TEXT BOUND INTO THE SPINE "A DISAGGREGATE TRIP GENERATION MODEL FOR THE STRATEGIC

PLANNING CONTOL OF PRIVATE CAR TRIPS TO LARGE FOODSTORES"

G. McL. HAZEL

VOLUME 1- THE TEXT

Ph. D. THESIS

CRANFIELD INSTITUTE OF TECHNOLOGY This thesis is dedicated to my Father and Mother who, through their love, sacrifice and vision made it all possible.

G. McL. Hazel, July 1985.

For I ras my father's son, tender and only beloved in the sight of my mother. He taught me also, and said unto me, Let thine heart retain my words : keep my canmandments, and live. Get wisdom, get understanding : forget it not; neither decline fran the words of my mouth. Forsake her not, and she shall preserve thee : love her, and she shall keep thee.

Proverbs 4: 3-6. ACKNOWLEDGEMENTS

The author wishes to express his gratitude to Professor M. Cordey-Hayes for his invaluable advice, experience and guidance and also to the staff of the Centre for Transport Studies, Cranfield Institute of Technology. Special thanks are due to Dr. J. Clark, Mr. I Black and Dr. M. Benwell.

The author also wishes to thank Napier College and Lothian

Regional Council for making the research programme possible through the provision of funding and the remission of staff time, Mrs M. Clark for typing this thesis and, last but not least, to my wife Fiona for putting up with the attendant neurosis of research study for the past six years and for proof-reading the thesis. CONTENTS PAGE

VOLUME 1

CHAPTER 1 THE GROWTH OF LARGE FOODSTORES IN BRITAIN 1

CHAPTER 2 THE DEFINITION OF STORE TYPE AND THE ESTIMATION OF PRIVATE-CAR TRIPS TO LARGE RETAIL CENTRES 10

CHAPTER 3

CONSUMER BEHAVIOUR 26

CHAPTER 4

THE ACCESSIBILITY OF LARGE RETAIL FOODSTORES 54

CHAPTER 5

DESIGN OF RESEARCH PROGRAMME 67

CHAPTER 6

THE PLANNING AND ORGANISATION OF THE ENQUIRY 84

CHAPTER 7

ANALYSIS OF THE DATA 117

CHAPTER 8

INTERPRETATION AND DISCUSSION OF THE FINDINGS 232

CHAPTER 9

SUMMARY AND CONCLUSION 263

CHAPTER 10

SUGGESTIONS FOR FURTHER RESEARCH 271

REFERENCES 276 CONTENTS

LIST OF TABLES (Contd. )

PAGE

Table 27(b) Principle Components Analysis - Factor Composition for Each Area (Dependent Factors) 151 Table 27(c) Principle Components Analysis - Factor Composition for Each Area (Dependent Factors) 152 Table 28(a) Principle Components Analysis - Factor Composition for Each Area (Independent Factors) 153 Table 28(b) Principle Components Analysis - Factor Composition for Each Area (Independent Factors) 154 Table 28(c) Principle Components Analysis - Factor Composition for Each Area (Indpendent Factors) 155 Table 29 Large Foodstores in Edinburgh District 161 Table 30 Spatial Accessibility Indices for Individual Areas 165 Table 31 Pearson Correlation Coefficients for Spatial Accessibility and Variables Exhibiting a Significant Relationship 166 Table 32 Canonical Correlation Analysis Between Variables B, C, D, E, F and G and I, K, L, M, N, O, P, Q and R for Each Individual Area 170 Table 33 Structure of First Order Canonical Variates for Individual Areas 171 Table 34 Canonical Correlation Analysis for Means Data Matrix with and without the Variable S 173 Table 35 The Structure of the Canonical Correlation Variates with respect to the Means Data Matrix with and without S 174 Table 36 Canonical Correlation Coefficients for Groups of Areas used in the Tests of Homogeneity 177 Table 37 Canonical Correlation Analysis for the Three Superstore Dependent Variables with respect to the Independent Variables 178 Table 38 Structure of First-Order Canonical Variates for Individual Areas using only Three Superstore Dependent Variables 180 Table 39 Canonical Correlation Analysis with respect to the Three Superstore Dependent Variables and Three Selected Independent Variables 181 Table 40 Canonical Correlation Analysis for - 1) All variables 2) Three Store Dependent and All Dependent Variables 3) Three Store Dependent and Three Independent Variables 182 Table 41 Pearson Correlation Coefficients Between Dependent and Independent Variables for Individual Areas 183 . Table 42 Means Data matrix in Order of Store Duration of Stay 192 Table 43 Means Data Matrix in Order of Store Expenditure 193 CONTENTS

LIST OF TABLES (Contd. )

PAGE

Table 44 Means Data Matrix in Order of Store Frequency of Visit 194 Table 45 Canonical Correlation Analysis for Groups of Areas based on an Aggregate Store Usage Index 197 Table 46 Order of Variable Addition in the Multiple Regression Analysis for F with I to R by Individual Area 199 Table 47 Order of Variable Addition in the Multiple Regression Analysis for B with I to R by Individual Area 200 Table 48 Order of Variable Addition in the Multiple Regression Analysis for D with I to R for Individual Areas 201 Table 49 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 203 Table 50 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 204 Table 51 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 205 Table 52 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 206 Table 53 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 207 Table 54 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 208 Table 55 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 209 Table 56 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 210 Table 57 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 211 Table 58 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 212 Table 59 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 213 Table 60 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 214 Table 61 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 215 Table 62 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 216 Table 63 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 217 Table 64 Multiple Regression Analysis - Table of Pearson Correlation Coefficients by Individual Area 218 Table 65 Multiple Regression Analysis F with I to R; F with B, D; B with I to R; and D with I to R for Total Data Matrix 221 CONTENTS

LIST OF TABLES (Contd. )

PAGE

Table 66 Multiple Regression Analysis -E with I to R for the Total Data Matrix 225 Table 67 The Prediction of Frequency Use, Duration of Stay and Expenditure at Large Foodstores in the Survey Area using the Developed Trip Generation Model 244 Table 68 The Prediction of Personal Accessibility from the Independent Variables using the Means Data Matrix 252 CONTENTS

LIST OF FIGURES

PAGE

Figure 1 British Hypermarket and Superstore Openings 1966-1981 3 Figure 2 Growth in Number and Sales Space of French Hypermarkets 4 Figure 3 Number of Parking Spaces as a Function of Sales Area in France 23 Figure 4 Summary of Factors Influencing Activity Scheduling 37 Figure 5 The Accessibility Window of a Household to allow a Private Car, Bulk-Purchasing Trip to a Foodstore to take place 60 Figure 6 Model of the Household Choice Process with respect to Bulk-Purchase Shopping by Private Car 69 7 Figure Theoretical Study Framework showing groups of Households and Store Locations 72 Figure 8 Conceptual Framework for Model Development Based on Sub-Area Models or Aggregated Data Model 73 Figure 9 Dependent Variable Structure 75 Figure 10 Structure of Dependent and Independent Variables 77 Figure 11 Two Models of the Conceptual Structure of Independent and Dependent Variables 81 Figure 12 Model of Conceptual Framework showing sub-models based on Factor Variables and Individual Variables 83 Figure 13 The Study Area 96 Figure 14 The Questionnaire Design 100 Figure 15 Map of Area Locations 103 Figure 16 Profiles of the Dependent Variables with respect to their Means 105 Figure 17(a) Profiles of the Independent Variables I to 0 with respect to their Means 106 Figure 17(b) Profiles of Independent Variables P to R with respect to their Means 107 Figure 18 Profiles of Dependent Variables with respect to Standard Deviation 111 Figure 19(a) Profiles of Independent Variables I to 0 with respect to Standard Deviation 112 Figure 19(b) Profiles of Independent Variables P to R with respect to Standard Deviation 113 Figure 20 Scattergram of the Store Expenditure/Week (D) v. Total Expenditure (E) - Area 1 (Moredun) 136 Figure 21 Total Expenditure/Week (E) v. No. of Half Days Employed (Q) - Area 1 (Moredun) 137 Figure 22 Expenditure/Week at Store (D) v. No. in House- Hold (M) - Area 1 (Moredun) 138 Figure 23 Location of Large Foodstores in Edinburgh District 164 CONTENTS

LIST OF FIGURES (Contd. )

PAGE

Figure 24 Freezer Ownership, Income, Number of Licenses, Number in the Household and Mean Age of the Household with respect to Expenditure at Large Foodstores 195 1

CHAPTER ONE THE GROWTH OF LARGE FOODSTORES IN BRITAIN

1.1 Introduction

This thesis sets out to provide a model for the calculation of private car trips to large foodstores based on local area household characteristics. It recognises the weakness in predicting private-car trips to large stores using trip-rates obtained from surveys of stores in other areas. The trip generation model that is sought must be easily applied and must use readily accessible data. It is proposed therefore that a relationship be sought between private- car trips to the stores and the household characteristics, obtainable from census data, of the local catchment area. The model thus obtained would

be used for strategic planning control.

The initial chapters set out the scale of growth of large foodstores in Britain and the consequent problems they

create for the road network and the local environment.

The conceptual framework and research objectives are then discussed and related to the research tasks and the methods of analysis required to achieve the research objectives. The resulting trip generation model is fully examined, to understand the relationships established within the model, and then applied to future development within an established aggregate distribution and assignment model.

The first chapter therefore highlights the growth of large foodstores in Britain and abroad. It discusses the

general factors which have combined to encourage this

growth and examines the future of this form of retailing in Britain. 2

1.2 The last 15 years have seen a phenomenal growth in large

food retailing stores in Great Britain. Jones(') estimates that this growth is the equivalent of providing

gross floor-space equal to 20 shopping centres the size of Brent Cross. In terms of their size, locational requirements and characteristics, these stores have altered the pattern of shopping centres and the shopping behaviour of millions of householders on a scale unprecedented in the history of the British retail trade.

The Unit for Retail Planning Information (URPI) estimates that a total of 230 large stores, comprising 42

hypermarkets and 188 superstores were open and trading at (2) the end of 1982. URPI define a hypermarket as a store with over 5,000 m2 selling area and a superstore as a store between 2,500 m2 and 5,000 m2 selling area. In 1977 there were 140 stores trading, based on these URPI definitions. Figure 1 shows the rapid growth in British

hypermarket and superstore openings between 1961 and 1981.

1.3 During the 1960's this form of retailing expanded rapidly

in Europe, accounting for 10% of the retail trade in some (3) countries. In France, for example, the first hypermarket was constructed in 1967 and there are now in excess of 305, each with a sales area greater than 2,500 2'3) M. The six largest hypermarkets in France are operated by Carrefour and include a store at Toulouse with

67 checkouts, parking for 4,000 private cars and is twice the size of the largest store operated by any of (3) Carrefour's competitors. Figure 2 and Table. 1 show the growth rate of these stores in France.

1.4 In the United States of America the out-of-town hypermarket showed a similar growth pattern. In 1950 the average store size was 400,000 ft2 of retail floor space; 2 by 1960 this had risen to over 750,000 ft . In this period the number of large retail centres rose from 2,000 Tod now RU~ of stAnnualae ow"WA SUPOS t (fpht Yia1ý) PON scow s.. wu umwIus r.... " u.

1 " FIGURE

British hypermarket and superstore openings 1966-1981 4

300

250

200

ä a. G Y

150 t ö a M A = 100 S

50

0

FIGURE 2

Growth in number and sales space of French hypermarkets

Source: Geography, Vol. 61, Pt. 4,1976 Sala area (m') 25oo- Number Soon-. 7500- 10 000- tg 000 of cmployccs 4999 749J 9'J99 14 999 and over Total Percentage

1-34 1 0 0 0 0 t 0 35-99 46 0 0 0 0 46 0 100-149 47 8 1 0 0 56 19 15o,- 199 s6 19 4 0 0 49 17 200-249 to 2g 7 3 0 4g t 250-299 3 26 17 2 0 48 t6 30O-399 0 i 11 14 1 35 t awl over 0 3 5 4 '5 3 ITI Jut I't s. l x! 11 1'ý. . ý ut.. gc" .1t. ;lu 1.1 U tuu

"I ciLcutage refers to ul1 305 stores. wukL: 6: "Attu (Ica hyper cj Iuperuurchls au to jauvier. 1976", Libre ServiceActualitts, 561-2.

TABLE 1

Size of French Hypermarkets in 1975 6

to 11,200 and this continued into the seventies adding (4) 1000 to 1200 new stores each year. The North American developments have taken the form of regional shopping

centres, usually situated well outside suburban areas.

1.5 The growth of the large retail foodstore has been

experienced in most developed countries. Documented evidence has been produced to substantiate this trend in Ireland(5), West Germany(6), Switzerland(7) and Japan(8) in addition to France and the USA mentioned above. This pattern of growth is continuing and Jones, in his paper on the saturation level of hypermarket and superstore G) provision, concludes that it is not possible to predict saturation in a market as dynamic and uncertain as retailing due to problems of definition, qualitative factors, changes in marketing and management techniques and the difficulty in forecasting development opportunity. This is supported by Sheth(9) in his examination of future retailing trends.

1.6 In contrast to the somewhat explosive development in certain countries the pattern of growth in Britain has been slower and smaller in scale. In 1970 24 large stores

were in existence, the majority operated by Asda and Woolco(2) and situated in the North of England. Since then development has accelerated, as shown in Table 2.

1.7 As in other countries this trend is continuing. Safeway Food Stores, for example, had 82 outlets in 1975 and 110 in 1983, giving a rise in total sales area from 781,000 2 2(10) ft to 1,005,000 ft. Kwik Save Discount Group PLC had 156 outlets in 1978 and 306 outlets in 1982, with 30 (11) planned for 1983. Associated Dairies Group PLC had 12 warehouses and 32 branches in 1972. This had risen in (12)- 1983 to 72 warehouses and 88 branches The British

situation, although less dramatic than the growth patterns of France or the USA, nevertheless constitutes a major 7

YEAR HYPERMARKETS SUPERSTORES TOTAL

1967 2 1 3

1971 6 22 28

1974 14 61 75

1977 31 109 140

1981 42 188 230

Source: URPI U7,1978

TABLE 2

Growth in Hypermarkets and Superstores in Great Britain, 1967-1981 8

change in retailing pattern with major consequences for the urban environment. The report, commissioned by (13) Glasgow Corporation, on shopping centres and hypermarkets in the Glasgow area highlights these

consequences and requires planning authorities to understand the reasons for this growth so that it can be effectively controlled.

1.8 The reasons for the emergence of hypermarket and superstore shopping have been well documented and can be classified into two broad areas; changes in methods of store management and retailing and changes in consumers' (1,2,9,13,14,15) mobility and accessibility.

The scale of operating enables the retailer to achieve economies by optimising merchandising techniques and by introducing new management methods such as direct

purchasing, mechanical goods handling in-store and devolved departmental management responsibility, thereby

offering discounted prices to the consumer. Increasing (16) car ownership levels and improving accessibility to large stores have enabled consumers to take advantage of

these economies of scale. These factors are more fully discussed by Parker(s). Pacione(14) and others. (9,15,17,18)

1.9 The increased accessibility offered by these stores coupled with traffic congestion and restraint policies within city centres, has led to an increase over time in store size. Tesco, for example, have increased the 25,000 percentage of total sales area of stores over ft2 (19) from 11% to 40%, between 1975 and 1981. Thus the

one-stop shopping, associated with large foodstore retailing, has become an established and growing section of the retail market. It is generally agreed, by most (9,14,17,18) retail commentators, that this growth will continue and this form of shopping will significantly 9 affect the retail hierarchy of Britain. The next chapter discusses the impact these stores have had on the traffic movements of urban areas and the local environment and the consequent problems that have been caused. 10

CHAPTER 2 THE DEFINITION OF STORE TYPE AND THE ESTIMATION OF PRIVATE-CAR TRIPS TO LARGE RETAIL CENTRES

2.1 Introduction

The chapter begins with an examination of store types and their definitions as this is related to trip rate. This is also necessary to establish the scope of any predictive model that may be developed.

It is shown that the definitions of hypermarkets, superstores and supermarkets are arbitrary and based on floor size. The regional centre can be separately identified. This thesis relates to the former three shop types.

The existing predictive methods, for trip arrival and parking provision at each store, are then discussed and specific are shown to be unsatisfactory; their and empirical nature being inappropriate in the general case. It is suggested that a trip generation model based on

local area characteristics be developed which must be easily applied and economical. For these reasons the model should be based on census data and provide an input to a compatible distribution model which allows for competition. 11

2.2 The Definition of Store Types with respect to Current Practise

2.2.1 In Britain large retail stores vary in size and title

from the regional shopping centre to the local supermarket. Therefore when attempting to model private-car trips to large retail stores one must establish the scope of the predicting model. To do this it is necessary to review the current definitions of each type.

(') 2.2.2 It has been quoted, in the previous chapter, that there were in 1981,230 superstores in Britain. This does not include 70 stores classed as supermarkets but marginally smaller than the cut-off figure, who generate (2) similar levels of private-car traffic. The form of definition can therefore significantly change the level of activity.

2.2.3 The regional shopping centre however, can be classified as distinct from the individual large store in that it differs in sales technique, range of goods offered, number (20) of shops involved and traffic characteristics. The regional shopping centre usually has at least one large department store in addition to many small shops. The sales approach is one of comparison shopping, the consumer being able to choose between several retail units.

2.2.4 The distinction between hypert xarkets, superstores and

supermarkets is less obvious. They all emphasise convenience goods displayed in one large store and previous and current definitions tend to emphasise the scale factor difference between store types. France (3) defines the hypermarket as follows

a) a retail unit of at least 2,500 m2 (22,500 ft2) sales area on one floor selling a wide range of low-priced food and a more general range of non-food goods. 12

b) a ground level car park at least three times the

sales area of the store.

c) self service principles throughout.

(21) Harris and Andrew define hypermarkets as having a sales area greater than 50,000 ft2 and superstores, a sales area greater than 25,000 ft2. Pacione(14), in

commenting that no definitions have been generally agreed, chooses a figure of 6,000 m2 (60,000 ft2) sales area to define the hypermarket.

2.2.5 The Unit for Retail Planning Information (URPI) defines

hypermarkets and superstores as single level, self-service stores offering a wide range of food and non-food merchandise, with at least 50,000 ft2 of sales area for the hypermarket and 25,000 ft2 of sales area for the

superstore. They must also be supported by adequate car parking. These definitions do not recognise characteristic differences but use the sales area as the basis of definition. Parker(5) again bases his

definitions on sales area but chooses 20,000 ft2 of selling space as the minimum area necessary for the

definition of a superstore.

2.2.6 The current literature therefore bases the definitions of hypermarkets and superstores on sales area and the variation in definition produces a wide range of defined

hypermarkets and superstores in Britain e. g. the number of hypermarkets varies from 3 to 38 depending on the definition adopted. The characteristics listed by the

authors mentioned above are vague and talk in generalities

lacking a uniform definition. Supermarkets are not mentioned except for Jones(l) commenting on the false

divisions imposed by definitions based on sales area. It

is clear that size of store is at best unsatisfactory and 13

that other factors such as local catchment area characteristics, location and competition must influence (22) the performance of any given store. This is

underlined by the fact that the same store could be defined as either a hypermarket or superstore, depending on the sales area definition used, and different trip rates would be applied.

2.2.7 It is proposed therefore to avoid such distinctions between hypermarkets, superstores and supermarkets and use the general term large foodstore where appropriate since in reality the differences in the British context are more to do with the locational factors mentioned above rather than any intrinsic difference caused by size. That is the location is directly related to the accessibility and

attractiveness of the store and will affect the distribution of shopping trips within the retail,

competitive framework of the area. If the trips generated are modelled on household characteristics and are separated from the distribution to individual stores the definition problem is irrelevant. This postulation will

be expanded in the chapter on conceptual framework.

2.2.8 The previous chapter outlined the historical growth of large retail centres and showed their continuing presence in today's retail hierarchy. The size of these stores

attracts large numbers of shoppers, predominantly arriving by private-car, and ensures a major impact on the local area, just as any major traffic generator. This raises issues of development control which must be faced by transport planners and leads to a trade-off between retail (24) efficiency and environmental protection. The traffic pattern generated and the consequent social and engineering problems have to be understood and dealt with and the next section of this chapter examines the current methods of trip generation estimation from which development control decisions are made. 14

2.3 The Problem of Private-Car Trips Attracted to Large

2.3.1 It has been argued that large, one-stop shopping stores

are an established part of the retail hierarchy and that their size and consequent traffic generation requires

control of their development. The particular characteristic that this thesis addresses itself to is the generation of private-car trips to these stores. The percentage of private car trips varies from store to store depending on store type and catchment area characteristics (21,22,23,24) but is invariably between 70% and 90%. (21) Harris and Andrew argue that the scale of trips

attracted and the parking provision required compares with the level of trip attraction of a regional airport the size of Turnhouse, Edinburgh and therefore requires the same careful planning and design. The Minworth-Carrefour store generated, in 1979, between 6,300 to 8,500 daily trips with the road access having to cope with up to 1000 vehicle arrivals in the peak hour. The store is situated adjacent to a major road. The Fine Fare, Stirling store was sited within a council housing estate with only a roof car park which catered for 180 cars. The result was that within weeks of the store opening Prohibition and Restriction of Waiting Orders had to be introduced on the residential streets around the store. The local residents (24), suffer severe vehicle intrusion. Aitken & Malcolm in studying this store, conclude that the introduction of a hypermarket or superstore into an area where the road network is not designed to carry the additional, generated traffic is undesirable. It would appear therefore that in

permitting a large, one-stop, foodstore development to

proceed, transport planners must ensure that the surrounding road network is capable of supporting the increased load that will be imposed upon it. The method of estimating these trips will now be discussed. 15

2.4 Current Techniques for the Estimation of Trips Attracted to Large Foodstores

2.4.1 The current method used for the estimation of trips to

large stores is either based on Gross Floor Area (GFA) or Net Floor Area (NFA), the latter can also be referred to as sales area, selling area or retail area. Parking requirements are calculated using the same empirical methods which involve comparison of trip rates, established from surveys of stores in other locations, (21) with the store being considered. Harris & Andrew

support this method.

2.4.2 Table 3 shows the results of two British studies which

suggest an optimum trip rate/1000 ft2 GFA when the store has built up a demand from its catchment area. The two

stores listed show an agreement between the sets of figures but this pattern is not substantiated when a wider

sample is taken. Table 4 is extracted from a letter, in (21) response to Harris & Andrew's paper, from Maltby & (25) Johnson of Salford University. The overall variation

in this small sample is approximately 80 trips/1000 ft2 NFA. For a medium-sized store of say 50,000 ft2 NFA this would mean a 4,000 trip variation, a significant difference in terms'of road network capacity. In addition there appears to be no obvious relationship between stores of comparable size. This is supported by URPI(2) who found that the link between turnover and floorspace was tenuous. In some instances stores of a similar size had turnovers differing by 100%. They also concluded that no relationship could be found between store size and turnover to floor-space ratios.

2.4.3 It can be seen, therefore, that store and locational characteristics influence trip rates. There is no evidence to suggest a simplistic relationship between 16

floor area and trip rate that can be generally applied (21) with confidence. Indeed, Harris & Andrew concede that trip rate per floor area is only applicable in certain

instances and that for practical applications a more refined technique is required which relates trip rates to catchment area characteristics. Leake(22) established,

from his Leeds study, a relationship between the peak two-way traffic flow and retail floor area

V- 20Y

where V- two-way flow of shopping vehicles (vehs/hr) Y- retail shopping area (per 100 ft2)

(24) Aitken & Malcolm dispute the value of the constant in

the equation and argue that from their study it appeared to have a value of 13.4. However they qualify their statement by acknowledging a range of values between 13.4

and 17.5. Thus the variation found in Table 4 is present in this equation which takes no account of the catchment (25) area characteristics. Kelly substantiates this store trip-rate variation by quoting figures from his Welsh

study. The attraction rates for Asda, Gwent and Asda,

Merthyr Tydfil are quoted at 59.3 vehs/1000 ft2 GFA and Leo's Superstore, Pyle at 54.4 vehs/1000 ft2 GFA. The

Asda stores each having 40,000 ft2 (59,000 ft2 GFA) of sales area and Leo's Superstore having 30,000 ft2 (45,000 ft2 GFA) of sales area.

2.5 Current Techniques for the Estimation of Private-Car Parking Provision at Large Foodstores

2.5.1 The provision of car-parking at large foodstores shows the same variation as trip-rates. Its under-provision

can create accident and environmental consequences and its overprovision is wasteful of land and is

environmentally obtrusive. One of the earliest studies of parking at large stores was carried out in 1965 by the 17

Total % of Trip Rate Trip Rate Trips Weekly 1000 ft 1000 ft Total GFA, GFA, Minworth Eastleigh

Tuesday 6,399 17.7 43.2 40.1

Wednesday 6,324 17.5 42.7 40.4

Thursday 6,929 19.2 46.8 50.5

Friday 7,896 21.9 53.3 59.1

Saturday 8,536 23.7 57.7 55.4

Total 36,084 100.0 243.7 245.5

Source: Harris & Andrew. (21)

TABLE 3

Trip Rate/1000 ft2 GFA at two British Superstores (24-hour total, mid-December) 18

Store Sales Trip Rate/1000 2 ft2 Location Area (ft ) Sales Area

Asda, Llandudno 31,000 129.5

Fine Fare, Stirling 34,000 59.1

Gem, Leeds 35,000 69.1

Carrefour, Caerphilly 55,000 70.2

Carrefour, Eastleigh 56,000 116.6

Carrefour, Minworth 70,000 112.8

Tesco, Iram 73,000 63.5

Fine Fare, Hyde 75,000 47.7

Carrefour, Bristol 90,000 87.7

Source: Maltby & Johnson. (25)

TABLE 4

Trip Rate/1000ft2 Sales Area -a Sample of British Stores 19

Urban Land Institute(26). The study was carried out at 270 shopping centres in the USA and Canada during the 12 busiest shopping days of the 1964 pre-Christmas period. The suggested design standard of 5.9 spaces per 100 m2 GFA was based on the results from 103 shopping centres having neither offices nor theatres.

2.5.2 Early British parking standards followed the (27) recommendations of the Multiple Shop Federation and 28). NEDO The former converted the American regional. standard to the circumstances applying in Britain by allowing for the different levels of car ownership between the two countries. Standards were produced relating to 1969 for high, average and low values of car ownership together with predicted standards for 1975 and 1980. The recommendations only applied to central area shopping facilities. NEDO produced minimum standards which depended on the location of the particular shopping facility. A reduced value of the parking index for

central areas reflected the motorist's dislike of congestion, lack of convenient parking facilities and better public transport systems. The higher value of

suburban stores reflected their dependence on the

private-car.

2.5.3 The NEDO study suggested a minimum of 5.0 parking spaces (24) per 1000 ft2 of sales area while Aitken & Malcolm suggest 6.7 spaces. Sainsbury superstore at Bretton, Peterborough used a value of 10 spaces per 1000 ft2 of sales area while Asda have a policy of providing 1 space for every 60 ft2 of sales area wherever possible, i. e. 16 spaces per 1000 ft2 sales area. Leake(22) initially

suggested a figure of 6.0 spaces per 1000 ft2 of sales area in areas of high car ownership but published a (29) reappraisal of his work in 1982 which took cognisance of household car ownership, petrol facilities and ease of circulation. Table 5 shows the suggested rates. Note 20

that superstores and supermarkets have been grouped

together substantiating the argument put forward earlier in the chapter and also the characteristics of the catchment area around the store are beginning to be

considered; in this case car ownership. This trend is

also seen in a paper by Codd(30) on his work with the City of Glasgow. He suggests a shopping parking prediction model based on shopping centre size and car ownership :

t.. 1-k f(diý) S(A ij

where tij - trips from zone i to shopping centre j over a given time period.

Cij - number of cars from zone i at shopping centre J.

A4 - effective area of shopping centre j, a function of convenience and non- convenience floor space.

k -a constant

f, g - functions

dij -a measure of deterrence between zone i and shopping centre J.

One aspect of his initial findings was the inadequacy of current procedures. The relationship of parking accumulation vis-a-vis GFA was found to have a poor fit to the least squares line. The relationship is not improved if convenience floor space is used.

2.5.4 In France the higher trip rates experienced at hypermarkets has led to the use of a higher parking design ratio than that used for regional shopping centres. The parking ratio for the latter is 6 spaces/100 m2 GLA, which provides sufficient parking for 350 days a year. As a 21

Parking Index (car spaces/100m2 retail area)

Without Petrol With Petrol

Supermarkets, Superstores 11.0 12.5

Hypermarkets - 20.5

Source : Leake & Turner. (29)

TABLE 5

Suggested Design P. I. Values for Various Types of Shopping Facilities 22

general rule the ground level car park of a hypermarket is

at least 3 times the sales area. The number of parking spaces can also be shown graphically as a function of sales area as shown in Figure 3, (note the discrepancy

between the observed and theoretical ratios).

ý31) 2.5.5 The Distributive Trades EDC in their report

recommended that the absolute minimum for parking provision for an out-of-town shopping centre should be 8.5 spaces/1000 ft2 sales area and that every effort should be made to improve this allocation. This standard was used for the Eastleigh, Carrefour store and was only just adequate to meet the demand. Minworth-Carrefour has 1,280 parking spaces; in line with the recommendations at that (25) time. Kelly suggests that, from experience in Europe, a design figure of 7 to 10 spaces/1000 ft2 GLA is more pertinent to hypermarkets and, superstores however he goes on to quote a value of 5.4 spaces for Asda, Gwent and Asda, Merthyr Tydfil and 3.3 spaces for Leo's Superstore, Pyle. He concludes that the suggested range of 7 to 10 spaces does not compare well with his study where a figure of 10 to 15 spaces/1000 ft2 GLA was more appropriate.

2.5.6 Maltby & Johnson(25) note that a comparison of parking

provision to 38 Carrefour stores on the Continent of Europe with 19 British stores revealed average provisions of 18.8 spaces/1000 ft2 sales area for European stores and 12.3 spaces/1000 ft2 sales area for British stores. The authors note that the difference was due to store location, catchment area characteristics and land costs rather than differences in trip generation. This conclusion was based on published data on European and British stores. 23

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a 45066 to eeo 1L Coo ý Sctircý G&rm*;

SC1N CM t) "KQ

FIGURE 3

Number of parking spaces as a function of sales area in France

------Ratios proposed by SETRA Observed ratios (sample of 257 centres) 24

2.5.7 It is clear therefore, that no generally accepted criteria for the estimation of parking spaces exists and the variation in rates is comparable to that found for trip rates. The Local Authority and developer can choose

from a wide range of rates to justify their policy decisions. As with trip rates the methods are empirically based on global factors which are shown to be dependent on the characteristics of the area surrounding the particular store being surveyed. There is no obvious correlation shown between store size and parking space, by inspection of the data. This is substantiated by inspection of a list of every hypermarket and superstore in Britain and their floor area and parking space

provision. The list shows no pattern between the two variables (see Appendix A).

2.6 Concluding Remarks

2.6.1 The present method of estimating trip rate and parking

allocation is unsatisfactory. The development control officer has to use data from areas considered to be equivalent to the catchment area under study. In practise this means the developer may choose rates sympathetic to his cause anc the Local Authority likewise and the resulting compromise may not model the actual demand.

2.6.2 The emerging trend, as indicated in the chapter, is to acknowledge the impact of catchment area characteristics on trip rate and parking provision. This thesis seeks to establish an objective basis which models the relationship between local area characteristics and the usage of large foodstores. The model will estimate the trips generated

from the local area and must be easily used by the

development control officer. This means that it must be

able to be applied quickly and economically and to a level of accuracy compatible with its strategic nature. 25

2.6.3 The implication of ease of application suggests a model based on area characteristics which can be abstracted from

census data. The model must be of a general nature enabling interaction with a general distribution model,

taking into account the competitive framework existing between stores in the area. Before discussing the conceptual theory behind this hypothesised relationship it is necessary to identify from consumer behaviour theory and known research findings the characteristics within an area that- influence store usage. Once these have been identified the conceptual design and research objectives can be established. 26

CHAPTER THREE CONSUMER BEHAVIOUR

3.1 Introduction

The chapter begins by examining store choice and defining it as comprising three parts - access to store, attraction to competing stores and consumer -characteristics and needs. A review is then undertaken of current shopping models which seek to emulate the consumer store-choice

process. These are also in three parts - the individual store assessment of trips arriving based on floor area, the aggregate models and the disaggregate models. The latter two seek to model the effect of competition between

stores.

The chapter 'argues that, for the strategic planning control of large stores, a disaggregate model is required that predicts private-car trips to stores from the household characteristics of an area. These trips would then be input into a conventional gravity model and

distributed to the existing stores. In order to develop this disaggregate model the effect of competition must be removed and this is achieved by randomly selecting groups of households, spatially together, and comparing the relationship developed within each group.

The latter section of the chapter identifies from previous studies the general factors which predict the shopping usage of a household. These are seen to be an employment factor, a household structure factor and an income and social grouping, which could be thought of as a lifestyle factor. From these general factors specific variables are

identified. The effects of personal accessibility and spatial accessibility are fully discussed in the following chapter, thus these two chapters form the basis from which the research objectives and conceptual framework can be developed. 27

3.2 Factors Influencing Store Choice

3.2.1 The previous two chapters have described the empirical method of traffic estimation from large retail stores.

This method is based on the relationship between trip rate and the floor area of the store and estimates the trip attraction to a given store from traffic surveys carried out at other stores. Although it is argued that the above (22,24) relationship exists the method does not perform well in practise due to the different characteristics of each store and its environs.

3.2.2 This study is concerned with the estimation of

private-car, shopping trips to large foodstores. This has been approached in three ways :

1) the empirical method, used by most development control planners, based on floor area. This pertains to an individual store.

2) aggregate methods of trip estimation within a competing framework of existing stores. Again based at the store-end.

3) disaggregate methods of trip estimation based at the consumer end. These methods seek to explain and model the nature of the individual consumer's choice.

3.2.3 The understanding of the consumer choice mechanism has attracted much study and a review of the work to date identifies three broad areas which make up this mechanism

i. e. the characteristics or attitudes/beliefs of the

consumer, the accessibility of the store and the (32,33) attraction of the store, or the store image. This i. can be thought of as store-based or consumer-based e. . 28

Accessibility of Store

Attractive Power Store of Store Image/Attraction

Consumer Character- Time allocation/ istics or attitudes/ personal beliefs accessibility

OR

Accessibility of Store

Consumer Store Characteristics Choice of Image/Attraction or Attitudes/ Store Beliefs

Time allocation/ personal accessibility 29

3.3 Analytical Approaches to Consumer Behaviour

3.3.1 The Gravity Model

One of the earliest and best known applications of the gravity model is the retail location model developed by (34) (35) Lakshmanan & Hansen from the work of Reilly. The

model allocates consumer expenditures from residential zones i to shopping centres j subject to the constraint :

s c1Pi ij -i£

Sij are the sales made by vendors in j to consumers in i

and Ci is the per capita expenditure on consumer goods in i. The model is,

x1 -X2 Si3 - AiCiPiW1 di1

1 where Ai -l- W.W d. . J 13 Wi is a measure of shopping centre attraction in j and are parameters of the model'.

In this type of model the attraction of the store (Wi) is

usually a simplistic measure such as retail floor area, although other terms reflecting range of goods, etc., can (36) be added. The accessibility term is the deterrence INldi3 -X` function Wi and the consumer term is modelled by population (Pi) and per capita expenditure (Ci). The advantage of such a model is its ease of application for strategic modelling and indeed the concept of "gravitational" attraction has been proved from empirical

studies to be sufficiently accurate for this type of (37) modelling. The disadvantage is its aggregate nature

which relies on the calibration of the model parameters and the deterrence function with respect to a given area. 30

The calibration of these parameters and function can be both costly and time consuming.

3.3.2 Probabilistic Choice Models

The search for a model which more accurately reflected the consumer's choice pattern led to the extension of the one-centre gravity model to an interacting network of shopping centres. To describe this many-centre interaction the probabilistic notion of competition is (38) introduced. Huff showed that the probability of a given centre j being chosen from a closed set of centres is . U. P3 UJ

where Ui is the utility associated with the jth centre. The utility of the centre j for a particular consumer is indexed by the ratio of the attractive power (A3) of the

centre to the distance (dii) of the centre to the consumer

" i. e.

Aý /d.. Pii sE Aý /dlýß

This can then be developed in terms of sales by multiplying Pjýi by the retail expenditure of the consumers in zone i"(3zr The model is similar in structure to the gravity model but attempts to relate the sales emanating from a zone to the probability that a consumer will choose a certain store. The concept of the competitive framework

is also an advance towards a more realistic modelling of the, choice mechanism of a consumer, compared with the one-centre gravity model. 31

Harris(39., from a model originally proposed by (40). Stouffer developed a retail location model which contained specific provision to vary demand for different classes of population and to vary consumer behaviour in

proportion to the density of shopping opportunities. The basic formalism derived by Harris is,

äQ ' f(L)Q

and the probability of a shopping trip continuing beyond V is, If(L)Q R dL L=O

where probability distribution function V- the number of opportunities L- the' probability that a consumer will be satisfied by a particular commodity Q- the probability that the consumer has not been satisfied with the first 'x' number of opportunities

Harris used a gamma distribution for f(L). This is developed to the generalised form,

a2 R (L + bl W)-al L(L + b2 D) -(cl W+ c2 D) e

where W-V+D D- distance to the next opportunity.

The model contains seven parameters and these can be reduced depending on the value these parameters take. For example, if a-b c-0 then the gravity model is obtained. For a detailed exposition of the model development the re ader is referred to 'a report by (32) Cordey-Hayes. 32

(41) Wilson developed an approach based on maximum entropy which had as its basic hypothesis that the probability of a particular trip distribution occurring is proportional

to the number of states that can give rise to the distribution. The number of distinct arrangements W(Tij) that can give rise to the distribution (Tij) is,

WET )-T! ij 7T T iJ 13

where T is the total number of trips. The total number of possible states is then,

W-EE W(Tij) ij

The procedure is to maximise W(Tij), subject to the imposed constraint, to give the most probable distribution. Both the gravity model and the intervening (39) opportunities model, developed by Harris, can be derived from this approach.

A further two models that have been developed are the

logit and probit models. The basic form of the logit model is,

P- exp V/E exp V cck kck giving the probability Pc that a traveller will be attracted to a grouping c of elementary alternatives, } given that a set K-{.... k ... of such groupings is available to him. Vk is the utility of grouping k. The model is disaggregate in the sense that it predicts the choice of an individual.

This model can be derived from the gravity model by considering each individual trip-maker in zone i as making one of the 0i trips from that zone, i. e. 33

T. B. f. ps1_3 '3 ýi 1 Bk fik k

where Pj - the probability that the individual in zone i will travel to zone J.

If Vk - log (Bk Fik) the basic logit model can be derived. A fuller explanation of this type of model is given by (42) Daly. An extension of the above is the multinomial logit model which can be written,

Udmt P(d, DMt) m: -e Udmt Ze where (P(d, m: DMt) is the probability that individual t will choose destination d and mode m from the full set of alternative destinations and modes open to him; DMt. Udmt is the utility 'individual t obtained from going to destination d by mode m. This model was used for the (43) distribution of shopping trips in Holland in 1975.

Probit analysis assumes that there'is a threshold P for degree of preference for the store to be chosen. That is a consumer will decide to shop at a store only if his or her degree of preference is above the threshold. A linear representation of this, as used by Malhotra(44), is

R PZ ßi Xi i=o

image where xl, x2, ... xR represent salient store characteristics. The model then attempts to explain store choice directly in terms of store image characteristics and thereafter to forecast consumer expenditure. 34

3.3.3 Attitudinal Approaches

A further approach to the understanding of the consumer choice mechanism involves attitudinal modelling. This

area of research', as applied to retail behavioural modelling, is comparatively new and falls into two categories,

a) Studies seeking to model individual psychological or attitudinal responses to choice alternatives, for (44) example the work of Hartgen.

b) Studies seeking to explore the relationships between attitudinal responses and observed behaviours, for (45), (46) example the work of Burnett Cadwallader and (47) Mackay et al

The theory assumes that when individuals evaluate the

desirability of alternatives they do so by cognitively integrating their perceptions of the alternatives. The process by which the perceptions of attributes associated with the alternatives are combined into overall

evaluations of attitudes may be represented by some form of a multi-attribute' utility or value equation. It is assumed that when selecting alternatives, the individual will choose that alternative for which he or she holds the highest overall value or utility.

One of the most influential models in this area of retail marketing research is Fishbein's behavioural intention (48) model which is represented, in summary form, as

B'v I- (AB) W1 + (SN)W2

where B-a specific overt behaviour under the

control of the individual. 35

I = the intention to perform that behaviour

AB - the attitude towards that behaviour SN = the subjective norm or belief that most people who are important to the individual think he or she should perform that behaviour W1, W2 = empirically determined standardised regression coefficients.

Burnkraut & Page(49), from their research, argued for the validity of this two-component conceptualisation of the determinants of behavioural intention.

(50) The work of Louviere & Meyer also provides preliminary support for the approach. They found that psychological judgements are shown to exhibit high correspondence with real behaviour. This means that if estimates of the psychophysical relationships can be obtained a priori, and such estimates are theoretically true, then the problem of multicollinearity between independent variables can be bypassed. A critical' link in the theory is the

combination rule or the multi-attribute utility function.

Without adequate knowledge of its true form considerable error in model specification is possible. This is a (50) general criticism of attitudinal studies. The major advantage is that they require no revealed behavioural data only observations of evolution under controlled circumstances.

3.3.4 Activity Models

In the last decade researchers have begun examining travel

in the context of activities which are demanded at the end of trips. This is a departure from the norm where trips

have been considered as commodities. The activity-based approach carries the promise of improved understanding of 36

travel behaviour and an implicit need to define the parameters of interest. These parameters relate to decisions, taken at household level, of whether or not to participate in an activity and for what duration.

Through a review of activity-based models of the past (51) decade Damm isolates three areas into which causal factors can be categorised i. e. activity needs, temporal constraints and spatial constraints. Lenntorp(52) focussed on the potential rather than the actual set of alternatives available. Figure 4 shows the framework of factors affecting activity scheduling. The data required to build activity schedules for each household falls into four categories :

1) the data should at least contain information on the duration of all daily out-of-home activities.

2) an identifier for a person's work status should be present.

3) transportation level of service as well as land-use

data is necessary.

4) data on the socio-economic characteristics of the (51) household are required.

This type of data is not readily available and Damm had to use transportation/land-use home interview surveys from Minneapolis/ St Paul. Five equations were then developed for whether or not someone decides to participate in an activity and another five equations for duration were estimated with data on people who participated in an activity in a particular time period.

The above work was of an exploratory nature and called for further research by specific activity e. g. shopping. The 37

LON GER R [N DECISIONS

II II HOUSEHOLDNEEDS CONSTRAINTS CONSTRAINTS

ACTIVITY SCHEDULE.

FIGURE 4

Summary of factors influencing activity scheduling

Source: Damm (51) , 38

theory requires information on in-home activities, opening

hours of major activities, degree of flexibility in worker's arrival and departure time and non-motorised movements.

Until this data is available activity-based analysis will not play a major role in transportation planning.

