate of SAMS urban area growth in non-Assisted Areas near motorways (15.6%). Within Assisted Areas motorway proximity appears to have a substantial impact on the growth of small urban areas (13.8%) and may have done something to reduce the rate of decline of larger SAMS urban areas (-1.4% compared with -2.5%).

4.10.SUMMARY

In this chapter we have used the SAMS urban areas data to extend our knowledge of current urbanisation processes in England and to assess the role played by SAMS urban areas in these processes. An important aspect of the change involved here is hierarchical (i.e., size-related), rather than simply spatial and the SAMS urban areas data have permitted the first detailed analysis of this element of change. However, in the absence of explicit data on linkages between


urban areas (i.e., travel-to-work data), it has been necessary to place SAMS urban areas in their functional region context in order to assess the way in which growth occurs.

Three broad types of population change among SAMS urban areas, in addition to the size-related aspects of change, have been distinguished:

(i) Decline (though often fairly slight), in population among some larger (Xl-100,000 population) SAMS urban areas forming continuous subdivisions of larger urban areas in the North West, Yorkshire/ Humberside and the North and in metropolitan functional regions and inner ring areas.

(ii) Decentralisation of population to mainly smaller SAMS urban areas which are still within the commuting penumbra of the large urban agglomerations.

(iii) Deconcentration of population away from the other metropolitan cores and their commuting hinterlands and into the region of free-standing SAMS settlements and rural areas typified by parts of East Anglia, the South West, the East Midlands and the South East beyond the metropolitan Green Belt.

The latter type of movement in particular has been led by new service employment and the provision of homes for owner occupancy attracting non- manual and - in certain locations - skilled manual workers and based on car-commuting to work.

The apparent simplicity of these patterns of growth is, however, considerably complicated by their overlapping nature (both people and jobs have been decentralising to SAMS urban areas from the major cities), and by the impact of policy measures to constrain or promote growth generally. In particular the planned growth of certain New and Expanded Towns has, in a sense, distorted the analysis of some aspects of the analysis of population change among SAMS urban areas whilst the existence of Green Belt controls has limited the growth of SAMS urban areas (though possibly not the smaller ones) which, on other criteria, might be growing more rapidly. An analysis of SAMS urban area growth by different combinations of policy area - some of which, on a priori grounds clash and others of which reinforce each other - though difficult to interpret, raises a number of issues for further analysis.

CHAPTER 5

A Classification of SAMS Urban Areas

5.1. INTRODUCTION

This chapter investigates the inter-relationships between multiple indices of structure and change in SAMS urban areas as a function of broader social change. It is concerned with data reduction and classification, and with examining groups of urban areas with similar characteristics within a policy area framework. The intention is to reduce the large amounts of data associated with the project, namely numerous indices of structure and change for the 957 SAMS urban areas, to a level that is more amenable to succinct description and analysis. Physically defined urban areas form the primary unit of analysis.

The use of physically defined urban areas differentiates this analysis from previous multivariate studies of settlement geography which have relied on local authority areas as the unit of analysis (Moser and Scott, 1961; Webber and Craig, 1978). As Openshaw and Charlton (1984), have noted, such studies arc not based on any consistent or meaningful notion of what constitutes a town or settlement. The classification of functional regions as carried out by Openshaw and Charlton (1984), clearly improves on a territorial base consisting of local authority areas, but it necessarily neglects the characteristics of individual settlements within functional regions. In similar vein, Champion and Green (1985) have developed a classification index of economic performance for these same functional regions in an attempt to locate the characteristics of Britain’s booming ‘towns’. If. as is clear from the current analysis, the location of such growth is becoming increasingly scattered across a large number of small and medium-sized settlements then functional regions, which may include several SAMS settlements, in addition to the urban core of the region, are an inadequate basis for studying small town demographic and economic change.

Physically defined urban areas thus form the primary unit of analysis but the classification of urban areas is also inter-related with other indicators of locational status, namely the functional role of SAMS settlements (e.g., employment cores as against commuter rings), Green Belt status, Assisted Area status. and nearness to motorway. Classifications are multivariate and general, for in constructing them a large number of indices of structure and change covering a range of topics were used rather than a few specific variables.

62


5.2. INPUT VARIABLES

A wide set of indices covering a range of topics form the basis of the urban area classification. A selection of variables drawing from each broad category in the study i.e., the demographic, socio-economic, housing and migration/change variables described in Chapter 2, was used to ensure as general a classification as possible (Table 20). Variables are chosen so that each broad category is not over-represented (Webber and Craig, 1978). The choice of input variables is intended to reflect the variety of functions performed by SAMS urban areas as commuting suburbs, as market towns, resorts or mining towns. Variables chosen to indicate such functions are included together with a range of indicators of

TABLE 20. Input variables, means and standard deviations

Variable Mean Standard deviation

Population, 1981 Children under 15 Ages 65+ New Commonwealth residents Average household size Owner occupancy Municipal renting Second/holiday homes Professional/managerial owner occupiers Skilled manual owner-occupiers Professional/managerial workers Other non-manual workers Skilled manual workers Semi-unskilled workers Work outside district of residence Car to work Bus to work Households owning one or more cars Households owning two or more cars Manufacturing workers Service workers Distribution/catering workers Agricultural workers Mining workers Unemployment Economically active women Population change (rate) Change in O-15 Change in 65+ Change in average household size Change in New Commonwealth born Change in employed residents Change in unemployment Change in economically active women Change in households (rate) Change in owner occupancy Change in municipal renting Change in one or more car owners Change in two or more car owners

22,095.9 23.0 14.7

1.4 2.8

62.5 27.1

0.6 16.4 17.1 15.8 28.9 23.7 21.5 32.9 54.9 11.4 65.8 17.1 28.8 32.4 19.3

1.3 4.5 8.4

44.3 11.4 -3.2

1.8 -6.0

0.3 9.5 3.8 5.5

18.5 7.5

-0.9 9.2 7.6

20,143.7 2.8 4.9 1.6 0.2

14.5 14.2

1.8 8.6 5.8 6.8 6.3 5.9 5.4

16.9 9.1 8.3

11.3 8.2

10.3 9.6 4.3 1.6 7.3 3.7 5.2

30.5 2.1 I.5 3.2 0.4

30.4 2.5 2.7

30.0 5.1 4.7 3.2 3.2


socio-economic and demographic composition, typical of many multivariate small area analyses. Change indices of age structure, tenure and car ownership, and of employment availability and activity are also included. The set of input variables includes several growth indices (of population, employment and housing), which ensures that ‘growth’ is properly represented or ‘weighted’ in the input variables and hence output analysis. The inclusion of growth variables also reflects possible discrepancies (such as employment surplus and deficit) between growth indicators in various categories of urban area. Simple housing tenure variables are supplemented by tenure indices specific for social group. in order to reflect social diffusion of owner occupancy.

In order to ensure that the variables each have approximately the same influence on the final classification (i.e., in order to correct for differences in scale between them) all input variables were converted to standardised scores.

5.3. CLUSTER Ai’iALYSIS

In cluster analysis, as opposed to principle components analysis another widely used method of classifying data, the focus is on grouping objects or areas rather than variables (Everitt, 1980). Each area is described by scores on a set of input variables and urban areas with similar scores are clustered together so that the characteristics of each cluster can be described. The cluster analysis method used (iterative relocation), ensures an optimal classification insofar as clusters are as homogeneous within themselves as possible but as different between each other as possible (Wishart, 1978).

Hierarchical clustering methods such as the ‘nearest neighbour’ and Ward‘s

method. would require that we consider the characteristics of all 900 or so urban areas in the data set and merge together those that are most similar on the input characteristics. To reach a manageable number of clusters (less than say. 15), then requires over 900 successive fusions. Once a particular urban area has been allocated to a particular cluster it cannot be re-allocated despite changes in the average characteristics of clusters as further fusions take place. In contrast. the iterative relocation procedure (Wishart, 1978) starts with a random allocation of urban areas to a relatively small number of clusters (such as 15) and repeatedly scans the set of urban areas to see if the allocation of each area to one of the clusters can be improved. During each relocation scan each area is considcrcd in turn and its similarities with all other clusters are computed.

Suppose that the similarity between urban area X and its parent cluster P is S(P,X) and the similarity between X and any other cluster Q is S(Q.X). then if S(Q,X) exceeds S(P,X). area X is moved from cluster P to cluster Q. The centroids of clusters P and Q arc recomputed to account for this change at the time that the switch occurs. The population is repeatedly scanned until no arcas are relocated during one full scan. when an optimum solution for 15 clusters in terms of the similarity function S will have been obtained. One may then go on


to carry out the same relocation procedure for successively reducing numbers of clusters based on the previous allocation. In the analyses carried out here such relocations were considered for between 12 and 15 clusters.

The effectiveness of the cluster analysis can be assessed by comparing the variation of the particular index between clusters with the original variation of that index between all the urban areas. A low loss of variance implies that the variable is well explained by the classification, i.e., within each cluster most urban areas have similar values of the variable and the major differences are between clusters (Webber and Craig, 1978). At the level of individual areas the quality of the classification can be assessed by the distances of areas in each cluster from the cluster average (centroid). A poorly classified case will be further away from the cluster average than most other areas in that cluster (Openshaw, 1983).

5.4. THE RESULTS

The results of the cluster analysis, based on an iterative relocation technique to reduce the 952 urban areas to a set of 12 clusters, are summarised in Tables 21 to 24, and Appendix 3. Table 21 presents the averages on the 39 input indices within each of the 12 clusters together with the global average (over all SAMS urban areas) and the percentage of the original variation in each index retained in the 12 clusters (the variation ‘explained’ by the cluster analysis).

It can be seen that the cluster analysis retains nearly three-quarters of the original variance in some indices - car ownership, high status workers, and perhaps most importantly, in population change - whereas other indices, notably population size and agricultural workers have relatively little variation over the clusters. Tables 22 to 24 show the distribution of urban areas both by cluster and by other types of locational status - position in the labour-market hierarchy, Assisted Area, Green Belt, New/Expanded Town status, and Standard Region.

Appendix 3 gives each cluster by its constituent urban areas, giving also the Standard Region and labour market type for each urban area, the 1981 population and 1971-81 population growth rate for each area, and the distance of each urban area from the average of the cluster in which it is located. This distance is calculated on the basis of the 39 input variables, so that an urban area with low distance from the cluster average is a typical member of the cluster, while large distances from the cluster average represent poorly classified cases (Openshaw, 1983). These distances are ranked within each cluster from 1 (the area closest to the average) to areas furthest away from the average.