3.4 Concepts and Approaches from Consumer Behaviour Theory

most Relevant to the Study

3.4.1 The previous section reviewed the theory used to model consumer behaviour. The gravity model, or a similar type of spatial interaction model, is widely used in practise to estimate consumer demand. The model has its origins in central place theory, developed by Christaller(53) & (54), Losch however the empirical evidence does not support (55) the central place concept of choice. One of the major reasons for this is that retail spatial competition introduces monopolistic elements that are absent from (56) aspatial competition as found in economic theory. This means that market area boundaries are less sharply defined as a result of complex travel patterns: Casparis(57) postulated that consumer preferences and income must influence retail patterns and to assume the consumer will always use the nearest store is questionable.

(58) The work of Hubbard also suggests that consumers are likely to bypass the nearest alternative if the extra

effort is compensated by better shopping (59) developed by opportunities. This notion was (60) Reilly into a "law of retail gravitation" which Although subsequently developed into the gravity model. - based on the initial central place theory framework the

gravity model incorporates a retail competition element represented by "size" of store. 39

3.4.2 The advantage of the gravity model is primarily its convenience which is suited to the strategic planning required for development control. It is also proven and (61) its limitations are well documented. Pankhurst & Roe, in comparing a gravity-type model with a decision-type model, found that the gravity model fitted observed data more closely but its generality was poor and showed sensitivity to lack of precision in the parameter values of the model. They suggested that sub-models be developed from disaggregated data enabling the local values of the model parameters to be determined and input to the gravity model.

3.4.3 The emergence of probabilistic theory applied to retail (38) choice was primarily due to Huff who argued that consumers may visit more than one store and the probability of visiting a particular store is equal to the ratio of the utility of that store to the sum of the

utilities of all stores considered by consumers. The Huff model (see para. 3.3.2) was the first to suggest that

market areas were complex, continuous and probabilistic rather than the non-overlapping geometrical areas of

central place theory. The calibration process involves dividing the market area into zones based on residential characteristics and traffic patterns and selecting, randomly, a number of households from each zone. These households are then surveyed to determine for each zone the proportion of store visits made to a particular store. Most empirical studies support the usefulness of this (62,63,64) approach however the accuracy is not good for predicting trips to individual stores. Stanley & (65) Sewall incorporated a store image utility function in

the model and improved its performance in this respect. However although the probabilistic framework and zoning method based on area characteristics are useful to this study the weakness of individual store trip prediction is

important to development control. 40

(66) 3.4.4 Nakanishi & Cooper have developed a more general spatial choice model called the Multiplicative Competitive Interaction Model (MCI). They considered store

characteristics as well as store attractiveness and

estimated the model parameters by log transformation and least-squares procedures. A number of retail companies (67) have employed this method but its main application is in site selection.

The multinomial logit and probit models, mentioned earlier suggest a good approximation to aggregate choice data but are again, at present, mainly applied to market (68) information and simulation experiments. More research is required to develop these models as retail choice

models.

3.4.5 The problems of the revealed preference models outlined

above, and with respect to this study, are twofold :

a) they assume consumer utility functions to be compensatory, i. e. individuals trade-off low levels of one attribute with high levels of another. Recker (69) & Schuler found that this compensatory assumption

did not predict shopping store choice well.

b) they are context dependent i. e. the estimated parameters reflect the characteristics of the area. (70) Rushton found that these area characteristics influenced the utility of the alternatives. Also a (58) number of studies have shown the distance decay parameter to be highly dependent on the characteristics of the spatial structure. The implication is that consumers consider not only the

distance to a particular store but the relative distances to other store alternatives. Therefore, depending on the area, consumers may add a 41

differential weighting to the impact of distance on store choice.

Continuing with this latter point Ghosh(71 found that

inclusion of the ratio of distances to the closest and farthest stores from each origin improved the revealed (72) preference model's predictive ability. Hubbard found that the socio-economic characteristics of the area affected the spatial structure and this implies that spatial choice models should be calibrated for different segments of the population, or disaggregate models should be used to directly assess the effect of socio-economic characteristics on store choice. The disadvantage of the latter approach is that it requires a large amount of data. However if a generalised form of disaggregate model could be developed linking the socio-economic characteristics of the area to store usage then this would negate this point.

3.4.6 The final group of models discussed in section 3.3 was attitudinal models. Their advantage is that they overcome the problems of context dependency, discussed with respect to revealed preferenced models, because they estimate consumer utility functions from simulated choice data. They do not, therefore, rely on past choices to reveal the utility function and the estimated weights do not reflect the effect of spatial structures. However these models have problems of definition in that the hypothetical store configurations must reflect the entire spectrum of possible values for a store attribute and they must also (73) be realistic to ensure a meaningful response. The design of-the questionnaire is also critical.

An important assertion, in this context, is made by (50) Louviere & Meyer who found that simple relationships exist between attitudes and trip behaviour. There are, however, major problems with these models with respect to 42

destination choice and there is little evidence of major improvements, at the present time, to the conventional (74) household or person-based models.

3.4.7 Present practise in the strategic transportation planning control of large foodstores is based on aggregate models, either an individual estimation of trip generation based on floor area or a more general gravity-type model distributing trips to all stores but based on store measures such as floor area and number of parking spaces. This is because these methods are convenient, economic and well-tried. Even though they have shortcomings these are well documented and can be allowed for in the decision-making process. The reluctance to change from

this situation to a more disaggregate approach is significant. Confronted with a choice between an imperfect but understood approach to another which is

potentially superior but has not stood the test of time, the transportation planner will not change the status quo. There is a need therefore to develop a method which can be applied within the efficiency and economic constraints of practise but which gives the planner a more accurate

prediction within which the decision can be made.

3.4.8 The previous chapter showed the unsatisfactory nature of individual store trip prediction based on floor area. This chapter has shown that the traditional gravity-type shopping model can be calibrated to fit observed data satisfactorily but the nature of the trip generation and subsequent parameter calibration means the model is context dependent. To calibrate this for every planning application would be costly in time and money and still relies on aggregate prediction techniques.

Craig, Chosh & McLaffery(59) suggested incorporating some

form of disaggregate consumer behaviour model into an aggregate model. That is to use the aggregate model to 43

distribute trips to individual stores but improve the trip

prediction and generality of the model by using a disaggregate model. This requires a study of consumer behaviour at the origin end of the trip, household-based

as opposed to store-based. The household being the

preferred unit for shopping activity rather than the (75) individual because of the nature of the activity.

3.4.9 The aim of this study is to identify and develop a disaggreagte model that will predict private-car shopping trips to large stores and will interface with the current gravity-type distribution model used in practise. This implies a split between the generation of these trips, at a disaggregate level, and the distribution of these trips

at the aggregate level. Thus it is assumed that further work, outwith this thesis, may be necessary on the distribution model replacing the gravity-type approach with some improved representation. This then is viewed as

a two stage process, this study seeking to develop a disaggregate trip generation model which can initially be linked to the conventional gravity model but can be further developed, at a later stage, into a more suitable

distribution model.

3.4.10 The probabilistic and attitudinal models can however contribute to the development of this model. The zone framework, based on residential characteristics, used in the probabilistic model is a useful basis when linked to Hubbard's(59) work on the socio-economic characteristics of an area and their influence on household trips. Revealed preference models show a link between spatial and (50) consumer behaviour and Louviere & Meyer established a link between consumer judgement and perception and the

characteristics of an area. Pirie(76) showed that spatial preference is linked to spatial behaviour and may be modelled from observing such behaviour. The outcome of store choice can either be viewed as a reflection of 44

motivation and household values or a reflection of the constraints of the environment and personal circumstances, that is spatial choice can be discovered by questionnaires designed to elicit preferences or by studying overt (77) behaviour. The latter approach is suited to the argument being developed in this chapter and indicates, given the links established between the socio-economic characteristics of an area and spatial choice, that there

is a relationship between the behaviour of shoppers and their area characteristics. Thus by studying their overt behaviour and comparing this with the area characteristics the nature of this relationship can be established and once established can be used' to predict private-car trips to foodstores at a disaggregate level. The generality of the relationship, if established, would also require investigation.

3.5 Development of Factors Relevant to the Establishment of a Disaggregated Trip Generation Model

3.5.1 The demand to use large foodstores can be conceptualised in many ways but is generally seen to comprise three

elements :

a) the needs of the household with respect to the activity b) the temporal constraints on the household c) the spatial constraints on the household

This was discussed under activity modelling, and the work (51) of Damm, earlier in the chapter. If it is true that consumer behaviour can be predicted from the area

characteristics then. it can be postulated that the needs of the household, with respect to bulk-buying of food, can

be predicted from the household characteristics. This also seems intuitively reasonable as a large, high income family, for example, would be more likely to bulk-buy 45

their food than an aged, single person of low income. The temporal constraints consist of availability of transport and the availability of the person, or persons, to carry (78) out the shopping. Benwell & White have called this

term "personal accessibility". The spatial constraints

consist of the opportunities to carry out this type of shopping, that is the distribution of shops with respect to the particular household. The definition of this spatial accessibility can contain store attraction terms

as well as a distance term. The latter two factors are discussed in the next chapter on accessibility.

3.5.2 The first term, which refers to the needs of the household with respect to bulk-buying of food, consists of household characteristics. If it is assumed that only car-owning households are studied and that given the need to bulk-buy the household will reallocate its use of the household car, or cars, to make that possible and given that the area being studied has a number of suitable shops

which provide opportunities to bulk-buy food, then the demand is reduced to the characteristics of the household and the personal accessibility of the shopper, or

shoppers, to carry out the trip. Since this study aims to predict private-car trips to large stores and Holman & (79) Wilson showed that it is. reasonable, given the household decision to use these stores, that a car can be made available either during the day or in the evening, or at the weekend, the first two assumptions are acceptable. The second assumption is true of all cities in Britain and indeed most large towns and these are the areas where most planning decisions will be required. It may be further argued that the shopper, or shoppers, can also allocate time so that the shopping is carried out, although certain

characteristics such as employment and family structure will limit the time available. 46

3.5.3 The above postulates that the demand can be predicted, given these assumptions, using the characteristics of the household with respect to the personal availability of the shopper, or shoppers. It can also be viewed in terms of

the aim to develop a disaggregate trip generation model based on household characteristics linked to a conventional gravity-type distribution model. Thus the generation is split from the distribution, the latter being distributed primarily with respect to the' spatial distribution of suitable stores.

3.5.4 This introduces the effect of competition between stores which can affect the choice of store with respect to the consumer. This is the difference between the prediction of trips to individual stores based on their floor area and using a shopping model whether aggregate or disaggregate. These models attempt to simulate the

competitive framework the consumer perceives from the basis of size of store, as used in the gravity model, to the discovery of attitudes and beliefs of the individual consumer, as used in behavioural/attitudinal modelling. If the distribution of trips is separated from their

generation then competition must be kept constant in order to establish that the relationship is between household characteristics and store usage. This implies that the households to be studied must have the same opportunities open to them so that the differences in their use of the stores is attributable to the differences in household characteristics. Thus the households must be spatially together. Having established a relationship between household characteristics and store usage in one group of households this can be compared with relationships in other groups of households. If the relationship is the same then the spatial separation, and the change in spatial distances to stores, has not altered the relationship and a general disaggregate trip generation model can be developed using the household characteristics 47

of an area. If there is a difference then either a competition variable could be added to accommodate the difference or a relationship based on household characteristics alone cannot be achieved.

3.5.5 One of the prime factors affecting the allocation of time within a household, as mentioned above, is employment structure. This is of considerable interest to researchers charting the shift of women into the (80,81) (82) workforce. Nichols & Metzen showed that the woman continues in her role of principle shopper even though her work status changes her time allocation.

3.5.6 The age structure and family size of the household also (83,84) affects the activity pattern of the household. it relates to employment which in turn relates to the income level of the household. The income level not only makes bulk-buying easier but can be used to increase the time

available by purchasing the time of others to perform certain tasks. Most empirical studies of shopping (85,86) behaviour include income as a variable. Related to

income is social class which is widely used as a predictor (87) (88) of retail usage. Potter showed that social class was related to the use of retail stores.

3.5.7 The general factors therefore, from past studies, which influence the shopping activity pattern of the household are the household and employment structures, income and social grouping. The ages and size of the household will determine the food requirement and the income and employment structure of the household will determine the ability to meet that demand. The social grouping of the household relates to the method of carrying out the (88) shopping to meet that demand.

3.5.8 This is supported by Neale & Hutchinson who related shopping activity to household structure, income, car 48

ownership and two walking variables specific to their (85) (86) study. Dix identified income, household structure and life-cycle, which related to employment, as the important household characteristics with respect to

household trips. Thus in general three effects influence

the shopping pattern of the household household structure, employment structure and income and social class.

3.6 Development of Variables Relevant to the Establishment of a Disaggregated Trip Generation Model

3.6.1 Car Ownership

This study relates to car-owning households however the level of car ownership within the household will improve the personal accessibility of the shopper, or shoppers. (89) The Eastleigh Carrefour study showed that car ownership rises with household size and that car ownership among large foodstore users is above average (see Table 6). The study also concluded that car availability is not critical since, given the decision to use the store, the household will reallocate their time to enable the

shopping trip to take place. Garland showed that a strong relationship exists between car ownership and employment and income and this is supported by the Coventry shopping (33) (89) studies. The Eastleigh Carrefour study identified level of car ownership and household size as the best predictors of private-car trips to large stores.

3.6.2 Income

A number of studies have established a relationship (90,91,92) between income and shopping usage. It is 49

Household Sample Households with Size Size own car or van (No. -of Households) Z

1 person 657 21

2 persons 1178 60

3 persons 631 75

4 persons 681 81

5 or more 460 77

Source : Department of the Environment

TABLE 6

The Relationship between Car Ownership and Household Size 50

obviously true that higher income groups have a greater

disposable income and a greater opportunity to bulk-purchase if they so desire. Again, the Eastleigh Carrefour study showed the high incidence of high-income (89) households using the store.

3.6.3 Employment Structure

The number of people employed in a household affects the income level and the time allocation, of the household. One of the advantages of the bulk-purchase of food at one centre is the time savings which are important in a high employment household and less so in other households with a lower employment level. Ochojna & Macbriar(75) showed that employment is a powerful predictor of shopping trips and this is supported by Garland & Neale & (33,85) Hutchinson.

3.6.4 Social Grouping

Retail studies exploring the effect of socio-economic

variables on shopping trips have shown that a relationship exists between shopping usage and social grouping. These

studies also show a relationship between store choice and (93,94,95) (96) social grouping. Wasson argues that social grouping is a good segregation base for types of shopping (98) and Schaninger(97) and Hirisch & Peters show that it is a predictor of food shopping.

3.6.5 Household Structure

It has already been intuitively argued that larger

families are more likely to bulk-buy than smaller families. This is given substance by- the Eastleigh, (89) (33) Carrefour study and Garland showed that it was a stronger predictor of shopping trips than income. The Transport & Road Research Laboratory concluded, on their 51

(99) Reading household work at , that size was a major predictor of trip rate.

3.6.6 House Type, Freezer Ownership and Number of Licenses in the Household

There are three other variables which it is proposed' to include i. e. house type, freezer ownership and number of licences in the household. There is no rigorous support for these variables in the literature but they are related to this study and should be included in the preliminary analysis.

House type is related to social grouping and, if

significant in predicting store trips, is available on census data. Freezer ownership is related to storage of food and hence the bulk-buying of food. Without a freezer an additional constraint is placed on the consumer. The (89) Eastleigh Carrefour study showed that store users were more likely to own a freezer than not.

The number of licenses is related to the personal

accessibility of the household and it is argued that it will be positively correlated with store usage since the more people in the household that can drive a car the easier it is for a shopping trip to take place.

3.7 The Variables to be Used and Their Measurement Problems

3.7.1 A common problem in consumer behaviour analysis is the mixing of nominal, ordinal, interval and ratio scale variables, for while parametric statistical analyses can

be performed on the latter two it is inappropriate for the former two. (For a fuller explanation of these variable

measures see The Analysis of Survey Data - Volume 1 ed. by C. A. O'Muircheartaigh and C. Payne, John Wiley & Sons). This is important to multivariate analysis which relies on 52

the application of parametric statistical techniques. Of the variables discussed above house type and freezer ownership have only nominal scale and social grouping has ordinal scale. This means that only non-parametric

methods are permissable for social grouping. The

remaining proposed variables, that is income, car ownership, number of licenses, level of employment, age structure and personal accessibility can all be measured in such a way as to give them ratio scale status and enable parametric statistical methods to be employed. The social grouping variable will be measured as the socio-economic grouping of the household member defined as the head of the household.

3.7.2 Income is difficult to measure due to a reluctance to divulge the household income. For this reason it has been

omitted in the past. However it is proposed to use income bands to overcome this reluctance and yet still collect the information in a suitable form. Car ownership and

number of licenses in the household will be straightforward, the number of cars, or vans, to include all cars the shopper, or shoppers, have access to for

shopping. Employment will be the number of days, or half days, employment per week in the household. This means that the measure will have ratio scale status. Age structure could be categorised according to the ages of the household members however it is better to retain a ratio measure and therefore it is proposed to use three numeric variables which will describe the structure. These are the number in the household, the mean age of the household and the standard deviation of the ages. Personal accessibility is discussed in the next chapter.

3.8 Concluding Remarks

3.8.1 This chapter has argued from existing consumer behaviour theory that there is a relationship between the 53

characteristics of a household and the bulk-buying of food for that household. The concept postulated combines an aggregate and disaggregate approach to achieve a strategic planning model for development control of large foodstores

based on a conventional gravity-type model with the

generation term calibrated from a disaggregate model based on household characteristics. Thus it is proposed to split the generation and distribution stages of analysis. This thesis is concerned with the development of the disaggregate trip generation model.

3.8.2 In order to achieve an understanding of the relationship between household characteristics and store usage the effect of competition must be removed. This will be achieved by grouping households together and comparing the relationships developed between groups; the effect of spatial accessibility can then be assessed.

3.8.3 The next chapter discusses in detail the nature and effect of both personal and spatial accessibility with respect to shopping and complements this chapter on consumer behaviour. The research objectives and conceptual' framework can then be developed in the following chapter. 54

CHAPTER 4

THE ACCESSIBILITY OF LARGE RETAIL FOODSTORES

4.1 Introduction

This chapter initially examines the nature of accessibility with respect to bulk-purchase shopping. It is developed as two terms which are called a personal accessibility term and a spatial accessibility term. The former term comprises three factors; the availability of the car, the consumer and the store, and is contained within the trip generation model. The latter term comprises two factors; the attraction of the store over all other stores and the spatial deterrence between the household and the store and is contained within the distribution model. The structure of the trip generation model can be developed as individual variables or as multivariate factors to eliminate multi-collinearity problems between the household variables and the personal accessibility term. 55

4.2 Application of Current Accessibility Concepts to this Study

(100) 4.2.1 The Independent Commission on Transport commented

that accessibility is the aim of transport and not (101) mobility, speed or the easing of congestion. Activities cannot exist in isolation; participants require to be transported from their origin to the Accessibility activity in which they wish to participate. therefore, is a measure of the ease of contact with some It is form of spatially distributed opportunities. dependent not only on the locations of opportunities

relative to the individual but also on the ease of traversing the spatial separation between the individual

and the activity.

have been 4.2.2 A range of aggregate and disaggregate models developed to simulate accessibility and a historical (102) by Baxter review of these models has been carried out and Pirie(103)" The basic aggregate models use spatial in separation as a direct measure of accessibility whether journey. An terms of distance, time, or cost of the to the extension of this concept is to add a term model for the attractive power of the activity, with respect to introduces other similar activities. This term a Hoggart(104) competetive element between activities. describes these two factors, the attraction of the between activity and the spatial separation consumer and location behaviour. activity, as travel behaviour and Accessibility therefore is not only the ease of access to

an activity but involves the ability of the consumer, relative to his location, to take advantage of the term in the available opportunities. This implies a third (102) Baxter model; that of consumer availability. identified three factors in the measurement of

accessibility : mobility, potential and ease of access. These apply to the consumer, the activity and the 56

transport link between the consumer and the activity respectively. Hoggart(104) identified three similar factors but called them consumer circumstances, attractiveness, and deterrence. Doling views

accessibility as the link between trip generation and (105) distribution. In terms of consumer accessibility to

large foodstores the representation can be seen as three factors, i. e.

Ability of consumer to take advantage of opportunities to shop.

if Accessibility of Attractive power of consumer to competing centres r foodstore

L-j Spatial separation from consumer to each store.

This corresponds with the basic store choice mechanism discussed in the previous chapter where these three terms together with the need to shop, represented by the household characteristics, determine store choice. The attraction term and the spatial separation term form part of the distribution of trips, as developed in the previous chapter, whilst the consumer term affects the trip rate at household level. This latter point requires to be substantiated and is discussed in the following section.

4.3 Accessibility and Trip Generation at Household Level

4.3.1 When including an accessibility measure within a trip generation model it is desirable to construct the measure 57

in such a way that changes in the system that affect the accessibility of the household to large foodstores will be accommodated. In most studies to date an accessibility measure based on a Hansen aggregate model has been (106,107,108,109) used. These studies have not shown the

accessibility measures to have a significant effect on trip generation. This may have been because the models are unsuited to the purpose. This view is supported by Leake & Hazayyin(110) who showed the necessity of having an accessibility term in the trip generation model. They identified the main requirements of such a term as:

a) it should be easy to understand and logical in expression.

b) it should be easy to formulate and calculate using a minimum input of data and computation time.

c) it should, preferably, utilise data normally collected in transport studies.

4.3.2 Vickerman(111) showed that accessibility has a significant association with variations in trip making and (112) this was specifically shown by Robinson to apply to shopping trips. One of the problems that both Vickerman &

Leake encountered was that of collinearity with other household descriptors. The intercorrelations between an accessibility measure and household characteristics can cause difficulties in identifying the effect of the accessibility measure. Vickerman found factor analysis techniques to be useful because of their structuring of the data to produce a simpler structure based on multi-variate factors, however no a priori structure can be used. If stable factors could be identified a basis

may be provided. on which to, build a generalised model of shopping trip generation. 58

4.4 Disaggregation by Mode and Trip Purpose

(105) 4.4.1 Doling showed the need for disaggregation by mode in terms of the fundamental differences between households

in levels of mobility and disaggregation by trip purpose in terms of the difficulty in combining measures of accessibility for different types of activity opportunity. Also some opportunities are not accessible to some types of households. This is supported by the work of Leake & (78), Huzayyin(110) and Benwell & White the latter authors showing that the accessibility of certain groups to certain activities varies depending on their car availability and the activity.

4.4.2 This study is by definition disaggregated by mode and trip purpose. The study of shopping trips has the advantage that all households take part in the activity although not all households shop at large foodstores on a weekly, or more infrequent, basis. The study also relates to private-car shopping trips and therefore car availability may influence the frequency of trips. The ability to make a trip by private car at a given time is influenced by the level of household car ownership, the

number of drivers within the household and the time (78 allocation priorities of the household. The first two factors can be easily measured.

4.4.3 If the household uses a large store for the bulk-purchasing of food then it must allocate a significant period of time for the activity. Lucarotti(113) developed a measure of perceived availability using a trip or person descriptor by asking directly whether a car was available and Gwilliam & (114) Bannister took account of observed car availability through the interpretation of a travel diary and household (78) information. Benwell & White describe a method of

indexing a survey population to enable a disaggregate 59

study of mobility to be undertaken. Unfulfilled transport need was viewed as a product of the interaction between

personal car availability and the timing and location of the available activities.

4.4.4 In applying car availability to shopping trip generation the time involvement for a car-borne trip to a large store will mean that normally one trip will be made per third of a day. That is, if the day is split into morning, afternoon and evening sessions it is reasonable to assume that no more than one car-borne shopping trip within a session will take place. Thus for a shopping trip to take place the consumer, the car and the store must be available simultaneously. This is shown in Figure 5. The size of the "accessibility window" within which' a car-borne shopping trip can take place will vary according to the household circumstances. The size of the window represents the level of opportunity the household has to

carry out this type of activity. Thus it is a measure of the temporal, or personal, accessibility of the household as opposed to the spatial accessibility of the stores relative to the household location. The time that a car

and consumer are available will influence duration of the shopping trip and therefore certain opportunities may be outwith that time constraint. The opening times for large foodstores in Britain is uniform; almost all stores opening on a five-day trading cycle, closing on Monday and opening late Thursday and Fridays. This pattern applies to stores in Edinburgh and means that three standard daily sessions can be defined.

4.5 Personal Accessibility within a Trip Generation Model

4.5.1 The personal accessibility of a household, or the shopper within a household, is seen to comprise three factors : 60

PRIVATE CAR AVAILABILITY

FIGURE 5

The accessibility window of a household to allow a private car, bulk-purchasing trip to a food store to take place. 61

a) the availability of the car b) the availability of the shopper c) the availability of the store

The previous chapter and this chapter have argued that

this term should be included within the trip generation model because of its relationship with household variables and its influence on the trip rate of the household.

Personal accessibility is a property of the household and the opportunities available to the household and is independent of the trips made. This is shown by Hensher & Stopher(115) and further supports the argument that the trip generation prediction can be separated from the distribution of the trips.

4.6 Spatial Accessibility within a Trip Distribution Model

4.6.1 The spatial accessibility of the household to the shopping opportunities comprises the remaining two factors

outlined above, that is the attraction of a store with respect to other stores and the spatial distance between the household and the store. The second factor is measured using journey time to the store, distance to the

store, or cost, in terms of fuel and consumer time. Leake

& Huzayyin(110) found that journey time was the most effective measure and this is borne out by the author's personal experience.

4.6.2 The distance to a store from a household has an inverse relationship with the usage of that store. The distance also relates to the frequency of use of the store in that the further a store is from the household the less frequent is the trip rate. The duration of stay increases (21,89) as the frequency decreases. Table 7 shows the relationship between frequency of visit and journey time. 62

Frequency of Visit Journey Time (%)

(Sample Size: 0-5 5-10 10-20 20-30 30 + min. min. min. min. min.

254 462 775 243 252

Three times per week + 11 3 2 1 -

Two times per week 14 6 4 2 1

Once per week 48 48 47 34 14

Once per fortnight 10 16 17 16 15

Once per month 8 13 15 24 26

Less often 6 9 11 13 21

1st Visit 3 4 5 10 22

Source : Department of the Environment

TABLE 7

The Relationship between Frequency of Visit to a Large Store and Journey Time 63

4.6.3 The catchment areas of large foodstores follow a uniform pattern in that ninety-five percent of the store's customers come from within a twenty-five minute (24) car-driving time. This is influenced by the

characteristics of the catchment area. For example, the Asda, Coatbridge store attracts fifty percent of its custom from within the local district boundary, this approximates to a twenty-five minute car-driving time. The area is densely populated and has a low car ownership level. Expenditure per trip is also affected by location in that the further a household is from the store the more (89) is spent per visit.

4.6.4 The other factor which comprises the spatial accessibility measure is store attraction. This factor represents the competition between stores. It is the attractive power of the store with respect to the other store opportunities. The simplest measure of this factor is store floor area. This can be modified to include (34,38,116) retail turnover per square metre. Consumer expenditure can be used to simulate competition, using the Family Expenditure Survey published annually by the Department of the Environment, but the transferability of the weightings from area to area has not been proven. Trip frequencies can also be used as frequency is proportional to expenditure.

4.6.5 Vickerman(111) argues that store attraction can be represented by certain key store variables weighted by expenditure and including the generalised cost of reaching the store. A further development includes measurement of (55), store attributes. Huff for example, used reputation, amenity value, range of goods, services and prices. (93) Davies developed a scheme of retail quality grading

which includes prices, quality of goods, range of goods, window display, shop appearance and degree of (117) specialisation. Downs listed nine similar cognitive 64

categories relating to store attraction. Thus with this measure we have the same choice discussed in the previous chapter relating to the motivation and values of the consumer determining store choice or the constraints of

the environment and personal circumstances determining the choice.

4.6.6 The second postulate has been argued in this study but the measures of attraction, listed above, relating to store image should be the subject of further work on the distribution of predicted shopping trips. Thus it is argued that the household makes a decision to bulk-buy its food and this decision can be modelled from the household

characteristics and the personal accessibility window of the household. The predicted trips from the households in an area are then distributed to the store opportunities in the area. These opportunities, because of the type of shopping, are easily identified. The distribution model,

although not developed in this thesis, should incorporate a spatial accessibility term to reflect the distance from the households to the store and the attraction of one store over another. This term should be in keeping with the aggregate form of the model and the purpose of the (110) model, as shown by Leake and Huzayyin.

(118) 4.6.7 Benwell & Seaton incorporated number of shops, retail floor space and retail turnover into an attraction term in an aggregate shopping model of the form,

k W. Fii d,

where F- trips from origin i to destination j Wj relative attraction of destination j; based on retail floor area;

retail turnover and number of shops di distance by road

ß- power function 65

ka constant

The concept of relative attraction is well-known. It has

been used extensively in environmental assessment where absolute measures are difficult to achieve but comparative measures can be related to known situations. The argument therefore is that the proportional differences between

stores is correct and can represent the attraction of one store relative to all other possible stores. In the context of the aggregate distribution model proposed in the previous chapter, this could be initially measured by distance and/or retail floor area using the same argument that was advanced i. e. that this method at the distribution stage has proved a satisfactory model in past studies for the purpose of development control of large stores.

4.7 Concluding Remarks

4.7.1 This chapter has examined the effect of accessibility on the generation and distribution of private-car trips to large foodstores. This is seen as comprising two parts which have been entitled personal accessibility and

spatial accessibility. The latter term is made up of two factors; an attraction factor and a spatial deterrence factor. It is argued that personal accessibility relates to the shopping trip rate of the household and should be contained within the trip generation model while spatial accessibility is contained within the distribution model.

4.7.2 The personal accessibility term is subdivided into three factors: the availability of the car, the availability of the household shopper and the availability of the store. The size of the time period when all three coincide is a measure of the personal accessibility of the household to carry out this type of shopping. This concept of shopping 66

accessibility can be combined with the theoretical behaviour formulate postulates developed from consumer to the research objectives and the conceptual framework of the thesis in the following chapter. 67

CHAPTER 5 DESIGN OF RESEARCH PROGRAMME

5.1 Introduction

The previous chapters on consumer behaviour and accessibility identified variables which affect large foodstore usage. This chapter relates these variables to the research objectives and conceptual framework of this thesis.

The chapter begins by listing four research objectives and explaining the concept of household choice of store as a two-stage process; that of decision to make a trip (generation) and decision to use a particular store (distribution). The effect of competition is then discussed and an explanation of the way the sampling framework keeps competition constant within each surveyed area.

Two model structures are proposed one based on combined variable factors and the other on individual variables. Each model seeks to establish a relationship between store

usage and identified household characteristics.

The structure of the dependent variable of store usage is defined, together with the independent variable structure, with respect to the two model concepts and a diagramaatic summary of both models and the conceptual framework is given. The latter diagram shows how each model will be examined at individual area, at means area, and at total area level to establish the level of disaggregation

possible within the objective of producing an applicable trip generation model for the development control of large foodstores. 68

5.2 Research Objectives

5.2.1 The research objectives of the study are as follows :

To develop a trip generation model which predicts the number of private-car shopping trips to large foodstores from a given area using household census data.

To build the model so that it can be developed as a strategic planning tool for the development control of large foodstores.

To examine the model at a disaggregated and aggregated level to confirm, or otherwise, if the relationships established at the disaggregate level can be generalised and to establish the level of generalisation that can be achieved.

To show how the model can be combined with a gravity-type distribution model to build an area-wide model of private-car trips to large foodstores.

5.3 Building the Conceptual Framework

5.3.1 The chapter on consumer behaviour theory highlighted a range of variables that have been shown to correlate with shopping trips. The major influences were found to be employment, household structure, income and SEG. The

following chapter on accessibility identified two types of accessibility: personal accessibility and spatial accessibility. The former relates to the availability of the household shopper and car and is to be included in the trip generation model. The latter is to be included in

the trip distribution model.

5.3.2 The household choice process identified from these chapters is shown in Figure 6. This shows the basic 69

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assumption of this study that the generation and distribution of bulk-purchase shopping trips can be modelled separately; the generation of trips being

modelled from household characteristics and the

distribution being modelled by standard gravity-model techniques. In other words the decision to bulk-purchase

food can be predicted from household characteristics, based on census data, and once the decision has been'made the household chooses from the store opportunities available.

5.3.3 The basis of this approach is the established relationships between household characteristics and store (117), trips as developed in Chapter 3. Downs Bender(119)1 (120) Brown & Fisk and Hansen & Deutsher(121) discovered that shoppers tend to overcome a dislike of the large store environment in order to achieve time and/or cost savings and this implies that the characteristics of the household govern the shopping behaviour of the household.

5.3.4 The shopping behaviour of the household may be measured using the revealed behaviour of the household and the characteristics of the household. If this can be acheived the household trips can be aggregated to a zonal level, which corresponds to transportation zones previously defined by the Regional Authority, and distributed to the stores in the study area i. e. within the Edinburgh City boundary. The model could then be calibrated to match the individual store arrival patterns. Thus the proposed method uses the standard gravity-type distribution model improved by the addition of a disaggregate generation model developed within this thesis.

5.4 The Effect of Competition

5.4.1 The consumer choice relating to the bulk-purchase of food is viewed as two distinct elements. One of these 71

elements, the distribution of trips, comprises the

attraction of the store with respect to other stores and the spatial accessibility of the store. Each store operates within a competitive framework and the effect of

ease of access and store attraction, with respect to the competitive framework, will determine the turnover of the store. Thus competition has a major effect on store usage. To investigate the relationship between use of these stores and household characteristics the effect of competition must be eliminated. The previous chapter proposed that the household sample would comprise groups of randomly selected households, so that within each group the households would have equal store opportunities. The

within-area relationships between household characteristics and store usage can 'be compared with between-area relationships and if randomly sampled, with the aggregate grouping of all household areas. Figure 7

shows this sampling framework.

In the latter two cases, that is between-areas and

aggregated areas, the competition effect is no longer constant because of the different spatial locations of the areas. This will require an additional spatial accessibility term to be added to the independent variables.

5.4.2 The conceptual framework showing development of the aggregated household data and the sub-area household data is shown in Figure 8. Each sub-area will have a model corresponding to its usage pattern and the comparison of each sub-area model may provide a general trip generation model. Equally by grouping the data together a second generalised model may also be. developed. This latter

option may be developed from individual households or from sub-area mean values. The calibrated model for the study area would enable the effect of a new store to be evaluated and hence its maximisation of use within an established area. 72

Key :0- groups of households

- foodstore

study area boundary

FIGURE 7

Theoretical study framework showing groups of households and store locations 73

Development control model for the estimation of private car trips to large foodstores

Calibration process for all zones in study area with respect to trip arrivals at each store

Store Trip distribution Attraction Trip distribution model for all model for all zones in study zones in study area Store spatial area Accessibility

Comparison of sub-area models to develop a general trip prediction Competition model growthed up for factor for all zones in study area sub-area differences

Individual trip Trip prediction prediction model model using Competition for sub-area aggregated data factor for group of from all areas between households household oppor- tunities

Personal Household Personal Household Accessibility Characteristics Accessibility Characteristic of principal of principal shopper in shopper in household household

FIGURE 8

Conceptual framework for model development based on sub-area models or aggregated data model 74

The purpose of the analysis therefore is to explore and develop the relationship between store use and household characteristics at sub-area and area level, developing an understanding of these relationships.

5.5 The Definition of Store Usage

5.5.1 Store usage involves three elements which are inter-related; expenditure, duration of stay and frequency of visit. If a household decides to use a large store for bulk-purchase of food and fixes, by decision or default, the level of expenditure it can afford, this will

influence the pattern of usage of the store. For a given level of expenditure a household may choose to spend less

and visit more frequently or decrease the frequency and spend more at each visit. This dependent variable structure is shown in Figure 9.

5.5.2 The inclusion of the total shopping budget is to show the bulk-buying expenditure relative to the total shopping budget of the household. This provides a relative budget figure which can be used in the analysis in addition to the absolute bulk-buying expenditure.

5.5.3 The household usage pattern of a store is influenced by the household accessibility window, as defined in Chapter 4, the income level of the household and the needs of the household. Two of the three usage variables will define the third variable. The frequency of visit will determine the arrival rate at a store which will affect road capacity and associated factors such as accident rate and environmental intrusion. The duration of stay will affect the size of car park required and the expenditure level is

of interest to the physical planner and developer. 75

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5.5.4 It may be that because of the inter-relation of the two sets of three dependent variables a combined usage index is required to model the usage. The disadvantage in the

combined usage index is the difficulty of application once the model is developed whereas if the three dependent store variables are kept separate the trip rate and duration of stay can be used to predict the arrival pattern and car parking requirements. The analysis will examine both of these options.

5.5.5 In examining the relationships between household characteristics and store usage the households who use and those who do not use these stores for bulk-purchase shopping by private-car will be included. It is also assumed that only food and general shopping will be included in the survey and that minor shopping trips will be excluded. Major purchases of furniture, clothing, etc., will bias the results relating to the bulk-purchase of food and minor purchases, such as newspapers, are assumed to be insignificant in relation to the household shopping budget. The dependent variables are related to a weekly rate, this being the smallest timescale for

bulk-purchase shopping by private car. This means that a

monthly trip-rate would be represented by 0.25 trips per week. The duration of stay will be in hours per week and the expenditure in pounds sterling per week on the same weekly basis as trip-rate.

5.6 The Structure of the Independent Variables

5.6.1 In Chapter 3 the major effects on household shopping activity related to the three broad areas of household structure, employment and lifestyle; the latter term comprising income and SEG. This is diagramatically represented in Figure 10. Within each of these three

areas are household variables which, in the main, can be obtained from census data. The independent variable 77

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structure is to be modelled in two ways; either as individual variables or as factor variables which represent an underlying data structure and are composed of a weighted variable grouping. This corresponds to the optional structure of the dependent variables and again both models will be examined in the analysis. The factor variables will eliminate multicollinearity problems, if these are encountered using individual variables.

5.7 Choice of Independent Variables within each Area and their Indexing

5.7.1 The Household Structure Factor

The size and structure of a household has a major influence on the use of large foodstores. This was shown (33) 3 is by Garland Ochojna & in Chapter and confirmed , Macbriar(75) and the Transport and Road Research Laboratory(89). It is related to car ownership and income

and affects the personal accessibility window of the (83,84) household. Census data on household size are

readily available and it is proposed to measure it in terms of the number of people in the household, the mean

age of the household and the standard deviation of the household ages. The latter two variables give the age profile of the household and are numerical descriptions thus avoiding a category-based classification. Each household will therefore have three variables describing the structure: the number in the household, the mean age of the household and the standard deviation of household ages.

5.7.2 The Employment Factor

The employment status of the household also affects the personal accessibility of the household, determines the SEG of the head of the household and is related to the 79

(93,94,95) income level of the household. There is an inverse relationship between SEG and the number of days members of the household are employed; as SEG rises the (95) number of days employed decreases. This will have a

direct effect on the availability, and hence personal accessibility, of the household shopper, or shoppers. It is proposed therefore to include three variables within this factor: the number of half days employed per week, the SEG of the head of the household and the personal accessibility of the principal shopper in the household.

It is assumed the principal shopper can be identified and this assumption will be tested in a pilot survey. The measurement of SEG is category-based. The standard classification system is used, as shown in Appendix B, with Group 17 including the retired and unemployed.

5.7.3 The Lifestyle Factor

Income, as discussed in Chapter 3, has an important

influence on the shopping behaviour of the household and (52) influences the lifestyle of the household. The

measurement of income has proved difficult in past studies, and surrogate variables have been included to (51) represent its effect, but it is proposed to use income bands to overcome consumer reluctance. These bands relate to the gross annual income of the household.

In addition to income it is proposed to add four other dependent variables. These are number of cars- in the household, number of license holders in the household, freezer ownership and house type. The reasons for their inclusion have been argued in Chapter 3.

The number of cars and number of licenses in the household affect car availability and hence the personal accessibility of the principal shopper. As discussed in Chapter 3, this does not have a dramatic effect on 80

shopping behaviour since the household arranges its time

allocation for shopping around the availability of the car. The number of cars and number of licenses however

can provide greater opportunities to shop and in this way may affect the shopping pattern of the household. They are related to household income.

Freezer ownership is also related to income. The Eastleigh, Carrefour(52) study showed that households with freezers were more likely to use a large store for shopping. This variable cannot be abstracted from census data but it will be included in the preliminary analysis.

The fourth variable, house type, again relates to the income level of the household. It is a social indicator but its inclusion may be rendered void because of the sampling framework; that is, certain areas may have little variation in housing type. Its effect may be significant when studying between-area relationships.

5.8 The Structure of the Variables Chosen

5.8.1 It is proposed that the dependent variables be divided into two groups of three, each group consisting of an expenditure, frequency of visit and duration of stay variable. One group relates to bulk-purchase shopping and the other to total household shopping. In addition, the variables are to be examined using two model forms. These are shown in Figure 11. One model examines the variables as a combined usage index and the other as individual variables. The latter enables submodels to be developed which will predict expenditure, frequency of visit and duration of stay variables separately for a given area.

The former, using factor analysis to identify the underlying data structure, produces a usage index which can be related to intensity of use from a given area. The usage index will be difficult to apply in practise but may 81

yo. says empioyea, bL IExpenditure per Employment of head of household, week at store Factor Per. Acces. of shopper.

household, IFrequency per H Store Usage Household No. in household. week at store index Structure Mean age of Factor

Household income, Duration of Lifestyle Freezer ownership, stay per week Factor House type, at store No. of Licenses, No. of cars.

MODEL I

Expenditure per week X3 + Xn at store X1 + X2 + .....

Frequency per week Xn at store X1 + X2 + X3 + .....

Duration of stay per X3 Xn week at store X1 + X2 + + .....

MODEL 2

FIGURE 11

Two models of the conceptual structure of independent and dependent variables 82

be the only alternative if multicollinearity problems seriously affect the individual variable model.

5.8.2 The independent variable structure is similar to the

above and is represented in Figure 11. The same comments

apply to the two models.

5.9 The Summarised Conceptual Frameowrk

The summarised conceptual framework is shown in Figure 12. The analysis will examine the relationship between store usage and household characteristics within zones, between zones and with the zones aggregated. The investigative comparison of the relationships established at these levels will determine the degree of generalisation possible with a view to establishing a trip generation model. If established the form of model can be applied at transportation zone level predicting the number of store trips generated within a zone. This will enable the calibration model to be calculated with an accuracy based on the local area and hence enable a more accurate distribution of trips to stores in the area. The following chapters deal with the collection and analysis

of data and their application to the research objectives and conceptual framework, discussed in this chapter. 83

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CHAPTER 6

THE PLANNING AND ORGANISATION OF THE ENQUIRY

6.1 Introduction

This chapter identifies the research tasks to be carried out following the research objectives and conceptual framework of the previous chapter. The implementation of the research tasks is then discussed and statistical techniques identified which will satisfy these tasks. The analysis assumes a linear relationship between the dependent and independent variables.

The survey area, sampling framework and questionnaire design are discussed and the organisation of the pilot and main surveys. This is followed by a presentation of the initial data characteristics using mean, standard deviation and skewness area profiles. These profiles show the intercorrelation between variables and provide a basis for the detailed analysis of the two conceptual models at area, mean and composite area level in the following chapter. 85

6.2 The Identification of Research Tasks with respect to the Research Objectives and Conceptual Framework

6.2.1 The conceptual framework developed in Chapter 5

identified two models based on factor variables and individual variables respectively. One of the research tasks is therefore to identify these factors from observed data. Once this has been carried out both models can be analysed to test their predictive ability with respect to private-car trips to large foodstores.

6.2.2 The research objectives demand an examination of these models at area level and at aggregated level. It is proposed to examine the relationship between the dependent and independent variables at area level by computing a matrix of variables, obtaining an aggregated form of the data based on the mean profile of the variables in each area, and an aggregated matrix of the households in all areas. These two forms of aggregation are proposed because the variability at household level may be too large to accommodate within a generalised model but the smoothing of within-area variations using mean variable values may be possible. The latter method is appropriate for the strategic planning of large stores as most transportation studies are based at zonal level.

6.2.3 The examination of the relationships mentioned above must include an investigation of the effect of each variable within each model and the strength of that effect. In addition it will be necessary to understand the strength of the relationships between variables and the spread and form of the data distribution of each

variable so that variation between areas can be analysed.

6.2.4 The investigation into the level of generality

achievable in the models will require examination of any apparent area groupings, for although all areas may not 86

show the same model relationships groups of areas may be similar. These research tasks imply the application of certain statistical techniques in the analysis of the data and these techniques will now be discussed with respect to the above.

6.2.5 In discussing these techniques it is initially assumed

that a linear relationship exists between the dependent and independent variables, this will aid the application of the developed strategic model. If a linear relationship has is not found then the proposed suite of programs the facility to identify the nature of the non-linear

relationship.

6.3 The Identification of Methods of Analysis with respect to the Research Tasks

6.3.1 Descriptive Statistics

be The basic statistics of the variables to used are in required for the matrix of variable means and to aid the explanation of the analysis. They will reveal to underlying distributional characteristics with respect dispersion a measure of central tendency, degree of around the mean and degree of skewness that will enable a more distribution precise definition of the variable's slope to SPSS be achieved. Throughout the analysis the suite of programs will be used except for the cluster analysis, which will use GENSTAT, and some minor self-written programs. The SPSS subprogram CONDESCRIPTIVE provides the user with the capability of obtaining the above measures less for any set of variables which is more or continuous Where and approaches an interval level of measurement. the variable is discrete, or a category variable, the fall into number, or proportion of cases which each category, will normally be examined. 87

6.3.2 Bivariate Correlation Analysis

The input to be used for the identification of the combined variable factors, for the canonical correlation and the multiple regression correlation is a correlation matrix. In addition when examining the relationships between the independent and dependent variable sets at area, mean and aggregate level a knowledge of the strength and sign of the bivariate correlation betwen single pairs of variables is useful. Bivariate correlation provides a single number which summarises the relationship between two variables. These correlation coefficients indicate the degree to which variation in one variable is related to variation in another. It is also possible to compare the strength of

the relationship between one pair of variables and another.

When comparing variables it is highly unlikely that a regression line will be found that perfectly fits the data. The Pearson product-moment correlation coefficient, symbolised by r, gives a measure of "goodness of fit" for linear regression. When the linear regression line is a

poor fit r will be near zero; +1.0 indicates a perfect fit. If r is close to zero this indicates either no relationship exists or there is a non-linear relationship. The latter will be checked, where appropriate, using a scatter diagram plot.

If the Pearson r coefficient is squared one gets a measure of the proportion of variance in one variable "explained" by the other. It is important, in the context of the research objectives discussed in the previous chapter, that the inter-correlations between variables are

understood. 88

The SPSS subprogram PEARSON CORR computes the Pearson product-moment correlations for pairs of variables. The formula used by SPSS is :

NNN E X. Y. -(EX. ) (EX. )/N i=1 11 i=1 1 i=1 1 NNN2N {[ E X. 2-(EX. )2 IN] [EY. 2-(EY1 )2 IN] } . 1 1 1 1=1 1=1 1-1 1=1

Significance tests are reported for each coefficient and

are derived from the use of Student's t with N-2 degrees of freedom for the computer quantity,

N-22 r[ 1-r

6.3.3 Principal Components Analysis

The research objectives seek to build a trip generation

model based on the relationship between household characteristics and private-car use of large stores and to

generalise this relationship as far as the data analysis permits.

The conceptual framework then identified two model forms to achieve these objectives. One of these models is based on three independent factors (employment, household (large structure and lifestyle) and two dependent factors store usage and total shopping usage). These factors are a combination of several inter-related variables. This

structure presupposes an underlying structure based on the intercorrelations of the variables which combine to form the factors. 89

This structure implies a form of factor analysis which enables one to identify whether some underlying pattern of relationships exist such that the data may be rearranged, or reduced, to a smaller set of factors, or components, that may be taken as source variables accounting for the observed inter-relations in the data.

The term "factor analysis" is not a unitary concept and it subsumes a variety of procedures. Generally it can be said to be organised around the major alternatives available at each end of the three customary steps of factor analysis i. e.

1) the preparation of the correlation matrix 2) the extraction of the initial factors 3) the rotation to a terminal solution

In identifying the initial factors the new variables may be defined as exact mathematical transformations of the original data, or assumptions may be made about the structuring of variables and their source of variation. It is proposed to use the former method which is called Principal Components Analysis. It is a relatively straightforward method of transforming a given set of variables into a new set of composite variables, or principal components, that are orthogonal to each other. No particular assumption about the underlying structure is required. The first principal component may be viewed as the single best summary of linear relationships exhibited in the data. The second component is the second best linear combination of variables, under the condition that the second component is orthogonal to the first. To be orthogonal to the first, the second component must account for a proportion of the variance not accounted for by the defined first one. Thus the second component may be as the linear combination of variables that accounts for the most residual variance after the effect of the first 90

component is removed from the data. Subsequent components are defined similarly until all the variance in the data is exhausted.

The principal components model may be expressed as,

Fn Zý aji F1 + aj2 F2 + ..... + ajn

where each of the n observed variables is described linearly in terms of n new uncorrelated components F1, F2 is in turn defined as a linear ..., Fn each of which combination of the n original variables.

The final stage of the analysis is the rotation of the factors to achieve a terminal solution. The exact is configuration of the factor structure not unique, regardless of whether factors are defined or inferred. One factor solution can be transformed into another

without violating the basic assumptions, or the Therefore mathematical properties of a given solution. one must choose the best rotational method to arrive at the terminal solution that satisfies the theoretical and The practical needs of the research objectives. two options are the orthogonal method and the oblique method. There is no compelling reason to choose one over the other and it is proposed to use the former of the two methods. It is further proposed that the VARIMAX method of rotation is is chosen since it is the most widely used and a modification of the alternative QUARTIMAX method.

6.3.4 Multiple Regression Analysis

framework The second model emerging from the conceptual

consisted of individual variables acting as unique dependent variables and related to the single variables of This frequency of use, expenditure and duration of stay. be model implies that multiple regression analysis would a 91

suitable statistical tool for the analysis of the model structure. Multiple regression is a general statistical technique through which one can analyse the relationship between a dependent, or criterion variable, and a set of independent or predictor, variables.

The basic principles of regression analysis, used. in the bivariate case mentioned earlier, may be extended to multiple regression. The general form of the equation is,

Y1 -A+ B1 %1 + B2 %2 + ... + BK XK where Y1 represents the estimated value for Y, A is the Y intercept and the Bi are the regression coefficients. The A and Bi coefficients are selected so that the sum of square residuals (Y - Y1)2 is minimised. This implies that the correlation between the actual Y values and the Y1 estimated values is maximised, while the correlation between the independent variables and the residual values

(Y - Y1) is reduced to zero.

The SPSS subprogram REGRESSION will be used for this analysis. As in the bivariate case the proportion of variance of Y explained can be evaluated by examining the square of the multiple correlation i. e.

Variation in Y explained by the combined linear R2 influence of the independent variable Total variation in Y

Regression procedures per se may be categorised as descriptive statistics, however, it is commonly performed on sample data which the researcher is interested in generalising to a population, as in this study. The goodness of fit test of the regression equation contained in the subprogram is the F-test. This is represented as, 92

SSREG/k F SSRES/N-k-1

R2/k (1 - R)/(N-k-1)

where SSreg is the sum of squares explained by the entire regression equation, SSres is the residual sum of squares, k is the number of independent variables in the equation and N is the sample size. The F-ratio is distributed

approximately as the F-distribution with degrees of freedom k and N- k- 1.

It is proposed to enter the variables using forward step-wise inclusion. The order of inclusion is determined by the contribution of each variable to the explained variance in descending order of importance.

6.3.5 Canonical Correlation Analysis

The preceding two sections have explained how the two model structures proposed will be analysed. The third research objective requires each of these models to be examined to determine the degree of generalisation that can be achieved. Thus it is necessary to examine the structure and each individual variable contribution within each model at area, mean and aggregate level. It is proposed to use canonical correlation analysis for this task as it not only yields the maximum strength of relationship between independent and dependent variables

but details the variable weightings which comprise that

relationship. These order weightings can then be compared at different levels of aggregation. 93

Canonical correlation analysis takes as its basic input two sets of variables, each of which can be given theoretical meaning as a set. This corresponds to the sets of dependent and independent variables contained in this study. Then the analysis derives a linear

combination from each of the sets of variables in such a way that the correlation between the two linear combinations is maximised. Many such pairs of linear

combinations may be derived. These canonical variates are essentially equivalent to the principal components discussed previously, except that the criterion for their selection is not the same. Whereas both techniques produce linear combinations of the original variables, canonical correlation analysis does so not with the object of accounting for as much variance as possible within one set of variables but with the aim of accounting for a maximum amount of the relationship between two sets of variables.

This type of analysis enhances the other forms of analysis

used and is suited to the conceptual framework of variables identified. The SPSS subprogram CANCORR will be

used and the correlation matrix will form the input data. As with principal components analysis CANCORR manipulates intercorrelations among the variables to search for particular data patterns. The structure and variable weightings of the canonical variates is not explained and must be interpreted with respect to the conceptual framework.

6.3.6 Cluster Analysis

The aim of the study, as discussed in the previous

chapter, is to build a generalised trip generation model from the relationship established at area level between, the dependent and independent variable sets. This generalisation may not be possible and if this is so two 94

alternatives exist. Either every area has a different relationship or groups of areas have the same relationship. It may therefore be necessary to apply a clustering technique to the data to identify common groups level of areas. The object of this being to assess the of data in generality, or aggregation, achievable in the set accordance with the third research objective.

A fundamental problem with cluster analysis is the lack of satisfactory definition of what constitutes a cluster. Most clustering techniques cannot be formulated in terms of a satisfactory model because of this problem. The be clusters, or groups, will tend to chosen on an intuitive basis. The SPSS suite has no cluster analysis GENSTAT program so it is proposed to use the sub-program.

6.3.7 Other Statistical Tests

be Other small programs will require to written as the

analysis proceeds and these will be explained within the analysis report..

6.4 Identifying'the'Survey Area

has 6.4.1 The study is based in the City of Edinburgh which a its boundary. Each number of large shopping stores within store exerts an attractive power over a considerable catchment area and forms an overlapping competitive framework within the City. The geography of the area the means that a green-belt buffer zone exists outside City boundary and the nearest urban centres to the-City

are on the periphery of the nearest store catchment areas. Although shopping trips do take place between these areas is to the and City stores the percentage small relative Regional Council total private-car trip generation. The 1% for found this figure to be of the order, of a

particular store on the City boundary. 95

6.4.2 It is proposed, therefore, that the City boundary be taken as defining the survey area. This corresponds with planning zones and hence ennumeration district boundaries

on which census data is based. The study area is shown in Figure 13.

6.5 The Sampling Framework and Technique

6.5.1 It has been established in the previous chapter that to examine if there is a relationship between household characteristics and store usage the effect of competition has to be held constant. It was -proposed to use stratified sampling to group households in areas which will have equal shopping opportunities and hence the same competitive framework.

6.5.2 The sampling element chosen is the household, as

previously discussed, and the area group of households will be randomly sampled so that they can be aggregated and retain their random nature. There are 1,529 Ennumeration Districts within the study area and these

represent 229,000 households (1981 census). The financial

constraint has limited the number of households to 400 which is a sample of 0.2% of the total households. From the experience of previous surveys of this nature undertaken at Cranfield Institute of Technology, Edinburgh University and Napier College and the experience of

statisticians at Edinburgh University and Napier College, this sample number is acceptable. Given the number of variables in the conceptual models and the sample number it is proposed to sample fifteen areas each of which have twenty-seven households.

6.5.3 Each Ennumeration District was then given a number,

omitting 'those districts which were defined as "institutions". These listings are shown in Appendix C. 96

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Fifteen districts were then chosen using a random number generator and these are highlighted in Appendix C. The

postcodes for these districts were obtained and a list of addresses compiled for each area.

6.5.4 The next stage in the sampling process was to identify the car-owning households within the list of addresses for each area. The vehicle licensing centre at Swansea was approached but would not divulge the information. The only way to secure the information is by inquiring at each household and if the household did not possess a car passing to the next household. This process will continue until the sample size in each area is achieved. It is not possible to assess whether this introduces a small bias to the sample.

6.6 The Questionnaire design

6.6.1 The principles of design for the questionnaire are as follows :

i) the questionnaire will be in four parts; a general

section, conforming to the lifestyle factor identified in the conceptual framework, a household

structure section which collects information related to individuals within the household, a personal accessibility section, which conforms to the principles discussed in Chapter 4 and a shopping diary section.

ii) the questionnaire should be structured in such a way that answers can be ticked in boxes given on the questionnaire wherever possible. This aids speed of response and the computerising of the data.

iii) the indexing of the variables identified in Chapter 3 will conform to the proposals in that chapter. 98

iv) the colour of the questionnaire to be light green as this elicits a better response rate from the

personal past experience of Dr M Benwell at Cranfield Institute of Technology. The

questionnaire will also be laid out neatly and

presented professionally on good quality paper with the Napier-College crest.

6.6.2 The general information section obtains information on house type, freezer ownership, number of cars in the household and income of the household. This corresponds

to the lifestyle factor in the conceptual framework of one model. The number of licenses in the household is not included in this section as it pertains to the individual members of the household. The second section identifies the age structure of the household, which follows the

principle employed for income and uses agebands, and the employment structure of the household. In addition a coding is given relating members of the household to the head of the household. This is included for possible further household The analysis of structure. . third section is' presented in tabular form so that the interviewer or interviewee can quickly tick the times when the principal shopper and car are available. These can then be visually checked to obtain a ratio based on eighteen periods when the car, principal shopper and store are available simultaneously. In other words it measures the accessibility window of the household. The fourth section is a standard household shopping diary which obtains information, based on a typical week, on shopping expenditure, duration of stay and frequency of visit. If

the household conducts a major shopping trip outside this period this should be noted, with the details, on the

diary. As proposed in the previous chapter shopping trips with expenditures under £2.00 are not to be noted in addition to major non-food items. 99

6.7 The Pilot Survey

6.7.1 A pilot survey was undertaken, during March 1981, to test the effectiveness of the questionnaire design. The survey covered one hundred and fifty households in four

areas of Edinburgh; Morningside, Grange, Buckstone and Balcarres. These areas are very different in character and provided a broad test for the questionnaire.

6.7.2 No major problems were encountered and the layout worked efficiently and effectively. The survey confirmed that the household could identify a principal shopper, usually the wife, and the personal accessibility table worked well. There was some confusion between gross and net household income so the questionnaire now has a heading

for gross income as this was the measure to which most people could relate. The finalised questionnaire is shown under Figure 14.

6.8 The Organisation of the Survey

6.8.1 The survey was undertaken by one person employed on a full-time basis over two weeks during September 1982.

This provided consistency of approach in the interviewing

and was an efficient way of carrying out the survey since the interviewer became both experienced and faster as the survey progressed. The interviewer was fully briefed and given a letter of authorisation from Napier College in addition to a badge showing his name and the College name. A letter was also sent to every potential householder explaining the purpose of the survey, the confidentiality of the data and the method of survey. The interviewer carried copies of the letter to cover households that had not received it. This method proved most successful. The letters of authorisation and information are shown in Appendix D. The District Authority and the police were

also informed. ääý 100 iý le ý 3 P161 C UJ. 0GItG a a Dy{P/J77ýL11 Of CIVIL RMOIKLlINO 8 III " a SHOPPING f0 K2RAACY IMfa THE ý SIWfiC .. ACT Or SUPSASTCASS

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The Questionnaire Design 101

6.9 Setting up the Data Files

6.9.1 A data file for each area was created and the variables indexed according to the questionnaire coding and the

conceptual framework. The field widths and indexing is shown in Table 8. The coded data for each area are set out in Appendix E. A further data file was created which aggregated all the areas into one file of four hundred households. The files were given names HAZELI to HAZEL16

and are mounted on a PRIME 750 computer. It will be noted that not all areas have twenty-seven households. This is because of invalid returns with respect to failure to answer certain questions. This can be clearly seen in area 11 where a large number of households refused to answer the questions on income and employment. It is considered that the remaining data will be sufficient for the analysis.

6.10 The Presentation of the Initial Data Characteristics

6.10.1 The locations of the fifteen areas are shown in Figure 15. They show a good spread over the City and reflect the various types of housing within the City.

6.10.2 In accordance with the research tasks the mean value of each variable was calculated, for all areas, and a means data matrix, MEANS1, was created. The means of the variables are shown in Table 9 and their profiles are plotted in Figures 16 and 17.

6.10.3 A number of points emerge from the initial inspection of the figures. The greatest variations are seen in Area 5 (Waverley), Area 6 (Westburn), Area 10 (Pilton), Area 12

(Turnhouse), Area 13 (Leith) and Area 14 (Craigleith).

Area 5 is an area of low bulk-buying habit preferring to

shop at smaller stores. It is an inner-city area of low 102

Col umns Variable Variable Remarks Name Description

1- 3 A Area number A number from 1- 15

4- 9 B Duration/week Number of hours to two at Store decimal places

10 - 14 C Duration/week As above for all shopping

15 - 20 D Expenditure per Number of £ to two week at Store decimal places

21 - 26 E Total Expenditure As above for all shopping trips

27 - 31 F Frequency/Week of The number of times in visit to Store the week the store is visited

32 - 36 G Total frequency/week As above but for all for all shopping trips shopping

37 - 38 H House type 1,2,3 or 4

39 I Freezer ownership 1 or 0

40 - 41 J Number of cars/vans Number of cars/vans used regularly by the household

42 - 43 K Income band of the Number from 1 to 8 household

44 - 45 L Number-of licences in Number of licences the household

46 - 47 M Number of people in Number of people in the household

48 - 49 N Mean age of people Mean age in the household

50 - 53 0 Standard deviation of Standard deviation ages of the household

54 - 56 SEG of the head of the SEG group number household

57 - 59 Number of half days/ Number of half days week of employment in the household

60 - 62 Personal accessibility A number between index of the principal 0 and 18 shopper

TABLE 8

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employment and income, low car-ownership and high mean age. It is an area with a large number of retired persons who neither have the demand, income, nor the accessibility to bulk-buy at large stores.

6.10.4 The Area 6 (Westburn) profile highlights the locational influence of the proximity of a large store. Area 6 is adjacent to a suburban shopping centre. Although freezer ownership, car ownership and income are all low the use of the store is high. The mean age of the household is low.

6.10.5 The Area 10 (Pilton) profile shows the depressed nature of this area. A large average number in the household but

low income, car and freezer ownership. The employment is above average, reflecting the large family size and this enables the weekly shopping budget to rise above average, although this is not translated into above average use of large stores.

6.10.6 The Area 12 (Turnhouse) profile shows it to be a prosperous area with a low mean age indicating a high proportion of young families. The shopping budget is above average both for total shopping and bulk-buying even although this area is on the outskirts of the City.

6.10.7 " The Area 13 (Leith) profile shows a high shopping activity but below average expenditure on bulk-buying of food. It is an area of low income with a significant proportion of large families.

6.10.8 Area 14 (Craigleith) is a rich, inner suburb similar in character to Cammo, Swanston and Turnhouse. It is an area of high income, freezer ownership and average number of

licenses in the household. The population is older but

not retired. The family size is small indicating many children have now left home to further their careers leaving the parents in a mid-life period. 109

6.10.9 From these profiles areas can be grouped according to

"wealth", or age, etc. For example areas 6,7 and 12 all have low household mean ages. It is also worthy of note

that certain variable profiles match. For example the number of licenses in a household and income. Even at this early stage of the analysis it can be seen from the description of the areas above that factors combine to influence the shopping pattern of the household.

6.10.10 A similar exercise to the means distribution was carried out for the standard deviations of each variable. Table 10 and Figures 18 and 19 show the details. It can be seen that the variable dispersion varies considerably from area to area. Freezer ownership, personal accessibility, socio-economic group (SEA) and employment are all relatively equal in profile but the other variables show a range in dispersion pattern. It may have

been expected that a uniformity of dispersion would have occurred in such closely located groups however this is not the case and begs the question as to what causes this variability in dispersion.

6.10.11 Tables- 11 and 12 show the same type of relative

comparison between areas with respect to standard error and skewness respectively. These tables will be referred to later in the analysis.

6.11 Concluding Remarks

6.11.1 This chapter has discussed the analytical implications of the research tasks emanating from the conceptual framework and research objectives. It is assumed that in

the first instance a linear relationship exists between household characteristics and store usage and the proposed analytical methods are based on this assumption. If a linear relationship does not exist it is possible to investigate non-linear relationships. 110

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The two conceptual models imply a multiple regression

analysis, with a preceding identification of principal components in one of the models. The input to these statistical techniques is from a bivariate correlation matrix which serves the additional purpose of indicating

the sense and strength of the bivariate variable relationships.

6.11.2 The nature of the relationship between the independent and dependent variables requires to be analysed in detail at various disaggregate and aggregate levels and the proposed canonical correlation analysis will identify the overall strength of the relationship at each level and the structure of that relationship. This will indicate the

level of generality of model that can be achieved. It may be that certain groups of areas exhibit the same

structural relationship and cluster analysis will be used to identify these groupings.

6.11.3 The chapter then explains the rationale behind the sampling framework, survey area and questionnaire design

and this is tested in a pilot survey. Following the pilot survey the organisation of the main survey is explained and the initial tabulation of the raw data.

6.11.4 The final section discusses the initial data characteristics and identifies certain variations between areas. Tables of means, standard deviations and degree of skewness are listed and an initial appreciation of the interdependence of the variables is gained. The means data matrix will be required for the detailed analysis.

6.11.5 The following chapter deals with the analysis of both

the proposed models and investigates the nature of the relationship between the independent and dependent

variables at area, mean and composite area level. This will enable a generalised trip generation model to be developed which can then be integrated into a standard,

aggregate trip distribution model. 117

CHAPTER 7

ANALYSIS OF THE DATA

7.1 Introduction

This chapter follows on from the preliminary profile analysis of areas and carries out the research tasks, identified from the research objectives and conceptual framework, developed within Chapter 6.

The analysis is divided into three sections :

i) the formation of the Pearson correlation matrices and principal components analysis to develop the conceptual Model 1.

ii) the canonical correlation analysis to analyse the strength and structure of the relationship between the two sets of variables.

iii) the multiple regression analysis to develop a disaggregate trip prediction model.

These tasks not only identify the appropriate form of the trip generation model being proposed but establish the level of disaggregation possible whilst retaining an acceptable level of trip prediction. The proposed model will then be examined for stability and application in the following chapter. 118

7.2 The Framework of the Analysis

7.2.1 The conceptual framework shown in Figures 11 and 12 identify two models; one based on composite, variable components and the other based on individual variables. One of the primary research tasks is therefore to confirm the component data structure of Model 1. In building a predictive trip generation model for private-car trips to large stores the level of generality that can be attributed to the chosen model must be found. The conceptual framework has shown this in two parts; one at the individual area level and the other at the total area and means area levels. The analysis must examine the structure of the relationship at area, means and total level to identify the level of generality able to be

achieved in the model. It may be that although not all the area relationships are the same, groups of areas may be the same and so this is an intermediate level of aggregation that will be examined.

7.2.2 If areal variation exists the analysis must discuss and explain this variation. The initial production of the Pearson bivariate correlation matrix at area, mean and total level will aid this understanding in addition to

providing the input matrix for the later stages of the analysis.

7.2.3 Once the level of generality is identified a predictive model can be developed at the appropriate level. The effectiveness of the model and its stability over each individual area will be tested.

7.2.4 The methods of analysis are shown in matrix form in Table 13 and relate to the levels of aggregation and

research tasks emanating from the research objectives and conceptual framework of Chapters 3,4 and 5. It is proposed to separate the analysis and the interpretation 119

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of the results so that this chapter will report on the analysis. The following chapter will then interpret the findings from the analysis. It is difficult to achieve a complete segregation of analysis and interpretation and certain points of interpretation will be discussed as they occur in this chapter. 7.3 Calculation of Pearson Product-Moment Correlations Matrix and Identification of Significant Variable Correlations with respect to the Conceptual Framework

7.3.1 Pearson Correlation Coefficients with respect to the Dependent Variables

The Pearson correlation coefficients, greater than or equal to 0.35, for the dependent variables are listed in Table 14. All correlation coefficients listed with respect to dependent and independent variables are statistically significant at the 5% level.

The table shows that the three store usage variables are strongly intercorrelated in all areas, the exception being in area 11 (Cammo) between duration of stay and frequency of visit to the stores. The three total shopping usage

variables are also strongly intercorrelated although the strength of the correlation varies from area to area. The significant relationship between store and total shopping usage relates to the shopping expenditure variables D and E. Area 1 (Moredun) and Area 13 (Leith) are the exceptions. Area 1 is an area remote from large shopping stores with a large retired persons grouping and Area 13 is an inner city area of low income and car ownership.

No relationship was found between duration of stay at the

stores and total time spent shopping. - This also applied to frequency of visit to large stores. In general the households regarded the bulk-buying shopping usage as a

separate entity from other household shopping. 121

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7.3.2 Pearson Correlation Coefficients with respect to the Independent Variables

As with the dependent variables only coefficients greater

than 0.35 and significant at the 5% level are shown. H Table 15 shows the listings of coefficients. Variable (House Type) has been omitted from the analysis because of

collinearity problems with other independent variables. little This is not of concern because each area showed have variation in house type and this variable would not added significantly to the analysis.

Inspection of the table indicates that income, the household structure variables, employment and the socio-economic grouping are the variables showing independent strongest intercorrelations with other from variables. Certain areas diverge significantly the 12 (Turnhouse) 1 general correlations. Areas and Area 1 (Moredun) show particular divergence. shows this its divergence with the dependent variables and in contributing factors were highlighted the previous City subsection. Area 12 is a remote suburb on the boundary and is predominantly young, middle-class families. The separation of the area from the other parts devergent the of the City may contribute to the nature of area. It will be noted that the personal accessibility variable (R) does not show a consistently strong it is correlation with any other variable although negatively correlated with income and employment and the household positively correlated with the mean age of and socio-economic grouping. 123

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7.3.3 Pearson Correlation Coefficients with respect to the Relationships between Dependent and Independent Variables

The correlation coefficients, greater than or equal to 0.35, between the dependent and independent variables are

shown in Table 16. Three values just below 0.33 are shown in brackets and all values shown are significant at the 5% level.

These relationships are of prime importance to the study as they indicate the strengths of the linear relationships between the predictor and predicted variables. It is evident, from Table 16, that the large store and total shopping expenditure variables (D and E) are the most important of the dependent variables and show correlation with the same independent variables discussed in the previous section i. e. income, the household structure variables and employment. Socio-economic grouping does (Turnhouse) not appear. Again areas 1 (Moredun) and 12 consistently do not show these relationships and in addition area 4 (Swanston) shows little relationship between the two sets of variables. Area 4 is similar to

area 12 in that it is an outer, middle-class suburb.

The correlation coefficients for the sum of all the areas combined are low, the highest being 0.57 between total shopping expenditure per week and the number in the household, which is not unexpected due to the areal variability.

7.3.4 These initial results show areal variation in the bivariate correlations and will be important in the interpretation of this variation. Some of the variation dominant can be attributed to the location or a characteristic in the area, such as high mean age of it population or proximity to a large store, and is expected that in general the areal variation will be shown 126

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to be the result of several local area effects working together. These initial indications do not encourage the development of a generalised model for all areas at household level. In particular areas 1 and 12 cause

concern in that they consistently diverge from the general

pattern. This effect may continue to be seen in the statistical analysis of each area's data.

7.4 Testing the Areal Data for Non-Homogeneity of the Bivariate Correlations

7.4.1 The tables showing the significant bivariate correlations also show an areal variation. It is important, in the context of a generalised model, to , establish whether these areal variations mean that these areas constitute a non-homogenous group. To test this each variable pair was pooled over the fifteen areas and the resulting single correlation compared to the bivariate

correlation obtained from the total data matrix for the same variable pair. The homogeneity of each variable pair was then tested using a computer program based on the parallel estimates of correlation coefficients. The method of averaging is via the Z-transformation (122) procedure. The bivariate comparisons are listed in Table 17. The variable pairs with a correlation

coefficient greater than 0.3 were subjected to a further analysis. The most divergent area was omitted first and the new whole correlation coefficient compared to the total matrix coefficient. If this did not establish homogeneity between the groups the next most diverse area

was omitted. This process continued until homogeneity was achieved. Table 18 shows the selected variable pairs and the areas omitted in order to achieve homogeneity.

7.4.2 The table shows that where homogeneity exists in a variable pair over all areas the pooled correlation 128

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Variable Pairing Total Pooled Remarks Matrix Coefficient Coefficient (All Areas)

Hrs. /Wk at Freq. /Wk 0.78 Not By omitting areas Cammo(ll) Store (B) - at Store(F) Homogeneous and Easter (15) homogeneity was achieved giving a whole factor of 0.85.

Tot. Hrs/Wk Tot. Exp. /Wk 0.45 Not By omitting Spottiswoode(2) Shopping(C) - Shopping(E) Homogeneous homogeneity was achieved giving a whole factor of 0.51.

Tot. Hrs/Wk Tot. Freq. /Wk 0.73 Not By omitting Cammo(ll) Shopping(C) - Shopping(G) Homogeneous homogeneity was achieved giving a whole factor of 0.51.

Exp. /Wk Tot. Exp. /Wk -0.57 Not. By omitting areas Moredun at Store(D) - Shopping(E) Homogeneous (1) and Leith(13) homo- geneity was achieved giving a whole factor of -0.65.

Tot. Exp. /Wk Tot. Freq. / -0.47 Not By ommitting areas Spottis- Shopping(E) - Weeks(G) Homogeneous woode(2), Westburn(6) and Cammo(l1) homogeneity was achieved giving a whole factor of -0.60.

Tot. Exp. /Wk No. in -0.57 Not By omitting area Swanston Shopping(E) - Household(M) Homogeneous (4) homogeneity was achieved giving a whole factor of -0.62.

Tot. Exp. /Wk Mean Age of -0.38 Not By omitting area St Peters Shopping(E) - Household(N) Homogeneous (7) homogeneity was achieved giving a whole factor of -0.45.

TABLE 18

Testing for Areal Homogeneity with Selected Variable Pairs 130

Variable Pairing Total Pooled Remarks Matrix Coefficient Coefficient (All Areas)

Tot. Exp. Employment/ 0.42 Not By omitting areas Moredun /Week(E) - Household(Q) Homogeneous (1) and Craigleith(14) homogeneity was achieved giving a whole factor of 0.49.

Exp. /Wk at No. in 0.33 Not By omitting areas Moredun Store(D - Household(M) Homogeneous (1) and Swanston(4) homo- geneity was achieved giving a whole factor of 0.37.

No. of Cars/ 0.53 Not By omitting area Easter(15) Household(J) - Income(K) Homogeneous homogeneity was acheived giving a whole factor of 0.50.

No. of Cars/ No. of Lic. 0.51 Not Homogeneity could not be Household - Holders(L) Homogeneous achieved after four groups had been omitted.

Income/ No. in 0.40 Not By omitting area Clermiston Household(K) - Household(M) Homogeneous (3) homogeneity was achieved giving a whole factor of 0.48.

Income/ S. D. of 0.36 Not By omitting area Turnhouse Household(K) - Ages(O) Homogeneous (12) homogeneity is achieved giving a whole factor of 0.40.

Income/ Employment/ 0.52 Not By omitting areas Moredun Household(K) - Household(Q) Homogeneous (1) and Westburn (6) homo- geneiry is achieved giving a whole factor of 0.63.

No. of Lic. S. D. of 0.39 Not By omitting areas Moredun Holders(L) - Aes(O) Homogeneous (1) and Easter(15) homo- geneity is achieved giving a whole factor of 0.48.

TABLE 18 (Contd) 131

Variable Pairing Total Pooled Remarks Matrix Coefficient Coefficient (All Areas)

No. in Mean Age of -0.62 Not By omitting areas St Peters Household(M) - Household(Q) Homogeneous (7) and Turnhouse(12) homo- geneity is achieved giving a whole factor of -0.74.

No. in S. E. G. of -0.35 Not By omitting area Pilton(10) Household(M) - Household(P) Homogeneous homogeneity is achieved giving a whole factor of -0.45.

No. in Employment/ 0.48 Not By omitting areas Moredun Household(M) - Household(Q) Homogeneous (1) and Turnhouse(12) homo- geneity is achieved giving a whole factor of 0.58.

Mean Age of S. D. of -0.57 Not By omitting areas St Peters Household(N) - Ages(0) Homogeneous (7) and Turnhouse(12) homo- geneity is achieved giving a whole factor of -0.67.

Mean Age of S. E. G. of 0.60 Not By omitting area St Peters Household(N) - Household(P) Homogeneous (7) homogeneity is achieved giving a whole factor of 0.64.

Mean Age of Employment of -0.45 Not By omitting areas Moredun (1) Household(N) - Household(Q) Homogeneous St Peter(7) and Turn- house(12) homogeneity is achieved giving a whole factor of -0.60.

Mean Age of Personal 0.32 Not By omitting areas Swanston Household(n) - Access. (R) Homogeneous (4), Turnhouse(12) and Craigleith(14) homogen- eity was acheived giving a whole factor of 0.42. All these areas have inverse relationships.

S. D. of Employment/ 0.40 Not By omitting areas Moredun Ages(0) - Household(Q) Homogeneous (1) and Turnhouse (12)homo- geneity was acheived giving a whole factor of 0.52.

TABLE 18 (Contd. ) 132

Variable Pairing Total Pooled Remarks Matrix Coefficient Coefficient (All Areas)

S. E. G. of Employment/ -0.47 Not By omitting area Moredun (1) Household(P) - Household(Q) Homogeneous homogeneity was achieved giving a whole factor of -0.55.

S. E. G OF Personal 0.31 Not By omitting areas Swanston Household(P) - Access. (R) Homogeneous (4) and Turnhouse(12) homo- geneity was achieved giving giving a whole factor of 0.46. These areas have a . negative relationship.

Employment/ Personal -0.41 Not By omitting area Moredun(1) Household(Q) - Access. (R0 Homogeneous homogeneity was acheived giving a whole factor of -0.48.

TABLE 18 (Contd. ) 133

coefficient and the total data matrix coefficient are the same. The occurrence of non-homogeneity can be shown on an area basis and, within areas, on a variable pair basis. Table 19 shows the number of times an area was responsible for causing non-homogeneity over all areas. This highlights area 1 (Moredun) and area 12 (Turnhouse) as

those areas showing the greatest tendency to exhibit non-homogeneity and area 4 (Swanston) and area 7 (St Peters) to be the areas showing non-homogeneity four and five times respectively. The first three areas have been

described previously but area 7 is a young, middle-class area in the inner city with a large proportion of households with no children and both husband and wife working.

7.4.3 If the variable pairs causing non-homogeneity are listed for each of the above four areas, household structure variables occur in all but two of the variable pairs in areas 4,7, and 12 and the employment variable occurs in all but three of the variable pairs in area 1. Table 20 lists these variable pairs. Before commenting on these relationships, further investigation to establish the nature of the relationships is undertaken using scatter diagrams. Typical examples of these plots are shown in

Figures 20,21 and 22. The remaining diagrams are shown in Appendix F.

7.4.4 The main points to emerge from these plots is the effect of non-use of the large store and the effect of the category variables. In Figure 20 the linear relationship is impaired by the presence of seven households who do not bulk-buy. The employment variable, although not classified in categories, occurs in categories, as is

evident in Figure 21. This effect also occurs with the number in the household, the number of licences in the household and personal accessibility. 134

Area Number No. of Time(s) Area was responsible for non- homogeneity

1 11 2 2 3 1 4 4 5 0 6 2 7 5 8 0 9 0 10 1 11 3 12 8 13 1 14 2 15 3

TABLE 19

Occurrence of Non-Homogeneity by Area 135

AREA 1: Expenditure/Week at Store & Total Shopping Expenditure/Week (Moredun) Total Shopping Expenditure/Week & Employment Household

Expenditure/Week at Store & Number in Household Income/Household & Employment/Household

No. of Licence Holders & S. D. of Ages No. of Licence Holders & Employment/Household No. in Household & Employment/Household No. in Household & Employment/Household

Mean Age of Household & Employment/Household S. D. of Ages & Employment/Household S. E. G. of Household & Employment/Household Personal Accessibility & Employment/Household

AREA 12 : Income/Household & S. D. of Ages (Turnhouse) No. in Household & Mean Age of Household No. in Household & Employment/Household

S. D. of Ages & Mean Age of Household Mean Age of Household & Employment/Household Mean Age of Household & Personal Accessibility S. D. of Ages & Employment/Household S. E. G. of Household & Personal Accessibility

AREA 4: Total Shopping Expenditure/Week & No. in Household

(Swans ton) Expenditure/Week at Store & No. in Household Mean Age of Household & Personal Accessibility S. E. G. of Household & Personal Accessibility

AREA 7: Total Shopping Expenditure/Week & Mean Age of Household (St Peters) No. in Household & Mean Age of Household S. D. of Ages & Mean Age of Household S. E. G. of Household & Mean Age of Household Employment/Household & Mean Age of Household

TABLE 20 Variable Pairs in Areas Showing Non-Homogeneity 136

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Appendix F-12 shows seven households with zero standard deviation of household ages indicating either one or two persons of the same age residing in the house. Appendix

F-21 shows a large group of single persons and young

couples with no children, as previously described in area 7. This is also seen in Appendices F-23 and F-24.

In area 1 (Moredun) twenty-five percent of households do not bulk-buy but low usage of large stores in other areas has not resulted'in the non-homogeneity present in this area.

7.4.5 The scatter plots do not show non-linear relationships

but attribute the non-homogeneity to either no relationship, the presence of households with zero use of large stores, or the tendency for certain variables to fall into categories.. In area 1 (Moredun) Appendices F-1

and F-3 support the statement that a significant proportion of retired, or unemployed, people reside in this area. This, combined with the relative isolation of the area from the nearest large store, accounts for the non-homogeneity of the area.

7.4.6 This section of the analysis has computed the correlation matrices at area, means and total matrix level for input to the later statistical analyses. In addition the variable pairs have been examined, as has their homogeneity across all areas. This is important for two reasons, firstly to build a generalised model for all areas the relationship between dependent and independent variables must be homogeneous and secondly when considering the reasons why areal variation occurs these

bivariate relationships will aid in the interpretation of

the variation. The initial analysis of the worst areas of non-homogeneity has indicated that part of that non-homogeneity is due to zero usage of large stores and categorising of variables. These effects and the reason 140

why no relationship exists in certain areas must he discussed once further analysis has been completed.

7.5 Identifying the Employment, Household Structure and

Lifestyle Factors of Model 1 using Principal Components Ana lays is

7.5.1 The second section of the analysis examines the postulate that the underlying data structure comprises two dependent factors and three independent factors. The two dependent factors are large store shopping, or bulk-purchasing of foodstuffs, and total household

shopping and the three independent factors are employment, household structure and lifestyle. It is argued that these three influences, made up of a combination of

household characteristics, determine both large store and total shopping usage.

7.5.2 The statistical technique selected in the previous chapter to determine this underlying data structure is principal components analysis. The analysis, which is carried out at area, area means and total matrix levels, does not use iteration because the without iteration method is a sufficiently good approximation for interpretation with a considerable saving in computer time. The rotation of the solution, using VARIMAX as discussed in the previous chapter, simplifies the (123) structure and aids interpretation.

7.5.3 Principal Components Analysis of the Total Data Matrix taking account of the effect of areal non-homogeneity on the data structure

The tests for non-homogeneity previously reported in this 12 chapter identified area 1 (Moredun) and area (Turnhouse) as the areas showing the greatest degree of 3 (Clermiston), 5 non-homogeneity. Conversely area area 141

(Waverley), area 8 (Saughton), area 9 (Craigleith Hill), area 10 (Pilton) and area 13 (Leith) showed either no, or an insignificant proportion of, non-homogeneity. These seven areas were subjected to a principal components

analysis to investigate their component data structure. The other areas were then added, in order of increased non-homogeneity presence, to see the effect these areas would have on the stability of the data structure. Table 21 lists the ordering of the area additions.

The principal components resulting from the six computer runs are shown in Tables 22 to 26. The first factor is of prime importance as are the extreme polar weightings of each factor. These tables show that the data structure remains stable as areas are added, so that the presence of non-homogeneity in certain areas, especially areas 1 and 12, does not impair the structure. This is important in the context of a generalised model based on component variables. The second order components do show change primarily because of the fifth component generated in the first two computer runs. However this stabilises as the areas are aggregated and affects only the minor components.