5.4.1. Cluster 1 - Service based employment centres

Cluster 1 contains primarily a mixture of relatively successful larger urban areas with a service bias or diverse economic structure, together with smaller rural urban

JPP 33:1-E


TABLE 21. Characteristics of 12 clusters

Cluster averages Global average 1 2 3 4

Population, 1981 22,094 27,770 17,869 25,062 15,277 Children under 15 23.0 21.9 22.4 17.7 25.4 Ages 65+ 14.7 14.8 14.0 27.4 10.8 New Commonwealth residents 1.4 1.6 1.6 1.0 1.2 Average household size 2.8 2.7 2.8 2.4 2.9 Owner occupancy 62.5 60.6 75.3 71.1 13.4 Municipal renting 27.1 24. I 15.1 14.3 18.8 Second holiday homes 0.6 0.2 0.3 5.8 0.2 Prof./managerial owner-occupiers 16.4 17.7 32.1 16.8 21.6 Skilled manual owner-occupier 17.1 12.8 12.0 14.7 19.8 Prof./managerial workers 15.8 17.0 28.2 17.8 18.7 Other non-manual workers 28.9 32.6 35.5 29.2 34.0 Skilled manual workers 23.7 18.5 16.3 22.1 22.2 Semi-unskilled workers 21.5 18.8 14.1 19.2 17.9 Work outside district of residence 32.9 29.9 51.9 19.7 47.8 Car to work 54.9 53.5 64.4 52.0 63.9 Bus to work 11.4 9.3 5.4 7.0 7.8 Household owning I+ cars 65.8 67.9 80.2 61.0 78.4 Households owning 2+ cars 17.1 17.7 32.4 13.1 23.3 Manufacturing workers 28.8 20.4 23.5 17.3 29.0 Service workers 32.4 45.4 40.6 35.9 33.7 Distribution/catering workers 19.3 18.1 19.2 27.6 19.1 Agricultural workers 1.3 1.0 1.3 1.4 I.4 Mining workers 4.5 I.9 2.0 2.0 2.6 Unemployment 8.4 6.4 4.8 10.7 5.8 Economically active women 44.3 45.4 44.3 32.7 48.3 Population change (rate) 11.4 3.6 6.7 9.8 26.8 Change in 0- I5 -3.2 -3.9 -4.1 -1.3 -3.4 Change in 65+ 1.8 2.4 2.4 1.5 1.3 Change in average household size -6.0 -7.7 -5.9 -3.7 -5.2 Change in New Commonwealth born 0.3 0.1 0.3 0.1 0.3 Change in employed residents 9.5 4.4 IO.1 3.5 30.7 Change in unemployment 3.8 2.6 I.7 3.4 2.5 Change in economically active women 5.5 6.9 7.4 3.3 7. I Change in households (rate) 18.5 12.3 13.2 14.1 33.8 Change in owner occupancy 7.5 7.4 5.7 6.7 6.5 Change in municipal renting -0.9 -0.9 -0.9 0.4 -1.9 Change in one or more car owners 9.2 8.4 5.9 9.8 7.9 Change in two or more car owners 7.6 7.4 12.0 5.4 10.9

areas with a service bias (including some garrison towns). These towns are mainly located in the South East, East Anglia and South West but are also significant in other regions (Fig. 4). Larger service centres are exemplified by urban areas such as Durham, Bath and Guildford and often combine high levels of one or

TABLE 21. (cont’d)


5 6 7

Cluster averages Variation

8 9 10 11 12 explained

11,693 36,883 15,525 29,390 33,769 47,22 1 32,869 15,091 16.6 21.6 30.9 25.1 22.8 25.1 24.4 23.5 23.6 45.6 18.6 7.0 12.2 15.0 10.3 13.8 13.4 13.0 62.9 0.8 4.9 0.9 1.3 2.3 9.6 0.8 0.4 62.2 2.6 2.8 2.8 2.7 2.9 2.8 2.8 2.8 47.3

62.2 23.7 66.5 62.3 42.6 63.2 40.8 43.3 55.7 25.7 73.2 25.9 28.0 51.5 27.3 49.9 44.4 59.5

0.8 0.3 0.2 0.2 0.1 0.1 0.0 0.0 54.9 14.9 7.3 14.9 11.5 10.5 11.6 6.9 6.2 13.2 16.7 6.0 22.5 20.5 13.4 20.4 14.2 19.0 40.9 14.8 14.9 14.2 11.9 11.7 11.7 8.5 7.9 69.9 26.9 28.7 27.3 25.8 27.8 26.3 22.9 20.3 49.8 25.3 20.4 27.9 26.9 24.0 25.4 24.8 32.8 57.2 23.3 20.3 21.7 25.5 24.9 25.3 27.2 27.5 61.6 20.6 20.9 35.2 24.9 28.2 23.0 32.7 27.4 40.4 53.2 58.4 59.2 50.4 53.6 50.0 46.3 42.7 51.6

5.2 13.5 12.3 13.1 13.2 16.3 24.4 25.9 56.1 66.6 64.7 61.3 58.9 65.0 55.4 48.1 49.5 75.2 15.1 10.6 16.1 12.2 14.9 11.9 8.3 7.9 76.3 24.2 29.3 36.5 37.9 36.1 40.9 35.1 23.0 49.1 32.0 32.5 26.6 26.1 27.9 27.8 27.1 22.5 53.2 23.1 20.8 17.5 18.6 18.3 17.7 18.0 16.0 40.1

3.3 0.1 1.1 0.8 0.6 0.3 0.5 0.8 22.9 2.1 1.2 5.4 3.4 2.1 1.7 4.5 26.9 70.2 7.8 13.5 8.3 9.1 10.5 11.0 16.1 10.8 58.1

40.1 48.9 46.4 45.6 49.7 47.5 44.0 42.2 54.5 14.1 728.6 24.7 2.9 11.2 4.8 -0.6 2.4 67.7 -2.3 7.8 -1.4 -2.9 -6.0 -1.7 -4.1 -4.1 31.9

2.0 -9.8 0.3 1.8 2.9 0.8 2.2 1.9 25.9 -5.7 2.6 -3.9 -5.7 -10.5 -3.1 -8.2 -8.0 28.5

0.2 3.2 0.3 0.3 0.5 2.0 0.2 0.1 42.3 10.1 675.3 20.4 -0.9 8.3 -2.5 -8.8 0.3 63.4 3.2 8.9 4.2 4.4 6.0 5.9 8.7 4.5 47.6 4.4 0.8 3.7 5.0 6.0 3.0 4.8 6.2 28.1

21.1 707.6 29.6 9.2 24.0 8.8 8.2 11.4 64.3 8.2 -24.1 9.3 6.7 13.3 5.1 4.4 12.4 23.1

-0.9 34.3 -2.2 -0.4 -9.8 1.1 2.9 1.4 30.0 8.9 26.1 12.9 10.3 6.2 10.2 8.8 11.3 44.2 6.1 5.9 8.5 5.9 6.8 5.6 4.0 4.4 63.5

more of administrative, university or other service employments. Smaller rural service centres are exemplified by urban areas like Northallerton, Newmarket, Devizes and Wantage. While most of the areas in this cluster have above average self-containment and a local employment base, a minority are primarily commuter

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FIG. 4. SAMS urban areas classification. Cluster 1 : Service employment centres.

centres (i.e., Epping). The service bias of this cluster’s employment structure accounts for its lower unemployment than average (and lower unemployment increases), even among its Northern region representatives.

Inner ring urban areas are under-represented in this cluster, but those that are present are often located in Green Belts (i.e., Bingley and Epping), and this has reduced the average growth rate of the cluster. Moreover, many county and university centres, while relatively prosperous, exibit only modest growth. Historic centres such as Wells, Bath and Salisbury, with other forms of development restrictions, may also undergo population loss.


5.4.2. Cluster 2 - High status commuter and service towns

Cluster 2 has an affinity with cluster 1 in its predominant service bias, but exhibits many more distinct high status socio-economic characteristics (e.g., professional and managerial owner-occupiers, and two-car owners). While primarily consisting of inner ring commuter urban areas near to conurbations or their satellites, it also includes some high status towns with substantial local employment such as Knutsford, Woking and Maidenhead.

The distinct metropolitan bias of this cluster is evident in the over-representation of SAMS urban areas in the South East and North West. Table 24 shows the high concentration of Green Belt urban areas in this cluster, with an equal mix of approved and interim/submitted status. Several high status dormitory centres in the rings of growing free-standing employment centres are. however, also found in

this cluster. Many commuter settlements near London or Manchester, which had their main

phase of growth in the 1950s or 1960s (or even between the wars), now have low growth. The main influence in constraining growth is location in approved Green Belt, which appears to override the impetus to development provided by motorway proximity (66 of the 119 areas in this cluster are close to a motorway). Nevertheless, continuing metropolitan decentralisation in certain locations (not in Green Belt) and growth of commuter suburbs in free-standing labour markets beyond metropolitan influence account for an average growth rate of 6.7%.

5.4.3. Cluster 3 - Resort and retirement towns

Urban areas in cluster 3 are mainly resort and retirement towns which resemble clusters 1 and 2 in a service based employment structure. They all have a coastal or near-coastal location, however, they are distinguished by an age structure weighted towards the elderly and by a high proportion of second/holiday homes. Despite the service bias, unemployment levels are high, partly because of seasonal tourist employment. Dependence on tourism is also apparent in the high levels of distribution/catering jobs.

The majority of SAMS urban areas in this cluster are in smaller rural (or outer ring) labour markets, with relatively high self-containment (only 20% on average of residents work outside their local district), but some are major employment centres (Worthing, Hastings, Eastbourne, Scarborough) with growth led by office decentralisation or expanded manufacturing, albeit from a low initial base. Several peripheral resort urban areas in the South West with unemployment problems have Development Area status (i.e., Newquay, Falmouth, Ilfracombe), while in

the North a number of urban areas in this cluster have Intermediate Area status. Among SAMS urban areas in this cluster migration generally acts to reinforce

the elderly age structure - though the increase in the over 65s also reflects in


situ ageing. The continuation of economically inactive migration is apparent in increases in employed residents lower than in total population.

5.4.4. Cluster 4 - Growth commuter areas with mixed employment

As in the case of Cluster 2, this growing group of SAMS urban areas primarily contains dormitory centres with low levels of self-containment and high levels of car commuting. However, the employment (and social) composition of this cluster is more diverse for it includes commuter settlements in the hinterlands of industrial towns in the Midlands (Fig. 5). These urban areas included many skilled manual as well as non-manual owner occupiers among their residents. But while

FIG. 5. SAMS urban areas classification. Cluster 4: Growth commuter towns with mixed employment.


the primary emphasis of cluster 4 is on small commuter settlements, a few larger employment centres with a mix of manufacturing and services lie in this cluster (i.e., Farnborough and Dunstable), as well as some smaller SAMS urban areas

where a significant local employment base is combined with commuting to larger centres - such as Saint Ives, Cambridgeshire.

This cluster also provides evidence for continuing metropolitan decentralisation: examples of growth in metropolitan (or satellite) hinterlands include Dronfield, near Sheffield, with a 32% population increase in 1971-51 and Eaglescliffe. near Stockton-on-Tees, with 52% growth. Part of the high average growth rate of this cluster (26.8%) does, nevertheless, reflect an expanded commuter function for towns in the hinterlands of free-standing employment centres: for example, Highworth near Swindon and Sawston near Cambridge. Above average population growth has occurred despite an over-representation in this cluster of ‘Green Belt’ towns - suggesting, that the effects of Green Belt policy on development interacts with the social composition of areas, with development more likely in middle income Green Belt settlements.