7.5.4 The Composition of the Total Data Matrix Components

In accord with the structure of Model 1 the principal components analysis on the total data structure separated the dependent and independent variables. The following

principal components were output from the analysis : 142

PCA Run 1 : Areas 3,5,8,9,10,13 PCA Run 2 Areas 2 and 14 added PCA Run 3 Areas 11 and 16 added PCA Run 4 Areas 3 and 15 added PCA Run 7 Area 7 added PCA Run 6 Area 1 and 12 added

TABLE 21

Total Data Matrix Principle Component Analysis - Testing the Effect of Areal Non-Homogeneity on the Component Structure 143

Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Areas: Areas: Areas: Areas: Areas: All Areas: 3,5,8,9, 2,3,5,8,9, 2,3,5,6,8, 2,3,4,5,6 2,3,4,5,6 1,2,3,4,5 10,13 10,13,14 9,10,13, 8,9,10,11, 7,8,9,10, 6,7,8,9, 14 13,14,15 11,13,14, 10,11,12, 13,14,15

Factor 1 Factor 1 Factor 1 Factor 1 Factor 1 Factor 1

Q 0.71016 Q 0.75152 Q 0.80799 Q 0.82217 Q 0.79658 Q 0.76408 K 0.67648 0 0.53059 M 0.71596 M 0.71445 M 0.66141 M 0.63038 0 0.51451 M 0.52133 0 0.69534 0 o. 68470 0 0.63669 0 0.59053 M 0.45823 K 0.48066 K 0.50530 K 0.52775 K 0.50597 K 0.53264 E 0.35814 E 0.37800 E 0.49094 E 0.48377 E 0.42941 E 0.40878 D 0.20663 D 0.18601 L 0.32621 L 0.33967 L 0.31046 L 0.30528 I 0.15328 I 0.16143 D 0.29483 D 0.28792 D 0.26456 D 0.25135 L 0.12242 L 0.14849 J 0.20159 J 0.22416 J 0.19769 J 0.20617 G -0.00921 H 0.09010 H 0.19341 H 0.18056 H 0.15468 I 0.06908 H -0.02234 J 0.03431 I 0.07020 G 0.07677 I 0.05623 G 0.04768 F -0.02456 C 0.00791 G 0.04817 I 0.05828 G 0.04730 F 0.02715 J -0.04578 G 0.00423 C 0.03008 C 0.03417 F 0.01989 H 0.14060 C -0.05190 F -0.03626 G 0.02608 F 0.01882 C -0.00357 C -0.01657 B -0.08419 B -0.07109 B -0.06492 B -0.08278 B -0.07522 B -0.06757 N -0.69750 P -0.71852 P -0.67654 R -0.65859 P -0.66705 R -0.65777 P -0.79251 N -0.72931 R -0.68088 P -0.66092 R -0.67181 P -0.68477 R -0.82530 R -0.83003 N -0.82209 N -0.79810 N -0.80079 N -0.78626

lb

TABLE 22

Total Data Matrix Principle Components Analysis - Testing The Effect of Areal Non-Homogeneity 144

Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Areas: Areas: Areas: Areas: Areas: All Areas: 3,5,8,9, 2,3,5,8,9, 2,3,5,6,8, 2,3,4,5,6 2,3,4,5,6 10,13 10,13,14 9,10,13, 8,9,10,11, 7,8,9,10, 14 13,14,15 11,13,14,

Factor 2 Factor 2 Factor 2 Factor 2 Factor 2 Factor 2

G 0.90686 F 0.94345 F 0.93088 F 0.92041 F 0.91840 F 0.92133 C 0.87985 B 0.90737 B 0.89507 B 0.89220 B 0.89568 B 0.89993 E 0.71981 D 0.87826 D 0.85216 D 0.86609 D 0.86609 D 0.87168 M 0.50766 E 0.34221 E 0.36092 E 0.37391 E 0.37566 E 0.38904 0 0.36905 K 0.14572 M 0.18045 M 0.14556 M 0.15955 M 0.15779 H 0.14955 1 0.12171 H 0.15880 H 0.12201 0 0.12843 K 0.12499 Q 0.13496 M 0.10968 R 0.14322 K. 0.11577 K 0.12486 0 0.10657 I 0.08050 H 0.10147 L 0.13331 R 0.10728 R 0.10847 L 0.09341 P 0.06258 0 0.09089 0 0.13278 L 0.10542 L 0.09509 R 0.09163 D 0.05154 L 0.09958 K 0.12716 0 0.10001 H 0.09389 I 0.09105 L 0.03183 R 0.06106 J 0.10936 J 0.08597 I 0.06549 H 0.07963 F 0.02449 C 0.02174 I 0.03966 I 0.05808 J 0.05704 J 0.05461 B 0.01927 J 0.00187 C 0.00898 C 0.00273 C 0.00235 C 0.01665

R 0.01503 P -0.00339 P -0.01409 P -0.00575 P -0.00578 Q -0.04005 K -0.06569 Q -0.02927 Q -0.01557 Q -0.00580 Q -0.02559 P -0.04840 J -0.08718 N -0.11109 G -0.12598 G -0.12552 N -0.14910 G 0.14728 N -0.36122 G -0.11857 N -0.15617 N -0.13175 G 0.15272 N -0.15325

TABLE 23

Total Data Matrix Principle Components Analysis -

Testing the Effect of Areal Non-Homogeneity 145

Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Areas: Areas: Areas: Areas: Areas: All Areas: 3,5,8,9, 2,3,5,8,9, 2,3,5,6,8, 2,3,4,5,6 2,3,4,5,6 10,13 10,13,14 9,10,13, 8,9,10,11, 7,8,9,10, 14 13,14,15 11,13,14,

Factor 3 Factor 3 Factor 3 Factor 3 Factor 3 Factor 3

F 0.95345 L 0.80545 J 0.76338 J 0.72525 J 0.72588 J 0.70530 B 0.91361 J 0.71809 L 0.69207 L 0.69207 L 0.69118 L 0.68533 D 0.88227 K 0.56519 K 0.66803 K 0.65363 K 0.67273 K 0.62593 E 0.28958 M 0.55083 I 0.56235 I 0.59964 I 0.60778 I 0.58362 M 0.13202 0 0.54422 R 0.28330 R 0.30684 R 0.30476 R 0.33277 0 0.10704 Q 0.37938 D 0.23375 0 0.21023 0 0.24642 0 0.24911 K 0.09758 R 0.29054 Q 0.22978 D 0.18368 Q 0.23135 D 0.20833 H 0.09357 E 0.27979 0 0.22585 Q 0.18128 D 0.20675 M 0.20375 R 0.08369 D 0.25501 E 0.21462 M 0.16979 M 0.19869 Q 0.20500 I 0.07900 I 0.09793 M 0.17768 E 0.14695 E 0.18436 E 0.17140 L 0.07127 F 0.04875 F 0.04539 F 0.04055 F 0.02396 F 0.03796 C 0.00189 G 0.02129 G 0.00073 N -0.03757 N -0.01325 N -0.02572 J -0.01954 B -0.07665 N -0.04057 G -0.03775 G -0.03877 B -0.04985 B G Q -0.02075 C -0.08292 -B -0.08170 B -0.06162 -0.06117 -0.04464 P -0.02470 H -0.10099 C -0.12530 C -0.10553 C -0.11368 C -0.11224 G -0.09351 P -0.19908 P, -0.31743 P -0.33722 P -0.33630 P -0.32889 N -0.12496 N -0.33800 H -0.55442 H -0.58093 H -0.56670 H -0.57811

TABLE 24

Total Data Matrix Principle Components Analysis -

Testing the Effect of Areal Non-Homogeneity 146

Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Areas: Areas: Areas: Areas: Areas: All Areas: 3,5,8,9, 2,3,5,8,9, 2,3,5,6,8, 2,3,4,5,6 2,3,4,5,6 10,13 10,13,14 9,10,13, 8,9,10,11, 7,8,9,10, 14 13,14,15 11,13,14,

Factor 4 Factor 4 Factor 4 Factor 4 Factor 4 Factor 4

J 0.79076 G 0.91917 G 0.91257 G 0.90588 G 0.89459 G 0.88693 L 0.78963 C 0.90110 C 0.88873 C 0.88692 C 0.87870 C 0.87701 K 0.60144 E 0.63607 E 0.56588 E 0.56798 E 0.60991 E 0.61498 Q 0.49787 M 0.36825 M 0.35663 M 0.36761 M 0.42149 M 0.45883 M 0.48002 0 0.24341 0 0.22463 0 0.25679 0 0.29087 0 0.33117 0 0.44145 H 0.13003 R 0.13192 R 0.16095 R 0.15655 R 0.16957 D 0.23501 I 0.10345 P 0.12827 P 0.07199 P 0.09307 P 0.07672 I 0.21296 P 0.07348 H 0.11816 L 0.04733 H 0.06786 Q 0.07467 R 0.20500 Q 0.06530 L 0.08332 I 0.02404 L 0.06478 L 0.07091 E 0.19607 R 0.02805 I 0.05193 H 0.02249 Q 0.06289 H 0.06657 H 0.03441 L 0.01712 Q 0.03581 B 0.01941 I 0.04492 I 0.03917 F 0.02173 B 0.01658 B 0.01098 Q 0.00614 B 0.01738 B 0.03142

G -0.04259 F 0.00927 F -0.00778 F -0.01335 D -0.01032 D -0.00711 B -0.08456 D 0.00574 D -0.03461 D -0.03686 K -0.02072 K -0.00837 P -0.09180 K -0.03586 J -0.03813 K -0.05494 F -0.02569 F -0.01016 C -0.11452 J -0.08652 K -0.05282 J -0.09497 J -0.05152 J -0.04564 N -0.26208 N -0.23148 N -0.13301 N -0.16271 N -0.11618 N-0.13656

TABLE 25

Total Matrix Principle Components Analysis -

Testing the Effect of Areal Non-Homogeneity 147

Run 1 Run 2 Run 3 Areas: Areas: Areas: 3,5,8,9, 2,3,5,8,9, 2,3,5,6,8, 10,13 10,13,14 9,10,13, 14

Factor 5 Factor 5 Factor 5

H 0.83627 I 0.71503 P 0.16926 K 0.41437 N 0.15413 J 0.32529 E 0.13829 L 0.10290 Q 0.13071 D 0.08345 M 0.04252 N 0.07395 C 0.02828 E 0.04348 L 0.02256 F 0.00273 R 0.01614 G 0.00036

B 0.01072 Q -0.00076 F 0.00224 R -0.02069 D -0.00527 B -0.05039 G -0.01111 C -0.07039 0 -0.08399 0 -0.15701 K -0.11461 M -0.21732 J -0.23358 P -0.30061 I -0.52075. H -0.63627

TABLE 26

Total Data Matrix Principle Components Analysis -

Testing the Effect of Areal Non-Homogeneity 148

Dependent Variables (79.6% variance explained)

Factor 1 (45.7%) Factor 2 (33.9%)

Variable Weighting Variable Weighting

Exp. at store (D) 0.93 Total shopping freq. (G) 0.91 Freq. at store (F) 0.91 Total shopping hours (C) 0.89 Hours at store (B) 0.86 Total shopping expend. (E) 0.68

Independent Variables (63.6% variance explained)

Factor 1 (38.2%) Factor 2 (15.2%) Factor 3 (10.2%)

Variable Weighting Variable Weighting Variable Weighting

Household No. of Personal Size (M) 0.89 Cars (J) 0.73 Access(R) 0.87

S. D. of Ages(0) 0.86 Income (K) 0.69 SEC (P) 0.59

Mean Age_ No. of Employment (N) -0.74 Lics. (L) 0.65 (Q ) -0.63

Freezer Owner- ship(I) 0.62

The data structure shows the dependent variables clearly split into a large store component and a total shopping component and the independent variables split into three components which correspond to the household factor, the lifestyle and the employment factor of Model 1. The dependent components explain 79.6% of the dependent variable variance and the independent components explain 63.6% of the independent variable variance. The household factor is the dominant component explaining 38.2% of the variance. The three variables comprising this factor have

weightings showing their importance. In the third factor note that as employment rises the personal accessibility 149

of the principal shopper falls reflecting the increasing number of working wives in the population.

The bulk-buying expenditure variable is of prime importance in the first dependent variable component but

the total shopping expenditure variable is of third order importance in the second component. This indicates that once the household decides to spend a certain budget at a large store the frequency of smaller shopping trips becomes more important rather than the amount of budget residue left in the household shopping account. This minor shopping trip budget shows less variation than frequency and duration of these trips.

7.5.5 The Principal Components Analysis of each Individual Area

The analysis described in the previous section indicated a meaningful overall data structure that corresponds with Model 1 of the conceptual framework. This structure was not affected by the non-homogeneity present in some areas. This section carries out a principal components analysis

on each area to investigate if the total data structure occurs at individual area level thus indicating that a generalised model, based on Model 1, can be achieved.

Tables 27 and 28 list the component structure by area of the dependent and independent variables respectively. The percentage of variance explained by each component after, rotation is listed underneath each component. The percentage of variance explained varies from eighty-five percent to one hundred percent.

The dependent variable structure in all areas, except area 11 (Cammo), conforms to the general structure. This is not the case with the independent data structures. The primary factor in the majority of areas is household 150

Area No. Factor 1 Factor 2 Factor 3 Variable Weighting Variable Weighting Variable Weighting

F 0.98 C 0.99 B 0.92 G 0.81 D 0.89 E 0.40 (60.8%) (39.2%)

2 B 0.99 C 0.96 D 0.70 F 0.87 G 0.79 E 0.70 D 0.49 (54.2%) (28.5%) (17.3%)

3 F 0.96 G 0.97 B 0.86 E 0.78 D 0.75 C 0.64 (70.4%) (29.6%)

4 D 0.98 G 0.85 F 0.85 C 0.83 B 0.84 E 0.59 (60.7%) (39.3%)

5 G 0.96 D 0.90 C 0.93 F 0.83 E 0.86 B 0.76 (62.2%) (37.8%)

TABLE 27a

Principle Components Analysis - Factor Composition for Each Area (Dependent Factors) t

151

Area No. Factor 1 Factor 2 Factor 3 Variable Weighting Variable Weighting Variable Weighting

6D0.88 C 0.98 B 0.85 G 0.91 F 0.83 D -0.36 E 0.78 (59.4X) (40.6x)

7B0.96 E 0.83 D 0.88 G 0.80 F 0.81 C 0.55 (62.1%) (37.9%)

8F0.95 G 0.95 D 0.93 C 0.92 B 0.89 E 0.65 (56.8X) (43.2%)

9D 0.96 G 0.89 F 0.85 C 0.81 B 0.82 (66.0%) (34.0%)

10 F 0.99 G 0.99 B 0.90 C 0.71 D 0.89 E 0.61 (60.7%) (39.3%)

TABLE 27b

Principle Components Analysis - Factor Composition for Each Area (Dependent Factors) 152

Area No. Factor 1 Factor 2 Factor 3 Variable Weighting Variable Weighting Variable Weighting

11 E 0.98 B 0.73 C 0.62 D 0.95 G -0.74 B 0.42 F 0.50 (56.8X) (27.1%) (16.0%)

12 D 0.99 C 0.89 F 0.84 G 0.84 B 0.75 E 0.58 E 0.70 (69.8%) (30.2%)

13 G 0.96 F 0.98 E 0.87 D 0.83 C 0.82 B 0.70 (69.5%) (30.5%)

14 D 0.96 G 0.92 F 0.89 C 0.88 B 0.87 E 0.52 (62.8%) (37.2%)

15 G 0.97 D 0.97 C 0.79 F 0.68 E 0.76 B 0.66 (53.4X) (46.6%)

TABLE 27c

Principle Components Analysis - Factor Composition for Each Area (Dependent Factors) 153

Area Factor 1 Factor 2 Factor 3 Factor 4 No. Variable Weighting Variable Weighting Variable Weighting Variable Weighting

1 R 0.92 N 0.94 J 0.85 L 0.79 P 0.87 0 0.94 K 0.58 Q -0.76 N 0.64 N -0.60 I 0.42 K -0.70. N -0.60 Q 0.40 L 0.38 (35.8%) (17.3%) (13.9%) (11.5%)

2 Q 0.87 M 0.63 J 0.61 I 0.48 K 0.78 0 0.63 R 0.56 L 0.47 L 0.74 P 0.54 N 0.40 J -0.64 M 0.70 R 0.48 I 0.38 0 0.67 I 0.62 K 0.35

(43.8%) (18.8%) (12.2%) (10.0%)

3 M 0.90 K 0.87 0 0.87 Q 0.54 Q 0.71 P -0.76 L 0.51 R -0.83 I -0.52 N -0.68 (44.4%) (22.2%)

4 K 0.80 M 0.87 Q 0.76 L 0.80 0 0.84 K 0.43 I 0.78 N -0.89 R -0.89 J 0.70 P -0.74

(39.3%) (20.5%) (15.0%)

5 Q 0.84 J 0.92 0 0.84 L 0.88 M 0.80 K 0.84 I 0.64 P -0.52 P -0.69 R -0.79 N -0.92

(58.0%) " (19.8%)

TABLE 28a

Principle Components Analysis - Factor Composition for Each Area (Independent Factors) 154

Area Factor 1 Factor 2 Factor 3 Factor 4 No. Variable Weighting Variable. Weighting Variable Weighting Variable Weighting

6 M 0.73 I 0.83 R 0.80 Q 0.72 J 0.77 L 0.65 0 0.63 Q 0.53 K -0.73 P -0.84 L 0.51 N -0.91

(37.8%) (19.6%) (13.4%)

7 Q 0.83 M 0.88 N 0.79 J 0.78 0 0.78 R 0.78 K 0.71 N -0.29 P 0.42 I 0.62 P -0.39 Q -0.21 L 0.48 P -0.36

(38.7%) (15.7%) (12.8%)

8 Q 0.84 L 0.81 I 0.81 M 0.81 J 0.76 J 0.46 0 0.78 K 0.51 R 0.31 K 0.78 Q 0.38 R -0.69 P -0.12 P -0.75 N -0.89 (50.4%) (12.7%) (11.7%)

9 K 0.81 I 0.89 L 0.82 Q 0.68 M 0.75 J 0.82 0 0.65 Q 0.49 R 0.65 N -0.78 0 0.48 P -0.95 N 0.43

(51.9%) (18.3%) (10.2%)

10 0 0.90 K 0.89 R 0.83 M 0.86 Q 0.85 P 0.75 L 0.47 J 0.72 L 0.44 N -0.85 I 0.54 J 0.37 Q -0.29

(37.9%) (18.6%) (14.7%)

TABLE 28b

Principle Components Analysis - Factor Composition for Each Area (Independent Factors ) 155

Area Factor 1 Factor 2 Factor 3 Factor 4 No. Variable Weighting Variable Weighting Variable Weighting Variable Weighting

11 Q 0.91 0 0.77 J 0.86 M 0.76 L 0.85 I 0.74 K 0.79 P -0.71 R -0.59 N -0.93 (58.1%) (14.4%)

12 M 0.87 J 0.80 Q 0.83 I 0.72 0 0.83 L 0.66 K 0.79 N 0.61 N -0.46 P -0.74 R -0.67

(28.1%) (17.1%) (12.9%) (12.1X)

13 0 0.84 J 0.91 R 0.93 L 0.96 M 0.81 I 0.77 P 0.67 Q 0.56 N -0.85 K 0.74 K -0.63 P -0.36 Q -0.63

(41.4%) (17.1%) (12.5%) (10.0%)

14 M 0.91 Q 0.67 0 0.87 I 0.65 L 0.70 K 0.50 K 0.68 J 0.49 P -0.73 P -0.41 N -0.94 R -0.76

(56.2%) (12.6%)

15 M 0.92 J 0.82 I 0.93 L 0.97 0 0.86 Q 0.79 N 0.45 N -0.39 K 0.83 R 0.73 P 0.41 N -0.73 P -0.73 (43.3%) (15.4%) (12.6%) (11.1%)

TABLE 28c

Principle Components Analysis - Factor Composition for Each Area (Independent Factors) 156

structure but it contains other prominent variables pertinent to the area. The other two general factors can be identified in some areas but were intermingled with

other variables. The number of components varied from two to four.

This result corresponds to the initial areal investigation based on the profile of means and standard deviations by area and the local disaggregation that could be achieved. It may be possible to use the stable total data matrix structure on an aggregated basis or if the same structure is confirmed by examination of the means data matrix. Conversely the second model using individual areas may be

able to respond to this areal variation. The initial criticism of Model 1 still pertains in that the measurement of the five factors would pose severe practical problems.

7.5.6 Area Means Matrix Principal Components Analysis,

Without a Spatial Accessibility Index

The area means matrix is listed in Table 9. These average values for each variable over all areas smooth the household variation within areas and provide an area measure commensurate with zoning in transportation modelling. The principal components analysis was carried out with the dependent and independent variables separated, as with the total matrix. The component structure is as follows : 157

Dependent Variables (100; variance explained)

Factor 1 (61.2%) Factor 2 (38.8%)

Variable Weighting Variable Weighting

Exp. at store (D) 0.93 Total shopping freq. (G) 0.99 Freq. at store (F) 0.93 Total shopping hours (C) 0.69 Hours at store (B 0.72 Total shopping expend. (E) 0.27

Independent Variables (84.8% variance explained) Factor 1 (47.1%) Factor 2 (24.7%) Factor 3 (13.0%)

Variable Weighting Variable Weighting Variable Weighting

Freezer Mean Age No. in Ownership(I) 0.92 (N) 0.83 Household(M) 0.92

Income (K) 0.90 SEG (P) 0.77 SD of ages(0) 0.87

No. of Employment Mean Age Lics. (L) 0.89 (Q) -0.61 (N) -0.28

Personal Access (R) 0.85

The dependent variable structure corresponds with the total data matrix structure showing the two shopping components explaining all the variance in the variable means. The independent structure also corresponds with the total data matrix structure except for the presence of personal accessibility in the lifestyle component, mean age in the employment component and the relegation of the household structure component to the third order. Note that the variable J is omitted due to collinearity problems and this has affected the composition of the first component. 158

7.5.7 Area Means Matrix Principal Components Analysis,

with a Spatial Accessibility Index

The combining of area means in one matrix means that

competition is no longer constant within the matrix. The spatial accessibility of each area differs according to the opportunities to bulk-buy with respect to that area. However the data structure shown in the preceding section indicated that the effect of competition did not radically

affect the stability of the data structure. In chapter 4 accessibility was argued as comprising three elements :

a) the personal accessibility of the principal shopper b) the spatial accessibility of the store c) the relative attraction of the store.

The first term has been included in the analysis, the second and third terms have not. It was proposed to use the basic form of the accessibility model :

n A. Si -E ý1. l ,1d..

where Si spatial accessibility to stores 1 to n from households in area i

attractiveness of store j

dij = straight-line distance from i to j

A= calibration factor.

In keeping with the concept of integrating the disaggregate trip generation model with a standard gravity-type distribution model it is proposed to use retail sales area as the measure of attractiveness and 159

straight-line distance as the measure of deterrence. These are also in keeping with the strategic role of the model in development control. If it is assumed that the

maximum driving contour is twenty-five minutes all sixteen large shopping stores in the study area are included. These stores, as identified by the Physical Planning Department, Lothian Regional Council are listed in Table

29. The locations of the stores with respect to the fifteen study sub-areas are shown in Figure 23.

The spatial accessibility indices were computed using a power factor of two for lambda. This has been proved by empirical studies to be the power function best suited to (124) shopping models. These values are listed in Table 30. However values for lambda of one and three will also be computed and compared.

The principal components analysis of, the means data matrix with spatial accessibility index was carried out with the dependent and independent variables separated. The resultant data structure is as follows :

Dependent Variables (100% variance explained)

Factor 1 (47.4%) Factor 2 (31.3%)

Variable Weighting Variable Weighting 0.99 Exp. at store (D) 0.93 Total duration of shopping(C) 0.69 Freq. at store (F)0.93 Total freq. of shopping(G) Duration at Total expend. of 0.27 store(B) 0.73 shopping(E) 160

Independent Variables (79.5% variance explained)

Factor 1 (44.4%) Factor 2 (23.1%) Factor 3 (12.0%)

Variable Weighting Variable Weighting Variable Weighting Freezer No. in Mean Age Ownership(I) 0.92 Household(M) 0.92 (N) 0.81

Income (K) 0.89 SD of ages(0) 0.84 SEG(P) 0.77

No. of Spatial Employment Lics. (L) 0.88 Access(S) 0.60 (Q) -0.61

Personal Mean Age Access (R) 0.85 (N) -0.32

The addition of spatial accessibility has the effect of raising the household structure factor to the second order thereby bringing the means data structure closer to the total data structure. The household structure factor is now a composite measure which includes spatial accessibility. Table 31 shows the variables with which the spatial acessibility index is correlated. Note that the

three large store variables, in addition to total shopping

expenditure, are correlated. Spatial accessibility is intercorrelated with the independent variables of income and household size. This indicates that the spread of bulk-buying opportunities influences the use of large stores and indeed the total shopping budget. This is supported bye the bivariate relationship with income. The positive relationship with household size is a measure of the marketing potential of the stores in that as household size rises the opportunity to bulk-buy rises. It is to be noted that none of these bivariate relationships is particularly strong and the overall effect on the

component structure is to reduce the percentage of the variance explained from 100% to 78.7% for the dependent structure. and from 84.8% to 79.5% for the independent structure. 161

Approximate Floorspace Gross Sales Data Located Ft2 M2 Ft2 M2 Source in Area

Local Centres

Safeway, East Craigs 20,000 1,860 16,000 1,490 1 1 Safeway, East Craigs 26,000 2,420 20,000 1,860 2 1 Safeway, East Craigs - - 19,000 1,770 6 1 Safeway, Jock's Lodge 14,250 1,350 - - 2 4 Safeway, Jock's Lodge 15,600 1,450 - - 10 4 4 Safeway, Jock's Lodge - - 10,000 930 6 Safeway, Davidson's Mains 14,250 1,320 10,330 960 2 2 Safeway, Davidson's Mains 15,800 1,470 - - 10 2 Safeway, Davidson's Mains - - 10,100 940 6 2

Stores Outwith Existing Centres

Asda, Milton Road 71,260 6,630 39,000 3,630 2 6 Asda, Milton Road - - 42,300 3,930 6 6 Asda, Milton Road 69,370 6,450 46,930 4,360 8 6 Asda, Milton Road 73,000 6,800 42,000 3,900 9 6 Trendcentre, Granton 43,500 4,050 30,000 2,790 2 3 Trendcentre, Granton - - 40,000 2,720 6 3 Trendcentre, Granton 40,000 3,700 30,000 2,800 9 3 Trendcentre, Chesser Ave. 45,000 4,190 18,500* 1,720* 2 14 Trendcentre, Chesser Ave. 41,000 3,180 22,000* 2,050 1 14 Trendcentre, Chesser Ave. - - 23,300* 2,070* 6 14 Tesco, Drumdryden Drive 29,000 2,700 18,000 1,670 4 15

* Food Only

TABLE 29

Large Foodstores in Edinburgh District 162

Approximate Floorspace Gross Sales Data Located Ft2 MZ Ft2 MZ Source in Area

City Centres

Templeton, St James Centre 13,000 1,210 9,800 910 1 8 Templeton, St James Centre 15,690 1,460 9,010 840 2 8

Marks & Spencers, Princes St. - - 12,040* 1,120* 3 9 British Home Stores, Princes St. - - 8,640* 800* 3 10 Littlewoods, Princes St. - - 7,120* 600* 3 11

Inner Suburbs

Laws, Nicolson Street 9,800 910 9,400 870 1 7 Fine Fare, Dalry Road -, - 11,000 1,020 1 12 Fine Fare, Dalry Road 18,620 1,730 11,540 1,070 2 12 Fine Fare, Dalry Road -- 11,460 1,070 6 12 Fine Fare, Dalry Road 14,400 1,340 11,500 1,070 5 12

District Centres

Scotmid, Portobello --- -- 5 Presto, Wester Hailes 59,000 5,490 39,000 3,630 2 16 Preston, Wester Halles 59,000 5,500 39,000. 3,600 2 16 Safeway, Morningside 26,110 2,430 18,690 1,740 7 13 Safeway, Morningside -- 20,770 1,930 6 13

TABLE 29 (Contd. ) Large Foodstores in Edinburgh District 163

MAJOR CONVENIENCE STORES IN LOTHIAN

Data Sources

1. Store Managers (January/February 1982) 2. Edinburgh District Floorspace Survey (1976) 3. Personal Inspection (June 1982) 4. Personal Inspection (September 1982) 5. Personal Inspection (October 1982)

6. IGD Large Stores Directory (1982) or Superstore Directory (1982) 7. Planning Application (January 1979)

8. Measurement of plans for reorganisation of Store (May 1982) 9. URPI List of Hypermarkets and Superstores (1982) 10. Montagu Evans & Son 'Shopping Survey Report' (September 1978) 11. New Towns Annual Report 1981.

TABLE 29 (Contd. ) Large Foodstores in Edinburgh District 16ý

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Area No. Si Area No. Si

1 0.4 8 1.8 2 2.0 9 1.1 3 1.5 10 1.3 4 0.5 11 1.6 5 1.6 12 0.8 6 19.6 14 1.2 7 3.2 15 1.2

NOTES:

1) Area 6 is adjacent to a superstore. 2) X-2. Other values of lambda are compared with this value later in the analysis.

TABLE 30 Spatial Accessibility Indices for Individual Areas 166

Variable Pair Pearson Correlation Significance Coefficient Level (x 100%)

S-B (Duration at Store) 0.52 0.02

S-D (Expenditure at Store) 0.44 0.05

S-E (Tot. Shopping Expenditure) 0.51 0.02

S-F (Frequency at store) 0.55 0.01

S-K (Income) 0.46 0.04

S-M (Household Size) 0.51 0.03

TABLE 31

Pearson Correlation Coefficients for Spatial Accessibility and Variables Exhibiting a Significant Relationship 167

If the variables are not separated and a principal components analysis is carried out, the addition of spatial accessibility creates a fifth order component of spatial accessibility, expenditure at store and total

duration and total expenditure of shopping. This component explains 7.5% of the variance in the variables. As is expected spatial accessibility to bulk-buying opportunities does relate to usage of these large stores but is of minor importance in explaining the measurement of trip rate.

7.5.8 Comments on the Principal Components Analysis

The purpose of the principal components analysis was to confirm the factor structure of Model 1 and thereby define a usage factor which could be predicted by independent factors based on household characteristics, i. e. the analysis indicates what factors are being measured by the dependent and independent variables. The analysis did confirm the postulated structure of Model 1 at total data matrix and means data matrix level and the non-homogeneity of variable pairs in some areas did not affect the stability of the structure.

However the analysis of the, individual areas showed that although the dependent structure held true the independent structure was unstable. The general factors were evident in over half the areas but were influenced by dominant local factors such as proximity of a large store and high proportion of retired persons. This can have the effect of increasing, or reducing, the bulk-buying capacity of an area in a way that would not be expected from the level of income in the area. This means that the effects of such external influences as proximity of a large store and internal influences such as a large proportion of retired households affect the stability of the general model. This consequently throws doubt on the ability to 168

generalise a structure over all areas at the househild

level. Both the individual areal profiles and areal data structures indicate that the shopping usage variables can be represented by a bulk-buying term and a total shopping

term. However this usage level is arrived at in different

ways depending on the variable strengths in that area. Each area has its own dominant characteristics that influence the shopping usage patterns of the area. The end result may be the same level of superstore usage but the influencing variable combinations may be different.

Thus this implies that a model based on the three factors of model 1 would be limited in that the general factor structure of Model 1 cannot cope with the within areal variations. As these variations are subdued through aggregation, Model 1 can represent the generalised structure. This is seen in the structure of the total data matrix and the means matrix. Therefore a trip prediction model based on Model 1 would apply at the aggregated level and not at individual area level. This should be evident from the multiple regression analysis.

7.6 Examination of the Strength and Structure of the Relationship between the Dependent and Independent

Variables using Canonical Correlation Analysis

7.6.1 Before developing the trip prediction capabilities of the postulated models it is necessary to examine the strength and structure of the relationship between the dependent and independent variables to see if there is a basis for model development. As has been discussed in chapter 6 canonical correlation analysis is a generalisation of multiple regression analysis which

enables the maximum relationship to be determined between a dependent and independent set of variables. The canonical correlation coefficient indicates the extent of the correlation between the two variable sets when the 169

variables are weighted so as to yield their maximum correlation. As with principal components analysis the canonical variates are independent and uncorrelated.

7.6.2 Canonical Correlation Analysis for Individual Areas

A canonical correlation analysis was carried out for each area using all the dependent and independent variables excepting House Type (H) and Number of Household Cars (J) both of which were causing multi-collinearity problems. Table 32 lists details of the analysis. The first order canonical variates significant at the 5% level are shown, except in areas 1,4,5,12 and 13 where no significant variate was found and the first order variate is listed. The eigenvalues can be multiplied by one hundred to obtain the percentage of the variance explained in the two variable sets. The addition of Number of Household Cars

(J) to area 13 brought the canonical coefficient up to 0.92 and the significance level to 0.03. The variable J

did not produce multi-collinearity in area 13. The addition of J to areas 1,4,5 and 12 did not alter the level of significance.

7.6.3 These individual area results show that a strong

relationship exists between the dependent and independent variables. The total data matrix however, shows only 41% of the variance explained and supports the conclusion, indicated by the principal components analysis, that the household characteristics of an area can predit usage of large stores but do so in different ways depending on the dominant characteristics of the area.

7.6.4 The Structure of the First Order Canonical Variates

The variables, weighted over 0.5, comprising the first order variates are shown in Table 33. There is no consistent structure to either the dependent or 170

Area Eigenvalue Canonical With S Chi-Square D. F. Significance No. Correlation Lambda Level Coefficient

1 0.88 0.94 0.01 59.38 54 0.29 2 0.98 0.99 0.00 73.78 54 0.04 3 0.93 0.96 0.00 76.24 54 0.03 4 0.81 0.90 0.03 58.26 54 0.32 5 0.77 0.88 0.04 52.80 54 0.52 6 0.94 0.97 0.00 78.98 54 0.02 7 0.82 0.91 0.01 80.26 54 0.01 8 0.84 0.92 0.01 88.78 54 0.00 9 0.92 0.96 0.01 81.13 54 0.01 10 0.87 0.94 0.01 81.42 54 0.01 11 1.00 1.00 0.00 89.80 54 0.00 12 0.88 0.94 0.02 70.69 54 0.06 13 0.74 0.86 0.02 71.42 54 0.06 14 0.87 0.93 0.01 72.08 54 0.05 15 0.87 0.93 0.02 73.44 54 0.04 16 0.41 0.64 0.47 271.84 54 0.00 (total)

TABLE 32

Canonical Correlation Analysis Between Variables B, C, D, E, F and G and I, K, L, M, N, 0, P, Q and R for Each Individual Area 171

Area Usage Index Independent Variable No. Structure of Canonical Structure of Canonical Variate Variate

1 1.8F - 0.6E - 1.9B 0.6R - 1.2M 2 1.2D + 1. OB - 0.8C - 1.6F 0.9M + 0.5P - 0.6J - 0.80 3 1- 11E 0.9M + 0.7R - 0.6P 4 1.6D + 1.3G - 0.7E - 1.3F 1.4M + 0.7N - 0.50 0.5R 0.8N 5 1.5G + 1.3D - 0.8C 1. OQ + 0.5P + - 0.70 - (+0.21) 6 1.5E + 1.0C - 2.4G - 2.7D -0.8M - 0.6N - 0.5J 7 1.3E - 0.6G 0.7K - 0.5Q - 0.7N 8 1.6D + 0.9C - 0.9B - 1.8E -1. OM + 0.70 9 1.9D - 1.1B 0.7Q + 0.51 (-0.4P) 10 0.6G + 0.5D - 0.9C 0.8M + 0.7Q + 0.6N - 0.7L 11 0.6D + 0.6F - 0.8B 1. OQ + 0.6J + 0.5K - 0.7P - 1.3L 12 0.5C - 1.2B 0.70 + 0.6K + 0.5N + 0.5P (-0.1L) 13 0.6D - 1. OG - 1.4F 0.93 + 0.5L - 0.80 - 0.8P - 1.2K 14 1.1B - 1.1D 0.6M + 0.5N - 0.5K - 0.8P - 0.9L 15 -0.9E (+0.4D) 0.9Q + 0.7N - 0.70 16 0.8E + 0.5D (-0.3B) 0.6M (-0.04N)

TABLE 33

Structure of First Order Canonical Variates for Individual Areas 172

independent variable sets over the fifteen areas. Groups

of similar areas can be identified, such as areas 2,4 and 13 whose poles are expenditure at the store (D) and frequency at the store (F), but the independent structure is- not the same for these areas. Household structure figures prominently on the independent side of the equation as does shopping expenditure on the dependent side. This is in line with the strength of the bivariate relationships previously examined. The total data matrix shows three significant canonical variates which explain 41%, 10% and 6% respectively of the variance in the variable sets. The structure of these variates does not relate to the individual area structures.

The analysis indicates that a general model to household level will not be possible due to the variation within each area. There is however a relationship between household characteristics and store usage which can be further explored. Also the possibility of combining areas will be explored, as discussed in the previous chapter, using cluster analysis.

7.6.5 Canonical Correlation Analysis using the Means Data

Matrix, with and without the Spatial Accessibility Index

The canonical correlation analysis for the means data matrix, with and without the spatial accessibility index, is shown in Table 34. It was expected, based on the results of the principal components analysis, that a strong canonical correlation would exist between the two sets of variables and this is shown to be true. The effect of adding spatial accessibility is the appearance of a third significant canonical variate.

The structure of the variates is shown in Table 35. The two dependent variates are based on shopping expenditure 173

Eigenvalue Canonical With S Chi-Square D. F. Significance Correlation Lambda Level Coefficient

Without S: 1.00 1.00 0.0 9999.0 60 0.00 1.00 1.00 0.0 9999.0 45 0.00 (0.96 0.98 0.0 32.17 32 0.46)

With S 1.00 1.00 0.0 9999.0 66 0.00 1.00 1.00 0.0 9999.0 50 0.00 1.00 1.00 0.0 9999.0 36 0.00 (0.96 0.98 0.06 25.18 24 0.40)

TABLE 34 Canonical Correlation Analysis for Means Data Matrix

with and without the Variable S 174

Variate Dependent Variables Independent Variables

Without S:

1 0.8D + 0.7B - 0.6E 1.1R + 0.9K + 0.70 - 1.4P - 2.5L

2 1.4G 1. OB - 0.6D - 1.4C 1.9J + 1.81 + 1.2R - 1.1N - 1.2P - 5.4L

With S

1 1. OB (-0.3E) 1.3R + 0.9K + 0.60 - 1.3P - 2.5L

2 1.3C - 0.8B - 1.4G 5.4L + 1.2P + 1.1N - 1.1R - 1.91 - 2. OJ

3 1.6E + 1.4B - 1.3C - 2.6D 1.3L + 0.9R + 0.5Q + 0.5P - 0.60 - 0.9S - 0.91 - 1.1J

TABLE 35

The Structure of the Canonical Correlation Variates with respect to the Means Data Matrix, with and without S 175

and total shopping usage and the independent variates are based on the number of licenses (L), the number of cars (J) and the personal accessibility of the principal shopper (R). The addition of spatial accessibility adds a third variate based on duration of stay at the store (B)

and expenditure at the store (E). These structures reinforce the previous analysis at total data matrix and individual area level that a common usage factor cannot be identified over all the areas at household level.

7.6.6 The Value of the Power Function, Lambda, in the Spatial Accessibility Index

As previously mentioned in the chapter the value of two (124), was assumed for lambda however values of one and

three were examined to investigate their effect. Appendix G shows the results of the investigation. Appendix G-1

shows the principal components analysis for the three

values of lambda. - Only the value of two generates the fifth spatial accessibility component and the structure is stable, for values of two and three. Appendix G-2 shows the canonical correlation coefficients for the three

values of lambda. The value of two shows a third significant variate with a canonical coefficient of one. Appendix G-3 lists the variate structures and shows that a value of three for lambda causes instability. On the basis of these results and the previous work referred to earlier it is proposed that the value of two for the power function lambda is accepted.

7.6.7 The Reduction in Strength of the Overall Canonical Correlation with Aggregation

It has been shown that as areas are aggregated the principal components structure tends to the general structure. However as areas are aggregated the effect of areal variation reduces the strength of the relationship 176

between the dependent and independent variables. Thus

although the canonical correlation for each area is strong the overall canonical correlation for the total data

matrix is 0.64. Table 36 shows the area groupings used to test if the non-homogeneity within areas affected the component data structure. These groupings were subjected to a canonical correlation analysis and the diluting effect of gradual aggregation can be clearly seen. No one area is responsible for the dilution of relationship indeed the addition of areas 1 (Moredun) and 12 (Turnhouse), areas which showed the greatest degree of non-homogeneity, decrease the overall canonical correlation by only 0.02%. This gradual dilution of relationship is expected since the proportional increase in the size of the aggregated group at each stage of the analysis does not exceed 10%. Thus the interpretation of each area describing usage in different ways is supported. The areal variations work against each other to dilute the strong areal relationships. The means data matrix smooths out the variation within areas, at household level, to produce a strong general canonical coefficient.

7.6.8 Canonical Correlation Analysis using only the Three Bulk-Buying Dependent Variables and all Independent Variables

The canonical correlation analysis carried out to date has been based on the six dependent variables. The conceptual framework, subsequently confirmed by the principal components analysis, defined two sets of dependent variables based on the bulk-buying of food and the total food budget of the household. The two proposed models

attempt to relate household characteristics and store

usage and therefore if the three store usage variables can be separately modelled the application becomes easier.

Table 37 shows a canonical correlation analysis using the 177

Areas Sample Eigenvalue Canonical With S Chi-Square D. F. Significance Size Correlation Lambda Level Coefficient

3,5,8, 155 0.57 0.76 0.28 185.53 60 0.00 10,13

+2,14 200 0.48 0.69 0.35 202.30 60 0.00

+6,11 237 0.48 0.70 0.35 240.16 60 0.00

+4,15 290 0.44 0.66 0.42 242.19 60 0.00

+7 317 0.44 0.66 0.43 260.72 60 0.00

+1,12 365 0.42 0.64 0.45 263.03 60 0.00

TABLE 36

Canonical Correlation Coefficients for Groups of Areas

used in the Tests of Homogeneity 178

Area Eigenvalue Canonical With S Chi-Square D. F. Significance No. Correlation Lambda Level Coefficient

1 0.72 0.85 0.09 33.96 30 0.28 2 0.89 0.94 0.04 35.82 30 0.21 3 0.70 0.83 0.11 34.18 27 0.16 4 0.54 0.74 0.28 22.72 30 0.83 5 0.65 0.80 0.22 25.72 30 0.69 6 0.82 0.90 0.06 37.55 30 0.16 7 0.70 0.84 0.13 38.15 30 0.15 8 0.69 0.83 0.10 41.01 30 0.09 9 0.89 0.94 0.04 59.78 30 0.00 10 0.83 0.91 0.06 52.43 30 0.01 11 0.98 0.99 0.00 62.42 30 0.00 12 0.39 0.63 0.35 18.67 30 0.95 13 0.69 0.83 0.12 40.06 30 0.10 14 0.84 0.92 0.08 45.39 30 0.04 15 0.52 0.72 0.27 24.60 30 0.74 16 0.28 0.53 0.66 146.14 30 0.00 (Total)

Means Data 1.00 1.00 0.00 9999.00 60 0.00 Matrix

TOTAL 37

Canonical Correlation Analysis for the Three Superstore Dependent

Variables with Respect to the Independent Variables 179

three store variables duration of stay (B), expenditure (D), and frequency of visit (F) and all the independent variables.

The results show strong correlation coefficients in all areas, except area 12 (Turnhouse) but eleven area relationships are not significant at the 5% level. The total data matrix canonical correlation has dropped from

0.64 to 0.53 but the means data matrix remains strong. Inspection of the canonical variable areal structure, in Table 38, again shows little stability.

7.6.9 Canonical Correlation Analysis using the three Bulk-buying Dependent Variables and three Independent Variables

The level of significance may be improved by reducing the number of independent variables. The independent

variables chosen for each area are the variables, shown by the areal principal components analysis, to be the most dominant in that area. Table 39 shows the result of the analysis. The result of reducing the independent variables means that eight areas are significant at the 5% level. Seven of these areas have strong canonical correlation coefficients the eighth, area 13 (Leith), has a coefficient of 0.63. The overall coefficient has fallen by 0.02% to 0.51 implying that the additional variables do not contribute in a significant way to the equation. The results of the last three stages of the analysis, are shown in Table 40. It shows the reduction of the areal canonical correlation coefficient as variables are removed. Seven areas, areas 3 (Clermiston), 6 (Westburn), 7 (St Peters), 8 (Saughton), 9 (Craigleith Hill), 10

(Pilton), and 11 (Cammo) show consistently strong correlation coefficients but their variate structures show little uniformity. It also shows three groups of areas: 180

Area Usage Index Structure Independent Variable Structure of Canonical Variate of Canonical Variate

1 2.7F -2. OB 0.9R + 0.61 -0.5N -0.5P -0.7Q-1.2M

2 (0.4F + 0.4B) - 1.2D 0.5J - 0.6L

3 1.4D - 0.63 - 1.3F 0.6R + 0.5M - 0.8P - 0.90

4 1.7F-1.3D 0.60-0.6L-0.7P-1.1M

5 LOB - 1.3D -1. IQ - 0.51' - 0.5R (+0.3M)

6 0.9D (-0.4B) 0.6J + 0.5M - 0.51 - 0.5N

7 1.1 D+0.5F - 0.5B 0.8K - 0.5Q - 0.9N

8 2. OB - 0.7D - 0.7F 0.8L-0.6I -1.5Q (+0.30) 9 LOB - 1.4D -0.7Q - 0.51

10 1.1D + 0.7F - 2. OB 0.6M - 0.8L 0.6Q 11 0.6D + 0.6F -0.8B 1.3K-0.5M-0.5N -

12 0.9D + 0.5B (-0.4F) 0.7Q + 0.6R + 0.5J -0.5N -0.7K-0.7L

0.6P 0.5K 0.8Q 13 2.1F - 1.9D 1.00 + 0.6N + + - 1.2P 14 0.9B - 1.8D 0.9+0.8M-0.6L- 0.61 15 0.7F - 1.1B 0.9L - 0.5K - 0. SR -

16 0.6B - 1.4D (0.3N - 0.4K)

TABLE 38

Structure of First-Order Canonical Variates for Individual Areas

using Only Three Superstore Dependent Variables 181

Area Eigenvalue Canonical With S Chi-Square D. F. Significance No. Correlation Lambda Level Coefficient

1 0.34 0.58 0.55 10.53 9 0.32 2 0.41 0.64 0.53 9.31 9 0.41 3 0.61 0.78 0.28 23.27 9 0.01 4 0.40 0.63 0.59 11.32 9 0.25 5 0.38 0.61 0.52 13.47 9 0.14 6 0.64 0.80 0.31 19.21 9 0.02 7 0.57 0.75 0.38 22.06 9 0.01 8 0.58 0.76 0.30 22.55 9 0.00 9 0.82 0.91 0.14 44.31 9 0.00 10 0.66 0.81 0.26 30.25 "9 0.00 11 0.87 0.93 0.03 39.25 9 0.00 12 0.13 0.36 0.81 4.42 9 0.88 13 0.39 0.63 0.43 18.78 9 0.03 14 0.32 0.56 0.65 9.16 9 0.42 15 0.38 0.61 0.55 13.35 9 0.15 16 0.26 0.51 0.70 128.15 9 0.00

TABLE 39

Canonical Correlation Analysis with Respect to the Three Superstore Dependent Variables and Three Selected Independent Variables 182

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i) areas showing a strong correlation with all independent variables and with three independent variables

ii) areas showing a strong correlation with only all the independent variables

iii) area not showing a strong correlation

If the Pearson bivariate correlations are inspected, as listed in Table 41, it is evident that the expenditure dependent variables (D and E) are the variables upon which a strong canonical correlation is based. The lack of correlation in the other dependent variables is because of the small variance in their measurement.

7.6.10 Examination of the Three Groups of Areas Based on the Results of the Canonical Correlation Analysis of Paras. 7.6.8 and 7.6.9

The first group of areas comprise areas 3,6,7,8,9,10, 11 and 13. Area 13 is added because of the strong canonical coefficient in the first two analyses :

i) Area 3- the strong coefficient is based on total shopping variables E and G therefore the coefficient drops

when these are omitted.

ii) Area 7- the expenditure variables E and D are the basis of the relationship.

iii) Area 8- the total expenditure variable E is dominant. 186

iv) Area 9- the expenditure variables E and D are dominant.

v) Area 10 - as for area 9.

vi) Area 11 - the expenditure variables D and E again strong in addition to the

frequency of use of store variable F.

vii) Area 13 - the absence of the store expenditure variable D accounts for the decrease in the coefficient.

The second group-of areas comprises areas 2,12,14 and 15

i) Area 2- expenditure variables D and E are dominant

ii) Area 12 - no relationship with any of the store variables B, D, F.

iii) Area 14 - again B, D and F almost totally absent. iv) Area 15 - as for area 14.

The last group of areas comprises areas 1,4 and 5 i) Area 1- very little relationship shown with the store variables B, D, F. ii) Area 4- no relationship with B, D, F. iii) Area 5- the relationship with the expenditure variables is dominant. 187

From inspection of Table 41 it can be seen that the expenditure variables D and E form the basis of strong areal canonical correlations. In group two the reliance

on the total shopping variables produces a reduction in the coefficient when they are removed. Group three requires all six dependent variables to achieve a strong correlation.

7.6.11 The canonical correlation analysis has established that a strong relationship exists, in each area, between the dependent and independent variables but that the structure of the relationship differs from area to area. The result of this variation is that on aggregation the canonical correlation coefficient is reduced. Thus the conclusion of the principal components analysis is supported by this analysis that a generalised model at household level using either of the proposed models will not produce a satisfactory prediction model.

The means data matrix however, continues to show a strong correlation between the variable sets and an areal model

based on the mean profile of each area is possible.

7.7 Examination of Area Groupings using Cluster Analysis

7.7.1 The third research objective and consequent research task addressed the problem of generality of the model and required the analysis to determine the level of disaggregation possible while still retaining a strong predictive relationship between the dependent and independent variables. The principal components analysis

and canonical correlation analysis showed that the areal variation between households was of such diversity that a general model structure was not possible. The means profiles of each area, however, did provide the basis for a general model but this model relates to average area 188

values. In the context of a strategic control model this is acceptable and compares with the zoning system of a conventional transportation model.

7.7.2 Certain areas did show similarities and before developing the predictive models, using multiple regression analysis, the middle-ground of aggregated area groupings should be examined using cluster analysis. It is apparent, from the canonical correlation analysis, that certain groups-of areas have similar variable poles to their canonical variates. If the dependent variate poles are inspected the following is shown :

All Dependent Variables

Four areas (3,6,8,15) have D-E as opposite poles Three areas (2,4,13) have D-F as opposite poles Three areas (9,11,14) have D-B as opposite poles Two areas (5,10) have C-G as opposite poles One area (7) has E-G as opposite poles One area (12) has B-C as opposite poles One area (1) has B-F as opposite poles

The immediate points of interest to note in these groupings is the continuing dominance of the two shopping expenditure variables D and E and the continuing isolation of area 1 (Moredun) and area 12 (Turnhouse).

If only the three store variables are included the following is shown :

Three Store Variables

Seven areas (5,6,7,9,10,11,14) have D-F as opposite poles Five areas (2,3,4,12,13) have D-B as opposite poles Three areas (1,8,15) have B-F as opposite poles. 189

7.7.3 The area groups using all six dependent variables exhibit common characteristics in that areas 3,6,8 and 15 are predominantly council-rented housing whereas 9,11,

and 14 are private housing. Areas 5 and 10 are areas of social deprivation. This socio-economic form of classification does not show for the three store variables.

7.7.4 A canonical correlation analysis was carried out on the four aggregated groups of areas which relate to the six dependent variables. The correlation coefficients for each group are as follows :

Area's 3, 6,8,15 0.79 Areas 2, 4,13 0.60 Areas 9, 11,14 0.78 Areas 5, 10 0.82

The diluting effect of aggregation is again evident in these results although the 0.79 value for areas 3,6,8 and 15 has remained relatively strong, explaining over 60% of the variance in the variables. The conceptual basis of these groups is however superficial and is not rigorous in that other council-rented areas such as area 10 (Pilton) are not included.

7.7.5 Cluster analysis was used to determine the minimum number of groups for which significance could be achieved at the 5% level. The analysis is based on the formula :

2/p Rcl - Rc2 n- cl c2 / ) F(c1, C2) - 7= -1} Rc2 02 cl

where R- residual sum of squares for clustering cl combination 1. 190

Rc residual sum of squares for clustering 2 combination 2.

(Note :c2> CO

p(n - cl) s numerator ui in F-test

p(n - c2) numerator 112 in F-test (i. e. degrees of freedom)

p number of variables

n= number of areas.

Thus between 1 and 2 clusters :

2/16 1595.41 978.29 15 F(c1i - { -12 C2) 978.29 15 -21 -1}

0.63/1.08 (0.92) -1

= 3.5

III - 16(1) - 16 at 5% level 1.6. which is 112 - 16(15-2) - 208 } significant

Between 2 and 3 clusters:

978.29 697.28 15 -230.125 F(c19 - /{ } Cd s 697.28 15 -3 2) -1

0.40 /1.08 (1.84) -1

0.4 ul - 16(1) - 16 at 5% level 1.6. which is 112 - 16(15-2) - 208 - significant 191

7.7.6 The two significant clusters comprised areas 1,2,3,5, 8,9 and 14 and areas 4,6,7,10,11,12,13 and 15.

Inspection of each group characteristics provides no meaningful classification and only marginally improves the

overall canonical coefficient. Thus although the cluster analysis provides significant clusters at the two cluster level, no meaningful interpretation can be ascribed to these clusters.

7.7.7 A further basis for the grouping of areas can be argued on store usage level, grouping areas of similar usage. This can be achieved using either duration of stay, expenditure or frequency of visit. Tables 42,43 and 44 show the areas in order of duration of stay, expenditure and frequency of visit to stores respectively. These tables show the linear relationship between certain independent variables and store usage and how this

relationship can be affected by a dominant area characteristic. For example four variables are plotted against area number in Figure 24. With each of these variables a linear trend can be seen but, for example area 6 (Westburn) appears to be consistently misplaced. It is an area of low income, freezer ownership and car ownership and yet it has a high store usage. This is because of the proximity of the area to a large store and the large number of walking trips that take place. This is reflected in the spatial accessibility index of 19.6. A further example is in Table 44 and area 9 (Craigleith Hill Avenue). This is in an area of high income and car and freezer ownership, however the low number in the household and the high average age indicates that there is a significant group of retired couples in this area whose food requirement is low. The area is therefore displaced further down the usage table. This exercise can be carried out for all areas and further supports the analysis results to date in that it shows the interplay 192

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between the independent variables. The linear trend however also supports the relationship established by the canonical correlation analysis and indicates that a model based on multiple regression is possible, albeit not based

at household level.

7.7.8 This section of the analysis has found no basis for classification of areas into usage groups. A range of classification criteria can be selected and each yields a different set of groups. For example, if the three store usage variables were added together, unweighted, and grouped on three usage classifications of high, medium and low, a possible grouping would be :

High - areas 6, 11, 14,12 Medium - areas 1, 2, 3,5,13 Low - areas 4, 8, 9,10,7,15

These areas group bear no resemblance to the previously established groups, based on cluster analysis or canonical variates. A canonical correlation analysis was carried out on these groupings and the effect of area aggregation on the correlation coefficient again observed. Table 45

shows the details. There is therefore no satisfactory justification for developing this approach.

7.8 Prediction of the Store Dependent Variables using Multiple Regression Analysis

7.8.1 This section of the analysis concerns the development of a predictive model using multiple regression analysis. The two models identified in the conceptual framework will be analysed to investigate their development into a strategic trip generation model for private car trips to large foodstores. The first model to be examined is Model

2 which is based on the individual variables. The three dependent variables of greatest interest are duration of 197

Areas Canonical Correlation Coefficient

6,11,14 0.77

6,11,14,12 0.72

1,2,3,5 0.68

1,2,3,5,13 0.62

1,2', 3 0.72

4,8,9 0.72

4,8,9,10 0.74

4,8,9,10,7,13 0.68

TABLE 45

Canonical Correlation Analysis for Groups of Areas

Based on an Aggregate Store Usage Index 198

stay at the store (B), expenditure at the store (D) and frequency of visit to the store (F). This model has the

advantage that trip generation, car park capacity and market potential can be modelled separately using frequency (F), duration (B) and expenditure (D) respectively.

7.8.2 Multiple Regression Analysis of the Individual Areas using Model 2

The details of the multiple regression analysis for each area are shown in Appendix H. This shows a step-wise analysis for each of the three dependent store variables

with the independent variables and frequency of use (F) with duration of stay (B) and expenditure (D). The asterisked values are those proved significant using the F-test.

It can be clearly seen from inspection of the appendix that the three dependent variables are strongly correlated

in all areas. It is therefore possible to predict one of these variables from the other two variables at household

level. The prediction of the individual dependent variables from the independent variables shows the areal variation corresponding to that found by the previous stages of the analysis.

The store expenditure variable (D) is the one variable which shows a relationship with the independent variables. Its multiple correlation coefficient varies from 23% to 95% and is significant in ten areas. This again shows the dominance of store expenditure in the dependent/independent relationships.

As expected, the prediction mechanism varies from area to area and does not show either a standard structure or groups of standard structures. Tables 46 to 48 show the 199

Frequency with I-R

Area 1 F - IQ0KRJLNPN

Area 2 F - RI0MLPJK

Area 3 F - 0KRIQMPL

Area 4 F - PMNRL0KQKI

Area 5 F - Q0KJPLM

Area 6 F - MJI0LRKQN

Area 7 F - NQKPLMRI0.

Area 8 F - IRMQPLJ0NK

Area 9 F - NQ IJRMP0L

Area 10 F = LIRJQK0MP

Area 11 F - PLJ0IQRMKN

Area 12 F - MJNKQRL0

Area 13 F - LRN0JKPMIQ

Area 14 F - 0QKMJRILPN

Area 15 F - PJQNI0R

Area 16 F. - KQNMR. IJP0

TABLE 46

Order of Variable Addition in the Multiple Regression Analysis

for F with I to R by Individual Area 200

Duration with I-R

Area 1 B - IQ KJRMNP

Area 2 B - RPJ0MNLQ

Area 3 B - IKROPNQM

Area 4 B - MNIR0PKJ

Area 5 B - KM0RNJQPI

Area 6 B - MI0JKQLRN

Area 7 B - NKQPMLRJ0

Area 8 B - IRLQMP0N

Area 9 B a RQMJ0LNPK I

Area 10 B a LIRQJ0MNP

Area 11 B - KQMRLJ0NP I

Area 12 B - NKJQM0P IL R

Area 13 B - PRLQ0JKNM I

Area 14 B - PKNMRQJK 10

Area 15 B - ILJR0KMNP

Area 16 B - NQKIMR0JL P

TABLE 47

Order of Variable Addition in the Multiple Regression

Analysis for B with I to R by Individual Area 201

Expenditure with I-R

Area 1 D - IQ0KLPJMN

Area 2 D - LR0MJQKP

Area 3 D - K0MRPI QN

Area 4 D - RJIMNKPL0

Area 5 D - QPLRIMNJK0

Area 6 D - MJIKRQL0NP

Area 7 D - KNRQI POMJL

Area 8 D - 0RIPJKQMN

Area 9 D - MINJL0RP

Area 10 D - 0KLJIRP

Area 11 D - JP0QRLKNIM

Area 12 D - JNMQKLRIP

Area 13 D - LRPN0QMJKI

Area 14 D - 0PKMNLJIQR

Area 15 D - LKJPIMQR0

Area 16 D - KMINRPLJ0

TABLE 48

Order of Variable Addition in the Multiple Regression

Analysis for D with I to R for Individual Areas 202

order of variables chosen in each area by the step-wise regression for each of the three dependent variables. The

standardised residual plots and the observed and predicted dependent variable values for each area are shown in

Appendix I. An analysis of the residuals is carried out later in this section of the analysis.

7.8.3 Interpretation of the Multiple'Regression Analysis at Individual Area Level with respect to the Pearson Correlation Matrices

The areal variation that has been observed at each stage of the analysis can be understood from inspection of the Pearson correlation matrices shown, for each area, in Tables 49 to 64. These tables show correlation coefficients greater than or equal to 0.4 and significant at the 5% level.

The upper-right quartile of the tables shows the number and strength of the relationships between the dependent and independent variables. A strong multiple correlation coefficient can be achieved either by a number of correlated variables of medium strength or by one or two strongly correlated variables. For example in area 2 (Spottiswoode) store expenditure (D) has a correlation coefficient of 0.78,61% of its variation being explained by the independent variables. Table 50 shows that this relationship is achieved from six moderately correlated independent variables.

The areas that have consistently shown a poor relationship between the two variable sets and those areas exhibiting a strong relationship can also be seen from the tables. Areas 1 (Moredun) and 12 (Turnhouse) show virtually no significant correlations between the store variables B, D and F and the independent variables, whereas areas 9 (Craigleith Hill) and 11 (Cammo) show strong correlations 203

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based on the shopping expenditure variables. This is generally true, and it is suppported by the canonical correlation analysis, in that the presence of a strong relationship between dependent and independent variables

has its basis in the expenditure variables D and E.

The correlations within the dependent and independent variables are generally strong and will tend to reduce the correlation between dependent and independent variables. The number of variables also influences the correlation coefficient and this is adjusted to yield a more conservative estimate. The method of adjustment is based on the formula :

r2 in the population error variance in Y in the population total variance in Y in the population

The adjusted r2 formula uses unbiased estimates of the error variance and the total variance in the population. The formula used in the SPSS computer package is

Adjusted ) (1 r2) r2 - r2 -( N---k -

where k- number of independent variables in the regression equation.

N- number of cases.

In Appendix H the adjusted r2 values have not been written into the standard output since the individual area model shows a low predictive capability.

7.8.4 Examination of Residuals

These comments on the examination of the residuals apply to the preceding multiple regression analysis and to the 220

subsequent multiple regression analysis. The residuals list for each output was checked for any regular pattern of positive and negative runs. They were also checked for any regular pattern of scatter about the mean and their randomness was checked using the Durban-Watson test statistic and the plotting of the residual values on probability paper to confirm their linearity. These checks confirm that no abnormality exists in the residuals.

7.8.5 Multiple Regression Analysis with respect to the Total Data Matrix

It is not expected that a predictive model, using Model 2 and the total data matrix, will provide a satisfactory prediction capability. The overall canonical correlation coefficient is low and inspection of Table 64 shows that only income (K) is correlated, at a low level of 0.4, with store expenditure (D), with the duration and frequency variables uncorrelated at, or above, the 0.4 level. This is confirmed by inspection of Table 65 and Appendix 1-16 where the r2 values of frequency (F), duration of stay (B) and expenditure (D) were 8%, 4% and 18% respectively. If the analysis is carried out for the total shopping expenditure (E) the equation explains 38% of the variance in the dependent variable. The interest in the difference between bulk-buying expenditure (D) and total shopping expenditure (E) relates to the elimination of the zero expenditure values found in D. The presence and effect of zero values in general is discussed in the next chapter on the interpretation of the analysis. The results of the multiple regression analysis based on total shopping expenditure are shown in Table 66 and Appendix J.

A further analysis was carried out using a relative household expenditure term, T, defined as the total shopping budget (E) minus the bulk-buying budget (D). 221

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This was done to equalise the proportion of total shopping budget spent at large foodstores. However, the results of the analysis showed that only 12% of the variance in T

was predicted by the independent variables. The results

of the analysis are shown in Appendix K.

7.8.6 Multiple Regression Analysis with respect to the Means Data Matrix, with and without the Spatial Accessibility Index

The means data matrix has been shown to form the basis of a predictive model at mean area level. It is expected that the development of a model based on multiple regression analysis will support this view. The analysis

was carried out in three stages :

i) with all the independent variables ii) with income (K) and freezer ownership (I) omitted iii) with personal accessibility (R), income (K) and freezer ownership (I) omitted.

The reason for this is that the three variables mentioned

in stages (ii) and (iii) are not contained within census data and as part of the research objectives require a strategic model based on census data these have been removed to see if their omission impairs the model significantly. The results of the analysis are shown in Appendix L. The adjusted r2 values have been added to the output listing.

Omitting all three variables the highest adjusted multiple

correlation coefficients, which are significant at the 5% level are:

i Frequency of visit (F) - 0.75 (adjusted r2 - 43%) Duration of stay (B), - 0.71 (adjusted r2 - 37%) 2 Expenditure per visit (D) - 0.80 (adjusted r 50%) 227

If personal accessibility (R) is included the values become :

Frequency of visit (F) 0.92 (adjusted r2 . 65%) Duration of stay (B) 0.96 (adjusted r2 . 86%) 2 Expenditure per visit (D) - 0.87 (adjusted r. 62%)

The values with all independent variables included are :

Frequency of visit (F) - 0.98 (adjusted r2 - 83%) Duration of stay (B) - 0.97 (adjusted r2 - 85%) Expenditure per visit (D) - 0.96 (adjusted r2 - 77%)

These figures show a good prediction of the three individual dependent variables. The store expenditure variable requires income (K) and freezer ownership (I) to be included to achieve a level of 77% prediction. The

duration of stay variable (B), important for car parking capacity, requires the addition of personal accessibility (R), raising the level of variance in B explained from 37% to 86%. The frequency of visit variable (F), important

for trip prediction, requires all the independent variables to achieve an 83% prediction. Thus each of the store dependent variables can be predicted using household characteristics. The application and stability of these

multiple regression equations will be discussed in the following chapter. Note that spatial accessibility (S) becomes the most important variable for frequency of use when personal accessibility (R) and income (K) are removed.

7.8.7 Multiple Regression Analysis with Respect to Model 1

The results of the analysis relating to Model 1 have supported the conceptual theory of three independent factors of influence on two store usage factors. However 228

the model based on this structure has shown no stability between areas and the analysis has indicated that the predictive capability of a factor-based model will be

poor. To investigate this further the multiple regression

analysis was carried out in four stages :

i) to define the usage index as a linear combination of all six dependent variables and to correlate this with the individual dependent variables by area.

ii) to define the usage index as a linear combination of the three store usage variables relating this to the principal components structure, using weighted and unweighted variables, for the total data matrix. This will test the effect of the component

weightings on the multiple-regression coefficient.

iii) as in (ii) but applied at individual area level.

iv) as in (ii) but applied to the means data matrix.

The results of stage (i) of the analysis are shown in

Appendix M. In seven areas the grouped usage index had a

lower correlation coefficient than the Model 2 structure. In the remaining eight areas the coefficients were of the same order of magnitude. There is therefore no advantage in this form of model. This is further exacerbated by measurement difficulties associated with the usage index. As with the preceding stages of the analysis there is no uniform variable structure between areas.

The second stage of the analysis applied to the total data matrix using the weighted and unweighted principal components structure. The results of the analysis are shown in Appendix N. Although the coefficient is greater than for the individual usage variables It is very low at

0.36 unweighted and 0.37 weighted. Thus only 12% and 13% 229

of the variance in the usage index is predicted with the component weighting making no appreciable difference to the prediction.

The third stage of the analysis related to the individual

areas using the model of stage (ii). The analysis was carried out using the total data matrix component weightings and with area component weightings. This was applied to only the first seven areas as it was not expected to yield high prediction levels. The results are shown in Appendix 0. These results show the same pattern as stages (i) and (ii) in that the correlation coefficient in all seven areas is lower than other forms of model and the component weightings make no significant difference to the prediction.

The final stage of this section of the analysis relates to

the means data matrix, with and without spatial accessibility. The results of the analysis are shown in

Appendix P. The model does not perform as well as the individual usage variables of Model 2 and the addition of the spatial accessibility variable marginally decreases

the value of the multiple correlation coefficient. In keeping with the results from the principal components analysis and the canonical correlation analysis Model 1 does not provide a basis for a trip generation model of private-car trips to large foodstores.

7.8.8 The multiple regression analysis examined the predictive capabilities of the two proposed models. Model 2, based on individual variables, showed the same areal instability as the previous stages of the analysis and, with the

exception of store expenditure, did not provide high

levels of prediction for the store usage variables. As the areal canonical correlation between the dependent and independent variables is consistently high this indicates that to achieve this relationship all the store variables 230

are required. Thus when separated the relationships, as seen by the Pearson correlation matrices, are not strong enough to produce high multiple correlations. The model based on the total data matrix explained 40% of the variance in the dependent variables.

This is a creditable performance when one considers that the model is attempting to predict the store usage of each household but is not satisfactory with respect to a trip prediction model for development control. As with the other sections of the analysis the means data matrix provided a basis for good prediction of the three dependent store variables. The structure and stability of these equations will be discussed in the following

chapter.

The-analysis based on Model 1, using the principal components structure, did not provide any advantage over Model 2 and in most cases yielded lower prediction levels. It is proposed therefore to develop the disaggregate trip generation model on Model 2 using the means data matrix. This is complementary to current transport modelling techniques based on transport zones, the difference being

these equations are based at a more disaggregate level and have been developed from the household characteristics of

an area. These characteristics are largely census based and can adapt to the changing land-use patterns of an area without recourse to further major household surveys.

7.9 Concluding Remarks

The analysis of the data has been carried out in three

sections : the production of the Pearson correlation

matrices and the principal components analysis to establish the form of Model 1, the canonical correlation analysis to establish the strength and structure of the relationship between the dependent and independent 231

variables and the multiple regression analysis to develop the predictive model.

The principal components analysis identified a stable data structure at an aggregated level but did not find that this structure was maintained at area level. The canonical correlation analysis established that a strong relationship existed between the two variable sets at area level but that this relationship required all the dependent variables to be present. As the areas were aggregated the canonical relationship diminished, in other words the independent variables were defining store usage with respect to the character of the area and as areas were added together these strong relationships were cancelling each other out because of the areal variation in bivariate relationships. Thus as areas were added the canonical coefficient declined in proportion to the area sample size.

The area means data smoothed out the areal variation and between enabled strong relationships to be established the individual dependent variables and the independent variables. This was-developed in the multiple regression analysis to provide three equations, for the three store This variables, with an 80Z-90Z level of prediction. model cannot be improved upon at individual area or total data matrix level or by using the principal components structure.

The development and application of this model will be considered in the following chapter. 232

CHAPTER 8

INTERPRETATION AND DISCUSSION OF THE FINDINGS

8.1 Introduction

This chapter gives an overview of the analysis and interprets the results with respect to the research objectives. The interpretation specifically addresses the problem of within and between area variations. The interpretation is then discussed with specific reference to the areal variation, level of disaggregation achieved and stability of the model. The chapter concludes with a discussion of the application of the three proposed sub-models and their interface with the distribution

model. The development and application of the combined model to development control procedures is also discussed. 233

8.2 An Overview of the Analysis

8.2.1 The conceptual framework of the thesis postulated that private-car trips to large foodstores could be split into two parts comprising the generation of trips and the

distribution of trips to the stores in the study area. The research objectives sought to identify this

disaggregate generation model, find the level of disaggregation able to satisfy the prediction accuracy

required and explain the model's interaction with an aggregate distribution model. The generation model could not be defined a priori and two conceptual models were proposed. These models were based on the belief that the

choice mechanism of the consumer could be modelled from observed behaviour which in turn could be related to

national census data on each household.

8.2.2 The inspection of the Pearson bivariate correlation matrix confirmed that the six dependent, shopping variables were inter-related and many of the independent variables were inter-related especially, as expected, the three household structure variables and income, employment and SEG. The inter-relationships between the dependent and independent variables were based on the two shopping expenditure variables, D and E. The areal variation, which has been mentioned at each stage of the analysis, could be seen at this preliminary stage where area 1 (Moredun) and area 12 (Turnhouse) showed significant variation in the strength of the bivariate correlations.

8.2.3 The principal components analysis examined model 1, based on five composite variable factors. The analysis sought to confirm this model structure so that a store usage index could be identified. However, although the aggregated data supported the conceptual structure the model broke down at individual area level. The model based on the aggregated data explained 80% of the 234

variation within the dependent variables and 64% within independent the variables. The structure based on the data means matrix explained 100% of the variation within the dependent variables and 85% within the independent

variables. The addition of a spatial accessibility index did not add to the level of explanation significantly. The above figures highlight the smoothing effect of the means data matrix, - which shall be discussed in detail later in the chapter.

8.2.4 The canonical correlation analysis sought to establish the strength and structure of the relationship between the

dependent and independent variables. The analysis showed that in all areas the strength of relationship was high, around 80% explanation, but when the areas were aggregated the correlation coefficient fell to 0.64,41% explanation, for the total data matrix. This supported the conclusion of the principal components analysis in that each area achieved the strong canonical correlation using a different model structure. The canonical correlation declined in proportion to a rising sample size. This means that as the within-area variation increases the

percentage variance explained decreases.

8.2.5 The 0.64 coefficient was achieved using all the dependent variables and all the independent variables. If the three store variables were used with all the independent variables the coefficient fell to 0.53, however if only three of the independent variables were used the coefficient only fell by 0.02 to 0.51. The three independent variables were chosen relative to the

canonical-weightings for each area and therefore differed

from area to area.

8.2.6 The multiple regression analysis showed that a general model could be achieved using the means data matrix but because of the areal variation a general model could not 235

be applied within each area at household level. As a consequence of this areal variation the total data matrix did not provide a strong predictive model. The means data matrix, without income (K), freezer ownership (I) and personal accessibility (R), attained multiple correlation

coefficients of 0.75,0.69 and 0.80 for frequency of visit (F), duration of stay (B) and expenditure per visit (D) respectively but the adjusted r2 values fell to 0.43,0.39 and 0.50. The addition of personal accessibility (R) raised the adjusted values to 0.65,0.86 and 0.62 and the further addition of income (K) and freezer ownership (I) raised the values to 0.83,0.79 and 0.77.

8.3 Interpretation of the results

8.3.1 The canonical correlation showed that the postulated

theory, that a relationship exists between shopping usage and household characteristics, is correct. It further showed that the relationship is strong, with an 80% explanation of the dependent variables. However the way in which this explanation is achieved varies from area to area. Tables 49 to 64 show the Pearson bivariate correlations by area. It can be clearly seen, by inspection of the upper-right quartile, that the prediction of store usage depends on the shopping expenditure variables and their relationship to the independent variables. These tables show the areal variation and why certain areas, such as area 1 (Moredun), do not produce a good relationship between the two sets of variables. They do not explain, however, why this areal variation exists.

8.3.2 A low correlation between a variable pair can be attributed to one of three reasons :

a) there is no relationship b) the variable variance is low 236

c) the relationship is not linear.

The third reason can be discounted as this was investigated, using scatter diagrams, in the initial

stages of the analysis. The first two reasons require further discussion.

Table 10 shows the standard deviations for the two sets of variables. If two extreme areas, i. e. an area which gives a poor relationship and an area which gives good relationship between the two variable sets, such as area 1 (Moredun) and area 11 (Cammo), are inspected the standard deviation of key independent variables income (K), number

in the household (M), mean age of household (N) and employment (Q) show a consistently lower value in area 1 than in area 11. This lack of dispersion is also evident in area 12 (Turnhouse), another' area which has

consistently shown a poor predictive quality. This lack of variance can be explained by the characterisitics of these areas. For example, in area 1 there is a high proportion of retired couples whose income level will be relatively uniform, given the quality of the area. Therefore' although it is an area of private housing the store usage level is low due to the low demand of this group and the lack of large store facilities within walking distance of the area.

8.3.3 The general situation is shown graphically in the sketch below. In certain areas the values of the independent variable are clustered between a narrow band on the x-axis (between xl and x2). This affects the ability of the

regression model to predict an accurate relationship. If there is a larger variation in the independent variable then there is a greater spread, and hence the standard deviation of the variable is increased. If this is achieved within a sample of equivalent size then the regression correlation coefficient of that sample will 237

compare favourable with the former (between x3 and x4). If, however, to achieve the increased standard deviation the sample size is increased then there will be the

negative effect of increased spread in the y-axis due to

the normally expected variation between households (from y1. Y2 to y3, y4).

Dependent Variable

- line Y, -- - Regression

o Y. ýw w

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rý,

xi, X3 X Xx Xti X.

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x Values with small standard deviation within x,, x, larger deviation Equivalent size of sample but with within x,, x, o Increased sample within x,, x..

8.3.4 This problem is not related to an optimisation of sample, or zone size, but rather a symptom of the variation between households and between the areas of households. The variation, in this case, is the variation from in the standard deviation of a particular variable is that the area to area. The implication of this be the developed trip generation model should applied at household data can most disaggregate level at which census district level. be extracted i. e. at the ennumeration as least-squares these areas are aggregated then the increased difference will increase because of the spread 238

around the regression line and the correlation coefficient will fall.

8.3.5 Thus the smallest practical zone size is the

ennumeration district, approximately 120 households. An analysis at this level would use the variable means within each district. The means data matrix giving a consistently accurate prediction capability. This is not to imply that the overall accuracy of the model has increased but that by taking the variable means values

within each area the areal variability has been reduced. This will yield a zonal base, rather than a household

base, which is required for the trip distribution model at a later stage. The variation within and between areas is explained in the sketch below.

Individual Area Mean

Variation - -` ------ariation ` in variable in variables in total Total data in mean data matrix mean Variation data matrix isvariables_ in area matrix

larger The diagram represents the areal variation and the

variation in the total data matrix. When the means of is area variables are taken the overall variation much less but the variance between means is sufficient to yield

a high predictive correlation.

8.3.6 From the foregoing it is clear that a predictive model data However the can be based on the means matrix. removed stability of the variable order, as areas are 239

one-by-one from the means data matrix, is subject to fluctuations. The strengths of the F-test values for each stage of the step-wise regression analysis were inspected

to identify a reduced number of variables which could predict the store usage variables to an acceptable level

and which would remain stable as areas were subtracted. This identified the following groupings :

Frequency of use (F) - Income (K), Mean age of household (N), Number of Licences (L), Personal accessibility (R).

Expenditure per

Visit (D) Income (K), Mean age of household (N), Number of licenses (L), Number in the household (M),

Personal accessibility'(R).

Duration of Stay (B) SEC (P), Standard deviation of household ages (0), spatial accessibility (S), personal accessibility (R).

These groupings were stable as each area was subtracted from the means data matrix and the adjusted r2 values for F, D and B are 72%, 70% and 42% respectively. It should be noted that, as indicated in the previous chapter, the adjusted r2 takes account of the number of variables and the sample size and the reduced values shown above reflect the small sample size. The unadjusted r2 values for F, D and B are 80%, 80% and 58%. Higher levels of prediction

can be achieved but the variable structure is not stable

as areas are removed.

8.3.7 The variables included, however, require justification

and an analysis was carried out removing variables one-by-one from each equation to examine the effect on the 240

multiple regression correlation coefficient. The results are as follows:

For frequency of use :

1. a) F- 1.000 + 0.346K - 0.027N + 0.119R - 1.117L r2 = 80%; adjusted r2 = 72% b) F 0.573 + 0.043K - 0.015N + 0.061R r2 = 59%; adjusted r2 - 48% c) F 0.875 + 0.119K - 0.01ON r2 - 47%; adjusted r2 = 38% d) F 0.416 + 0.131K r2"= 32%; adjusted r2 = 27%

For expenditure at store :

6.055K 2. a) D- 13.121 - 15.315L - 0.459N + + 1.821R + 2.272M r2 = 80%; adjusted r2 = 70% b) D 21.222 - 14.758L - 0.504N + 6.170K + 1.768R

r2 = 76%; adjusted r2 - 66% 3.269L 0.290N + c) D- "22.668 - - 4.510K r2 = 65%; adjusted r2 = 55% d) D 17.042 - 8.698L - 0.221N r2 - 57%; adjusted r2 = 50%

For duration of stay at the store

0.0050 3. a) B- 0.810 - 0.026P + 0.001S + 0.027R + 42% r2 - 58%; adjusted r2 - 0.029R b) B- 0.834 - 0.027P + 0.001S + 46% r2 - 58%; adjusted r2 - 0.002S c) B- 1.251 - 0.033P + r2 - 48%; adjusted r2 s 39% d) B- 1.416 - 0.040P 241

r2 = 34%; adjusted r2 = 29%

8.3.8 From the above the three equations which yield the

highest levels of prediction of the independent variables and are stable are as follows:

F-1.0 + 0.346K - 0.027N + 0.119R - 1.117L B-0.81 - 0.026P + 0.0015 + 0.027R D- 13.1 - 15.315L - 0.459N + 6.055K + 1.821R + 2.272M

where

F- Frequency of visit to store/week/household B= Duration of stay at store/week/household D= Expenditure at store(s)/week/household K Income/household

L- Number of licenses in the household M= Number in the household

N- Mean age of the household P= SEG of head of household R- Personal accessibility of principal shopper S- Spatial accessibility of household.

These are the variables and coefficients of major predictive importance relative to the usage of large foodstores. This means that as household income and personal accessibility rise the frequency of use of the store increases and as the mean age drops frequency of use increases. The latter relationship indicates the presence of children in a household and the increased food demand of a family with young children and teenagers. The

inverse relationship between the number of licenses and

frequency of use relates to two factors. It is evident, from the experience of working with the data, that households with a high number of licenses tend to be low-income, large families with several people working in

low SEG employment. This is supported by the Pearson 9

242

in number the household rises and SEG falls the number of licenses increases. In addition there is no correlation between the personal accessibility of the shopper and the number of licenses. This was discussed in the previous

chapter on accessibility when it was seen that the household reallocates the availability of the household car, or cars, to carry out a major shopping. Thus it is expected that as the number of licenses in the household

is associated with households whose frequency of use will be lower, the relationship with frequency of use will be inversely proportional.

The expenditure at the store rises with income, family size and personal accessibility. The negative correlation with the number of licenses has been discussed above. As

the mean age of the household falls expenditure rises. The strong positive relationship between the number in the

household and the mean age of the household supports the conclusion that larger families have a greater food demand

and will generally spend more, all other things being equal.

For the duration of stay equation the time spent at the store rises as the accessibility of the store and shopper rises and as the SEC of the household rises. There is a positive correlation between SEC and personal accessibility but a negative correlation between expenditure at the store and SEC. Thus although households with a lower income and frequency use of the store shop longer, they spend less. It is expected that if frequency of use declines duration of stay will increase. This can be accompanied by increased expenditure if the household budget is high but in this

case indicates more careful budgeting of scarce resources.

The increase in the adjusted r2 when the standard deviation of household ages (0) is omitted, and the fact 243

that the unadjusted r2 does not decrease, indicates that this variable does not significantly contribute to the model and should not be included.

8.3.9 If these models are applied to the fifteen means data profiles the prediction of the three store usage variables is high. Table 67 lists the results of the analysis. These results per household must be multiplied by the number of households in the ennumeration district to predict the weekly number of trips, duration of stay and expenditure emanating from the area to large foodstores.

8.4 Discussion of the Results

8.4.1 The research objectives seek to identify a disaggregate trip generation model that can be combined with an aggregate distribution model to provide a strategic model for the development control of large foodstores based on the household characteristics of an area. The application of this model will be eased if these characteristics are based on census data, thereby eliminating the need for

expensive shopping surveys. The level of disaggregation possible with respect to the model was also to be investigated. At best this would accurately model each household, however this extreme level of disaggregation

did not prove satisfactory due to the variation between areas and households. The model developed does however achieve a level of disaggregation acceptable within the objectives of the thesis by basing the model at ennumeration district level, which is the most disaggregate level for census date information.

8.4.2 It was the postulation of the thesis that the choice mechanism of bulk-purchase shopping could be split into a generation stage, where the household characteristics determine the necessity to use these stores, and the decides distribution stage, where the household where to 244

Actual Usage Values Predicted Usage Values

Frequency Duration Expenditure/ Frequency Duration Expenditure/ Area of use of stay Visit of use of stay Visit No. (F) (B) (0) (F) (B) (D) (trip) (hrs) (£) (trip) (hrs) (£)

1 0.7 0.9 17.9 0.7 0.9 17.7

2 0.6 0.9 17.0 0.8 0.9 17.3

3 0.7 1.0 15.7 0.6 0.9 13.5

4 0.9 1.1 21.0 0.9 1.1 22.7

5 0.7 0.9 13.9 0.6 0.8 13.6

6 1.1 1.0 28.2 1.0 0.9 24.2

7 0.8 0.9 20.8 0.9 0.9 20.8

8 0.9 1.0 21.6 0.8 0.9 20.3

9 0.7 0.9 18.7 0.8 0.9 19.9

10 0.6 0.8 18.5 0.6 0.8 17.6

11 1.1 1.1 26.3 1.0 1.1 26.1

12 1.1 1.3. 28.1 1.1 1.1 26.1 . 13 0.8 1.1 16.7 0.8 0.9 20.8

14 1.0 1.1 25.8 1.1 1.0 25.4

15 1.1 1.2 20.1 1.1 0.9 23.4

TABLE 67

Large The Prediction of Frequency of Use, Duration of Stay and Expenditure at

Model Foodstores in the Survey Areas Using the Developed Trip Generation 245

shop. The analysis supports this postulation and the final trip frequency model does not contain the spatial accessibility variable. It is included in the duration of stay model as there is a relationship between the distance

between a household and a store and the time spent at the store.

8.4.3 Having split the choice process into two parts it was necessary to find the strength and structure of the relationship between the two sets of variables. The canonical correlation showed that all six dependent variables were necessary to obtain areal canonical coefficients of around 0.9 but the structure was not stable between areas. Two levels of variable variation exist; the within-area variation between households and the between-area variation. Neither of the two models postulated could predict the aggregated effect of the two levels of variation at household level but if the means of the variables were taken for each area a model, based on Model 2, could be developed satisfactorily. This model has the advantage over Model 1 in that it separates frequency of trips, duration of stay and expenditure per

visit into three multiple regression models.

8.4.4 The issue of accuracy of representation of the sample size requires discussion. Finance dictated a sample size of four hundred households and the sampling framework was designed around this constraint. The fifteen areas were randomly chosen and provide a well-distributed coverage of the city. It must therefore be assumed that if another fifteen areas were randomly chosen the same relationships would be found. Although competition was kept constant,

the effect of competing stores on the trip generation of households was found to be insignificant. This may not be true in an area with no large store if a store was then introduced and these results must be taken in the context of an accessible, existing bulk-buying trading pattern. 246

The proven value of the developed model is in building an accurate mathematical representation of an existing area, which can be updated as census data is updated, thereby providing a base against which the effect of a new store can be measured.

8.4.5 One of the reasons for the inability of either proposed model to cope with the areal variations at household level is the presence of car-owning households who never bulk-buy. The tables of raw data, Appendix E, show that the worst cases are area 10 (Pilton), where 37% of households do not bulk-buy, area 3 (Clermiston), with 29%,

and area 1 (Moredun), with 32%. The multiple regression models do not anticipate zero usage and therefore the correlation coefficient is reduced. However a comparison of the store variables and the total shopping variables, which contain no zero values, show that the presence of

zero values did not make a major impact on the results of the analysis. The zero values must be included otherwise the sample would be biased and the means data matrix overcomes their presence by smoothing out the variation. from The effect is an average' trip prediction an area, which is the desired result- from the trip generation model. It is recommended that the effect of the zero values be further investigated so that the model prediction within areas may be improved thereby developing

a greater understanding of the consumer choice mechanism at household level.