5.4.5. Cluster 5 - Rural and free-standing towns

Cluster 5 contains a number of smaller self-contained SAMS settlements in rural areas. These are found to a lesser extent in clusters 1 and 3 but cluster 5 is distinctive for its balanced economic structure. It represents the main cluster of smaller SAMS urban areas in non-metropolitan locations - either in outer rings of employment cores, in rural areas or in the daily urban systems of free-standing urban cores. In this cluster the latter are typically medium-sized centres (i.e., Grantham, Kendal, Bury St. Edmunds) with extensive agricultural hinterlands. The cluster shows a pronounced bias towards those Standard Regions that are less urbanised i.e., the South West, East Anglia and the North but arc also evident in the outer South East and the outer West Midlands (Fig. 6).

Population and household growth among SAMS urban areas in this cluster arc slightly above average, but growth of employed residents is slightly below average. The fact that this pattern is not accompanied by higher than average increases in unemployment is suggestive of the occurrence of distinct migration streams. for example, both employment-led migration and retirement migration. Retirement migration has dispersed from traditional larger coastal resorts to several attractive inland locations (e.g., Gillingham in Dorset, Pickering in Yorkshire) and has contributed to an above average percentage of retired people in this cluster - though this feature also reflects ageing of the population in more static rural towns.

Growth in several SAMS urban areas of this cluster may have been facilitated by Development Area status - for example, growth of new industries has occurred in the Devon urban areas of Barnstaple, Bideford and Braunton - suggesting that the urban-rural shift of employment and population may, in some cases. be related to Assisted Area status.


FIG. 6. SAMS urban areas classification. Cluster 5: Rural and free-standing towns.

5.4.6. Cluster 6 - Milton Keynes

This cluster consists of Milton Keynes alone. It is distinguished by exceptionally high growth, and to a lesser extent by high numbers of children and few retired people. The employment structure resembles that of cluster 4, with an equal mix of manufacturing and service jobs. However, its tenure structure is distinctive - with nearly three-quarters of households renting from public authorities.


5.4.7. Cluster 7 - Growth through manufacturing prosperity

Cluster 7 represents small-town growth through manufacturing prosperity, evidenced by employment levels (in 1981) and percent point increases in unemployment only slightly above the average, and certainly lower than the other manufacturing clusters. This pattern is confirmed by high increases in one-car ownership, and increases in two-car ownership exceeded only by high status non-manual commuter urban areas. The socio-tenurial composition of this cluster is biased towards skilled workers owning their own homes and municipal renting is below average.

Particular concentrations of urban areas in this cluster are found in the Midlands, the North West and Yorkshire/Humberside. The bias of the cluster is towards inner ring SAMS settlements in the hinterlands of satellite and free-standing employment cores - an example being Rothwell near Kettering. However, many such urban areas have local manufacturing jobs (shoe manufacturing in the case of Rothwell), and the cluster also includes some substantial larger centres such as Kidderminster and Braintrce, and also several small self-contained centres (in outer rings or rural areas), such as Middlcwich and Higham Ferrers.

Inner ring urban areas in this group have gained employment through manufacturing decentralisation (20% on average) and population and household growth through (predominantly) private sector residential development. with owner occupancy ranging from 57% to 67%. Such development has probably been

facilitated by the absence of green belt designation.

5.4.8. Cluster 8 - Manufacturing stability

This cluster resembles cluster 7 in its industrial bias (38% of employed residents in manufacturing), and relatively low unemployment or increases in unemployment. It is distinguished by its much lower growth rates (only 3% population growth on average, and a 1% fall in employed residents), and by its inclusion of many employment cores. The primary emphasis here is towards satellite and free-standing employment cores (i.e., Barrow-in-Furness, Crewe. Lancaster, Chesterfield, Rugby, Mansfield, Banbury). although there are also some smaller employment centres - in outer rings or rural areas - such as Barnoldswick. Goole and Skipton (Fig. 7).

The economic stability of this cluster is evidenced not only by relatively low unemployment but also by increases in (one) car ownership above average. As in the case of cluster 7, owner occupancy is the predominant tenure. and skilled manual owner occupancy is also above average. However, a higher proportion (26%) of workers in this cluster are in semi-skilled/unskilled jobs.

Employment centres in this cluster have benefited from metropolitan job decentralisation, New or Expanded Town schemes, sub-regional advantage (e.g., Cheshire as against the rest of the North West), or the particular nature of


FIG. 7. SAMS urban area classification. Cluster 8: Manufacturing stability.

the employment structure. However, in some areas (such as Barrow-in-Furness and towns in its hinterland) relative prosperity may have been maintained by Development Area status. Nevertheless, the predominant pattern, again as in cluster 7, is of urban areas with Intermediate Area status which may contribute to growth of free-standing and rural employment centres - particularly in Standard Regions such as the East Midlands, where Assisted Area designation is not total in its coverage.

5.4.9. Cluster 9 - Planned decentralisation

Cluster 9 contains a mix of relatively high growth manufacturing centres, often under New/Expanded Town schemes (i.e., Ellesmere Port, Haverhill, Daventry),


as well as some inner ring urban areas (New Addington, South Ockendon) and second generation New Towns (Hatfield. Welwyn) with relatively low growth. Lower growth rates in such towns may reflect Green Belt restrictions, particularly in the South East - nearly half of this cluster lies in Green Belt land - as well as the ageing of the original immigrant cohorts. Hence the average population growth rate for this group is only 11% which is comparable with the national average for SAMS settlements.

The distinguishing feature of this cluster is a high level of municipal renting - often a concomitant of New/Expanded Town status or overspill schemes. While the average unemployment rate is higher than in cluster 8, this is partly due to the inclusion of a number of unemployment blackspots (Corby, Madeley. Winsford).

5.4.10. Cluster 10 - Manufacturing towns with ethnic communities

Cluster 10. which contains 20 SAMS urban areas, is similar to cluster 9 in its manufacturing bias, and its levels of unemployment in 1981 and of unemployment increase between 1971 and 1981. It also consists predominantly of medium or large employment centres with 19 of its constituent urban areas forming functional region cores (or parts of such cores). Thirteen of the urban areas in this cluster

are located in or around the Manchester and West Yorkshire conurbations. This cluster is. however, mainly distinguished by high ethnic group representation and by its increase in New Commonwealth born residents from 7.6% to 9.5% of the population. While ethnic group immigration (and high natural increase) may offset net out-migration by whites, population and household growth in these towns is much below average, with numbers of employed residents in decline. Over half (12 out of 20) of the urban areas in the cluster have Intermediate Area status as a means of promoting growth and countering employment loss (Table 24).

5.4.1 I. Cluster I1 - Manufacturing areas with high unemployment

It is Cluster 11, however. that contains the largest concentration of unemployment blackspots with an average 1.5% unemployment rate and an 8 percentage points increase in unemployment. While population totals in this cluster are virtually static between 1971 and 1981, there was an average 8% decline in number of employed residents. It can be seen from Table 24 that nearly half of the 81 Special Development Areas in the total set of 952 urban areas lie in this cluster. In some urban areas, large increases in young adult populations in municipal overspill areas (i.e., Kirkby, Longdendale and Partington) have contributed to increases in unemployment.

The predominant locational bias of the cluster is towards urban areas in or near metropolitan and satellite towns in the North (and to a lesser extent. North West). Examples are Hartlepool and Newton-le-Willows, in Warrington’s outer ring. However. some free-standing centres (Carlisle, Darlington, Doncaster) with relatively high unemployment are also included. Other distinguishing features of


this cluster are high numbers of semi- or unskilled workers, and high use of bus to work - a reflection of low car ownership as well as the metropolitan nature of many urban areas in the cluster.

5.4.12. Cluster 12 - Mining towns

Finally, Cluster 12 consists of SAMS urban areas with high proportions of workers in energy (usually mining) and water, located on the Northumberland and Durham and South Yorkshire coalfields. Unemployment rates in 1981 averaged 11% but increases in unemployment were only slightly more than average. Mining areas thus appear to have had less drastic increases in unemployment than manufacturing areas although they did have relatively high unemployment in 1971. The picture of qualified affluence is also apparent in large increases in home and car ownership. though such ownership remains at below average levels in 1981, and the use of the bus to get to work remains important. The skilled character of the mining workforce is also apparent.

5.5. SUMMARY

The purpose of this chapter has been to isolate sub-groups of SAMS urban areas that are relatively homogeneous with respect to data measuring structure and change. In this respect the cluster analysis has proved remarkably sensitive in delimiting groups of urban areas based on combinations of size, function and location. It is important to bear in mind, however, that the clusters identified in this stage of the study are (a) derived from a very general set of variables selected as being representative of the total data set, and (b) descriptive rather than explanatory of the patterns or trends they depict. The resultant major types of urban area and the dimensions of change form a basis for the explanatory stage of the project based on modelling growth and change that follows in the following chapter.

CHAPTER 6

Modelling Growth and Change

6.1. INTRODUCTION

This chapter addresses two main questions, first, to what extent is the change in individual urban areas a consequence of structural changes which affect all urban areas uniformly, and how far is it the consequence of localised conditions? Second, precisely how is urban area type related to growth, where urban area type may be defined by locational variables, policy status and type of economic specialisation. This focus on elements of change - structural and localised - reflects major themes in the recent analysis of change, one of which is concerned with the process of causality underlying change, and another which distinguishes between different types of change. Simple change measures, such as percentage point differences, do not fully express the causal influence of the starting value of an index on change. In this analysis the different types of change have been broken down into their constituent parts in order to assess how far change in individual units of observation is a consequence of change that affects all urban areas uniformly, and how far it is due to particular factors operating in just a few urban areas.

The analysis of growth undertaken here is intended to reflect the contribution of local patterns of economic specialisation, distinctive social change, policy status, etc. to differentials in population growth between SAMS urban areas. Several major processes are investigated as sources of small and medium-sized town growth, the balance between them depending on the standard and functional region context. These include employment decentrahsation and consequent employment-led population growth; housing availability (particularly the

impact of private sector housing developments); the impact of selective migration which is expected to reflect one or both of employment and tenurial change; the extension of commuter hinterlands, as a result of increased car ownership and car commuting and improved road accessibility and the balance between autonomous and policy-led growth.

6.2. THE COMPONENTS OF CHANGE

Recent models of change in socio-economic systems have been concerned to do

80


three things: distinguish between broad structural trends and localised change; to reflect the causal impact of starting value on change; and to evaluate the relative importance of different components of change (Congdon and Shepherd, 1985, 1988).