8.4.6 Thus the three multiple regression models developed

provide a set of sub-models that address three specific large tasks. The estimation of private-car trips to based foodstores, the design of car parking capacity on

duration of stay and the assessment of market potential from the expenditure model. The first two predict the trip rate and duration per week. Further work requires to

be carried out to enable arrival rate throughout the week 247

be to estimated. The raw data contains information on time and day of each shopping trip but a further detailed

research programme will be required to build a daily, or hourly, sub-model within the proposed set of sub-models.

The present trip estimation can be proportioned over the week based on existing flows to stores in the city and the shopping diaries.

8.5 Concluding Remarks on the Development of the Trip Generation Model

8.5.1 This section, following an overview of the analysis of the two- conceptual models, has primarily addressed the problems of variance within the model, the level of

disaggregation of the model and model stability. These problems have been discussed with respect to the sample

size and sampling framework of the thesis. The variance of the sample comprises the within-area variance and the

between-area variance. The aggregated effect of the two kinds of variance make it impossible to predict private-car store usage at the individual household level to an acceptable degree of accuracy. This can be achieved

within each area using a different model structure but this lack of stability makes it unsuitable for strategic planning.

8.5.2 If however, the areal variation is smoothed, by taking the mean of each variable, a strategic model for all areas can be developed. The areal variation is caused by two effects. Firstly a lack of spread in the major variables in certain areas due to uniformity of population groups.

For example, in area 1 (Moredun) the uniformity of income due to the high proportion of retired couples. Secondly the variation due to the randomness of human behaviour.

Whenever a model seeks to predict human response an element of human individuality will always be present. Many factors impinge on the lifestyle of a household 248

making it difficult to predict all permutations with respect to strategic control. Indeed the two are, by definition,, ýlargely opposites. An example of this, from the survey, was a high-income household with children and

good access to large stores who did not use bulk-buying stores because of a particular vegetarian/whole-food philosophy to life.

There are many such cases each differing in detail but causing variation in the model. The effect of these two factors cause the areal variation. A third factor which could cause the same effect is the omission of critical

variables. However in each area the canonical correlation coefficient indicated that the independent variables

explained around 90% of the variance in the dependent variables thereby indicating that this was not the case.

8.5.3 The level of disaggregation of the model was examined using the area data, total data and means data matrices. As indicated above the total variation within and between areas produced a low multiple regression correlation coefficient for the total data matrix. The areal

variation produced a patchy- response in the predictive models even though the structure of the models changed to accommodate the variation. The means data matrix however, provided three models that accurately predicted the three store variables of frequency, duration of stay and expenditure. Thus it is possible to produce a predictive model disaggregated to areas of twenty-seven households. The most disaggregate level at which census data can be abstracted is the ennumeration district. This represents

an aggregation of the model from groups of twenty-seven households to around one hundred and twenty households.

This also- gives the zoning base from which all transportation and physical planning zones are aggregated. It is, therefore, unviersal in its application. This does not represent an increase in accuracy from the household 249

level but a practical model base from which a development control model can be built. The result of the aggregation

to area means level is an excellent model prediction of

shopping trips emanating from the area.

8.5.4 The stability of the three models was achieved using a reduced set of variables based on the F-test of significance. The models are as follows :

Frequency of use, of storef household/

week (F) - 1.0 + 0.346K - 0.027N + 0.119R - 1.117L

Duration of stay at store/ week household/ (B) s 0.81 - 0.026P + 0.001S + 0.027R

Expenditure at store/ household/ 1.821R week (D) 13.1 - 15.315L - 0.459N + 6.055K + + 2.272M

where

K= income of the household L= number of licenses in the household M= number in the household

N mean age of the household P SEG of head of household R= personal accessibility of principal shopper S= spatial accessibility of household to stores. 250

The structure of these models has been discussed earlier in this chapter and they support the postulated theory of the thesis that the bulk-buying shopping behaviour of a

car-owning household can be thought of as a two-stage process. Firstly the characteristics of the household

determine the decision to use a large foodstore then the household looks at the alternatives available and chooses a store. Thus the process is split into a trip generation stage and a trip distribution stage. The objective of this thesis is to produce the generation model at a disaggregate level, based on household characteristics.

The above set of models achieves this objective.

8.5.5 Thus data is abstracted from each enumeration district (ED) in the defined study area for each variable in the equation. The three variables that cannot be abstracted directly from census data are income, personal accessibility and spatial accessibility. The third of these variables is related to the distribution of stores and will be discussed later in the chapter. It is a physical measurement of the distribution of stores linear relative to each ED. Income has a strong relationship with the number of licenses in the household. The correlation coefficient is 0.87 and the adjusted r2 value is 87%. The predictive equation, from the means data matrix, is :

K- -0.44 + 2.55L

where K- household income L- number of driving licenses in the household

Personal accessibility can also be predicted using the but it inter-correlations of the independent variables figure requires four variables to achieve an adjusted r2 251

of 0.68. The step-wise regression output is listed in Table 68. These four variables are stable as areas are subtracted from the means data matrix, except that employment (Q) and the number in the household (N)

exchange places after two areas are withdrawn. Further work requires to be done to achieve a better understanding of personal accessibility.

8.5.6 The output from these three models is a trip rate, a duration of stay and an expenditure per household per week at bulk-buying foodstores respectively. These rates are then multiplied by the number of households in the ED to get the total for each ED in the study area. The total

number of private-car trips within the study area would then be used to calibrate the trip distribution model, constrained by the pattern of arrivals at the existing stores. Thus a strategic model is developed which can be updated from census data and onto which any new store proposal can be mapped to examine its effect. This concept will now be discussed in greater detail.

8.6 The Interface with an Aggregate Trip Distribution Model

8.6.1 This section of the chapter discusses the interface of the proposed trip generation model with an aggregate distribution model. It's purpose is to put the developed model in the context of the whole design package, showing how the model would be applied, in conjunction with the distribution model, to the development control of large foodstores. It is not the purpose of this thesis to

develop the applications package required for the but implementation of such a model in practise rather to built prove that a trip generation model can be on household characteristics. 252

Variable Multiple r2 Adjusted Name Correlation value r2 value

No. of Cars (J) 0.65 0.42 0.38

Employment (Q) 0.74 0.54 0.47

No. of Licences (L) 0.79 0.62 0.52

No. in Household (N) 0.88 0.77 0.68

TABLE 68

The Prediction of Personal Accessibility from the Independent

Variables using the Means Data Matrix 253

8.6.2 The research objectives sought to establish a reliable basis for private-car trip prediction to large foodstores based on household characteristics. The developed model comprises three sub-models, one of which relates to trip-rate. This model is based on the means data matrix and on the variables income (K), number in the household (N), personal accessibility of the principal shopper (R) and the number of licences (L). The form of the model is given below :

F-1.0 + 0.346K - 0.027N + 0.119R - 1.117L

8.6.3 The zonal base chosen is at Ennumeration District (ED) level as this is highly disaggregate, with respect to a strategic development control model, and data at a lower level cannot easily be obtained. By multiplying the trip-rate for each ED in the study area by the number of households the potential number of weekly, bulk-buying, car trips is obtained. Thus for each ED the generated number of trips per week is calculated.

8.6.4 The private-car arrivals at each store in the survey area would require to be surveyed either by customer interview or by vehicle count, as the total sales figure cannot be used because it includes all shopping trips. There is also a units incompatability in that trips to each store is equated to sales at each store so that the generated trips would require to be multiplied by an expenditure per trip factor. This latter problem can be accommodated since there is an expenditure sub-model for each ED.

8.6.5 There is therefore a choice between either a singly or doubly-constrained distribution model. The doubly- constrained model has the advantage that the distributed traffic can be assigned to the road network but has the 254

disadvantage that the modal split at each store would have to be found so that the total sales per week from car trips could be calculated. Given that the number of large stores is relatively small, it is proposed that a doubly-constrained distribution model should be used.

8.7 The Form of the Proposed Distribution Model

8.7.1 The form of the model is :

Si kik Oicisi/f(Ti j- j' j)

'' ki [E kiS where - j/f(ti j)j

1 kj- [Z k101Ci/f (Ti and j))

Oi - trips from ED i to store in ED1 Ci " expenditure per trip in EDi Si . private car based sales at store in ED j f(Ti1) - deterrence function and Sit - total number of car-borne shopping trips from zone i to store in ED J.

The product of the constants ki and kj, for each production zone and attracting store respectively, ensures that both the row and column totals of the model matrix equal the row and column totals of the survey matrix. The model is subject to the two constraints,

E Sij -01Ci at the production end, and

i 01 C1 "Es at the attraction end.

8.7.2 The generation term for each ED,, OiCi, is the number of trips from the ED multiplied by the expenditure per visit, as predicted by the multiple regression sub-models. The

I 255

attraction term, Si, is the number of sales at each store. Further work in this area may prove a more complex attraction term to be a better estimator of the distribution pattern but in the context of the philosophy of the conceptual framework number of sales in considered an adequate proxy for store attraction. This is discussed further in Chapter 10.

8.7.3 The form of the deterrence function is usually based on (125) one of the three mathematical functions :

a a) the power function, f(Tij) Tij b) the exponential function, f(T1ý) - exp(ß Tip) Tanner's function, f(Ti Ti c) j) - ja exp(ßTi j)

The calibration should include a testing of different functions to choose the function that best fits the empirical survey data, whether based on trip length or journey time; however this is seldom carried out in

practise. Calibration involves finding the numerical

value of the parameter or , or the two parameters the trip length journey and , which control mean or time. The simplest procedure is to run the model for a

range of parameter values thereby calibrating the model by trial and error. A more efficient method is to adopt a systematic search routine such as the Fibbonaci, or (126) golden-section, search algorithm 0 or the (127,128). Newton-Rapheon method It is proposed that journey time should be used, as argued in Chapter 4, on grounds of ease of implementation and the required strategic level of control unless further work suggests a more complex function is better.

8.8 The Design Procedure for the Distribution Model

8.8.1 The distribution of the trips generated at ennumeration district level may be achieved using a fully constrained model of the form, Ii

256

a Sii kik101CiSi/ Tij

-1 where ki - [E k1 Si, Ti1a]

and kýý - (E k1 01Ci/ Tija)-1

subject to the constraints,

ESij - 01Ci J

and iUiC, -ES J

This model would produce a distribution matrix whose origins would be the ennumeration districts in the study area and whose destinations will be the stores. A matrix of journey times would also be produced using the same origin and destination base. The mathematical function of transport impedance proposed is the power function based on journey time.

8.8.2 An initial value of the calibration parameter a, is then selected and the zoning balancing factors, ki and kj calculated. The balancing factors are solved using an iterative process by assuming any intial value for either ki or ks'. Once the calculation converges the balancing factors are substituted into the distribution model and the model origin and destination matrix is calculated. Then the mean journey time of all trips in the model origin and destination matrix is calculated and this is compared to the mean survey journey time. The value of may then require to be adjusted. If necessary another

calibration parameter may require to be selected until the calibration criteria are satisfied.

8.8.3 The survey area chosen has the advantage that few trips come from the rural areas outwith the green belt around 257

Edinburgh City boundary. Within the study area every household is within a twenty minute driving time contour of a large foodstore. However in an area where the study area cannot be so closely defined boundary problems will exist. This will mean that the study area will require to be enlarged until a suitable watershed in the market exists. The enlarged area will mean that other stores may be included.

8.8.4 The problem of catchment area definition relates to the distribution of shopping trips and the attraction of competing stores. It does not relate to the generation, or potential generation, of this type. of shopping trip. The development of measures of attractiveness, although discussed in the context of spatial accessibility in Chapter 4, is the subject of further work. This should include the value of the power function within the deterrance measure and the degradation of the power of attraction with distance.

8.8.5 The above paragraphs have stated a preference for a doubly-constrained model on the grounds of a better representation of the actual shopping pattern and the

ability to assign the predicted trips to the road network thereby directly assessing the impact on specific roads

and junctions. However a simpler procedure would be a singly-constrained model where.

ETiý Oi

This would not involve the expenditure sub-model nor the need to survey each store to establish modal trip patterns. The store attraction distribution would then be

a probability function based on some proxy measure of attraction, such as floor area, and a deterrance function.

The general form of the probability function would be, 258

X1 -A2 Pa , dl, ij E WýA1 did-X2 J

where Pit is the probability of a consumer in zone i shopping at store j and Wi is the store attraction index. The model would then be of the form

Äd Wi -"2 Sid oidi ii EW -fis

This agrees with the probabilistic nature of the shopping (38) market as proposed by Huff.

8.9 Concluding Remarks on the Interface with an Aggregate Distribution Model

8.9.1 The objective of the thesis is to establish a disaggregate trip generation model, for private-car trips to large foodstores, using household characteristics based

on census data. This having been achieved the model has been put into the context of the total applications

package required for development control of these stores. The proposed trip generation model requires to be tested over the whole of Edinburgh City and in other areas if it is to be used generally.

8.9.2 The development of a strategic development control model will require the following outlined stages :

i) Census data for the relevant household variables extracted for every ED and the total number of trips and total expenditure calculated from the equations. This needs to be done only once and thereafer would be updated every ten years, or if a major 259

residential land-use change occured. The duration of stay, which is a by-product of the development of the trip generation model potentially offers the possibility of estimating car parking requirements. Further work requires to be done to distribute the

total duration of stay from each ED to the large foodstores.

ii) The first stage provides a zonal system with numbers of private-car trips for each zone. These require to be distributed to the stores in the designated study area. If the doubly-constrained model is used the total expenditure will have to be calibrated with the total car-borne sales. Alternatively a singly-constrained distribution model using a probability function, as described earlier in the chapter, and a deterrance function based on journey time or cost could be used. These alternatives require to be tested as part of the future development of the total applications package.

8.9.3 The major advantages of the proposed method over existing methods are ease of application, both at the generation and distribution stages, economy of time, in that neither household nor store interview surveys require to be carried out, increased accuracy based on local area characteristics rather than extrapolation from other surveys in other areas and the ability to build an area-wide model thereby being able to assess the impact of a new store in the area or of a store ceasing to trade in the area. The trip generation model developed in this thesis combined with a standard aggregate distribution technique provides a unique solution to the dual problems of the context dependence of aggregate models and the data acquisition demands of disaggregate models. 260

8.9.4 From a developer's point of view once the total application model has been constructed new store locations could be chosen to maximise the number of potential trips. The trip generation model could be used to predict the number of trips in any given area, without giving the distribution of these trips with respect to the new and existing stores in the area. Thus the three sub-models could be used in a store-specific application. This would mean that a catchment area was defined around an existing store and the models applied to the ennumeration districts within the catchment area. Thus for any store the number of trips, duration of stay and expenditure per week could be estimate for its catchment area. This has the advantage over existing methods in that the models relate to local characteristics, the disadvantage, as mentioned above is that the effect of other stores in the system is not included. There is no guarantee that the households within the catchment area of a store will use that store, however it may be a guide to possible usage and is potentially superior to trip generation calculations based on floor-area. A comparison of the two methods in a store-specific application should be the subject of further work.

8.10 A Listing of the Major Points Emerging from the Analysis

8.10.1 The bulk-buying of food by private-car can be

represented as a separate, two stage process, i. e. the decision to make the trip and the subsequent choice of store. This means that it can be structured as a two-stage modelling process of trip generation and distribution.

8.10.2 There is a strong linear relationship between the use of large foodstores by private-car and certain houshold characteristics which can be extracted from census data. 261

8.10.3 Bulk-purchase food shopping by private car can be identified as distinct from general shopping thereby providing a dependent variable of usage with which the relationships with household characteristics can be measured.

8.10.4 This usage variable is best measured by the three

separate variables of trip frequency, duration of stay and expenditure per visit rather than a usage factor. A relative shopping term, where bulk-purchase food shopping is expressed as a percentage of total shopping, does not add to the explanation of the usage variable.

8.10.5 Two models were investigated based on factor variables and individual variables. The factor-based model in all cases is inferior to the individual-based model.

8.10.6 The strong relationship, based on the canonical correlation analysis, between household characteristics and large store usage indicates that no major independent variables have been omitted.

8.10.7 The proposed model predicts 41% of the total variation in large store usage at individual household level. Although this level of prediction is not satisfactory in the context of a strategic development control model it is a creditable performance when one considers the large variation at this maximum level of disaggregation.

8.10.8 This base level of accuracy means that when the model is aggregated to ennumeration district level the prediction is extremely high at 95%. Ennumeration district is the

base zoning system for all transportation and physical planning zoning, therefore the generation model can be readily interfaced with a standard trip distribution model. 262

8.10.9 The proposed model comprises three sub-models which not only predict the number of trips per ennumeration district but the duration of stay and the bulk-buying expenditure.

8.10.10 The model shows that the spatial accessibility of a household to large stores does not affect the number of trips emanating from the household. This supports the theory that the choice process can be regarded as two separate stages.

8.10.11 The advantages of the proposed model are :

i) ease of application and economy of time because the required data is extracted from census data.

ii) increased accuracy because the model is based on local characteristics.

iii) an ability to develop an area model, in conjunction with a standard trip distribution model, incorporating the interacting forces of different shops within the distribution stage.

iv) the combination of the advantages of a disaggregate model at the generation stage interfacing with a standard, aggregate model at the distribution stage, overcoming the disadvantages of the data requirements and the context dependency of the two types of models respectively.

v) its use in a store-specific application if a catchment area is defined around the store in question. 263

CHAPTER 9 SUMMARY AND CONCLUSION

9.1 Introduction

This chapter summarises the research programme highlighting the salient points of the thesis. The

summary includes all chapters of the thesis. 264

9.2 The objective of the research programme is to produce a private-car trip generation model, based on local household area characteristics, for use in the strategic control of large foodstores. Also to discover the level of disaggregation at which a general model can be developed and to show how the developed model interfaces with a standard, aggregate distribution model. The thesis began with the historical background to the study discussing the reasons for the growth of this type of shopping in Britain. The following chapter discussed the problems caused by these stores and the current methods used to predict their traffic attraction. These methods are based on the floor-area of the store and it is shown that this is unreliable and varies with local area characteristics. Various recent research papers recognise the. need for a generalised model of trip generation based on local area characteristics but no model exists at the present time. The estimation of parking spaces is also unreliable when based on the same criteria as trip-rate.

9.3 The chapter on consumer behaviour relates consumer behaviour theory to the observed characteristics of bulk-buying shoppers. This identifies important variables which are related to store usage and consumer choice. The chapter on accessibility discusses the place of accessibility within consumer choice and store competition. Accessibility is seen to comprise three elements :

a) the availability of a private car b) the availability of the principal shopper c) the availability of the store

For a private-car shopping trip to take place all three have to coincide. These elements represent the personal accessibility of the household and are part of the trip i

265

generation model. The spatial accessibility of each store comprises an attraction term and a spatial deterrence term. This relates to the distribution of trips within the competing framework of stores. It only applies in an area where the consumer has an existing

range of store choice and may not apply where no store exists and a store is to be provided. To study the

relationship between household characteristics and store usage competition had to be held constant. This is achieved by selecting groups of households which have equal store opportunities.

9.4 The research objectives and conceptual framework were

then developed. It was not possible to develop an a priori. model of store usage based on household

characteristics. The study is therefore designed as an investigative research programme based on two conceptual models. The first model proposed that store usage is a composite variable comprising frequency of use, duration of stay and expenditure per visit and is predicted by three composite, independent factors of household variables. The three factors are based on household structure, employment and lifestyle. If this model proved acceptable a graduated table of store usage, based on the composite usage index, would be constructed. The disadvantage of this model is the application of the usage index. The second model proposed that the three store usage variables are linearly related to household characteristics and can be predicted from them directly. The application of this model is much easier than the previous model as the three variables are identifiable.

9.5 The model defined in principle by the research design relates to the strategic control of large foodstore development. This implies that the model must' be efficient in time and effort with a predictive capability superior to existing procedures. The data requirements 266

of the model should not therefore be onerous and should be based on census data. These data are accessible and regularly updated. The variables not included in census data will be estimated from other intercorrelated

variables or external sources.

9.6 The methods of analyses used were multi-variate,

statistical techniques based on the SPSS computer suite of programs. The survey area was defined as the Edinburgh city boundary and fifteen sub-areas, each of twenty-seven households, were chosen at random- using a hierarchical sampling technique based on postal-codes. The household questionnaire was designed to measure the

identified variables, as indexed, and a pilot survey was carried out to test the design. Following the main survey of four hundred households, fifteen sub-area data

files were constructed and initial sub-area profiles prepared.

9.7 The analysis of the data commenced with the establishment

of a Pearson bivariate correlation matrix which shows the strength of the relationship between each variable pair. The two shopping expenditure variables are strongly

related to the independent variables but the other dependent variables show few consistent relationships. The non-homogeneity of certain sub-areas was investigated and its presence found to be insignificant in its

influence on the data structure. No evidence of non-linear relationships was found. A principal components analysis was carried out to establish what is being measured on the dependent side of the equation and how it is measured by the independent variables. The

total data matrix verified the factor model, postulated by the conceptual framework, but the structure does not consistently exist at sub-area level. Conversely the canonical correlation shows that within each area a strong relationship exists between the two variable sets, 267

but at total data matrix level the canonical coefficient drops from 0.90 to 0.64. This implies that although the objectives of the research can be achieved within the

sub-areas, at household level, a general model for all sub-areas cannot be used.

9.8 The second model, based on the individual variables, was then investigated using multiple regression analysis. Again the areal variation is large and the prediction level is variable. However, if the mean of each variable within the sub-area is calculated and a means data matrix constructed, the three store usage variables of frequency of use, duration of stay and expenditure per week can be predicted accounting for approximately 90% of the variance in these variables.

These multiple regression equations were developed to achieve three sub-models that are stable from area to area. These models are :

F-1.0 + 0346K - 0.027N + 0.119R - 1.117L B-0.81 - 0.026P + 0.001S + 0.027R D- 13.1 - 15.315L - 0.459N + 6.055K + 1.821R + 2.272M

where

F- Frequency of store use/week B- Duration of stay at store/week D- Expenditure at store/week K- Income of the household L- Number of licences in the household M- Number in the household

N- Mean age of the household P- SEG of head of household R- Personal accessibility of principal shopper S- Spatial accessibility of household to stores

p 268

The trip frequency and expenditure variable models account for 70% of the variance in the independent

variable. The duration of stay model explains 42%.

These percentages are adjusted for the number of

variables and sample size. The sample size (i. e. fifteen) of the means data matrix depresses the actual percentages which are 80% and 58% respectively. A higher

level of prediction can be achieved but the stability of the model decreases.

9.9 The areal variation comprises two elements; within-area and between-area variation. When areas are aggregated the total variation becomes so large that the prediction level drops significantly. The means data matrix smooths

the within-area variation to provide sufficient, but not excessive, between-area variation to achieve a high level of prediction. The areal variation between areas is caused by a lack of variance in variables in certain

areas, due to uniform groupings, and the individuality of human behaviour.

9.10 It is possible, therefore, to develop a stable, predictive trip generation model based on the means data matrix. The model consists of three sub-models relating to trip-rate, duration of stay and expenditure per week. These apply to trip generation, car parking capacity and market potential respectively. The model based on composite factors proved unsatisfactory.

9.11 The level of disaggregation achieved is small, based on groups of twenty-seven households, and is impractical. The smallest disaggregation achievable in practise is at ennumeration district (ED) level, which is of the order

of one hundred and twenty households. This is also the zoning base from which all other transportation zones are aggregated. The study area would therefore be divided into ED's and the trip-rate, duration and expenditure 269

levels calculated for each ED. If this was unacceptable on cost grounds the ED's can be aggregated to form sub-areas within transportation zones.

9.12 The effect of competition, represented by spatial accessibility, only appears in the duration sub-model.

This implies that in an area of existing stores a new store will redistribute the existing car-borne trips,

increase or decrease the duration of stay at these stores and redistribute the expenditure. Trips by other modes, notably walking trips, around the new store will be generated. This supports the theory of the thesis in

that the generation and distribution stages of the car-borne shopping trip can be separated. Household

characteristics determine the need to bulk-buy and once this need has been recognised the household looks at the alternatives and chooses a store.

9.13 Although not part of the thesis the interface of the trip

generation model with the aggregate trip distribution model is discussed and is based on a systems approach. This means that a model of the study area pattern for

car-borne, bulk-buying shopping is constructed based on the 'local area trip generations discussed above. The distribution model proposed is a doubly-constrained aggregate distribution model of the form,

a Sij - kikjOiCiSi/Tip

-1 where ki [E k' Sý/Tina]

1 and ký' _[E k10 C1/Tia)

subject to the constraints,

sij - oici 270

and ZOiCi ESQ J

where Sij - the total car-borne sales from zone i to store j

Oi total car-borne trips from zone i

Ci weekly expenditure/household from zone i

Sý - total car-borne sales at store j

Tina -a deterrance power function

a ki, k3', ' model parameters

9.14 Once the model is calibrated a mathematical understanding of the shopping pattern in the study area is achieved. The doubly-constrained distribution model also means that the trips can be assigned to the road network to investigate potential capacity problems. A proposed store can then be added to the model to study the effects on the trip distribution. The zonal trip-rates will be updated as census data is updated.

9.15 The research objectives of the thesis have therefore been achieved in that a strategic, disaggregate trip

generation model for private-car use of large foodstores has been developed. The level of disaggregation of the model has been fully investigated and its interface with an aggregate distribution model explained. The following chapter identifies areas of further development. 271

CHAPTER 10 SUGGESTIONS FOR FURTHER RESEARCH

10.1 Introduction

This chapter suggests a number of areas where further work could be undertaken. Many of these areas have been mentioned in the text. They range from the city-wide development and testing of the proposed model to the improved measurement of specific variables. 272

10.2 The Development and Testing of a City-Wide Model

10.2.1 The proposed generation model has been tested with respect to the four hundred household sample, however,

the development of a city-wide model needs to be investigated. This would initially be an extension of the existing study in Edinburgh but studies in other towns and cities require to be undertaken to test the national generality of the model.

10.2.2 Comparison must be made with existing methods of trip prediction ensuring that the catchment areas are

comparable. This would involve including all the stores in a study area so that the city-wide model could be compared to the store-specific models. It would also be of interest to compare the store-specific approach using both the existing disaggregate methods and the new disaggregate equations.

10.3 Estimating the Hourly and Daily Trip Pattern

10.3.1 The time and day of each shopping trip is recorded on

the shopping diary questionnaire for the household sample. At the strategic level the weekly total of trips combined with the daily pattern of bulk-buying shopping, which is well-documented, is sufficient. However the accuracy and flexibility of the model would be improved if a sub-model could be developed on an hourly and/or daily basis. This could provide the basis for the study of a range of critical scenarios.

10.4 Further Development of the Model

10.4.1 The model was developed in an area of competing large stores. Every household in the area, with a car, has access to a large store within a twenty-five minute driving time. A further development of the model would 273

be the study of areas without such stores, to evaluate the potential generation I rom the area. The investigation would adopt the same disaggregate model philosophy but would involve attitudinal surveys to establish the potential level of use with respect to household characteristics.

10.4.2 The model could be further developed for other modes of travel. This may involve modal sub-models for walking and bus trips. Each modal sub-model would require its

own network system but would use a common zoning system due to the highly disaggregate ennumeration district base.

10.4.3 The philosophy of this thesis, using a disaggregate

generation model with an aggregate distribution model could be investigated for other areas of development control, such as housing, offices and recreation. Each , area constitutes a major study but the possibility of developing a family of development control models based on census data is most attractive and should be pursued.

10.5 Measurement of the Variables

10.5.1 The previous suggestions have dealt with the general

development of the model and its testing. This area of further work is more specific in that it relates to the

measurement of individual variables. Freezer ownership, income and SEG were category-based variables; although income can be thought of as numeric. Some of the other variables were in effect category variables in that the

range, of values was limited. Car ownership and number of licenses are examples of this categorisation. The dependent variables of frequency and duration also

suffered from the effect in that the consumer rounded off the duration to the nearest hour or half- hour and frequency was either once per week, fortnight, or month. 274

The latter benefited from the weekly standardisation.

This stepped-effect worked against the linear correlation since the measurements grouped around certain values. Further work requires to be carried out on the indexing

and measuring of these variables so that a more continuous measure is obtained. The inclusion of income

and personal accessibility in the model means that census data cannot supply all the information for the model. Further work requires to be carried out to establish

independent measures for income and personal accessibility, thereby obviating the need to rely on other independent variables or on global data such as the National Expenditure Survey.

10.5.2 The presence of zero values in the dependent usage variables did not impair the predictive capacity of the model based on the means data. However the zero values did affect the sub-area models. Further work requires to be carried out to investigate whether it is possible to predict zero usage of large stores in a car-owning household. If the model could be amended to anticipate zero usage the prediction at household level would be improved.

10.6 The Development of the Distribution Model

10.6.1 The proposed distribution model is compatible with the research objectives in that it is aggregate and proven in its application. The attraction term is based on either number of sales, if the proposed model is used, or floor-area, if the singly-constrained model is used. However the chapter on accessibility highlighted the work to date on attraction terms for large stores. These more

complex measures should be tested once a city-wide model has been developed to judge whether the increased complexity is justified with respect to cost-effectiveness and distributional accuracy. 275

10.6.2 The same philosophy applies to the deterrance function

where journey time is used. Again the chapter on accessibility discussed more complex measures based on- generalised cost and these should also be investigated to

establish their contribution to the model's accuracy.

10.6.3 Finally other forms of distribution model should be investigated and their results compared to the standard spatial interaction model. A range of distribution

models is available, including those which operate at a disaggregate level. These models should however be sympathetic to the philosophy of the design method in

that they should provide an easily applied and efficient tool for the strategic planning control of large foodstores. 276

1. JONES, P. M. Hypermarkets and superstores - saturation or future growth? Retail and Distribution Management, July/ August 1982, pp20-27.

2. JONES, P. M. Trading features of hypermarkets and superstores. URPI U7,1978.

3. DAWSON, J. A. Hypermarkets in France. Geography, Vol. 64, Part 4,1976 pp259-262.

4. BELL, R. A. Transportation aspects of out-of-town shopping centres. Journal of the Institution of Municipal Engineers, Vol. 98, Oct. 1971, pp267-272.

5. PARKER, T The development of planned shopping centres in the Republic of Ireland. Retail and Distribution Management, March/April 1982, pp25-29.

6. SHAW, G. Shopping centre development in West Germany. Retail and Distribution Management, May/June 1984, pp47-51.

7. SMITH, B. Switzerland - retailing at a " crossroads. Retail and Distribution Management, Sept. / Oct. 1983, pp40-47.

8. DAWSON, J. A. SATO, T. Controls over the development of large stores in Japan. The Services Industries Journal, 1982, ppl37-145.

9. SHETH, J. N. Emerging trends for the retailing industry. Journal of Retailing, Vol. 49, No. 3, Fall 1983, pp6-18.

10. COMPANY PROFILE Safeway Food Stores Ltd. Retail Business No. 276, February 1981, p37.

11. COMPANY PROFILE Kwik Save Discount Group PLC. Retail Business No. 303, May 1983, p45.

12. COMPANY PROFILE Associated Dairies Group PLC. Retail Business No. 304, June 1983, p39. 277

13. MANSLEY, R. D. VERRICO, R. Shopping centre and hypermarket developments in and around Glasgow. Report for the Corporation of Glasgow, 1971.

14. PACIONE, M. The in-town hypermarket : an innovation in the geography of retailing. Regional Studies Vol. 13,1978, pp15- 24.

15. NEDC REPORT The future pattern of shopping. National Economic Development Office Report, HMSO London, 1971.

16. BASIC ROAD STATISTICS (1978) British Road Federation Report, London 1980.

17. GERN, R. C. The middle-aged spread of regional shopping centres in the 1970's. Traffic Engineering and Control, 1970.

18. DICK, A. C. Transportation aspects of new shopping developments. Traffic Engineering and Control, October 1971, pp249-251.

19. COMPANY PROFILE Tesco Stores (Holdings) Ltd. Retail Business No. 286. December 1981, p31.

20. BELL, R. A. Transportation aspects of out-of-town shopping centres. Journal of the Institution of Municipal Engineers, Vol. 98, Oct. 1971, pp267-272.

21. HARRIS, M. R. ANDREW, H. R. The traffic implications of hypermarket development. Traffic Engineering and Control, January 1979, pp2-8.

22. LEAKS, C. R. TURNER, D. J. Shopper and vehicle characteristics at large retail shopping centres. Traffic Engineering and Control, January 1982, pp8-13.

23. CODD, J. A. Parking requirements at suburban shopping centres - an investigation. Traffic Engineering and Control, March 1983, pp119-124.

24. AITKEN, C. P. MALCOLM, J. F. Parking, traffic generation and superstores : an underestimate? Traffic Engineering and Control, April 1977, pp199-201. 278

25. KELLY, R. W. Parking at a Hypermarket - six years on. Traffic Engineering and Control, May 1979, pp257-262.

26. URBAN LAND INSTITUTE Parking requirements for shopping centres. Technical Bulletin 53, Washington, DC, 1965.

27. MULTIPLE SHOP FEDERATION Car parking for shoppers. London, 1973.

28. NATIONAL ECONOMIC DEVELOPMENT OFFICE Future pattern of shopping. HMSO, London, 1971.

29. LEAKE, G. R. TURNER, D. J. Parking demand at large retail shopping stores :a reappraisal. Municipal Engineer, Vol. 109, May 1982, ppll3- 117.

30. BARRET, R. Trip generation and modal split of shopping trips. Traffic Engineering and Control, February 1975, pp72-74.

31. DISTRIBUTIVE TRADES EDC The future pattern of retailing. Report, 1971.

32. CORDEY-HAYES, M. Retail location models. Centre for Environmental Studies, CES WP16, October 1968.

33. GARLAND, R. N. The use of cluster analysis to identify and explain patterns of household travel. MSc Thesis, Cranfield Institute of Technology, September 1978.

34. LAKSHMANAN, T. R. HANSEN, W. G. A retail market potential model. Journal of the American Institute of Planners, 31, ppl34-43,1965.

35. REILLY, W. J. Methods for the study of retail relationships. Bulletin No. 2944, University of Texas, Houston, Texas, 1929.

36. BATTY, M. Urban modelling : Algorithms, calibrations, predictions. Cambridge Urban and Architectural Studies, 1976. 279

37. LANE, R. POWELL, T. J. Analytical transport planning. SMITH, P. P. Duckworth, London, 1971.

38. HUFF, D. L. A probabilistic analysis of shopping centre trade areas. Land Economics 39 pp81-89 (1963).

39. HARRIS, B. Models of locational equilibrium for retail trade. (Mimeo) and Journal of Regional Science, Vol. 5, No. 1,1964.

40. STOUFFER, S. A. Intervening opportunities :A theory relating mobility and distances. American Society Review 5, No. 6,1940.

41. WILSON, A. G. A statistical theory of spatial distribution models. Transportation Research 1, November, 1967.

42. DALY, A. Estimating choice models containing attraction variables. Transportation Research Vol. 16B, No. 1, pp5-15,1982.

43. RICHARDS, M. G. MARS, N. J. I. Gednaggregeerde simultane vraagmodellen voor winkelritten in het stedelijk verkeer. Le Clercq, F. et al. (eds) Colloquium wervoersplanologisch speurwerk, 1975, praktijk en model in de vervoersplanning, Delft, 1975, pp5l- 74.

44. HARTGEN, D. T. Attitudinal and situational variables influencing urban mode choice : Some empirical findings. Transportation 3, pp377-392,1974-

45. BURNETT, P. The dimensions of alternatives in spatial choice processes. Geography. Anal. 5, ppl81-204,1973.

46. CADWALLADER, M. A behavioural model of consumer spatial decision making. Econ. Geography 51, pp339-349,1975.

47. MACKAY, D. B. Cognitive maps and spatial behaviour 7, OLSHARSKY, R. W. of consumers. Geograph. Anal. SENTELL, G. ppl9-34,1975.

48. FISHBEIN, M. Belief, attitude, intention and AJZEN, I. behaviour : an introduction to theory and research. Reading, MA : Addison- Wesley, 1975. 280

49. BURNKRANT, R. E. An examination of the convergent, PAGE, T. J. (Jr. ) discriminant and predictive validity of Fishbein's behavioural intention model. Journal of Marketing research, Vol. XIX, pp550-561, November 1982.

50. LOUVIERE, J. J. A composite attitude-behaviour model MEYER, R. J. of traveller decision making. Transportation research Vol. 15B, No. 5, pp4ll-420,1980.

51. DAMM, D. Parameters of activity behaviour for use in travel analysis. Transportation research, Vol. 16A, No. 2, ppl35-148, 1982.

52. LENNTORP, B. Paths in space-time environments. Lund studies in geography, series B, No. 44, CWK Gleerup, Lund, Sweden, 1976.

53. CHRISTALLER, W. Die Zentralen orte in Sudlentschland. Jena, E. Germany :G Fischer (1935).

54. LOSCH, A. The economics of location. New Haven, Conn., Yale University Press (1954).

55. HUFF, D. L. Determination of intra-urban retail trade area. Real Estate Research Program, Los Angeles, University of California (1962).

56. EATON, B. The theory of market pre-emption: LIPSEY, R. the persistence of excess capacity and monopoly in growing spatial markets. Economics 46, ppl49-158,1979.

57. CASPARIS, J. Metropolitan retail structure and its relationship to population. Land Economics 43, pp213-218,1967.

58. HUBBARD, R. A review of selected factors conditioning consumer travel behaviour. Journal of Consumer Research 5, pp7-21, 1978.

59. CRAIG, C. S. Models of the retail location process GHOSH, Aa review. Journal of Retailing, McLAFFERTY, S. Vol. 60, No. 1, pp5-37, Spring 1984.

60. REILLY, W. J. The law of retail gravitation. New York, Knickerbocker Press, 1931. 281

61. PANKHURST, I. C. An empirical study of two shopping ROE, P. E. models. Regional Studies, Vol. 12, pp727-748, 1978.

62. HUFF, D. L. A programmed solution of estimating BLUE, L. retail sales potential. Center for Regional Studies, Laurence, Kansas, 1966.

63. FORBES, J. D. Consumer patronage behaviour. Marketing and the New Science of Planning, Chicago, American Marketing Association, pp381-385,1968.

64. HAINES, G. H. Maximum likelihood estimation of SIMON, L. S. central city food trading ALEXIS, M. areas. Journal of Marketing Research, 9, ppl54-159,1972.

65. STANLEY, T. J. Image inputs to a probabilistic model SEWALL, M. A. predicting retail potential. Journal of Marketing, 40, pp48-53,1976.

66. NAKANISHI, M. Parameter estimation for multiplicative COOPER, L. G. interactive choice model : least squares approach. Journal of Marketing Research, 11, pp303-311,1974.

67. ACHABAL, D. MULTILOC :A multiple store location GOOR, W. L. decision model. Journal of Retailing, MAHAJAN, V. 58, pp5-25, Summer 1982.

68. LOUVIERE, J. Design and analysis of simulated WOODWORTH, G. consumer choice or allocation experiments : an approach based on aggregate data. Journal of Marketing Research, 20, pp350-367,1983.

69. RECKER, W. Destination choice and processing SCHULER, H. spatial information : some empirical tests with alternative constructs. Economic Geography, 57, pp373-383, 1981.

70. RUSHTON, G. Analysing spatial behaviour by revealed space preference. Annals of the Association of American Geographers, 59, pp391-400,1969. 282

71. GHOSH, A. Parameter nonstationarity in retail choice models. Journal of Business Research, forthcoming, 1984.

72. HUBBARD, R. Parameter stability in cross-sectional modeling of ethnic shopping behaviour. Environment and Planning A, 11, pp977- 992,1979.

73. GREEN, P. E. Cojoint analysis in consumer research SRINIVASAN, V. issues and outlook. Journal of Consumer Research, 5, ppl03-123,1978.

74. RICHARDS, M. Disaggregate demand models - promises and prospects. Transportation Research A, Vol. 16A, No. 5-6, pp339-344,1982.

75. OCHOJNA, A. D. A pragmatic application of category MACBRIAR, I. D. analysis to small-scale household travel surveys. Centre for Transport Studies, Cranfield Institute of Technology, 1976.

76. PIRIE, G. H. Measuring accessibility :a review and proposal. Environment and Planning A,  Vol. 11, pp299-312,1979.

77. PAHL, R. E. Whose city? Penguin Books, Middlesex, / 1975.

78. BENWELL, M. Car availability and transport need. WHITE, I. Traffic Engineering and Control, pp4lO- 414, August/September 1979.

79. HOLMAN, R. H. Temporal equilibrium as a basis for WILSON, R. D. retail shopping behaviour. Journal of Retailing, pp58-81, Spring, 1982.

80. STROBER, M. H. Working wives and major family WEINBERG, C. B. expenditures. Journal of Consumer Research, No. 4, ppl41-147, December 1977.

81. JOYCE, M. The professional woman :a potential GUILTINAN, J. market segment for retailers. Journal of Retailing, No. 54, pp59-70, Summer 1978.

82. NICKOLS, S. Y. Housework time of husband and wife. 7, METZEN, E. J. Home Economics Research Journal, No. pp85-97, November 1978. 283

83. WILLIAMS, R. H. A policy oriented typology of grocery PAINTER, J. J. shoppers. Journal of Retailing, No. 54, NICHOLAS, H. R. pp27-42, Spring, 1978.

84. BERKOVITZ, E. N. In-home shoppers : the market for WALTON, J. R. innovative distribution systems. WALKER, O. C. Journal of Retailing, No. 55, ppl5-33, Summer 1979.

85. NEALE, J. L. Analysis of household-travel activities HUTCHINSON, B. G. by information statistics. Transportation Research A, Vol. 15A, ppl63-171,1981.

86. DIX, M. C. Report on investigations of household travel decision-making behaviour. Working paper 27, Transport Studies Unit, University of Oxford, England, 1977.

87. SPENCER, A. H. Deriving measures of attractiveness for shopping centres. Regional Studies, Vol. 12, pp713-726,1978.

88. POTTER, R. B. Spatial and structural variations In the quality characteristics of intra- urban retailing centres. Research paper, Bedford College, University of London, October 1979.

89. DEPARTMENT OF THE The Eascleigh Carrefour -a hypermarket ENVIRONMENT and its effects. Research Report 16, Department of the Environment, 1976.

90. KATONA, G. A study of purchasing decisions. New MUELLER, E. York University Press, New York, 1970. CLARK, L. H.

91. THORELLI, H. B. Concentration of information power among consumers. Journal of Marketing Research, No. 8, pp427-432, November 1971.

92. NEWMAN, J. W. Multivariate analysis of differences in STAELIN, R. buyer decision time. Journal of Marketing Research, No. 8, pp192-198, May 1971.

93. DAVIES, R. L. Effects of consumer income differences on shopping movement behaviour. Tijdshr. econ. soc. Geogr., No. 60, ppll2- 122,1969. 284

94. LOYD, R. Shopping behaviour and income : JENNINGS, D. comparisons in an urban environment. Econ. Geogr., No. 54, pp154-167,1978.

95. NADER, G. A. Socio-economic and consumer behaviour. Urban Studies, No. 6, pp235-245,1969.

96. WASSON, C. R. Is it time to quit thinking of income classes? Journal of Marketing, No. 33, pp54-57, April 1969.

97. SCHANINGER, C. M. Social class versus income revisited : an empirical investigation. Journal of Marketing Research, Vol. XVIII, pp192- 208, May 1981.

98. HIRISCH, R. Selecting the superior segmentation PETERS, M. correlate. Journal of Marketing, No. 38, pp60-63,1974.

99. DOWNES, J. D. Variation of household and personal travel time budgets in Reading Research Report LR966. Department of the Environment, 1980.

100. MONTEFIORE, H. et al Changing directions. A report from the Independent Commission on transport. Coronet, 1973.

101. SCHAEFFER, K. H. Access for all. Penguin Books, SCLAR, E. Middlesex, 1975.

102. BAXTER, R. et al Mobility, accessibility and potential in urban and regional planning. Research report, Marketing centre for Architectural & Urban Studies, University of Cambridge.

103. PIRIE, G. H. Measuring accessibility :a review and proposal. Environment and Planning A, Vol. II, pp299-312,1979.

104. HOGGART, K. Transportation accessibility : some references. Exchange bibliography 482, Council of Planning Libraries, 1974.

105. DOLING, J. A comment on the measurement of spatial opportunity. Regional Studies, Vol. 13,1979.

106. MAKKASH, T. Z. Activity-accessibility models of trip- generation. PhD Thesis, Purdue University, USA, 1969. 285

107. GUR, Y. J. An accessibility sensitive trip generation model. PhD Thesis, Northwestern University, USA, 1971.

108. DOUBLEDAY, C. Spatial mobility and trip generation. Urban transport Planning Conference, Leeds University, March 1976.

109. DALVIE, M. Estimation of non-work'trip-demand MARTIN, K. a disaggregated approach. Urban Transport Planning Conference, Leeds University, March, 1976.

110. LEAKE, G. R. Accessibility measures and their HUZAYYIN, A. S. suitability for use in trip generation models. Traffic Engineering and Control, pp566-572, December 1979.

111. VICKERMAN, R. W. Accessibility, attraction and potential: a review of some concepts and their use in determining mobility. Environment and Planning A, 6, pp675- 691,1974.

112. ROBINSON, R. V. F. Methodological problems in the study of HEBDEN, J. J. shopping travel., Proceedings of Retail VICKERMAN, R. W. and Local Planning Seminar, PTRC, London, 1974.

113. LUCAROTTI, P. S. K. Car availability - the fundamental split. Transportation Planning and Technology 3, pp203-213,1977.

114. WILLIAM, K. M. Patterns of car usage and restraint BANISTER, D. J. modelling. Transportation 6 (4), pp345-364,1977.

115. HENSHER, D. A., Conference Report : international STOPHER, P. R. conference on behavioural travel modelling. Traffic Engineering and Control, 18, pp319-322,1977.

116. RHODES, T. Forecasting shopping demand. Journal WHITAKER, R. of the Town Planning Institute, 53, ppl88-192,1967.

117. DOWNS, R. M. The cognitive structure of an urban shopping centre. Environmental Behaviour 2, ppl3-39,1970. 286

118. BENWELL, M. Accessibility and the shopping SEATON, R. A. F. activity. C. T. S. Report, Cranfield, 1977.

119 BENDER, W. C. Consumer purchase costs - do retailers recognise them? Journal of Retailing, 40, ppl-8, Spring 1964.

120. BROWN, F. E. Department stores and discount houses FISK, G. who dies next? Journal of Retailing, 41, ppl5-27, Fall 1965.

121. HANSEN, R. A. An empirical investigation of attribute DEUTSCHER, T. importance in retail store selection. Journal of Retailing, 53, pp59-72, Winter 1978.

122. RAO, C. R. Linear Statistical Inference and its applications. Wiley.

123. O'MUIRCHEARTAIGH, C. H. PAYNE, C. Exploring data structures. Wiley.

124. BATTY, M. Urban modelling : algorithmns, calibrations, preictions. Cambridge University Press, 1976.

125. OPENSHAW, S. Empirically derived deterrance CONNOLLY, C. J. functions for maximum performance spatial interaction models. Environment and Planning A, 9, ppl067- 79,1977.

126. BATTY, M. Exploratory calibration of a retail location model using search by golden section. Environment and Planning, 3, pp4ll-32.1971.

127. BATTY, M. The calibration of gravity, entropy, and related models of spatial interaction. Environment and Planning, 4, pp131-250.1972-

128. BLACK, J. Urban transport planning. Croom Helm, 1981. "A DISAGGREGATE TRIP GENERATION MODEL FOR THE STRATEGIC

PLANNING CONTROL OF PRIVATE CAR TRIPS TO LARGE FOODSTORES"

G. McL. HAZEL

VOLUME 2- APPENDICES

Ph. D. THESIS

CRANFIELD INSTITUTE OF TECHNOLOGY CONTENTS

VOLUME 2

APPENDICES

PAGE

Appendix A 1 A-1 Map and Table Showing Distribution of Hypermarkets and Superstores and their Sizes 2

Appendix B 24 B-1 SEG Standard Classification System 25

Appendix C 26 C-1 List of Numbered Ennumeration Districts 27 C-2 List of Institutional Ennumeration Districts 50

Appendix D 51 D-1 Letter of Authorisation given to Interviewer 52 D-2 Information Letter sent to all Potential Households 53

Appendix E 54 E-1 Data File for Area 1- Moredun 55 E-2 Data File for Area 2- Spottiswoode 56 E-3 Data File for Area 3- Clermiston 57 E-4 Data File for Area 4- Swanston 58 E-5 Data File for Area 5- Waverley 59 E-6 Data File for Area 6- Westburn 60 E-7 Data File for Area 7- St Peters 61 E-8 Data File for Area- 8- Saughton 62 E-9 Data File for Area 9- Craigleith 63 E-10 Data File for Area 10 - Pilton 64 E-11 Data File for Area 11 - Cammo 65 E-12 Data File for Area 12 - Turnhouse 66 E-13 Data File for Area 13 - Leith 67 E-14 Data File for Area 14 - Craigleith 68 E-15 Data File for Area 14 - Easter 69

Appendix F 70 F-1 Income (K) V. No. o f Half Days Employed (Q) - Area 1 (More dun) 71 F-2 No. of License Hold ers (L) v. S. D. of Ages (0) - Area 1 (More dun) 72 F-3 No. of License Hold ers (L) v. No. of Half Days Employed (Q) - Area 1 (Moredun) 73 F-4 No. in Househo ld (M ) v. No. of Half Days Employed (Q) - Area 1 (Moredun) 74 F-5 Mean Age Struc ture (N) v. No. of Half Days Employed (Q) - Area 1 (Moredun) 75 CONTENTS

APPENDICES (Contd. )

PAGE

Appendix F F-6 S. D. of Age Structure (0) v. No. of Half Days Employed (Q) - Area 1 (Moredun) 76 F-7 SEG of Head of Household (P) v. No. of Half Days Employed (Q) - Area 1 (Moredun) 77 F-8 No. of Half Days Employed (Q) v. Personal Accessibility (R) - Area 1 (Moredun) 78 F-9 Income (K) v. S. D. of Ages (0) - Area 12 (Turnhouse) 79 F-10 No. in Household (M) v. Mean Age of Household (N) - Area 12 (Turnhouse) 80 F-11 No. in'Household (M) v. No. of Half Days Employed (Q) - Area 12 (Turnhouse) 81 F-12 Mean of Age Structure (N) v. S. D. of Age Structure (0) - Area 12 (Turnhouse) 82 F-13 Mean of Age Structure (N) v. No. of Half Days Employed (Q) - Area 12 (Turnhouse) 83 F-14 Mean Age (N) v. Personal Accessibility (R) - Area 12 (Turnhouse) 84 F-15 S. D. of Ages (0) v. No. of Half Days Employed (Q) - Area 12 (Turnhouse) 85 F-16 SEG of Head of Household (P) v. Personal Accessibility (R) - Area 12 (Turnhouse) 86 F-17 Total Expenditure/Week (E) v. No. in Household (M) - Area 4 (Swanston) 87 F-18 Expenditure/Week at Store (D) v. No. in Household (M) - Area 4 (Swanston) 88 F-19 Mean Age (N) v. Personal Accessibility (R) - Area 4 (Swanston)* 89 F-20 SEG of Head of Household (P) v. Personal Accessibility (R) - Area 4 (Swanston) 90 F-21 Total Expenditure/Week (E) v. Mean Age of Household (N) - Area 7 (St Peters Place) 91 F-22 No. in Household (M) v. Mean Age of Household (N) - Area 7 (St Peters Place) 92 F-23 Mean Age of Household (N) v. S. D. of Age Structure (0) - Area 7 (St Peters Place) 93 F-24 Mean Age of Household (N) v. SEG of Head of Household (P) - Area 7 (St Peters Place) 94 F-25 Mean Age of Household (N) v. No. of Half Days Employed (Q) - Area 7 (St Peters Place) 95

Appendix G 96 G-1 Principle Components Analysis for all Variables X-1,2 and 3 97 G-2 Canonical Correlation Analysis for A-1,2 and 3 102 CONTENTS ter, APPENDICES (Contd. )

PAGE

G-3 Canonical Variate Structures for = 1, 2 and 3 103

Appendix H 106 H-1 Area 1 - Multiple Regression Analysis with I to R; F with B. D; B with I to R; and D with I to R 107 H-2 Area 2 - Multiple Regression Analysis F with I to R; F with B, D; B with I to R; and D with I to R ill H-3 Area 3 - Multiple Regression Analysis F with I to R; F with B, D; B with I to R; and D with I to R 115 H-4 Area 4 - Multiple Regression Analysis F with I to R; F with B, D; B with I to R; and D with I to R 119 H-5 Area 5 - Multiple Regression Analysis F with I to R; F with B, D; B with I to R; and D with I to R 123 H-6 Area 6 - Multiple Regression Analysis with I to R; F with B, D; B with I to R; and D with I to R 127 H-7 Area 7 - Multiple Regression Analysis F with I to R; F with B, D; B with I to R; and D with I to R 131 H-8 Area 8 - Multiple Regression Analysis wi th I to R; F with B, D; B with I to R; and D with I to R 135 A-9 Area 9 - Multiple Regression Analysis F with I to R; F with B, D; B with I to R; and D with I to R 139 H-10 Area 10 - Multiple Regression Analysis F with I to R; F with B, D; B with I to R; and D with I to R 143 H-11 Area 11 - Multiple Regression Analysis F with I to R; F with B, D; B with I to R; and D with I to R 147 H-12 Area 12 - Multiple Regression Analysis F with I to R; F with B, D; B with I to R; and D with I to R 151 H-13 Area 13 - Multiple Regression Analysis w ith I to R; F with B, D; B with I to R; and D with I to R 155 H-14 Area 14 - Multiple Regression Analysis F with I to R; F with B, D; B with I to R; and D with I to R 159 H-15 Area 15 - Multiple Regression Analysis F with I to R; F with B, D; B with I to R; and D with I to R 163 CONTENTS

APPENDICES (Contd. )

PAGE

Appendix I 167 I-1 Area 1- Plot of Standardised Residuals and Table of Observed and Predicted Values 168 1-2 Area 2- Plot of Standardised Residuals and Table of Observed and Predicted Values 169 1-3 Area 3- Plot of Standardised Residuals and Table of Observed and Predicted Values 170 1-4 Area 4- Plot of Standardised Residuals and Table of Observed and Predicted Values 171 1-5 Area 5- Plot of Standardised Residuals and Table of Observed and Predicted Values 172 1-6 Area 6- Plot of Standardised Residuals and Table of Observed and Predicted Values 173 1-7 Area 7- Plot of Standardised Residuals and Table of Observed and Predicted Values 174 1-8 Area 8- Plot of Standardised Residuals and Table of Observed and Predicted Values 175 1-9 Area 9- Plot of Standardised Residuals and Table of Observed and Predicted Values 176 I-10 Area 10 - Plot of Standardised Residuals and Table of Observed and Predicted Values 177 I-11 Area 11 - Plot of Standardised Residuals and Table of Observed and Predicted Values 178 1-12 Area 12 - Plot of Standardised Residuals and Table of Observed and Predicted Values 179 1-13 Area 13 - Plot of Standardised Residuals and Table of Observed and Predicted Values ý 180 1-14 Area 14 - Plot of Standardised Residuals and Table of Observed and Predicted Values 181 1-15 Area 15 - Plot of Standardised Residuals and Table of Observed and Predicted, Values 182 1-16 Area 16 - Plot of Standardised Residuals and Table of Observed and Predicted Values 183

Appendix J 190 J-1 Plot of S tandardised Residuals and Table of Observed and Predicted Values for Total Data Matrix wi th respect to the Variable E 191

Appendix K 199 K-1 Multiple Regression Analysis -T with I to R for the T otal Data Matrix 200 K-2 Plot of S tandardised Residuals and Table of Observed and Predicted Values for Total Data Matrix wi th respect to the Variable T 201 CONTENTS

APPENDICES (Contd. )

Appendix L 209 L-1 Multiple Regression Analysis -F with I to S; F with B, D; B with I to S; D with I to S for Means' Data Matrix 210 L-2 Plot of Standardised Residuals and Table of Observed and Predicted Values for Means Data with respect to Variable D 214 L-3 Multiple Regression Analysis -F with J, L, M, N, 0, P, Q, S; F with B, D; B with J, L, M, N, 0, P, Q, S; D with J, L, M, N, 0, P, Q, R, S for Means Data Matrix 215 L-4 Plot of Standardised Residuals and Table of Observed and Predicted Values for Means Data with respect to Variable D 219 L-5 Multiple Regression Analysis -F with J, L, M, N, 0, P, Q, S; F with B, D; B with J, L, M, N, 0, P, Q, S; D with J, L, M, N, 0, P, Q, S for Means Data Matrix 220 L-6 Plot of Standardised Residuals and Table of Observed and Predicted Values for Means Data with respect to Variable D 224

Appendix M 225 M-1 Area 1- Multiple Regression Analysis for U with Variables I to R 226 M-2 Area 1- Plot of Standardised Residuals and Table of Observed and Predicted U Values 227 M-3 Area 2- Multiple Regression Analysis for U with Variables I to R 228 M-4 Area 2- Plot of Standardised Residuals and Table of Observed and Predicted U Values 229 M-5 Area 3- Multiple Regression Analysis for U with Variables I to R 230 M-6 Area 3- Plot of Standardised Residuals and Table of Observed and Predicted U Values 231 M-7 Area 4- Multiple Regression Analysis for U with Variables I to R 232 M-8 Area 4- Plot of Standardised Residuals and Table of Observed and Predicted U Values 233 M-9 Area 5- Multiple Regression Analysis for U with Variables I to R 234 M-10 Area 5- Plot of Standardised Residuals and Table of Observed and Predicted U Values 235 M-11 Area 6- Multiple Regression Analysis for U with Variables I to R 236 M-12 Area 6- Plot of Standardised Residuals and Table of Observed and Predicted U Values 237 CONTENTS

APPENDICES (Contd. )

PAGE

Appendix M M-13 Area 7- Multiple Regression Analysis for U with Variables I to R 238 M-14 Area 7- Plot of Standardised Residuals and Table of Observed and Predicted U Values 239 M-15 Area 8- Multiple Regression Analysis for U with Variables I to R 240 M-16 Area 8- Plot of Standardised Residuals and Table of Observed and Predicted U Values 241 M-17 Area 9- Multiple Regression Analysis for U with Variables I to R 242 M-18 Area 9- Plot of Standardised Residuals and Table of Observed and Predicted U Values 243 M-19 Area 10 - Multiple Regression Analysis for U with Variables I to R 244 M-20 Area 10 - Plot of Standardised Residuals and Table of Observed and Predicted U Values 245 M-21 Area 11 - Multiple Regression Analysis for U with Variables I to R 246 M-22 Area 11 - Plot of Standardised Residuals and Table of Observed and Predicted U Values 247 M-23 Area 12 - Multiple Regression Analysis for U with Variables I to R 248 M-24 Area 12 - Plot of Standardised Residuals and Table of Observed and Predicted U Values 249 M-25 Area 13 - Multiple Regression Analysis for U with Variables I to R 250 M-26 Area 13. - Plot of Standardised Residuals and Table of Observed and Predicted U Values 251 M-27 Area 14 - Multiple Regression Analysis for U with Variables I to R 252 M-28 Area 14 - Plot of Standardised Residuals and Table of Observed and Predicted U Values 253 M-29 Area 15 - Multiple Regression Analysis for U with Variables I to R 254 M-30 Area 15 - Plot of Standardised Residuals and Table of Observed and Predicted U Values 255 M-31 Area 16 - Multiple Regression Analysis for U with Variables I to R 256 M-32 Area 16 - Plot of Standardised Residuals and Table of Observed and Predicted U Values 257

Appendix N 264 N-1 Area 16 - Multiple Regression Analysis for U with Unweighted El, S1, Hl 265 N-2 Area 16 - Multiple Regression Analysis for U with Unweighted El, S1, in 266 CONTENTS

APPENDICES (Contd. )

PAGE

Appendix 0 267 0-1 Area 1- Multiple Regression Analysis for U with El, S1, H1 (Area Weighted) 268 0-2 Area 1- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. E1, H1, SI (Area Weighted) 269 0-3 Area 2- Multiple Regression Analysis for U with El, Si, H1 (Area Weighted) 270 0-4 Area 2- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. El, H1, Si (Area Weighted) 271 0-5 Area 3- Multiple Regression Analysis for U with El, Si, H1 (Area Weighted) 272 0-6 Area 3- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. El, H1, S1 (Area Weighted) 273 0-7 Area 4- Multiple Regression Analysis for U with El, Si, H1 (Area Weighted) 274 0-8 Area 4- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. E1, H1, S1 (Area Weighted) 275 0-9 Area 5- Multiple Regression Analysis for U with El, Si, H1 (Area Weighted) 276 0-10 Area 5- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. El, H1, SI (Area Weighted) 277 0-11 Area 6- Multiple Regression Analysis for U with El, S1, H1 (Area Weighted) 278 0-12 Area 6- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. Ei, H1, S1 (Area Weighted) 279 0-13 Area 7- Multiple Regression Analysis for U with El, S1, H1 (Area Weighted) 280 0-14 Area 7- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. E1, H1, Si (Area Weighted) 281 0-15 Area 1- Multiple Regression Analysis for U with E1, Si, H1 (Total Weighted) 282 0-16 Area 1- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. E1, H1, Si (Total Weighted) 283 0-17 Area 2- Multiple Regression Analysis for U with Ei, S1, H1 (Total Weighted) 284 0-18 Area 2- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. E1, H1, Si (Total Weighted) 285 0-19 Area 3- Multiple Regression Analysis for U with E1, Si, H1 (Total Weighted) 286 CONTENTS

APPENDICES (Contd. )

PAGE

Appendix 0 0-20 Area 3- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. El, S1, HI (Total Weighted) 287 0-21 Area 4- Multiple Regression Analysis for U with El, Si, H1 (Total Weighted) 285 0-22 Area 4- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. El, H1, S1 (Total Weighted) 289 0-23 Area 5- Multiple Regression Analysis for U with El, Si, H1 (Total Weighted) 290 0-24 Area 5- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. El, H1, S1 (Total Weighted) 291 0-25 Area 6- Multiple Regression Analysis for U with El, Si, H1 (Total Weighted) 292 0-26 Area 6- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. El, H1, S1 (Total Weighted) 293 0-27 Area 7- Multiple Regression Analysis for U with E1, S1, H1 (Total Weighted) 294 0-28 Area 6- Plot of Standardised Residuals and Table of Observed and Predicted U Values w. r. t. El, H1, S1 (Total Weighted) 295

Appendix P 296 P-1 Multiple Regression Analysis for Usage with House, Employment & SEG (Means Data without Variable S) 297 P-2 Plot of Standardised Residuals and Table of Observed and Predicted Usage Values w. r. t. House, Employment and SEG (Means Data without Variable S) 298 P-3 Multiple Regression Analysis for Usage with House, Employment & SEG (Means Data with Variable S) 299 P-4 Plot of Standardised Residuals and Table of Observed and Predicted Usage Values w. r. t. House, Employment and SEG (Means Data with Variable S) 300 APPENDIX A 2

',

. f KEY o 4 t " Open Superstores oa" o' * Planned Superstores v°_ " Open Hypermarkets

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Superstores, Open Map and Table showing Distribution of Hypermarkets and and Planned, and their sizes 3

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date Sim; Sq. m" Spaces (Sq. ft. ) (Sq. ft. )

AVON

Bristol Asda 3,200 4,900 564 1978 (Bristol City) (34,000) (53,000)

Bristol Carrefour 8,400 16,500 1,700 1978 (Northavon) (90,000) (178,000)

Nailsea, Super Key 2,500 5,000 - Planned Bristol (27,000) (54,000) (Woodspring)

Yate Tesco 3,300 6,300 500 Planned (Northavon) (35,000) (68,000)

BEDFORDSHIRE

Kempston J. Sainsbury 3,200 5,900 500 1975 (North Bedfordshire) (34,000) (63,000)

BERKSHIRE

Bracknell SavaCentre - 14,500 735 Planned (Bracknell) (156,000)

Calcot, Reading SavaCentre 7,000 13,900 1,350 Planned (Newbury) (75,000) (150,000)

Lower Earley Asda 3,900 6,500 680 1979 (Wokingham) (42,000) (70,000)

Reading Tesco 3,300 5,100 Shared 1976 (Reading) (35,000) (55,000)

Tilehurst Super Key 3,000 5,000 221 1978 (Reading) (32,000) (54,000)

Wokingham Tesco 3,000 5,100 300 Planned (Wokingham) (32,000) (55,000)

CAMBRIDGESHIRE

Bar Hill Tesco 3,000 4,600 500 1977 (S. Cambridgeshire) (32,000) (50,000)

Cambridge Co-op (Cambridge 4,300 7,200 496 1978 (Cambridge City) Society) (46,000) (77,000) 4

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date Sq. m. S . m. Spaces (Sq. ft. ) (Sq. ft. )

CAMBRIDGESHIRE (Contd. )

Peterborough Big I 4,100 6,600 600 1979 (Peterborough City) (44,000) (71,000)

Wisbech Super Key 2,600 4,600 400 1974 (28,000) (50,000)

CHESHIRE

Chester Tesco 4,600 7,400 550 Planned (Chester City) (50,000) (80,000)

Crewe (Crewe Asda 4,100 6,700 600 1979 & Nantwich) (44,000) (72,000)

Ellesmere Port Lewis's 6,300 9,600 3,000 1976 (Ellesmere Port) (68,000) (103,000)

Macclesfield Co-op (North 4,200 5,600 - Planned (Macclesfield) Midland Soc. ) (45,000) (60,000)

Warrington Fine Fare 7,400 10,100 1,500 Planned (Warrington) (80,000) (109,000)

Widnes Asda 4,400 6,200 750 1969 (Halton) (47,000) (67,000)

Widnes Co-op 4,600 7,200 800 1975 (Halton) (Warrington Soc. ) (50,000) (78,000)

Winsford Fine Fare 4,600 6,300 750 1976 (Vale Royal) (50,000) (68,000)

CLEVELAND

Hartlepool, Fine Fare 4,600 7,400 480 Planned (Middleton Grange (50,000) (80,000) Hartlepool)

Middlesbrough Fine Fare 5,600 8,500 688 Planned (Middlesbrough) (60,000) (91,000)

South Bank Asda 3,700 5,600 - Planned (Langbaurgh) (40,000) (60,000) 5

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date S . m. S . m. Spaces (Sq. ft. ) (Sq. ft. )

CLEVELAND (Contd. )

Stockton-on- Asda 3,900 6,700 454 1970 Tees (42,000) (72,000) (Stockton-on-Tees

Thornaby Woolco 6,200 9,600 950 1968 (Stockton-on-Tees) (67,000) (103,000)

CORNWALL

Camborne Big I 2,800 3,700 400 Planned Merrier) (30,000) (40,000)

Truro Tesco 2,600 4,600 Shared 1978 (Carrick) (28,000) (50,000)

CUMBRIA

Barrow-in Asda 2,900 4,600 550 1974 Furness (Barrow (31,000) (50,000) in-Furness)

DERBYSHIRE

Chesterfield Big I - 3,700 - Planned (Chesterfield) (40,000)

Chesterfield Preston 3,300 5,100 150 1969 (Chesterfield) (35,000) (55,000)

Derby, Spondon Asda 3,700 5,600 - Planned (Derby) (40,000) (60,000)

Mickleover Hillards 3,200 4,600 371 1979 (Derby) (34,000) (49,000)

Sinfin Fine Fare 3,300 4,600 850 1979 (Derby) (35,000) (50,000) (1977)

DEVON

Exeter Big I 3,000 4,300 - Planned (Exeter City) (32,000) (46,000)

Lee Mill Tesco 2,800 4,600 5,000 Planned (South Hams) (30,000) (50,000) 6

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date Sq. m. S . m. Spaces (Sq. ft. ) (Sq. ft. )

DEVON (Contd. )

Newton Abbot Tesco 3,700 5,600 400 Planned (Teignbridge (40,000) (60,000)

Plymouth Asda 2,900 4,600 557 1976 (Plymouth City) (31,000) (50,000)

DORSET

Bournemouth Big I 5,700 8,500 500 Planned (Bournemouth) (61,000) (91,000)

Bournemouth Woolco 6,900 10,300 1,650 1968 (Bournemouth) (74,000) (111,000)

Weymouth Big I 2,900 3,800 550 1978 (Weymouth & (31,000) (41,000) Portland)

DURHAM

Darlington Big I 3,100 5,200 - Planned (Darlington) (33,000) (56,000)

Darlington Fine Fare 3,000 5,400 300 1978 (Darlington) (32,000) (58,000)

Darlington Wm.Morrison 3,400 5,000 - 1980 (Darlington) Supermarkets (37,000) (54,000)

Newton Aycliffe Fine Fare 2,600 4,100 280 1979 (Sedgefield) (28,000) (44,000)

Peterlee Fine Fare 4,500 6,300 240 1975 (Easington) (48,000) (68,000)

Stanley Fine Fare 4,500 5,900 340 1977 (Derwentside) (48,000) (63,000)

EAST SUSSEX

Eastbourne Tesco 3,700 5,900 400 Planned (Eastbourne) (40,000) (63,000) 7

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date S . m. Sq. m. Spaces (Sq. ft. ) (Sq. ft. )

ESSEX

Basildon SavaCentre 6,500 13,900 1,000 1980 (Basildon) (70,000) (150,000)

Chelmer Village Asda 2,800 4,600 520 1979 (Chelmsford) (30,000) (50,000)

Colchester Tesco 2,800 4,600 400 Planned (Colchester) (30,000) (50,000)

Colchester Tesco 4,200 6,500 700 Planned (Colchester) (45,000) (70,000)

Colchester Tesco 2,800 4,800 250 1979 (Colchester) (30,000) (52,000) (1978)

Harlow Tesco 6,500 9,500 - Planned (Harlow) (70,000) (102,000)

Pitsea Tesco 7,600 9,800 1,000 1978 (Basildon) (82,000) (105,000)

South Woodham Asda 3,200 5,200 600 1978 Ferrers (Chelmsford) (34,000) (56,000)

Wickford Not Available 2,700 3,600 100 Planned (Basildon) (29,000) (39,000)

GREATER LONDON

Croydon J. Sainsbury 3,300 6,000 500 Planned (L. B. Croydon) (36,000) (65,000)

Erith Asda 4,300 7,200 500 Planned (L. B. Bexley) (46,000) (78,000)

Finchley Tesco 3,300 4,800 300 1978 (L. B. Barnet) (36,000) (52,000)

Ilford Cartiers 3,300 - 800 1979 (L. B. Redbridge) (Ilford) (35,000) (1976)

Park Royal Asda 4,600 7,200 622 Planned (L. B. Ealing) (49,000) (78,000) 8

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date Sq. m. S . m. Spaces (Sq. ft. ) (Sq. ft. )

GREATER LONDON (Contd. )

Stratford Big I 2,500 4,900 - Planned (L. B. Newham) (27,000) (53,000)

Sutton Tesco 4,600 7,400 400 Planned (L. B. Sutton) (50,000) (80,000)

Thornton Heath Tesco 4,700 6,500 400 Planned (L. B. Croydon) (51,000) (78,000)

Tottenham Tesco 3,700 5,600 300 Planned (L. B. Haringey) (40,000) (60,000)

Walthamstow J. Sainsbury 2,600 4,200 350 Planned (L. B. Waltham Forest) (28,000) (45,000)

Wood Green Big I - 5,600 - Planned

GREATER MANCHESTER

Ashton-under Big I 3,100 4,800 500 Planned Lyne (330,000) (52,000) (Tameside)

Bolton Asda 3,200 5,900 500 1970 (Bolton) (34,000) (64,000)

Chadderton Asda 3,300 5,800 560 1972 (Oldham) (36,000) (62,000)

Failsworth Co-op (Norwest 3,700 5,800 530 1975 (Oldham) Society) (40,000) (62,000)

Failsworth Wm.Morrison 5,600 - 200 1978 (Oldham) Supermarkets (60,000)

Farnworth Asda 3,300 5,800 450 1979 (Bolton) (35,000) (62,000)

Golborne Asda 2,800 5,000 500 1972 (Wigan)

Gorton Co-op 2,800 - - Planned (Manchester (Co-operative (30,000) City) Retail Services) 9

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date S . m. S . m. Spaces (Sq. ft. ) (Sq. ft. )

GREATER MANCHESTER (Contd. )

Harpurhey Asda 4,300 6,800 700 Planned (Manchester City) (46,000) (73,000)

Hindley Co-op 2,700 4,100 500 1977 (Wigan) (Greater (29,000) (44,000) Lancastria Soc. )

Horwich Tesco 2.800 4.600 600 1974 (Bolton) (30,000) (50,000)

Hyde Fine Fare 7,000 9,800 600 1976 (Tameside) (75,000) (105,000)

Ince-in Wm.Morrison --- 1979 Makerfield Supermarkets (Wigan)

Irlam Tesco 6,800 9,700 980 1976 (Salford City) (73,000) '(104,000)

Manchester, Asda 3,200 5,400 475 1978 Longsight (34,000) (58,000) (Manchester City)

Middleton Woolco 6,000 9,300 816 1971 (Rochdale) (65,000) (100,000)

Oldham Co-op (Pioneers 4,600 7,500 450 1976 (Oldham) Society) (50,000) (81,000)

Rochdale Asda 3,800 6,600 1,000 1969 (Rochdale) (41,000) (71,000)

Rochdale Tesco 2,800 4,200 550 1973 (Rochdale) (30,000) (45,000)

Sale Tesco 4,100 5,900 234 1977 (Trafford) (44,000) (64,000)

Walkden Tesco 5,400 7,400 900 1978 (Salford City) (58,000) (80,000) 10

Location Retailer Net Gross Car 0 epning (District) Floorspace Floorspace Parking Date Sq. m. Sg"s. Spaces (Sq. ft. ) (Sq. ft. )

GREATER MANCHESTER (Contd. )

Whitefield Preston 2,600 4,200 325 1975 (Bury) (28,000) (45,000)

Wigan Asda 3,000 5,400 550 1970 (Wigan) (32,000) (58,000)

Wythenshawe Co-op (Notwest 5,600 9,800 1,400 1979 (Manchester City) Society) (60,000) (106,000) (1976)

HAMPSHIRE

Basingstoke Not available 3,700 5,000 Shared Planned Chineham (40,000) (54,000) (Basingstoke & Deane)

Burlesdon " Tesco 3,700 5,600 632 Planned Towers (40,000) (60,000) (Eastleigh)

Chandlers Ford Carrefour 5,200 11,500 1,100 1974 (Eastleigh) (56,000) (124,000)

Gosport Asda 3,000 4,700 347 1977 (Gosport) (32,000) (51,000)

Gosport Big I 3,200 4,100 300 1973 (Gosport) (34,000) (44,000)

Littlepark Co-op (Portsea 6,500 10,700 1,200 Planned (Havant) Island Society) (70,000) (115,000)

Portsmouth Tesco 3,700 5,900 Shared 1978 (Portsmouth City) (40,000) (64,000)

Southampton Tesco 3,700 5,600 500 Planned (Southampton City) (40,000) (60,000)

Waterlooville Asda 3,300 5,600 600 Planned (Havant) (36,000) (60,000)

HEREFORD AND WORCESTER

Bromsgrove Fine Fare 3,500 4,900 380 1979 (Bromsgrove) (38,000) (53,000) 11

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date S . m. Sq. m. Spaces (Sq. ft. ) (Sq. ft. )

HERTFORDSHIRE

Boreham Wood Tesco 2,900 4,600 300 Planned (Hertsmere) (31,000) (50,000)

Hatfield Woolco 6,700 9,500 1,250 1972 (Welwyn Hatfield) (72,000) (102,000)

Stevenage Tesco 3,200 4,800 600 1973 (Stevenage) (34,000) (52,000)

Watford Tesco 4,200 6,000 450 Planned (Watford) (45,000) (65,000)

HUMBERSIDE

Scunthorpe Asda 2,800 4,500 5113 1976 (Scunthorpe) (30,000) (48,000)

Scunthorpe Co-op 2,800 3,700 - 1979 (Scunthorpe) (Co-operative (30,000) (40,000) Retail Services)

ISLE OF WIGHT

Newport Big I 3,000 5,300 - Planned (Medina) (32,000) (57,000)

KENT

Broadstairs Co-op (Royal 6,200 9,900 900 1977 (Thanet) Arsenel Soc. ) (67,000) (107,000)

Canterbury Super Key 2,800 4,500 400 1979 (Canterbury City) (30,000) (48,000)

Chatham Fine Fare 3,100 6,000 400 1978 (Medway) (33,000) (65,000)

Chatham Tesco 5,900 8,200 500 Planned (Medway) (63,000) (88,000)

Chatham SavaCentre 5,900 13,000 2,000 1978 (Hempstead Valley Medway) (64,000) (140,000)

Swanley Asda 4,000 6,700 410 Planned (Sevenoaks) (43,000) (72,000) 12

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date Sq. m. Sq. M. Spaces (Sq. ft. ) (Sq. ft. )

LANCASHIRE

Blackburn Co-op 3,200 4,100 330 1977 (Blackburn) (Blackburn (34,000) (44,000) Society)

Blackpool Fine Fare 4,700 9,100 500 1979 (Blackpool) (51,000) (98,000)

Blackpool, Co-op (Greater 5,300 9,800 1,000 1979 Marton Lancastria (57,000) (106,000) (Blackpool) Society)

Colne (Pendle) Asda 3,100 5,700 500 1971 (33,000) (61,000)

Preston Asda 3,600 8,900 247 1967 (Preston) (39,000) (96,000)

Rawtenstall Asda 3,200 4,900 430 1977 (Rossendale) (34,000) (53,000)

Whittle-Le- Asda 3,200 4,900 536 1978 Woods (34,000) (53,000) (Chorley)

LEICESTERSHIRE

Braunstone Asda " 3,700 6,100 660 Planned (Blaby) (40,000) (66,000)

Hinckley Asda 2,600 4,400 320 1972 (Hinckley & (28,000) (47,000) Bosworth)

Leicester, Woolco 5,900 8,500 600 1967 Oadby (63,000) (92,000) (Oadby & Wigston)

Leicester, Co-op 5,800 7,700 800 1975 Thurmaston (Leicestershire (62,000) (83,000) (Charnwood) 13

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date S . m. S . m. Spaces (Sq. ft. ) (Sq. ft. )

LINCOLNSHIRE

Gainsborough Big I 3,300 4,600 400 1977 (West Lindsay) (35,000) (50,000)

Lincoln Co-op (Lincoln 2,500 3,400 280 1978 (Lincoln City) (27,000) (37,000)

Lincoln Tesco 3,300 4,600 200 1974 (Lincoln City) (36,00) (49,000)

Spalding Super Key 2,700 5,400 400 1978 (South Holland) (29,000) (58,000)

MERSEYSIDE

Birkenhead Asda 3,100 5,600 425 1976 (Wirral) (33,000) (60,000)

Birkenhead Co-op 4,400 6,800 250 1972 Woodchurch (Co-operative (47,000) (73,000) (Wirral) Retail Services)

Huyton Asda 3,200 4,900 536 1977 (Knowsley) (34,000) (53,000)

Kirkby, Co-op. (Greater 5,900 10,000 - 1978 (Knowsley) Lancastria (64,000) (108,000) Society)

Liverpool Co-op 3,100 4,900 325 1979 (Liverpool City) (Co-operative (33,000) (53,000) Retail Services)

Southport Big I 4,200 5,900 500 Planned (Sefton) (45,000) (64,000)

St Helens Co-op 2,700 3,600 - 1978 (St Helens) (St Helens Soc. ) (29,000), (39,000)

St Helens Wm.Morrison 3,100 3,700 500 1978 (St Helens) Supermarkets (33,000) (40,000) (1971) 14

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date S . m. S . m. Spaces (Sq. ft. ) (Sq. ft. )

NORTH YORKSHIRE

Harrogate Wm.Morrison 5,100 - - Planned (Harrogate) Supermarkets (55,000)

York Asda 3,000 5,600 628 1974 Huntingdon (32,000) (60,000) (Ryedale)

NORTHAMPTONSHIRE

Northampton, Tesco 9,900 13,900 1,600 1974 Weston Favell (107,000) (150,000) (Northampton)

NORTHUMBERLAND

Blyth Presto 2,800 4,200* Shared 1972 (Blyth Valley) (30,000) (45,000)

NOTTINGHAMSHIRE

Arnold Fine Fare 4,600 7,200 620 1978 (Gedling) (50,000) (77,000)

Bulwell Co-op (Greater 2,700 4,200 130 1978 (Nottingham Nottingham (29,000) (45,000) City) Society)

Mansfield Tesco 2,800 4,400 519 Planned (Mansfield) (30,000) (47,000)

Nottingham Asda 5,100 9,700 1,000 1966 (Rushcliffe) (55,000) (104,000)

Nottingham Tesco 5,600 7,400 Shared 1978 (Nottingham (60,000) (80,000) City)

Sutton-in- Fine Fare 5,900 8,600 860 1978 Ashfield (64,000) (93,000) (Ashfield) 15

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date S S . m. . m. Spaces (Sq. ft. ) (Sq. ft. )

OXFORDSHIRE

Banbury Big 1 2,700 3,900 - 1977 (Cherwell) (29,000) (42,000)

Wheatley So-Lo 2,700 3,500 350 1978 (South Oxfordshi re) (29,000) (38,000)

SALOP

Telford Carrefour 5,200 10,900 1,000 1973 (Wrekin) (56,000) (117,000)

Telford J. Sainsbury 2,600 4,400 900 1973 (Wrekin) (28,000) (47,000)

SOMERSET

Taunton Big I 4,600 7,100 650 Planned (Taunton Deans) (50,000) (76,000)

Yeovil Tesco 2,900 4,600 Shared 1978 (Yeovil) (31,000) (50,000)

SOUTH YORKSHIRE

Carcroft Asda 3,000 4,800 320 1974 (Donacaster) (32,000) (52,000)

Dinnington Asda - - - Planned (Rotherham)

Rotherham, Asda 3,200 5,600 500 1969 Eastwood (34,000) (60,000) (Rotherham)

Sheffield Not Available 4,000 6,000 527 1976 Sheaf Valley (43,000) (65,000) (Sheffield City)

Sheffield, Asda 2,800 4,700 400 1976 Chapeltown (30,000) (51,000) (Sheffield City)

Sheffield, Asda 2,700 5,500 478 1970 Handsworth (29,000) (59,000) (Sheffield City) 16

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date Sg.. m. Sq. m" Spaces (Sq. ft. ) (Sq. ft. )

STAFFORDSHIRE

Burslem Co-op (North 2,800 3,400 450 1973 (Stoke-on- Midland (30,000) (37,000) Trent) Society)

Hanley Tesco 5,600 7,600 500 1977 (Stoke-on-Trent) (60,000) (82,000)

Long ton Co-op (North 3,100 4,100 - Planned (Stoke-on Midland (33,000) (44,000) Trent) Society)

Newcastle- Co-op (North 2,800 4,600 500 1973 Under-Lyme Midland (30,000) (50,000) (Newcastle- Society under-Lyme)

Stafford Tesco 2,600 4,500 400 Planned (Stafford) (28,000) (48,000)

Talke (Stoke- Co-op (North 6,500 8,000 1,000 1975 on-Trent City) Midland (70,000) (86,000) Society)

Tame Valley Co-op (Tamworth 3,300 4,600 700 Planned Wilnecote Society) (35,000) (50,000) (Tamworth)

SUFFOLK

Ipswich, Boss Co-op (Ipswich 2,800 4,900 603 1977 Hall (Babergh) Society) (30,000) (53,000)

Lowestoft Fine Fare 4,200 5,900 600 Planned (Waveney) (45,000) (64,000)

SURREY

Woking Fine Fare 3,100 5,600 1,000 1977 (Woking) (33,000) (60,000)

TYNE AND WEAR

Killingworth Woolco 6,300 9,800 1,030 1970 (North Tyneside) (68,000) (105,000) 17

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date Sg. m. S . m. Spaces (Sq. ft. ) (Sq. ft. )

TYNE AND WEAR (Contd. )

North Shields Presto 3,900 5,600 Shared 1978 (North Tyneside) (42,000) (60,000)

Washington Presto 2,600 3,400 Shared 1978 (Sunderland) (28,000) (37,000)

Washington SavaCentre 6,500 15,100 1,300 1977 (Sunderland) (70,000) (163,000)

Washington Woolco 6,300 10,400 3,000 1973 (Sunderland) (68,000) (112,000)

WARWICKSHIRE

Bedworth Tesco 3,300 4,800) 450 Planned (Nuneaton) (35,000) (52,000)

Leamington Spa Asda 3,300 5,200 624 Planned (Warwick) (35,000) (56,000)

WEST MIDLANDS

Aston Asda 4,600 7,200 706 1979 (Birmingham City) (50,000) (78,000)

Birmingham Not Available - 8,700 800 Planned (Birmingham City) (94,000)

Birmingham, Tesco 4,600 6,300 - 1979 Edgbaston (50,000) (68,000) (Birmingham City)

Birmingham, Co-op 3,900 5,600 500 Planned Small Heath (Birmingham (42,000) (60,000) (Birmingham City) Society)

Brierly Hill Asda 3,300 5,400 510 1978 (Dudley) (36,000) (58,000)

Coventry Asda 3,500 5,900 659 Planned (Coventry, City) (38,000) (64,000)

Coventry J. Sainsbury 2,500 5,300 400 1977 (Coventry City) (27,000) (57,000) 18

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date Sq. m. Sq. m. Spaces (Sq. ft. ) (Sq. ft. )

WEST MIDLANDS (Contd. )

Coventry Tesco 3,700 5,600 500 Planned (Covntry City) (40,000) (60,000)

Darlaston Asda 3,500 5,600 270 1978 (Walsall) (38,000) (60,000)

Minworth Carrefour 6,500 13,700 1,300 1977 (Birmingham City) (70,000) (148,000)

Oldbury SavaCentre 5,600 12,100 1,000 . Planned (Sandwell) (60,000) (130,000)

Smethwick Co-op - 4,600 - Planned (Sandwell) (Birmingham (50,000) Society)

WEST SUSSEX

Worthing Tesco 2,600 4,400 300 Planned (Worthing) (28,000) (47,000)