Structural change refers to that part of total change in an urban area (between 1971 and 1981), which is expected on the basis of common shifts in the average of particular census indices and in the scatter of urban areas around the average. Changes in the scatter around the average imply either reduced or increased polarisation that is, convergence or divergence of values. On the other hand, changes in the relative ordering of urban areas on an index occur only when the end period value of an index in an area is not what would be expected on the basis of structural changes and the area’s start period value. Certain areas may therefore undergo unexpected patterns of change, which are dissimilar to the general pattern (Colver and Smirnov, 1979), and it may be valuable for explanatory or policy reasons to be able to isolate such ‘non-conforming’ areas.

It is also desirable that a quantitative approach to urban change should reflect both the flow of causality and the distinction between structural and localised change. The model outlined below attempts to do this. It is designed to answer three questions:

(a) Have the averages of population indices for the whole range of SAMS urban areas increased, decreased or stayed the same?

(b) Have differences between urban areas in terms of ‘scatter’ around possibly changed averages increased or lessened?

(c) Have individual urban areas changed in their position relative to the system of small and medium-sized urban areas as a whole?

The model uses a linear regression of 1981 values of an index on 1971 values of the same index in order to express the causal influence of the initial value of the index on its final values. The regression approach also permits us to distinguish between the different components of change, that is, average, scatter and relative position. A regression equation which isolates these different elements of change predicts 1981 values in urban areas on a particular census index on the basis of their 1971 values has the form:

(1981 Urban Area Value - 1981 Average) = Regression Slope x

(1971 Urban Area Value - 1971 Average)

This equation incorporates two structural elements of change, namely the shift in averages and changes in the scatter around the average, expressed in the regression slope. For example, a slope valued less than 1 implies convergence, since 1981 deviations from the average will be less scattered around the average than 1971 deviations. Similarly, a slope value greater than 1 implies divergence, and a widening of differentials. An extended discussion of the method can be found in Appendix 4.

The difference between the actual value of the index in the urban area in 1981 and the predicted value provides a measure of the third component of change,

JPP 33:1-F


that is in the relative position of individual urban areas to all other urban areas; for example, if the deviation of the actual from expected rates is positive then an urban area has risen on the index more than is expected on the basis of the prediction from regression. Changes in the relative position or rank order of individual urban areas reflect local influences on change. To summarise, total change (measured by a percentage point difference) in a particular urban area, may be expressed as the sum of three elements: a change in England wide

averages; a feedback component, representing the tendency to convergence or divergence according to the 1971 score in the urban area; and a locafised element, the difference between the 1981 predicted score in an urban area and the actual score. In addition the relative importance of these three elements of change can be numerically evaluated.

The significance of this approach is that it can be used to assess the validity of different models of demographic and economic change, among regions or urban areas. With regard to indicators of economic activity, for example, there has been some debate about whether regional differentials have narrowed or widened over time (Keeble, 1977, 1984; Gillespie and Owen, 1981). It has been argued on the basis of ratios of percentages measuring change that regional policy and dispersal of public sector jobs have narrowed regional differentials in unemployment. On the other hand, percentage point differences in unemployment suggest divergence

with larger increases in economically depressed regions. The model discussed above is intended to clarify the different interpretations of the nature of change derived from the use of different change measures (Crouch, 1982).

Similar questions have arisen in the analysis of social status and housing tenure change. It has been argued (Hamnett, 1984) that the relationship between social class and tenure is such that the increase of the two main tenures (owner occupancy and municipal renting), is associated with tenurial and social bi- polarisation. However, as the above discussion makes clear, changes in average levels of indices need to be distinguished from changes in scatter around these averages. In the present context, the question is whether owner-occupied household growth in SAMS urban areas between 1971 and 1981 implied increased tenurial polarisation, or whether the increase in owner occupancy has been accompanied by tenurial diffusion resulting largely from increases in owner occupancy among skilled and unskilled manual workers.

6.3. COMPONENTS OF CHANGE, 1971-1981

Table 25 gives for selected census indices the elements of change necessary for a calculation of expected and positional change for individual SAMS urban areas, and for assessing the relative importance of structural from localised changes over all urban areas. The regression slope is calculated by multiplying the ratio of the 1981 standard deviation (S.D.) to the 1971 standard deviation (given in columns 3 and 4) by the correlation between 1981 and 1971 values (column 5). Several classes of indices can be distinguished. For indices where the standard deviation

TA

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E

25.

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19

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81

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0.87

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26.9

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0.91

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.9

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56

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65.9

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0.86

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79

0.63

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76

4.84

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0.97

57

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0.

86

1.13

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has increased, the correlation between 1971 and 1981 values is generally high enough to ensure that the slope of change exceeds 1 (given in column 6). Such widening of differentials is most apparent for unemployment. It also occurs for indices of high social status, such as two-car ownership.

However, changes in the rank order position on these indices have also occurred as a result of particular localised influences on change and are substantial in the cases of unemployment (25% of total change). For the retirement ages index, slight increases in dispersion are not sufficient to imply increased polarisation (the regression slope here being 0.95). Selective migration by retired people seems to have little effect in increasing polarisation of the old - perhaps because such migration is to some extent being directed away from traditional retirement towns.

Some indices show reduced dispersion despite increases in their average levels. For example, the reduced dispersion implies a narrowing gap between SAMS urban areas in terms of owner occupancy (a regression slope of 0.87). a finding which conflicts with the tenurial polarisation hypothesis. Similar trends arc apparent in one-car ownership, implying a similar diffusion through the social scale as rates of one-car ownership have increased among manual workers. Nevetheless. there may be substantial locational variation in the process of reduced differentials - as implied by the changes in rank order particularly apparent for owner occupancy. The increase in female economic activity is also accompanied by reduced scatter around the average - reflecting large increases in such activity in regions with rates below average in 1971.

Finally, a third major class of indices - those measuring housing amenity and overcrowding - is characterised by large decreases in average levels, and large falls in dispersion around these averages.

6.4. POSITIONAL CHANGE, 1971-81

Table 25 reveals that local variations in change are substantial for several indicators. These significant localised changes imply a change in the relative position of particular SAMS urban areas on the indicator concerned, both in terms of rank order and in terms of deviation from the average. Table 26 sets out different measures of change for different urban areas using unemployment as an example. It can be seen that Hornsea (a Yorkshire coastal urban area) and Salford have similar ratios of unemployment rates (approximate doubling), but the real increase in Salford is greater. In fact. Hornsea fell down the rank order of unemployment (from 340th to 352nd), while Salford rose in the rank order of unemployment from 38th in 1971 to 19th in 1981 and its positional increase is

reflected in the change in measure. Similarly, Great Yarmouth and Wroughton (near Swindon) have approximately

equal percent point differences (around +3.5 percent points) but the true increase in Great Yarmouth is lower (only a third in ratio terms, compared with more than a doubling in Wroughton). The positional change measure thus gives an


TABLE 26. Positional change compared with differences and ratios

1971 1981 Difference Ratio Structural Positional Unemployment Unemployment in percent of percents change change

Salford 8.40 18.34 9.94 2.18 6.10 3.84 Hornsea 4.50 8.45 3.95 1.87 3.86 0.09 Great Yarmouth 10.00 13.49 3.49 1.35 7.02 -3.53 Wroughton 2.60 6.17 3.57 2.37 2.77 0.80

Note: The 197 1 and 1981 averages for total unemployment are 4.38 and 8.17, so the regression predicting 1981 unemployment is y’ - 8.17 = 1.57 (X - 4.38) e.g., for Salford, y’ = 8.17 + 1.57 (8.40 - 4.38)

= 14.50 (i.e., 8.40 + 6.10)

unequivocal picture of change, whereas percent point differences and ratios distort

the relative nature of change. There now follows a discussion of positional change among SAMS urban areas

on a number of key variables. In the original report (DOE, 1986 a,b), positional change was mapped for each variable but for reasons of space the maps have been omitted here. Table 27 presents three classes of positional change in each indicator, and presents a profile on a number of census statistics of each positional change class. The categorisation is on the basis of the standard deviation of positional change: the highest (lowest) positional increases are above (below) this

TABLE 27. Profiles of positional change categories

Index

(a) Owner Occupancy Professional/managerial migration Other non-manual migration Skilled manual migration Semi/unskilled migration Professional/managerial owner occupiers Other non-manual owner occupiers Skilled manual owner occupiers Semi/unskilled owner occupiers Population change (rate) Change in O-14 year olds Change in O-4 year olds

(b) Municipal Renting Skilled manual migration Semi/unskilled migration Skilled manual workers Semi/unskilled workers Population change (rate) Change in O-14 year olds Change in amenity sharing Change in overcrowding

Positional change category Low Medium High

1.20 1.49 2.52 1.88 9.72 8.55

15.17 6.55

16.18 -3.21 -2.41

1.97 1.86 2.10 1.27

17.65 13.46 17.03 7.11 9.19

-3.13 -2.47

2.23 2.09 2.71 1.28 1.27 1.91

23.71 23.31 26.48 21.28 20.98 19.61 20.94 8.98 25.11 -3.97 -3.08 -2.90 -6.97 -9.14 -15.02 -2.64 -2.06 -3.18

1.96 1.99 2.35 1.29

14.93 11.99 19.46 7.73

22.11 -3.26 -2.12



Index Positional change category

Low Medium High

(c) Car Ownership (one or more) Car commuters Bus commuters Train commuters Professional/managerial workers Other non-manual workers Skilled manual workers Change in owner occupancy Professional/managerial owner occupiers Skilled manual owner occupiers

(d) Car Ownership (two or more) Professional/managerial workers Other non-manual workers Skilled manual workers Car commuters Professional/managerial owner occupiers Other non-manual owner occupiers Skilled manual owner occupiers

(e) Children under 15 Migrants under I5 Households with 5+ persons Municipal renting Professional/managerial owner occupiers Other non-manual owner occupiers Skilled manual owner occupiers Population growth New Commonwealth Born

(f) Retirement Ages Migrants over 65 Pensioner one person households Owner occupancy Municipal renting Retirement owner-occupiers Population change (rate) Change in one person households

(g) New Commonwealth Born Children under 15 Households with 5+ persons Owner Occupancy Households at densities over 1 ppr Population change (rate)

(h) Unemployment Manufacturing workers Service workers Professional/managerial workers Semi/unskilled workers Municipal renting Owner occupancy Population change (rate)

52.27 55.06 57.00 15.81 10.37 13.20 2.91 3.66 2.10

13.67 16.43 14.18 26.97 29.36 27.85 23.81 23.27 26.20

6.67 7.24 10.15 13.31 17.23 14.84 14.59 16.74 21.37

17.46 14.97 18.35 27.93 28.56 31.16 21.95 24.15 23.11 52.47 54.21 60.67 17.83 15.29 20.69 11.91 12.43 14.85 14.20 17.09 19.35

1.89 2.05 2.32 10.46 10.49 11.20 26.20 27.22 27.03 19.77 15.87 16.12 13.75 12.40 13.44 13.99 17.13 19.67

1.18 7.81 41.08 1.20 1.06 1.31

0.76 0.73 0.95 12.54 13.55 15.00 66.52 61.87 62.42 23.40 27.67 26.94 18.37 19.28 24.85 41.95 8.04 2.40

1.90 3.83 5.12

23.38 22.92 24.14 11.06 10.48 11.50 61.90 62.17 59.71

2.07 2.42 3.12 8.82 10.55 24.39

24.42 28.13 36.08 34.38 33.17 26.75 17.30 16.44 11.12 20.56 20.95 25.02 22.42 25.34 40.02 66.22 63.96 51.76 15.38 10.38 13.64



Index Positional change category

Low Medium High

(i) Male Unemployment Manufacturing workers Service workers Professional/managerial workers Semi/unskilled workers Municipal renting Population change (rate)

6) Female Unemployment Manufacturing workers Service workers Professional/managerial workers Semi/unskilled workers Municipal renting Population change (rate)

(k) Female Economic Activity Manufacturing workers Service workers Children 4 and under Professional/managerial workers Other non-manual workers Skilled manual workers Semi/skilled workers

(1) Overcrowding New Commonwealth born Semi/unskilled workers Owner occupancy Municipal renting Private renting Change in owner occupancy Change in municipal renting

(m) Amenity Sharing New Commonwealth born Semi/unskilled workers Owner occupancy Municipal renting Private renting Change in owner occupancy Change in municipal renting

26.30 28.03 35.93 33.18 33.12 27.27 16.91 16.38 11.14 21.22 20.98 24.84 24.11 25.18 41.51 15.66 10.45 14.13

24.24 28.55 33.73 36.37 32.73 27.72 19.83 16.21 10.64 18.60 21.22 25.01 18.22 26.24 37.99 17.71 10.15 12.21

28.09 29.15 27.83 29.57 32.18 36.58

6.52 6.09 5.79 14.38 15.81 17.23 25.61 28.82 32.53 25.90 23.78 21.09 22.23 21.81 19.01

1.01 1.02 1.78 21.20 21.01 24.75 56.95 63.98 58.16 30.85 25.63 32.76

5.71 6.67 6.11 10.07 7.37 6.29 -2.38 -0.95 0.67

0.54 1.15 1.33 24.65 20.98 22.01 50.07 64.24 61.87 40.95 25.69 23.90

5.79 6.14 9.62 7.90 7.47 7.70 1.58 -1.38 0.00

standard deviation. Table 28 cross-tabulates the categories of positional change by the clusters of Chapter 5.

6.4.1. Changes in owner occupancy

Over a quarter of the total change in this index is due to shuffling in rank order (Table 25). It can be seen from Table 27 that urban areas with high positional


TABLE 28. Categories of positional change by clusters

Cluster Positional Change Category on Index: 1 2 3 4 5 6 7 8 9 10 11 12

Owner occupancy Low Medium High

Municipal renting Low Medium High

Car ownership (one +) Low Medium High

Car ownership (two +) Low Medium High

Children under 15 Low Medium High

Retirement ages Low Medium High

New Commonwealth born Low Medium High

Unemployment Low Medium

Male unemployment Low Medium High

Female unemployment Low Medium High

Female economic activity Low Medium High

9 1 2 11 6 100 115 54 89 85

7 3 2 16 11

7 5 0 27 6 101 112 58 84 90

8 2 0 5 6

9 14 4 7 8 100 104 51 91 87

7 1 3 18 7

18 26 19 89 66 38

9 27 I

27 34 15 88 79 42 I 6 1

1 26 58 75 57 1

13 7 70 87 33 8

4 8 12 90 77 33 22 34 13

15 12 3 100 99 54

I 8 1

24 4 88 77

4 21

6 4 101 95

9 3

II 16 27 14 14 102 103 30 97 82

3 0 1 5 6

9 13 15 12 18 103 106 42 100 81

4 0 10 4 3

13 37 14 23 18 99 82 41 88 72

4 0 3 5 12

3 3 31 10 21 81 86 25 74 81 32 30 2 32 0

1 0 0

0 0 1

0 0 1

0 1 0

0 0 I

1 0 0

0 0 1

0 0 1

0 0 1

0 0 1

1 0 0

8 12 2 2 42 17 73 126 29 18 21 31 30 3 9 0 1 16

22 6 22 81 120 18

8 15 0

0 9 23 45 123 17 66 9 0

0 1 3 19 24 38

1 39 23

0 33 14 19 31 42

1 0 8

1 15 2 0 10 5 68 123 37 19 54 59 42 3 1 1 0 0

0 4 15 0 9 3 57 131 22 11 51 58 54 6 3 9 4 3

48 3 0 4 3 5 63 129 28 16 56 54

0 9 12 0 5 5

1 4 3 0 1 1 103 129 26 2 63 63

7 8 11 18 0 0

6 8 I 0 3 13 84 115 21 10 16 45 21 18 18 10 45 6

5 10 1 0 3 90 117 20 10 19 16 14 19 10 42

8 4 2 0 0 81 109 32 14 23 22 28 6 6 41

33 8 4 5 10 76 117 25 14 50

2 16 11 1 4

12 50

2

2 41 21

11 50

3



Cluster Positional Change Category on Index: 1 2 3 4 5 6 7 8 9 10 11 12

Household overcrowding Low 17 13 3 23 9 0 5 2 12 0 8 13 Medium 95 103 52 88 87 1 93 108 21 2 40 43 High 4 3 3 5 6 0 13 31 7 18 16 8

Amenity sharing Low 3 0 1 7 2 1 22 7 1 0 21 33 Medium 96 113 37 109 86 0 83 107 38 15 37 24 High 17 6 20 0 14 0 6 27 1 5 6 7

increases in owner occupancy are characterised by high levels of middle income migration (by other non-manual and skilled manuals workers) as well as by migration of high income workers, and also by high levels of manual worker owner occupancy. This suggests that increases in the rank order of urban areas on owner occupancy are associated with the diffusion of home ownership to affluent manual workers - hardly indicative of tenure-based social polarisation (Hamnett, 1984).

Increases in working class owner occupancy tend to occur on the peripheries of large industrial towns in the East Midlands and Yorkshire/Humberside: regions of relative manufacturing prosperity during 1971-81. Table 28 shows that high positional increases are concentrated in clusters 4 and 8 - middle income owner occupied suburbs, and in cluster 12 - affluent mining communities. This table also shows that urban areas with high positional increases in owner occupancy have higher than average population growth, though not necessarily higher than average increases in children.

6.4.2. Changes in municipal renting

Positional increases in municipal renting have been highest in metropolitan cores (typically SAMS urban area subdivisions) in North and North West England, though individual urban areas in non-metropolitan locations may show unexpectedly large increases as a result of New/Expanded Town schemes (examples are Telford and Bodmin). Larger than average improvements in household conditions in urban areas with higher than expected municipalisation are also apparent. A major concentration of positional increases on this type of tenure is revealed in cluster 11 - manufacturing unemployment blackspots, and to a lesser extent cluster 12 - mining towns (Table 28).


6.4.3. Changes in car ownership (one or more)

Positional increases in one-car ownership show some resemblance to those in owner occupancy, with the highest averages in the East Midlands and Yorkshire/ Humberside, and in small self-contained outer ring urban areas such as Raunds and Minster as well as inner ring areas. The lowest positional increases occur in the less prosperous Northern region, and in rural East Anglia where there were high levels of car ownership at the start of the period.

As Table 27 indicates, the largest positional increases arc occurring in urban areas with high levels of skilled manual owner occupiers, indicating a diffusion of car ownership and lessening of socio-economic differentials (Button, 1982). By contrast, high status areas - where one-car ownership was already high in 1971 - have only medium levels of positional change. Higher than expected increases in car ownership are also associated with the highest level of car commuting to work (57% of employed residents). Table 28 shows a particular concentration of high positional increases in car ownership in cluster 7 (prosperous manufacturing commuter towns or small employment centres).

6.4.4. Changes in two-car ownership

In contrast to one-car ownership, increases in the rank order on two-car ownership have been most evident in South East England, and in prosperous commuter suburbs of North West England, particularly those near satellite towns. For example, in Hullbridge (near Southend) two-car ownership rose from 10% to 27%, an increase double the expected, and a rise in rank from 375 to 124. There is also a distinct association between high positional increases in two-car ownership and high commuting by car - over 60% in the top category of positional change. Positional increases on this indicator tend to occur in high status areas but have also extended to urban areas with high levels of middle income owner occupancy.

6.4.5. Changes in percentages of children

Examination of individual urban areas on this variable suggests that unexpectedly large increases have occurred in middle income dormitory towns, often near New/Expanded Towns (for example, Yaxley near Peterborough) as well as in small manufacturing towns with high levels of family migration (i.e. Raunds, Higham Ferrers). The association between such positional changes and total population growth is marked - with urban areas where the numbers of children have risen (or declined less than expected), averaging 41% population growth.


6.4.6. Changes in retirement age groups

Positional increases in older age groups reflect both recent trends in retirement migration and the ageing declining populations of urban areas within, and on the periphery of, large metropolitan agglomerations. The recent shift of retirement migration to East Anglia is particularly noticeable. Positional increases in rural areas reflect similar trends, while the larger than expected increases in metropolitan cores primarily reflect ageing, as do those in Green Belt towns.

The cross-classification of positional change categories by clusters demonstrates both the ageing effect in high status Green Belt towns (cluster 2 contains 34 of the 125 high positional increases), as well as the retirement migration effect (apparent in cluster 5, urban areas in rural locations and cluster 1).

6.4.7. Changes in unemployment

Moves up the rank order on this indicator have been among SAMS urban areas in the West Midlands, and to a lesser extent in the North West and North. By contrast, unemployment increases in the greater South East (South East, South West, and East Anglia) have been less than expected. The profile of positional change categories also shows that unexpectedly large increases in unemployment have tended to occur in areas with a manufacturing base, many semi/unskilled manual workers, and high levels of municipal renting. Examples are steel towns (Consett, Scunthorpe), and overspill settlements (Partington, Longdendale).

Table 28 shows the high concentration of positional increases in unemployment in cluster 11, but also shows a scatter through more prosperous manufacturing clusters (such as 7 and 8). However, more rapid increases in population than in employment account for several larger than expected increases in unemployment in New Towns. On the other hand the lowest positional increases in unemployment have occurred in SAMS urban areas with a pronounced service bias and above average numbers of professional/managerial workers. Employment centres (i.e., Winchester, Durham) and dormitory towns (Virginia Water), fall into this category.

6.4.8. Changes in female economic activity

Positional increases on this indicator have been highest in the cores and inner ring areas of metropolitan and satellite labour markets, particularly in the North West. The profile of high positional increases suggests that female economic activity has risen more than expected in urban areas biased to service rather than manufacturing jobs, and therefore to non-manual rather than manual jobs.


6.4.9. Overcrowded households

Greater than expected reductions in overcrowded households have occurred in smaller towns and some towns in the North, while declines less than expected have tended to occur in larger employment centres. The profile of positional (larger than expected) declines in overcrowded households shows them to be particularly associated with increases in owner occupancy (as in some mining towns). Positional increases (lower than expected declines) on this index have occurred in some municipal overspill developments (Longdendale, New Addington), towns with increased ethnic communities (Nelson, Rochdale) and some resort towns with high levels of private renting (e.g., Scarborough).

6.4.10. Amenity sharing

Greater than expected reductions in amenity sharing also show a size effect - more frequent in smaller towns. and with particular concentrations in Yorkshire/ Humberside and the North.

6.5. MODELLING GROWTH AMONG SAMS URBAN AREAS

The previous stages of the analysis have identified major elements of the social structure and change in urban areas. It is now necessary to turn to an approach to modelling change in urban areas that incorporates the geographical differences and the great variation in SAMS urban area type explicitly. The analysis now relates population growth in urban areas to indicators of:

(a) local urban area economic specialisation and employment availability; (b) policy status; (c) type of growth (in terms of selective migration); (d) distinct patterns of tenure change; (e) the commuter function of small towns. These are intended to be direct measures of the processes of change in SAMS

urban areas and the analysis is carried out at both the national (England) level and for Standard and functional regions, in order to distinguish between non-metropolitan growth as against metropolitan decentralisation, and to reflect differential growth processes in different types of regions.

The modelling approach used here differs from that used in most other studies. First, physically defined urban areas are set in a functional (labour market) context which permits an assessment of the locational nature of small town growth. Second, the model used here is estimated within Standard and functional regions, whereas other studies tend to use ‘dummy’ regional indicators relating to both geographical location and position in the functional hierarchy. Third, the model considers both the form and distribution of the metric of growth resulting


from a variety of causal processes. The model thus attempts to take account of the fact that analyses based on percentage growth rates may be distorted by a few exceptionally large percentage increases in towns with a small initial population (e.g., certain New/Expanded Towns). Large percentage increases from a low initial base tend to impart positive skewness (a few very large values) to the distribution of the dependent variable of the regression analysis. This invalidates the normality assumption and thus requires transformation.

6.6. GROWTH PROCESSES AND URBAN AREA TYPES

In the following analysis SAMS urban areas are further classified by indices of economic specialisation, distinct patterns of change, or policy status using a simple threshold procedure used to define sub-classes of urban areas. Thus, retirement towns are defined as those with high retirement migration and increases in pensionable age groups. Note than an urban area may have more than one type of specialisation. Table 29 shows average rates of growth in urban areas classified in this way according to measures of: the commuting function and car ownership; changes in housing tenure, employment structure and growth; policy status; and selective migration.

6.6.1. Car ownership and commuting

As Table 29 shows there is considerable variation in the rates of growth (or decline) of SAMS urban areas according to urban area type, and also interactions between urban area type and the location and size of urban areas. Columns 1 and 2 of this table show to what extent growth of small towns is associated with increased car commuting and accessibility stemming in part from more widespread car ownership. Thus the 98 urban areas with high car commuting average 24.3% population growth compared to the 11.4% average for all 952 urban areas. The association between growth and car commuting is most pronounced in urban areas in non-metropolitan locations.

The contribution of increased commuting accessibility to counter-urbanisation is also evident in the size effect: 29% growth in small urban areas (under 10,000 population in 1971) with high car commuting. Increased car ownership is, however, also associated with growth (through decentralisation) in metropolitan hinterlands, probably because car ownership has increased most in those metropolitan areas with a lower initial base. Conversely, SAMS urban areas with low levels of self-containment in general (regardless of travel to work mode) show a less marked growth advantage (column 3), since they include a number of dormitory towns in inner metropolitan rings with declining populations, and with train to work as an important travel mode (particularly near London). Motorway proximity (column 4) is also less clearly associated with growth, perhaps because many motorway towns are in northern industrial areas or inner metropolitan rings,

TA

BL

E

29.

Gro

wth

ra

tes

in u

rban

are

as,

1971

41,

by u

rban

are

a ty

pe (

colu

mns

) an

d lo

cati

ons/

size

(r

ows)

g

1 2

3 4

5 6

7 8

9 10

11

12

13

14

15

16

Stan

dard

R

egio

n So

uth

Eas

t W

est

Mid

land

s N

orth

W

est

Yor

ks/H

umbe

rsid

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orth

E

ast

Mid

land

s So

uth

Wes

t E

ast

Ang

lia

19.7

26

.6

12.6

17

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19.5

25

.6

21.1

-2

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14.2

14

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54.8

15

.0

5.7

- 13

.2

25.3

11

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14.6

12

.0

18.2

11

.1

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19

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53

.7

15.3

1.

1 20

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5 5.

2 5.

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6 5.

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6.

5 5.

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7 24

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Col

umns

(T

ype

of u

rban

ar

ea)

1. C

ar

com

mut

ing

area

2.

Hig

h in

crea

se

in c

ar

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rshi

p 3.

Low

se

lf c

onta

inm

ent

4. N

ear

mot

orw

ay

5. H

igh

grow

th

in o

wne

r oc

cupa

ncy

6. H

igh

grow

th

in m

unic

ipal

re

ntin

g 7.

Em

ploy

men

t su

rplu

s ar

ea

8. U

nem

ploy

men

t bl

acks

pot

9. M

anuf

actu

ring

to

wn

10.

Bas

ic

serv

ices

sp

ecia

hsat

ion

1 I.

New

/Exp

ande

d T

owns

12

. In

dai

ly

urba

n sy

stem

of

NE

T

13.

Gre

en

Bel

t 14

. A

ssis

ted

Are

a

15.

Hig

h pr

ofes

sion

al/m

anag

eria

l m

igra

tion

16.

Hig

h sk

illed

m

anua

l m

igra

tion

17.

Hig

h re

tirem

ent

mig

ratio

n 18

. H

igh

fam

ily

mig

ratio

n 19

. A

ll ur

ban

area

s of

giv

en

loca

tion/

size


and fewer are in less urbanised regions. Nevertheless, in some regions there does appear to be a growth advantage from such proximity. In the West Midlands, New or Expanded Towns with good road accessibility such as Telford, Droitwich and Daventry have high growth rates.

6.6.2. Housing tenure

The growing residential function of small towns is here seen to be particularly associated with expansion of owner occupied housing (column 5) with a 16% growth rate in small towns characterised by high increases in owner occupancy. Such growth occurs in both metropolitan and satellite hinterlands, and in non- metropolitan locations. Increases in municipal housing, on the other hand, are particularly associated with growth in free-standing and rural labour markets, typically New or Expanded Towns. Municipalisation in metropolitan and satellite towns includes not only high growth overspill developments but reduced populations in conurbation subdivisions.

6.6.3. Employment growth

While the commuting and residential functions of small towns have increased, there has also been an increase in employment opportunities in many such towns, so that population growth is linked to employment-led decentralisation or deconcentration and to variations in employment availability. The role of employment availability is illustrated in column 7, of Table 29 which relates to 251 ‘employment surplus’ towns where the growth of employed residents outstrips that of population. Population growth averages 19% in small towns with employment surpluses (i.e., growth in employed residents in excess of growth in population), and is more pronounced in urban areas located in free-standing and rural labour markets, suggesting interdependence of population and employment deconcentration. Conversely, population growth rates are generally lower than average in unemployment blackspots, with increases in unemployment rates much above the regional average. The growth in 122 urban areas averages 4.7%. The exceptions to this pattern are some ‘employment deficit’ New/Expanded Towns (for example, in the West Midlands) where rapid population growth has exceeded the growth of jobs.

6.6.4. Employment structure

Columns 9 and 10 of Table 29 reflect the impact of employment structure on growth, and illustrate the influence both of the urban-rural shift, of planned


decentralisation, and of continuing disparities between Standard Regions. Thus population growth in manufacturing towns in outer rings and rural locations exceeds the average growth of urban areas in these locations. However, medium sized towns with manufacturing specialisation, often New or Expanded Towns, also have high growth. By contrast, larger manufacturing SAMS urban areas and especially those in the North have low growth.

Growth in basic services (such as financial services) represented one of the few growth sectors in the recent recession. The evidence here suggests that basic service specialisation has been important in the growth of free-standing employment centres and in rural towns, suggesting a leading role for service growth in the process of deconcentration.

6.6.5. Policy effects

A major influence on the pattern of residential and economic growth in

SAMS urban areas has been policies of growth and restraint in both spatial and functional contexts. New and Expanded Town policies (column 11) have a pronounced growth effect (an average of 42%). This extends throughout the functional hierarchy from large satellite towns (Washington) to free-standing towns (Wellingborough) and small towns in rural areas (Thetford). The growth stimulus of larger New/Expanded Towns spills over into small towns in their hinterlands (column 12). Complementary Green Belt restrictions on growth in inner metropolitan rings have tended to deflect housing and employment growth to sites further from the metropolis (the so-called leap-frog effect). Thus in South East England, Green Belt towns average only 5.7% population growth.

Assisted Areas schemes have been intended to have selective regional impacts (lessening disparities in unemployment) rather than promote metropolitan decentralisation. Hence, assisted urban areas in metropolitan regions have a slight growth advantage. However, such status also appears to benefit peripheral rural areas, for example in South West England.

While job-led and policy-led explanations account for much of the variation in growth of small and medium sized towns, people-led explanations emphasising a change in residential preferences towards small town life also receive some support. High status areas whose social composition is being reinforced by above average levels of professional and managerial migration average 22% growth, with the growth differential most marked in small towns and in free-standing and rural regions. Skilled manual migration is more associated with decentralisation from metropolitan cores and with the growth of medium sized as well as small towns (often New or Expanded Towns). Migration of the economically active population is typically associated with family migration (column 18) which exhibits a pronounced size effect and contributes to both decentralisation and non-metropolitan growth. Retirement migration, on the other hand, contributes most to growth in free-standing and rural regions, particularly in environmentally attractive locations in East Anglia and the South West.


6.7. MULTIVARIATE CONTROL IN SMALL TOWN GROWTH

Finally, the analysis of growth rates in Table 29 does not control for interactions between SAMS urban area characteristics such as the coincidence of high car commuting with areas of high growth in owner occupancy. In the main report a multiple regression was therefore undertaken in order to provide a measure of the effect on population growth of each urban area characteristics which controls for correlations with other characteristics (DOE, 1986b). It is also described, in an extended version in Congdon and Shepherd (1986).

6.8. SUMMARY

The analysis of selected indicators of age, tenure, employment availability, housing conditions and car ownership has to a considerable extent confirmed the urban area classification of Chapter 5. For example, higher than expected increases in unemployment have occurred in SAMS urban areas with concentrations of manufacturing industry, and in those with more municipal tenants and unskilled workers. This chapter has also shown both the importance of localised change in SAMS urban areas and the contribution of local restructuring to differentials in employment availability (between employment surplus towns and unemployment blackspots); the increase and social diffusion of owner occupancy; the increased accessibility of small towns based on car commuting; and the impact of selective migration. These trends are apparent both as changes in the relative social position of urban areas and in the definition of urban area types. Policy impacts also appear to have been substantial, with New/Expanded Town and Green Belt policies particularly affecting the distribution of urban area growth in metropolitan and satellite regions and in Standard Regions with major conurbations. Assisted Area status, on the other hand. may have counteracted restrictions on metropolitan overspill in parts of the North but seems to have had less effect on promoting non-metropolitan growth, except in the South West.

JPP 33:1-G

CHAPTER 7

Conclusion

The research described here is the first to use OPCS/DOE urban areas data in an investigation into changes in the distribution of the urban population of England. Its focus has been on small and medium sized (SAMS) urban areas defined as those settlements with populations in the range 5,000 to 100.000 in 1987. As the first study of its type the project has had two aims: to evaluate the urban areas definition and document the census data based upon that definition and to examine the nature and causes of recent population growth among SAMS urban areas.

In relation to the first aim of the study a general finding is that a simple. rigorously applied land-use definition of urbanism as presented in the OPCS approach is an essential ingredient in the analysis of contemporary urban (and, by subtraction, rural) population change. The urban area definition is a major addition to our ability to chart the course of the changing distribution of population and a vast improvement on the inaccurate and outdated administrative definition of towns as used, perforce, in the OPCS Preliminary Report for Towns,

1981. It also represents an essential complement to definitions of the urban system based on the notion of labour markets, travel-to-work areas (TWAs) or functional urban regions (Goodman, 1970; Howson, 1979; Coombes et al., 1982).

In relation to the substantive aim of the investigation, the present research has revealed the pivotal role played by SAMS settlements in the major redistribution of the population that took place in England between 1971 and 1981. At a time when national population growth was a mere 0.4% (representing some 203.000 people) and when the population of the largest urban areas fell by nearly 2.5 millions (-9.0%), the numbers of people living in SAMS areas rose by 1.8 millions or 6.0%. Of equal significance, however, is the geography - both national and local - of that growth. The growth of SAMS urban areas is evident in all parts of the country - even in those regions in overall population decline and is locally scattered in form and variable in its impact. To underline the point made above, it is the existence of census data based upon detailed definitions of urban areas that has made it possible to demonstrate the nature of this, the most recent phase of urbanisation in England.

The growth of SAMS urban areas has been pivotal to the recent redistribution of population in England in a number of respects. First, it represents a continuation of the long-standing trend towards population growth in increasingly

98


lower levels of the urban hierarchy at the expense of the older, larger and more congested urban centres. The success of smaller, free-standing urban places in attracting both population and economic actiyity thus stands in stark contrast to the decline of inner city and, indeed, some suburban areas, and policies for the revitalisation of the latter should be informed by propensity for growth in the former.

Secondly, SAMS urban area growth has taken place in a remarkable diversity of geographical contexts ranging from areas receiving decentralised population and jobs in metropolitan hinterlands and satellite towns (especially in the West and East Midlands and South East) to free-standing towns and their hinterlands and rural areas (the South West, East Anglia and YorkshireIHumberside). The

tendency towards non-metropolitan growth is thus more pronounced in some Standard Regions (and parts of such regions) than in others and this location well away from the major urban centres and the highly scattered nature of its distribution raises questions about the nature and cost of the future provision of infrastructure and services.

There are a number of implications for transport policy that flow from SAMS urban area growth. The residents of SAMS urban areas have higher car

ownership levels than the average for England and tend to use the car for the journey to work. These high car ownership levels are a function of both the migration of higher status workers (particularly in small SAMS urban areas) and the deterioration in rural public transport facilities which have induced the indigenous population to acquire cars. It seems questionable whether the deregulation of bus services will assist in reversing this trend. The connection between SAMS growth and transport accessibility (in the form of motorway provision/access) has not been rigorously demonstrated by the statistical analyses carried out in the course of this research. Studies carried out in the past have found it difficult to identify significant development impacts attributable to improved accessibility (Holroyd, 1983), and the most that can be said at this stage is that motorways (or more generally high quality road access) should be seen as a necessary rather than sufficient condition for growth.

Thirdly, SAMS urban areas are represented across the spectrum of economic success (as measured by numbers and types of employed residents), from high unemployment among metropolitan and satellite urban areas of the North and North West to growth through the expansion of manufacturing and service industries in certain parts of the North, the East Midlands, East Anglia and the South West. As the location and environmental characteristics of these areas indicate, the economic success of SAMS urban areas is built on good communications and on an environment that is attractive to both professional and skilled workers as a place of residence and the entrepreneur as a location

for production. An important planning issue for such areas (and areas wishing to attract new employment) is the maintenance of the environmental quality and character of these areas in the face of demands for further physical expansion or intensification.

Finally, the recent growth of SAMS urban areas encapsulates a number of the


aspirations and changes in life-style that typify an important part of the social and demographic structure of England in the late 20th century. There is increasing

owner occupation in SAMS urban areas, increasing numbers of professional and skilled-manual workers, more one and two-car ownership and in certain types of area more retired persons. Having moved to SAMS urban areas for a better quality of life for themselves and their children it is reasonable to assume that such people will show an increased interest in maintaining the quality of the physical environment that attract them to a small town in the first place.

7.1. POLICY AREAS AND POLICY STATUS

The attempt to assess the impact of central and local government policy on SAMS urban growth has been less successful than was expected largely because of the limited nature of the data available to the study (see below) and the geographical scale at which it has been conducted. Some policy impacts are clear, if unremarkable, such as the rapid growth of SAMS urban areas that are New and Expanded Towns, although the urban area definition itself has enabled clarification of the contribution of planned versus spontaneous growth of population in non-metropolitan areas. This study has also shown the more pronounced effect of Green Belt constraints on growth in the South East and West Midlands compared with those in the northern half of the country. In the South East and West Midlands there are many SAMS urban areas that would have grown more rapidly or not been in actual population decline but for the presence of Green Belt controls. These controls continue to preserve the character and physical integrity of such settlements but the implications of population decline on future demographic structures and levels of economic activity may need further examination.

The aggregate study of policy impacts (Chapters 4, 5 and 6) and a series of case studies carried out on individual SAMS urban areas but not reported here.’ uncovered a further aspect of policy effects that has hitherto been given

little attention in the literature on urban policy. By their nature SAMS urban areas may lie in the interstices of policy areas which are meant to apply more rigorously to neighbouring larger centres, or they lie in areas where policies with opposing objectives intersect, or they are profoundly affected by positive measures for attracting growth to neighbouring towns. The declining case study area of Newton-le-Willows is an excellent example of such a town located as it is between Merseyside and Greater Manchester, within an Assisted Area and in a Green Belt and within a few miles of Warrington New Town. Other case study towns, on the other hand, have gained by their location relative to towns/areas with policy status (Highworth near Swindon, Southwell just beyond the Nottingham Green Belt). These ‘backwash’ effects of policy on the fortunes of SAMS settlements need to be examined more closely.

One further policy effect on SAMS urban area growth probably needs more attention and that relates to location near/access to the motorway or trunk


road network. The results here suggest the possibility of some effect of transport infrastructure provision on SAMS settlement growth but more rigorous detailed measures of access need to be applied to the analysis to confirm this result.

7.2. TECHNICAL ISSUES

The urban areas definition and the data based upon it are a major contribution to the analysis of urban change in England. Its focus on land use also addresses a number of key issues in planning policy. This research has, however, raised several technical problems associated with the urban areas definition and data including the updating of urban areas and the measurement of change, the data requirements for the study of growth and decline among urban areas and the form in which urban areas data are stored and utilised.

Indirect assessment of the area bases of the data, i.e., via census indices rather than cartographic delineation, revealed a generally close fit between the 1981 census data and the census tract definition used to derive change indices. Substantial discrepancies between population on the two area bases are limited to a few urban areas and can sometimes be attributed to the demographic or social characteristics of the urban areas concerned or the population definition (usual resident versus enumerated population) used. The impact of such discrepancies can be reduced by the use of percentage indices of structure for 1981 and the use of percentage point differences over time. As Chapter 6 shows, however, the choice of such indices can affect conclusions on the extent to which certain characteristics of SAMS urban areas are converging or diverging over time.

Of greater significance, however, is the direct measure of change based upon the actual boundaries of the urban areas. If the OPCYDOE exercise on urban areas is repeated for the 1991 census a direct measure of change for 1981-1991 will, automatically, emerge and ideally the delineation of enumeration districts for the 1991 census would take cognizance of the new urban areas definition.

Turning, therefore, to the data used in this study we would suggest a number of additions to the OPCS urban areas data that are aimed at enhancing further research into the determinants of growth among SAMS and even smaller settlements. In this study we have approached the problem of assessing the functional status of urban areas in an indirect fashion by allocating SAMS urban areas to functional urban regions and the zones of such regions. This approach has its merits on a broad geographical scale but is less successful in assessing, say, the contrasts between inner ring dormitory residential developments and inner ring urban areas with substantial local employment as well as commuting. There is a need, therefore, to enhance the functional description of urban areas with additional data. This requires data on the workplaces as well as the usual residence of urban area populations. Data on working populations within urban areas would permit both an evaluation of the functional order of urban places and a more accurate measure of employment change in urban areas and its relation to population change.


The socio-economic processes underlying growth in SAMS urban areas would also be further clarified by the provision of (a) change data from the 10% Small Area Statistics Files on socio-economic categories of the working population, travel-to-work mode and industry of employed residents and (b) data on out- migration from. and in-migration to, urban areas for periods of one and five years preceding the census in addition to SAS data on migration and local turnover. The mainly indirect measures of population movement used in this study suggest that recent migration is the major source of SAMS urban area growth and this should be documented more accurately.

The provision of ‘10% indices on an urban base would entail statistical problems with regard to assessing the significance of rates of change in the presence of sampling errors, especially for the smallest urban areas. These problems could. however, be mitigated by the use of aggregated data for occupational or industrial groupings as has been applied, with some success, in other studies. The indices involved here would be particularly relevant to the analysis of change that is. to some extent, peculiar to SAMS settlements including: employment restructuring (the shift of manufacturing and services to small towns); the increasing importance of car commuting: the character of selective migration by age and socio-economic group and the balance of in and out-migration as sources of SAMS urban area growth.

Finally, it is pertinent to note here that the urban areas data are ideally suited to storage and manipulation in the form of an integrated Geographical Information System (G.I.S.). Such a system was initiated for this study and given the acronym UAGIS: Urban Areas Geographical Information System. (Shepherd and Green,

1987). The database thus established contained all the census data used in the study, the digitised boundaries of SAMS urban areas and policy areas (Green Belts, AONBs, Assisted Areas. etc.), the complete road network (Present Year Network File) of England and the functional region location of urban areas etc. UAGIS thus represents a prototype data-organising model of the urban system of England which, within the limits of the data available, brings together the physical and the functional aspects of urbanism in an integrated yet highly flexible instrument of analysis.

NOTES: CHAPTER 7

1. The case study SAMS settlements were: Epping (Essex). Hartlepool (Cleveland). Highworth (Wiltshire). Maidenhead (Berkshire), Newquay (Cornwall), Newton-le-Willows (Lancashire). Rothwell (Northamptonshire). Southwell (Nottinghamshire) and St. Ives (Cambridgeshire). The cluster analysis was instrumental in the selection of the case study towns (DOE. 1986 c),

APPENDIX 1

The Definition of Urban Areas

The starting point in the definition of urban areas is the identification of areas with land use which is irreversibly urban in character. The definition used to identify urban land-use is modelled on the developed area classification produced by DOE which, in turn, is based on the National Land Use Classification. Land included as urban land comprises:

(i) permanent structures and the land on which they are situated, (ii) transportation corridors (roads, railways and canals) which have built-up sites on one or both sides

or which link up built-up sites which are less than 50 metres apart, (iii) transportation features such as railway yards, motorway service areas and car parks (operational

airfields and airports are also included),

(iv) mineral workings and quarries, and

(v) any area completely surrounded by built-up sites. Areas such as playing fields and golf courses are excluded unless they are completely surrounded by built-up sites as at (v) above.

The prerequisite for the recognition of an urban area is a continuous area of urban land extending for 20 hectares or more. Separate areas of urban land each of 20 hectares or more are linked if less than 50 metres apart.

The critical factor in the recognition of an urban area is a minimum population of approximately 1,000 persons. However, as there was no prior information on the 1981 populations of areas of urban land, a proxy threshold was applied by excluding areas with less than four 1981 Census Enumeration Districts (EDs). This resulted in the exclusion of some areas of urban land with more than 1,000 persons, but very few of above 2,000 persons. In addition, areas of urban land each of 20 or more hectares, but less than 200 metres apart are joined to form a continuous area. Major urban agglomerations, such as the metropolitan counties, are then subdivided into parts to provide a more useful set of statistics and to enable some comparisons to be made with previously published census statistics. Smaller urban agglomerations are also subdivided where appropriate, and, where possible, twin urban centres where urban land has merged are also subdivided.

103

APPENDIX 2

Indices, Normality and Transformation

Index Normal (Y=Yes, N=No)

Skew (Pos, Slight,

Neg) Transformation

One year migrants Population density Children under 15 Young adults Young middle age Older middle age Persons over 65 Average household size One person households Residents in New Commonwealth Households professional/managerial Other non-manual Skilled manual Semi and unskilled manual Professional/managerial migrants Manufacturing workers Service workers Unemployment Economically active females Economically active migrants (rate) Households owning one or more cars Households owning two or more cars Car to work Bus to work Train to work Work outside district Owner occupancy Municipal renting Private renting Second homes Share amenities Overcrowding Rate of population change Pert. point diff. in under 15s Pert. point diff. in over 16s Pert. point diff. in I person households Pert. point diff. in 5+ person households Pert. point diff. in New Comm. born Rate of change in employed residents Pert. point diff. in unemployment Pert. point diff. in econ. active females Pert. point diff. in households owning

1 or more cars

N Y Y N Y N N Y Y N N Y Y Y N Y N N N N Y N Y N N Y N N N N N N N Y N Y N N N N Y

Y

104

Pos Inverse square root Slight None Slight None Pos Inverse square root Slight None

Neg Square Pos Logit Slight None Slight None Pos Logit Pos Logit Slight None Slight None Slight None Pos Angular Slight None Pos Log Pos Logit

Neg Cube Pos Log Slight None Pos Logit Slight None Pos Logit Pos Logit Slight None

Neg Square Pos Angular Pos Angular Pos Angular Pos Angular Pos Logit Pos Log Slight None

Neg Square root cubed Slight None

Neg Square Pos Square root Pos Log Pos Log Slight None

Slight None


Rate of change in households N Pos Log Pert. point diff. in owner occupancy N Pos Square root Pert. point diff. in municipal renting N Pos Square root Pert. point diff. in amenity sharing N Neg cube Pert. point diff. in overcrowding N Neg Power Five

APPENDIX 3

A Model of Change Among SAMS Urban Areas

This appendix describes in more detail the model for change which is used in Chapter 6 to decompose change into structural and localised elements. The model uses a linear regression of 1981 values of an index on 1971 values of the same index in order to express the causal influence of the initial value of an index on its final value. A regression approach enables us to distinguish the three types of change: (i) shifts in the average over all urban areas, (ii) changes in the scatter about the average (convergence vs divergence) and (iii) changes in the relative position (rank order) of individual urban areas.

The regression equation expresses two components of change, namely the change in the average level of an index over all urban areas, and the extent to which there is change in the scatter of urban values around the average. It has the form:

Y’i - r = &Xi - x, (1)

where Yi is the predicted value of an index in urban area i (i = 1, 952) Xi is the actual value of that index in 1971, and Yand xare the 1981 and 1971 averages over all urban areas.

The regression slope, B = RSy/Sx is found by multiplying (a) the correlation R between 1981 and 1971 values by (b) the ratio of the 1981 standard deviation of the index to the 1971 standard deviation. It thus expresses changes in the scatter around the average (the tendency to divergence or convergence). If the slope exceeds unity the deviations of 1981 urban area values from the 1981 average will exceed the deviations of 1971 values from the 1971 average (i.e., divergence), while a slope less than unity means that the scatter around the mean is reduced (i.e., there is convergence). In the former case, urban areas which have a value of an index (such as unemployment) above average in 1971 will be even more above average in 1981. The effect of 1971 starting values in inducing greater or lesser concentration in 1981 is known as feedback. This is positive if differentials are enhanced and negative if they are reduced.

Equation (1) defines the two basic elements of change, structural and positional, which sum to give total change. Structural change in area i, given by Y’i - Xi is that part of total change resulting from knowledge of X and the England-wide trends in the average and dispersion of the index. It is evident from Equation (2) that structural changes do not alter the relaiive positions of different areas on the index between 1971 and 1981. B merely scales the deviations Xi - X up and down and transforms them to Y’i - Y without altering relative positions. Structural change can be further decomposed as follows:

y’i-Xi= Y+B(Xi-X)-Xi

=(F-&+(B- I&X-x). (2)

The first element of this equation represents change in the England-wide average, the second the effect of feedback and initial (1971) position of an urban area in relation to the 1971 mean. The latter term is positive if B exceeds unity (positive feedback) and an area is above average in 1971 or, alternatively, negative if E is under unity and an area is below average in 1971. Therefore, if there is negative feedback, the highest structural increases are occurring in areas that were below average in 1971.

Structural change is not the only element in urban area socio-economic change. In individual urban areas or clusters of areas, changes can occur which exceed or are much less than the England-wide trend. The difference Yi - Y’i between the actual value of the index in the urban area in 1981 and the predicted value provides a measure of the third component of change, that is in the relative position of individual urban areas. If the deviation of the actual from expected rates is positive then an urban area has risen on the index more than is expected on the basis of the regression. If the deviation is negative, with the actual rate less than the predicted rate, then an urban area has declined in relative position.

106


Changes in the relative position or rank order of individual urban areas also reflect localised conditions, i.e., to the extent that local change does not match change expected on the basis of general structure shifts. Hence the residual measure of change - the difference between 1981 actual and expected values - may be termed positional change. Such changes in position or rank are expected to cancel our overall: in mathematical terms, this condition is satisfied by the residuals in a regression of 1981 on 1971 values of an index, since the measures Yi - Yi sum to zero over all urban areas.

The ratio of positional change in an urban area to the standard deviation of positional change:

(Yi - ri) / sy (I - Ry (3)

can therefore be compared with values of the t statistic for 952 - 2 = 950 degrees of freedom. For example, a significant positional increase (Yi in excess of the prediction Y’i) would mean the urban area has risen in the rank order of the index concerned.

The deviation of actual from expected 1981 values of an index in a particular area has another more general interpretation as a ‘true’ measure of change. It is the residual when the feedback effect of the origin value has been controlled, and is thus independent of origin, whereas percentage point differences or ratios are not independent of the origin - hence the conflict of interpretation that occurs between these two latter approaches in the case of indices such as unemployment. However, it is because the ‘localised’ measure of change is base-free that it makes sense to classify urban areas according to levels of positional change, and calculate profiles on other indices. This enables us to answer such questions as, for example, are areas with high positional increases in owner occupancy characterised by high income owner occupancy or middle income owner occupancy?

Total change, as measured by the simple absolute difference in percentages, can be written as a sum of structural and positional components:

n-xi=(Yi-ri)+(ri-Xi) (4)

and, from Equation (2) this can in turn be written as follows:

Yi-Xi=(r-X)+(B- l)(Xi-x)+(Yi- Y’i). (5)

The simple absolute difference in percentages in an urban area can be broken down into three components, namely, change in England-wide means, change in scatter or concentration around the mean, and positional change. Now, in order to assess the aggregate amount of each type of change, the variances of total structural and positional change are best used, since the averages sum to zero over all areas. Squaring both sides of (5) and summing over N = 952 urban areas gives:

Z(Yi-Xi)2/N=(~-X)2+(B-l)2S~2+Sy2(1-R2) (6)

since cross-product terms cancel out. The second and third terms on the right-hand side are the variances of structural and positional change, respectively it can be seen that the size of positional change is inversely related to the correlation coefficient. Taking the first term on the right-hand side from each side gives:

Var(Y-X)=Var(Y-X)+Var(Y-r). (7)

This type of decomposition is useful in showing the relative importance of each type of change, structural changes (in means and scatter about the mean) as against positional change.

APPENDIX 4

Regression Modelling of Urban Areas Growth

The regression model of urban area growth uses indicators of urban area type to predict growth in individual urban areas. The resulting coefficients control for the simultaneous impact of other influences, so that the coefficient for a particular urban type (e.g., retirement town) or policy status (Green Belt) provides an indication of the net impact of the indicator concerned.

In order to reflect possible skewness in the dependent growth variable, estimation of the impacts of each independent variable is performed both in a multiple linear regression with untransformed percentage growth rates as dependent variables, and by using growth rates transformed to minimise skewness. In the presence of skewness the assumptions of linear regression may be invalidated by non-linearity and heteroscedasticity (with the variance to the observations dependent on their expected value).

Let Y denote the original growth rates, then the intention is to find the power T defined by:

Yen zz yT T#O = log Y T=O

such that the transformed growth rates fin are normally and independently distributed with constant variance S* and expectation specified by:

E( Y(n) = X8.

For example, PO.5 produces a square-root transformation, and T=-1 a reciprocal transformation. The values of 8, 9, and Tare found by maximum likelihood methods.

The maximised log likelihood is:

logL=nlogT-n/210gS2+(7’-l)XlogYi

where n is the number of urban areas. A constant is added to the Yi to ensure positivity, namely one minus the (regional) minimum growth rate.

Urban areas types are dummy indicators defined as one for urban areas exceeding theshold levels for census indicators of structure or change or with certain policy status, and zero for other urban areas. For some indices, thresholds are defined with respect to the Standard Regions in which they are located, in order to reflect regional variations in employment, tenure or commuting patterns; for example, average levels of manufacturing specialisation are higher in the North than elsewhere.

108

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