WEST YORKSHIRE

Bradford Wm.Morrison 3,300 4,600 452 1976 (Bradford City) Supermarkets (36,000) (50,000)

Bradford Tesco 5,600 7,400 450 Planned (Bradford City) (60,000) (80,000)

Huddersfield Hillards 3,300 4,800 450 1979 (Kirklees) (35,000) (52,000)

Huddersfield Asda 3,600 7,400 600 1980 Birkby (Kirklees ) (39,000) (80,000)

Hunslet (Leeds Wm. Morrison 3,400 4,900 - 1976 City) Supermarkets (37,000) (53,000)

Leeds (Leeds Big I - 5,700 - Planned City) (61,000)

Leeds (Leeds Wm. Morrison 3,300 4,600 146 1972 City) Supermarkets (36,000) (50,000) 19

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date Sq. m. Sq. m. Spaces (Sq. ft. ) (Sg. ft. )

WEST YORKSHIRE (Contd. )

Leeds, Meanwood Tesco 3,000 4,800 400 Planned (Leeds City) (32,000) (52,000)

Leeds, Tesco 3,700 5,600 - Planned Middleton (40,000) (60,000) (Leeds City)

Pudsey (Leeds Asda 4,200 8,300 615 1969 City) (45,000) (89,000)

Wetherby Co-op 2,800 3,900 150 1978 (Leeds City) (Harrogate Soc. ) (30,000) (42,000)

WILTSHIRE

Swindon Carrefour 4,000 6,500 - Planned (Thamesdown) (43,000) (70,000)

Trowbridge Tesco 3,000 4,600 - Planned (West Wiltshire) (32,000) (50,000)

WALES

CLWYD

Kinmel Bay Asda 3,500 5,100 500 Planned (Colwyn) (38,000) (55,000)

Wrexham Tesco 2,800 4,600 480 1977 (Wrexham Maelor) (30,000) (50,000)

DYFED

Llanelli Tesco 3,700 5,600 440 1978 (Llanelli) (40,000) (60,000)

GWENT

Cwmbran Woolco 6,300 10,900 900 1975 (Torfaen) (68,000) (117,000)

Newport Asda 3,100 5,400 746 1973 (Newport) (33,000) (58,000) 20

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parkin Date Sq. m. Sq. m" Spaces (Sq. ft. ) (Sq. ft. )

GWYNEDD

Llandudno Asda 2,900 4,600 700 1977 (Aberconwy) (31,000) (50,000) (1975)

Bridgend (Ogwr) Big I 3,400 5,500 750 Planned (37,000) (59,000)

Bridgend, Pyle Co-op 3,100 3,700 400 1973 (Ogwr) (Co-operative (33,000) (40,000) Retail Services)

Caerphilly Carrefour 5,100 10,900 1,100 1972 (Rhymney Valley) (55,000) (117,000)

Llantrisant Tesco 3,300 5,000 500 1979 (Taff Ely) (35,000) (54,000)

Merthyr Tydfil Asda 3,200 4,900 540 1977 (Merthyr Tydfil) (34,000) (53,000)

SOUTH GLAMORGAN

Barry (Vale of Presto 2,600 3,300 280 1971 Glamorgan) (28,000) (35,000)

Cardiff Co-op 4,200 7,000 500 1973 (Cardiff City) (Co-operative (45,000) (75,000) Retail Services)

Cardiff Tesco 3,000 4,600 - Planned (Cardiff City) (32,000) (50,000)

WEST GLAMORGAN

Neath (heath) Tesco 4,000 5,600 Shared 1978 (43,000) (60,000)

Swansea Asda 3,300 5,200 430 Planned (Swansea City) (36,000) (56,000)

SCOTLAND

CENTRAL

Falkirk Fine Fare 4,100 5,600 365 1978 (Falkirk) (44,000) (60,000) 21

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date Sq. m. Sg. m" Spaces (Sq. ft. ) (Sq. ft. )

CENTRAL (Contd. )

Stirling Fine Fare 3,200 4,600 180 1974 (Stirling) (34,000) (50,000)

DUMFRIES AND GALLOWAY

Dumfries Co-op 2,600 3,500 150 1976 (Nithsdale) (Dumfries Soc. ) (28,000) (38,000)

FIFE

Dunfermline Fine Fare 3,000 5,000 400 1975 (Dunfermline) (32,000) (54,000)

GRAMPIAN

Aberdeen Co-op (Northern 4,600 6,400 - 1977 (Aberdeen City) Aberdeen Soc. ) (50,000) (69,000)

Aberdeen, Fine Fare 3,700 5,600 400 1970 Bridge of Dee (40,000) (60,000) (Aberdeen City)

Aberdeen, Fine Fare 2,900 4,400 210 Planned Bridge of Don (31,000) (47,000) (Aberdeen City)

Aberdeen, Dyce Asda 3,900 5,900 600 1976 (Aberdeen City) (42,000) (63,000)

LOTHIAN

Edinburgh Asda 3,600 6,800 528 1972 (Edinburgh City) (39,000) (73,000)

Edinburgh, Co-op 2,800 3,700 - 1979 Granton (St Cuthberts (30,000) (40,000) (Edinbugh City) Edinburgh Soc. )

Livingston Co-op 2,800 4,100 600 1976 (West Lothian) (Blackburn (30,000) (44,000) Supermarket Ltd. ) 22

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date Sg. m. Sq. m. Spaces (Sq. ft. ) (Sq. ft. )

LOTHIAN (Contd. )

Livingston Woolco 6,200 10,400 2,000 1976 (West Lothian) (67,000) (112,000)

STRATHCLYDE

Ayr (Kyle & Tesco 3,100 4,700 - 1974 Carrick) (33,000) (51,000)

Bishopbriggs Fine Fare 4,400 5,400 350 Planned (Glasgow City) (43,000) (58,000)

Blantyre Asda 3,400 5,600 420 Planned (Hamilton) (37,000) (60,000)

Clydebank Fine Fare 4,300 5,900 500 1978 (Clydebank) (46,000) (63,000)

Coatbridge Asda 4,000 6,400 460 1976 (Monklands) (43,000) (69,000)

Cumbernauld Wm.Low 2,600 3,900 440 Planned (Cumbernauld & Kilsyth) (28,000) (42,000)

Cumbernauld Woolco 6,000 10,000 950 1974 (Cumbernauld & Kilsyth) (65,000) (108,000)

East Kilbride Fine Fare 2,900 3,700 1,200 1973 (East Kilbride) (31,000) (40,000)

Glasgow, Fine Fare 4,000 7,600 350 1977 Bearsden (43,000) (83,000) (Bearsden & Miingavie)

Glasgow, Co-op (C. W. S. 3,700 5,600 476 Planned Maryhill Retail Operations (40,000) (60,000) (Glasgow City) Group)

Greenock Tesco 2,800 4,400 430 1977 (Inverclyde) (30,000) (47,000)

Irvine Tesco 2,800 4,600 Shared 1975 (Cunninghame) (30,000) (50,000) 23

Location Retailer Net Gross Car Opening (District) Floorspace Floorspace Parking Date SSC. m. Sq. m _ Spaces (Sq. ft. ) (Sq. ft. )

STRATHCLYDE (Contd. )

Motherwell Fine Fare 3,200 5,600 400 1979 (Motherwell) (34,000) (60,000)

Pollock Co-op (C. W. S. 3,100 5,200 1,200 1979 (Glasgow City) Retail Operations (33,000) (56,000) Group)

Pollock Presto 2,600 3,700 Shared 1979 (Glasgow City) (28,000) (40,000)

Pollock Tesco 5,100 7,400 Shared 1979 (Glasgow City) (55,000) (80,000)

Summerston Asda 3,200 4,900 510 1979 (Glasgow City) (34,000) (53,000)

TAYSIDE

Dundee Asda 2,500 4,000 368 1977 (Dundee City) (27,000) (43,000)

Dundee Tesco 4,700 6,300 600 1978 (Dundee City) (51,000) (68,000)

NORTHERN IRELAND

Bangor Stewarts 2,500 4,200 900 1975 (North Down) Supermarkets (27,000) (45,000)

Glengormley Stewarts 2,800 5,200 1,000 1973 (Newtonabbey) Supermarkets (30,000) (56,000)

Newtonards Stewarts 2,600 4,200 1,400 1976 (Ards) Supermarkets (28,000) (45,000)

Newtonards Woolco 4,100 7,200 1,400 1976 (Ards) (44,000) (78,000) 24

APPENDIX B 25

Sucio-economic groups CIrs. ification by socio"cconon%ic groups was (0) Junior non-manual workers introduced in 1951 in and extensively amended Employees, not exercising general planning or 1961. The classification aims to bring together supervisory powers, engaged in clerical, sales jobs of social economic people with similar and and non-manual communications The classification is applied to the status. occupations, excluding 'those who have active, retired and permanently sick economically additional' and' formal 'supervisory functions by considering their employment status Lnd (these are included in- group 5,2). occupation. (7) Personal service workers (1) Employers in and managers central and Employees engaged in service occupations local industry, commerce etc government, caring for food, drink, clothing and other large - establishments personal needs. 1.1 Employers in industry, commerce etc. Persons who employ others in non- (11) Foremen and supervisors - manual employing 25 ur agricultural enterprises Employees (other than managers) who more persons. lormally and immediately supervise others 1.2 Managers in central and luul government, cngaged in manual occupations, whether or industry, commerce, etc. nut thetnsclveii engaged in such occupations. Persons who generally plan and supervise in enterprises employing manual non-agricultural (9) Skilled workers 25 or more persons. Employees engaged in manual occupations which require considerable and specific skills. (2) Employers and managers in industry, commerce etc. small establishments - (1U) Semi-skilled manual workers 2.1 Employers in industry, commerce etc. - Employees engaged in manual occupations small establishments. which require slight but specific skills. As in 1.1 but in establishments employing fewer than 25 persons. (I Ij Unskilled manual workers 2.2 Managers in industry, commerce ctc. - Other employees engaged in manual small establishments. uccupations. As in 1.2 but in establishments employing 'fewer than 25 persons. (12) Own ' account workers (other' than profcisional)' (9) Professional workers - self-employed Self-employed persons engaged in any trade, Self-employed persons engaged in work personal scrvlce`tor ' mdniial 'occupation not normally requiring qualifications of university ' iequii'ing"'training of university degree uormrlly standard. degree 'standard'Fand having no employees other khan family workers: (4) Professional workers - employees Employees engaged in work " normally L managers , (1! ) Farmers empläyers'and qualifications of university degree requiring Persons who''own rcni"or'gtanage farms, standard. at. ukc)"ývardens orforests, employing people Intermediate (5) non-manual worker 1' other than family'workcis in the work of the 5.11.. Ancillary workers and anists Employees H engaged in non-manual occupations ancillary 'to' the professions, (14) Farmers - own1account' 'normally requiring qualification; of not l crsons who own or 'rent fangs. maritct university degree standard; persons gardens'orforests and having: nolemployFes in artistic and not employing t..: engaged work ..., other than fuintlyºyorkers.... others therein. ', Self-employed nurses, medical auxiliaries,; teachers, work study (15) Agricultural workers in engineers and technicians are included. Persons engaged tending crops, animals, game or forests, or'operating'agricultural or 5.2 Foremen and non-manual supervisors forestry machinery. Employees (other than managers) engaged in included in 6, occupations group who (16) Members of the Armed Forces. formally and immediately supervise others in engaged such occupations. (17) Inadequately described and not stated occupations. B-1

SEG Standard Classification System 26

APPENDIX C 27

01 26 AA 01 1 AE 02 2 03 3 27 04 4 AF 01 05 5 28 06 6 AG 01 29 07 7 02 03 30 08 8 31 09 9 04 05 32 10 10

01 33 AB 01 11 AH 02 34 02 12 03 35 03 13 04 36 04 14 05 37 05 15 * 38 06 16 06 39 07 17 07 08 40 08 09 41 09 10 42 10 11 43 12 44 13 45 AC 01 18 46 02 19 14 15 47 03 20 16 48 04 21 49 05 22 17 18 50 06 23 19 51 07 20 52 21 53 AD 01 24 22 02 25

C-1 Districts List of Numbered Ennumeration 28

AJ 01 54 AL 07 85 02 55 08 86 03 56 09 87 04 57 10 88 05 58 11 06 59 01 89 07 60 AM 90 08 61 02 91 09 62 03 04 92 10 63 05 93 11 64 06 94 12 65 95 13 66 07 08 96 14 67 09 97 15 68 10 98 16 69 99 17 70 11 12 100 18 71 13 101 19 72 14 102 20 73 15 103 16 104 AK 01 74 105 02 75 17 18 106 03 76 19 107 04 77 20 108 05 78 109 06 21 22 110 AL 01 79 111 02 80 23 03 81 24 04 82 25 05 83 26 06 84

C-1 (Contd. ) Districts List of Numbered Ennumeration 29

09 144 AN 01 112 AP 145 02 113 10 03 114 12 146 147 04 115 13 148 05 116 14 149 06 117 15 150 07 118 16 151 08 119 17 152 09 120 18 153 10 121 19 154 11 122 20 155 12 123 21 156 13 124 22 157 14 125 23 24 158 15 126 25 159 16 127 26 160 17 128 161 18 129 27 28 162 19 130 20 131 29 30 21 132 22 133 23 134 163 24 135 AQ 01 164 25 02 03 165 04 166 AP 01 136 167 02 137 05 168 03 138 06 07 169 04 139 08 170 05 140 09 171 06 141 10 172 07 142 11 173 08 143 12 174

C-1 (Contd. ) Districts List of Numbered Ennumeration 30

28 207 AQ 13 175 AR 208 14 176 29 30 209 15 177 300 16 178 31 301 17 179 32 33 302 18 34 303 35 304 AR 01 180 305 02 181 36 306 03 182 37 38 307 04 183 308 05 184 39 309 06 185 40 41 310 07 186 42 311 09 187 10 189 01 312 11 190 AS 02 313 12 191 03 314 13 192 04 315 14 193 316 15 194 05 317 16 195 06 318 17 196 07 08 319 18 197 09 320 19 198 10 321 20 199 11 322 21 200 12 323 22 201 324 23 202 13 14 325 24 203 01 326 25 20 AT 02 327 26 205 03 328 27 206

C-1 (Contd. ) Districts List of Numbered Ennumeration 31

360 AT 04 329 AV 19 05 330 20 361 06 331 21 362 07 332 22 363 08 333 09 334 AW 01 364 10 335 02 365 11 336 03 366 12 337 04 367 13 338 05 368 14 339 06 369 15 340 07 370 371 16 341 08 17 18 AX 01 372 02 373

AU 01 342 03 374 02 343 04 375 03 344 05 376 04 345 06 377 05 346 07 378 06 347 08 379 07 348 09 380 08 349 10 318 09 350 11 382 10 351 12 383 11 352 13 384 385 12 353 14 13 354 15 386 14 355 16 387 388 15 356 17 16 357 389 17 358 AY 01 18 359 02 390

C-1 (Contd. ) Districts List of Numbered Ennumeration 32

01 424 AY 03 391 BA 04 392 02 425 426 05 393 03 427 06 394 04 428 07 395 05 06 429 08 396 . 430 09 397 07 431 10 398 08 432 11 399 09 433 12 400 10 434 13 401 11 435 14 402 12 436 15 403 13 14 437 16 404 438 17 405 15 439 18 16 17 440 441 AZ 01 406 18 19 442 02 407 443 03 408 20 444 04 409 21 445 05 410 22 23 446 06 411 24' 447 07 412 448 08 413 25 26 449 09 414 27 450 10 415 28 451 11 416 29 452 12 417 30 453 13 418 14 419 01 454 15 420 BB 02 455 16 421 03 456 17 422 18 423

C-1 (CONTD. ) Districts List of Numbered Ennumeration 33

488 BB 04 457 BD 01 05 458 02 489 06 459 03 490 07 460 04 491 08 461 05 492 493 09 462 06 494 10 463 07 495 11 464 08 12 465 09 496 494 13 466 10 14 467 11 15 468 498 16 469 BE 01 499 17 470 02 500 18 471 03 19 472 04 501 05 502 06 503 BC 01 473 504 02 474 07 505 03 475 08 506 04 476 09 507 05 477 10 508 06 478 11 509 07 479 12 510 08 480 13 511 09 481 14 512 10 482 15 11 483 16 513 514 12 484 17 515 13 485 18 516 14 486 19 517 15 487 20 518 16 21 22 519

C-1 (CONTD. ) Districts List of Numbered Ennumeration 34

553 BE 23 520 BF 21 554 24 521 22 25 522 23 555 556 26 523 24 557 27 524 25 28 525 558 29 526 BG 01 559 30 527 02 560 31 528 03 561 32 529 04 562 33 530 05 563 34 531 06 564 35 532 07 08 565 09 566 BF 01 533 567 02 534 10 568 03 535 11 569 04 536 12 570 05 537 13 571 06 538 14 572 07 539 15 573 08 540 16 574 09 541 17 18 575 10 542 19 576 11 543 20 577 12 544 21 578 13 545 579 14 546 22 23 580 15 547 24 581 16 548 25 582 17 549 26 583 18 550 27 584 19 551 28 585 20 552

C-1 (CONTD. ) Districts List of Numbered Ennumeration 35

685 BJ 13 652 BK 11 14 653 12 686 15 654 13 16 655 14 687 17 656 15 18 657 16 19 658 20 659 BL 01 688 21 660 02 689 690 22 661 03 691 23 662 04 692 24 663 05 692 25 664 06 693 26 665 07 694 28 667 08 695 29 668 09 696 30 669 10 31 670 11 697 32 671 12 698 699 33 672 13 700 34 673 14 701 35 674 15 16 702 17 703 BK 01 675 704 02 676 18 705 03 677 19 20 706 04 678 707 05 679 21 708 06 680 22 23 709 07 681 24 710 08 682 711 09 683 25 26 712 10 684 27

C-1 (CONTD. ) Districts Lists of Numbered Ennumeration 36

20 619 BG 29 586 30 587 21 620 31 588 22 621 32 589 23 622 33 590 24 623 624 34 591 25 26 625 35 -592 36 593 27 626 37 594 28 627 38 595 29 628 629 39 596 30 40 597 31 630 41 598 32 631 42 599 33 632 34 633 35 634 BH 01 600 635 02 601 36 636 03 602 37 637 04 603 38 638 05 604 39 639 06 605 40 07 606 01 640 08 607 BJ 641 09 608 02 03 642 10 609 04 643 11 610 05 644 12 611 645 13 612 06 07 646 14 613 08 647 15 614 09 648 16 615 10 649 17 616 11 650 18 617 651 19 618 12

C-1 (Contd. ) Districts List of Numbered Ennumeration 37

BM 01 713 BM 35 02 714 36 03 715 747 04 716 BN 01 748 05 717 02 749 06 718 03 750 07 719 04 751 08 720 05 752 09 721 06 753 10 722 07 754 11 723 * 08 755 12 724 09 756 13 725 10 757 14 726 11 758 15 727 12 759 16 728 13 760 17 729 14 761 18 730 15 16 762 19 " 731 763 20 732 17 764 21 733 18 765 22 734 19 23 735 20 24 736 766 25 737 BP 01 767 26 738 02 768 27 739 03 769 28 740 04 770 29 741 05 771 30 742 06 07 772 31 743 773 32 744 08 774 33 745 09 775 34 746 10

C-1 (CONTD. ) Districts List of Numbered Ennumeration 38

BQ 01 776 BR 03 809 02 777 04 810 03 778 05 811 04 779 06 812 05 780 07 813 06 781 08 814 07 782 09 815 08 783 10 816 09 784 11 817 10 785 12 818 11 786 13 819 12 787 14 820 13 788 15 821 14 789 16 822 15 790 17 823 16 791 18 824 17 792 19 825 18 793 20 826 19 794 21 827 20 795 22 828 21 796 23 829 22 797 24 830 23 798 25 831 24 799 26 25 800 27 26 801 28 27 802 29 28 803 30 832 29 804 30 805 BS 01 833 31 806 02 834 03 835

BR 01 807 04 836 02 808 05 837

C-1 (CONTD. )

List of Numbered Ennumeration Districts 39

10 869 BS 06 838 BU 07 839 11 870 08 840 12 871 * 09 841 13 872 873 10 842 14 11 843 15 874 875 12 844 16 13 845 17 876 14 846 18 877 878 15 847 19 879 16 848 20 880 17 849 21 881 18 850 22 882 19 851 23 883 20 852 24 884 21 853 25 26 885 27 886 BT 01 854 887 02 855 28 888 03 856 29 889 04 857 30 890 05 858 31 891 06 859 32 33 892 07 34 893 35 894 BU 01 860 895 02 861 36 896 03 862 37 897 04 863 38 39 898 05 864 40 899 06 865 41 900 07 866 42 901 08 867 43 902 09 868

C-1 (CONTD. ) Districts List of Numbered Ennumeration 40

BU 44 903 BX 01 935 45 904 02 936 03 937

BW 01 905 04 938 02 906 05 939 03 907 06 940 04 908 07 941 05 909 08 942 06 910 09 943 07 911 10 944 08 912 11 945 09 913 12 946 10 914 13 947 11 915 14 948 12 916 15 949 13 917 16 950 14 918 17 951 " 15 919 18 952 16 920 19 953 17 921 20 954 18 922 21 955 19 923 22 956 20 924 23 957 21 925 24 958 22 926 25 959 23 927 26 960 24 928 27 961 25 929 28 962 26 930 29 963 27 931 30 28 932 29 933 BY 01 964 30 934 02 965 03 966

C-i (CONTD.) Districts List of Numbered Ennumeration 41

BY 04 967 BZ 12 1000 05 968 13 1001 06 969 14 1002 07 970 15 1003 08 971 16 1004 09 972 17 1005 10 973 18 1006 11 974 19 1007 12 975 20 1008 13 976 21 1009 14 977 22 15 977 16 979 CA 01 1010 17 980 02 1011 18 981 03 1012 19 982 04 1013 20 983 05 1014 21 984 06 1015 22 985 07 1016 23 986 08 1017 24 987 09 1018 25 988 10 1019 11 1020

BZ 01 989 12 1021 02 990 13 1022 03 991 14 1023 04 992 15 1024 05 993 16 06 994 07 995 CB 01 1025 08 996 02 1026 09 997 03 1027 10 998 04 1028 11 999, 05 1029

C-1 (CONTD. )

List of Numbered Ennumeration Districts 42

CB 06' 1030 CC 10 1063 07 1031 11 1064 08 1032 12 1065 09 1033 13 1066 10 1034 14 1067 11 1035 15 1068 12 1036 16 1069 13 1037 17 1070 14 1038 18 1071 15 1039 19 1072 16 1040 20 1073 17 1041 21 1074 18 1042 22 1075 19 1043 23 1076 20 1044 24 1077 21 1045 25 1078 22 1046 26 1079 23 1047 27 1080 24 1048 28 1081 25 1049 29 1082 26 1050 30 1083 27 1051 31 1084 28 1052 32 1085 29 1053 33 34

cc 01 1054 02 1055 * CD 01 1086 03 1056 02 1087 04 1057 03 1088 05 1058 04 1089 06 1059 05 1090 07 1060 06 1091 08 1061 07 1092 09 1062 08 1093 09

C-1 (CONTD. )

List of Numbered Ennumeration Districts 43

CE 01 1094 CG 04 1225 02 05 1126 06 1127

CF 01 1095 07 1128 02 1096 08 1129 03 1097 09 1130 04 1098 10 1131 05 1099 11 1132 06 1100 12 1133 07 1101 13 1134 08 1102 14 1135 09 1103 15 1136 10 1104 16 1137 11 1105 17 1138 12 1106 18 1139 13 1107 19 1140 14 1108 20 15 t109 21 16 1110 17 1111 CH 01 1141 18 1112 02 1142 19 1113 03 1143 20 1114 04 1144 21 1115 05 1145 22 1116 06 1146 23 1117 07 1147 24 1118 08 1148 25 1119 09 1149 26 1120 10 1150 27 1121 11 1151 12 1152 13 1153 CG 01 1122 02 1123 14 1154 03 1124 15 1155

C-1 (CONTD.)

List of Numbered Ennumeration Districts 44

CH 16 1156 CJ 26 1188 17 1157 27 1189 18 1158 28 1190 19 1159 29 1191 20 1160 30 1192 21 1161 31 1193 22 1162 32 1194 33 23 1195

CJ 01 1163 CK 01 1196 02 1164 02 1197 03 1165 03 1198 04 1166 04 1199 05 1167 05 1200

06 1168 06 1201 07 1169 07 1202 08 1170 08 1203 09 1171 09 1204 10 1172 10 1205 11 1174 11 1206 12 1174 12 1207 13 1175 13 1208 14 1176 14 1209 15 1177 15 1210 16 1178 16 1211 17 1179 17 1212 18 1180 18 1213 19 1181 19 1214 20 1182 20 1215 21 1183 22 1184 CL 01 1216 23 1185 02 24 1186 25 1187

C-1 (CONTD. ) Districts List of Numbered Ennumeration 45

CR 01 1282 Cs 17 1314 02 1283 18 1315 03 1284 19 1316 04 1285 20 1317 05 1286 21 1318 06 1287 22 1319 07 1288 08 1289 CT 01 1320 09 1290 02 1321 10 1291 03 1322 11 1292 04 1323 12 1293 05 1324 13 1294 06 1325 14 1295 07 1326 15 1296 08 1327 16 1297 09 1328 17 10 1329 11 1330

Cs 01 1298 12 1331 02 1299 13 1332 03 1300 14 1333 04 1301 15 1334 05 1302 16 1335 06 1303 17 1336 07 1304 18 1337 08 1305 19 1338 09 1306 20 1339 10 1307 21 1340 11 1308 22 1341 12 1309 23 1342 13 1310 24 1343 14 1311 25 1344 15 1312 26 1345 16 1313 27 1346

C-1 (CONTD. )

List of Numbered Ennumeration Districts 46

CM 01 1217 CP 03 1249 02 1218 * 04 1250 03 1219 05 1251 04 1220 06 1252 05 1221 07 1253 06 1222 08 1254 07 1223 09 1255 08 1224 10 1256 09 1225 11 1257 10 1226 12 1258 11 1227 13 1259 12 1228 14 1260 13 1229

14 1230 CQ 01 1261 15 1231 02 1262 16 1232 03 1263 17 1233 04 1264 18 1234 05 1265 19 1235 06 1266 20 1236 07 1267 21 1237 08 1268 22 1238 09 1269 23 1239 10 1270 24 1240 11 1271 25 1241 12 1272 26 1242 13 1273 14 1274 CN 01 1243 15 1275 02 1244 * 16 1276 03 1245 17 1277 04 1246 18 1278 19 1279 CP 01 1247 20 1280 02 1248 21 1281

C-1 (CONTD. )

List of Numbered Ennumeration Districts 47

CT 28 1347 CU 19 1380 29 1348 20 1381 30 1349 21 1382 31 1350 22 1383 32 1351 23 1384 33 1352 24 1385 34 1353 25 1386 35 1354 26 1387 36 1355 27 37 1356 28 38 1357 29 39 1358 40 1359 CW 01 1388 41 1360 02 1389 42 1361 03 1390 04 1391

CU 01 1362 05 1392 02 1363 06 1393 03 1364 07 1394 04 1365 08 1395 05 1366 09 1396 06 1367 10 1397 07 1368 11 1398 08 1369 12 1399* 09 1370 13 1400 10 1371 14 1401 11 1372 15 1402 12 1373 16 1403 13 1374 17 1404 14 1375 18 1405 15 1376 19 1406 16 1377 20 1407 17 1378 21 1408 18 1379 22 1409

C-1 (CONTD. ) Districts List of Numbered Ennumeration 48

cw 23 1410 04 1442 24 1411 05 1443 25 14112 06 1444 26 1413 07 1445 27 1414 08 1446 28 1415 09 1447 29 1416 10 1448 30 1417 11 1449 31 1418 12 1450 13 1451 cx 01 1419 14 1452 02 1420 15 1453 03 1421 16 1454 04 1422 17 1455 05 1423 18 1456 06 1424 * 19 1457 07 1425 20 1458 08 1426 21 1459 09 1427 22 1460 10 1428 23 1461 11 1429 24 1462 12 1430 25 1463 13 1431 26 1464 14 1432 27 1465 15 1433 16 1434 CZ 01 1466 17 1435 18 1436 DA 01 1467 19 1437 20 1438 DB 01 1468

CY 01. 1439 DC 01 1469 02 1440 03 1441

C-1 (CONTD. )

List of Numbered Ennumeration Districts 49

DE 01 1470 DH 01 1500 02 1471 02 1501 03 1502

DF 01 1472 04 1503 02 1473 05 1504 03 1474 06 1505 04 1475 07 1506 05 1476 08 1507 06 1477 09 1508 07 1478 10 1509 08 1479 11 1510 09 1480 12 1511 10 1481 12 1512 11 1482 14 1513 12 1483 15 1514 13 1484 16 1515 14 1485 17 1516 15 1486 18 1517 16 19 1518 20 1519 1520 DG 01 1487 21 02 1488 22 1521 03 1489 23 1522 04 1490 24 1523 05 1491 25 1524 06 1492 26 1525 07 1493 27 1526 08 1494 09 1495 DJ' 01 1527 10 1496 11 1497 DK 01 1528 12 1498 13 1499 DL 1529

C-1 (CONTD. )

List of Numbered Ennumeration Districts 50

"Ixbuaca M rnicr I tusTIrjTl " enJk LtION ui: rxcars

InrtttutIonal ES Postco44 of which Inrtttuttw. Enumeration Institution Unit DI at riet forma t, "rt

29 .A 11 29 &. 06 : H1 14N Crltcn Hotel . tj J UL ey A4 VI 1141 2UA U..1. "y 1I4t4L :ye ug iq i. n uu Ell1 2HJ Caotla Tr;. Ieu Hotel 9 ;J 1u e9 : J! u4 lOl l IPP Church or :1cu(l. J Auawr141y huw. o (CO11. F. ) 19 .. L 4 .19 6C ul E11ä ';;CH Kinn Jour. Hotel ey .. R J: 0.9 aE 61 EN2 52H North Bratrah Ilotol Dw. .4 JI :d .y All J( k113 1,FE Fdinuur)th Ac: dwry, d.. r Howe ;, I. '. Q .9 Ax %). E113 7: 1`41 Ilotal 19 .. L 11 29 iL u'( 2113 uAP L4.lytud Haurat Crovo Urreh 19 .N J4 29 AN 14 PH3 5EE huyaL Luflnnury :y . Jt :5 9 AN 1ý EIl3 WE :itwpcat'u NaNruty Iluupttal 29 4M 26 29 AA 03 EH) 91X1 St. Joseph's 01d People's hove 29 AM 25 29 : 1l 04 IN14 1QLY Faitaa Collage 29 AP 29 29 aP 2t. E114 2DL Royal Victoria Houpital 25 iP 30 29 AP 16 9114 21Jr Western Coneral Hospital 29 A tU 29 AQ 07 FJ14 36 Eurocreat Rotelr Quranaforry hi. 29 LT 17 29 AT 04 £114 6H? Carailfield School 25 Al'"IS 25 A.L. 0b EH4 6T11 Dunferolina College 29 LY 1d a"9 AY 17 E115 21)(: Northern Uaneral Hospital

t9 bC tc 29 BC 17 E116 oT0 Leith Hospital 49 DD 11 19 aU u5 I-ltu 713 K..atern General Hospital 29 UK 13 29 LK 02 EIIN LAX Queensberry House Hospital 29 DI 15 29 1iK 00 ElIb LDP Veterans Residence 25 of 16 25 DK 10 Etld fIN Elate Inells Maternity Hoapttai 29 DL :7 29 116.17 Elio 9h1 Deaconess Hospital 29 BN 35 29 DM 20 EH9 ILA Sick Childran'a Hospital 29 bN 36 29 bM 31 E119 1311 Lon6aorr Hoapttal 29 bN .0 29 bN 01 E119 2143 Artl. y Atnulla Hospit. l 29 Lh 26 29 Ila 22 1,400 50J 3t. Donta School 29 hR 27 29 ! Lk 2J E1110 511E Royal Edinburgh Iloupttal 21 III 2u 29 slit 09 LH10 5w Cruldhouw 29 bh 29 29 Jilt 112 £1110 5112 City Hospital 29 b'r 07 29 Bf 05 01110 71M Prtnaar Margaret hors NOrptt4l 29 BX 30 29 M 19 EH11 3LJ Oaughton Prison ly DZ 22 25 B2 20 01112 5£L Crownnor Hotel 29 C: 16 29 CA 07 EH12 6TU Corstorphtns Hospital 29 CC 33 29 CC 30 111112tlrY 3t. Ilarraretts Collate 29 CC 34 29 CC 01 E1112 4W Royal Scot Hotel 25 CD C9 29 CU. 07 ER12 9311 Codarburn Hospital 29 CE C2 29 CE 01 EH12 010 Turr. hous. Airport, Tarnanal Buildings

c' CC iu 29 CO 02 EH13 OPD Convent or the Cood Sluprre i9 CC 21 !9 CC 09 EH 13 OPR kodford Barracks 29 CH 23 29 CH 02 01114 1DW Sacied Heart Convent .9 CL 0: 29 CL 01 EM14 4AP Rtccarton Campus - H. rtot. Natt University 29 CR 17 29 CH 01 EH15 2RJ With Nautical College

29 CU 27 29 CU 25 EH16 530 Pollock Halls of Residence 29 CU 25 29 CU 11 EH16 512 Royal Blind School 29 CU 29 29 CU 12 F]116 5M Edinburgh Women's Boatel 29 DY 16 29 DIP'12 EN244114 Cliftonhall School.

C-2

List of Institutional Ennumeration Districts 51

APPENDIX D 52

LOTHIAN REGIONAL COUNCIL Principal WATurmeau, BSc.PhD, CEng, FIMechE

Napier Colle g e COMMERCE &TECHNOLOGYOF

Pleasereply to Colinton Road. Edinburgh, EHIu SOT 055-44' "o-o 0 Sighthill Court. Edinburgh, EHu 48N oys-4v) i

Your ref

Our ref

Dare

Dear Sir/Madam

The Traffic Impact of Superstores: Letter of Authorisation

This is to certify that has been employed by Napier College as an interviewer on the above research project.

If there are any queries or problems arising from the survey please do not hesitate to call me at Napier College - telephone number 031-447-7070 Ext. 252.

Yours sincerely

George Hazel BSc MSc CEng MICE MCIT MIHE Lecturer Department of Civil Engineering

D-1 Letter of Authorisation given to Interviewer 53

Napier College COMMERCE & TECHNOLOGY

Owner/Occupier, 20 Victoria Street, PWNrr#y it EH1 2HG " Cobncon Road, Edinburgh, [NU toT eN"w ram 0 Slghthtll Court, Edinburgh, ! Nn 1v. evwt wt

Yr d(

Or.. f

DAM

Dear Sir/Madam

The Traffic Impact of Superstares

I am at present a lecturer at Napier College involved in researching the traffic impact of supergtores in Edinburgh for a part-time Ph. D. degree.

As you may appreciate these large stores cause parking and road safety problems to their immediate surroundings and can be damaging to the local environment. The research I am engaged in is trying to forecast the level of traffic generated by these superstores so that the local authority and the developer can ensure the optimum siting for the store.

An essential part of the research is relating usage, or non-usage, of these stores with household characteristics and for that I need your help. Your area has been chosen at random from GPO postal codes and an interviewer will be calling on you in the near future with a questionaire and shopping diary. The questionaire will take approximately tea minutes to complete and the shopping diary requires to be filled in for a period of one week. The interviewer will be carrying a latter of authorisation which you can ask to be shown.

The information is strictly confidential and the questionaires/ shopping diaries have no names and addresses attached to them. I do not know the origin of any questionairest this is of no consequence to my research. There is therefore no way survey details can be traced to a particular house.

The survey, however, is entirely voluntary and you may decline to take part if you so desire. The survey also only applies to car-owning households so if there is no car or van attached to your household tell the interviewer and he/she will pass to the next house.

I thank you in advance for your trouble and hope you will agree to take part in the research.

Yours sincerely

George Hazel BSc MSc CEng MICE MIT MIRE Lecturer Department of Civil Engineerinq

D-2 households Information letter sent to all potential 54

APPENDIX E BEST COPY AVAILABLE

Variable print quality 55

co I 3.5 CIO20. c b35. of i5. ß0 1 1113/a51f? 1 14 18. 23. 23.14, co 1 I .6 c 1.35 11 12 361 l_ 55 C' I 10 1ß' 00 1 4.0 4g. ?7Q 4e. 2 '2.00 11 1"514 45 13 0 30 013 301 1.0 C! oowf ! '. ?.3.5i 1. 'Z2 1 ' 1 .112 511 1' 1^ +3I 1$. 001 4.5 33" oc 5"0 P1 122 it 2[A 14 12 14 1s 001 3.00 2.6 20" (! C : 3I4. o 11 I413 2ß 17 ti4 10 08 001 1.0 015"00 17. ßC 1.25 11 2524 32 27 24 15 1$ 001 1.00 1. 131 1: 4 47 27 17 23 19 001 2.0C 1 cc oi34 @@X21.21 1 2.1 1 38 2 12 22 08 001 2. Q 325.0. 25.0( 1 -3e 1 1.. 1 23 65 8 17.10 I$ 00 1 1.0 035.22 35.3: 1.00 1 1 5_2 2 38 C 05.02 05 01 2. C 035.02 335. ^i 1.00 11 133.1 it 2 17 2' 12 13 001 2.52 32 5^"5: 2. cc, 1- 122 14 23 17 2Z 03 25 y! , : 331 3.0_ 2 2.0? 2. c: 1: 1212 73I ^ 1' 12 1£ý 00 1 1.00 11 l. i7 1' i^c 12 r" o 025.02o4c. a cc _ý . 001 1.5 _0"25" ?2 131 1 123 24 23 1 'l 24 12 28 20 1 1.2 ©20.30 20.00 1.00 11 11 2'2 '0 317 3Z 18 CC I 124 23 17 ?5,1ý 3.0 3i a0 1138. 5.30 11 .1J '05 CC1 1.02 333.32 3:. 32 1 32'11 122122 3 45 1 t< . 1; ;0 13 1.5, 22.32 1.3t'31C 1'^ 1 ^_ ('3) 1'11 1^ 13 01 1G. nn R, n'n n`a h1Ll: ^S 1^ 1^ r1 I. 1 !1Q "5. l r^ IC, ? ell x1 1 0r n3' ý nn n` ý" r` !. "' 1,! I

E-1

Data File for Area 1- Moredun 56

00101.5c04.51034.25051.2100.2103.25p 11o 103p103 191 9p07010008

00 01.5 04.5 034.2 51.2 00.2 03.25 1 03 03 19 00011 8 00 O 'r 02.0 @@b:-015.2 06ýr 05.00 02 02 70 10 0108 00 01.1 01.1 012.5 12.5 01.0 01.00 102 01 70 1 00 018 00 O1.5 01.5 034. O 34.0 01.0 01.00 1 04 03 28 12 0 01 18 00 02.5 909 16.7 05.00 1 02 02 63 10 1 00 18 00 69-. 2.5 0OO 0 12.55.00 1 i02 01 70 1 00 18 00 01.0 01.5 013.5 21.5 01.0 02.00 1402 01 38 00 01 13 00_%2.0 02.0g025.00p25.0 01.0 01.00 1ý 02 02 70 01 00 18 00 0.3 01.30015.0 80.0 00.2 2501.1, '1,006 02 31 10 0 02 08 002! 00.5ý O. 50015.015.0 00.5000.50 ý,00102p 0 27 26 0 01 08 00202. OO104.50011.50b26.0 01.0006.00ý tg0302D 1,0 47 20 1 02 18 0008 002100.6701.1? +030.055.0 00.5001.50 9 0Z0401j0 24 00 02 002b 1.00J01. Od023.50D23.5 01. Od01.00 1!1'b 101 56 0 0710101008 002044 OÖ03.0d00ß: 00; 042.04Ae. " Oä06.000 LCaO103010 40 27 1015013 00201.0001.00040.00040. Odo i. odo i. 00b 1.0010302041 32 21 10008 00202.0002.5d017.00022. OLIO 1.0C102.00030010210101 24 0p 18!0001008 00200.0002.50000 : 016011.0d0&. 0002.000i00101 O 101 56,0017000018 00202.0002.00015.0 15. Og01. OO1.00014 L0 g 02p00 t 701 0017000008 00200.75100.75037.5 37.5000.2 00.25i0ý101040304 40ý 18a 17'020018

E-2 Data File for Area 2- Spottiswoode 57

1. OOj04. OOO 16.001031.00101.00b2. .p 1o 1

00 01.0004.0 16.0 31.0 1.00 2.00 10051 10 08 00 03.0003.001012.0 12.0 1.00 1.00 li 04 01 56 0 10)09 00 01.00j)1.0 28.00029. O1.00 1.00 1 102)202 56 0 15)09 00 02.0 2.0 la 1.00 .0 1.00 1 03 01 51 00)16 000 02.18.0 5.0 30.0 57. O 1.00 6.00 103)204 40 10 20)18 00 1.0 1.0 35.001035.0 1.00 1.00 03 0 56 0 20 08 00 1. odO 08.0ý" 1.00 0 . 01 56 0 10 08 00 01.00 1.00,017.0 17.00101.00 1.00 1,02 0 56 1 00 18 00 Ge -ße101.0000 : 0Or015.20b 0001.00 0102 10 70 0 12 08 00 AQp2.5010 &. 1.00 Zb . -eop17.000000 P 03 0 45 10,22 08 00310&rOO 1.25; 0041.00027.00ý(1ß.. 00 1.001 02 10 56 10010 18 00ä01.50 3.503015.001024.00301.00 3: 00p 01,02 10 56 17j010 18 , 00zo 1.00 1.00016.00016.00101.00 1.000 b102 10 56 0$019101ob 18 00 1.00132.50017.00027.5001.00 4.000 ö102 0 63 10; 0O08b13 00308~00,1.00000W. 04033.00001 1.-000200103 20 5010007025Q08 003101.00p3.50045.00077.00101.00 6.00i0200104 LO 271 1 002035p08 00^' G:- OOb1.50000.00029.0000: O© 2° 000200102 1.051 20! 16012015008 00301.50 Ö1.50015.00015. Od01.00 1.0002001021010ä 701 0017000018 00302.00 ' 2.00025.00025.0001.00 1.0002101; 03 1.0 561 g009020O08 00302.5002.50043.00043.00.01.00,1.000010104 L'0ý 381 01002010008 0030$: ýº9O 1.30000.. 00028. ooof-©oe1.004210 03 t0.1 351 1Ei009020009 0001.25' 1.25028.00028.0001.0001.00ý020ot0 ], 0 381 0009022008 00: 100.00,1.50000.00021.0000.0003.00000102101101 701 0017000018

E-3

Data File for Area 3- Clermiston 58

7501.75020.001028. OOj01. 24

00 0.75 1.7 20.0 028.0 01.0 02.0 0 Jo ;o002 ao oa 008 00 1.0 3.5 16.0 031.5 01.0 04.0 0 1010 002 1o of Ole 00 1.00 3 1.00 35.0 035.0 01.0 01.0 00011 010 2 ao of 013 00 1.00 1.00 20. O 020. dO1. O 01. OO0000131) 0 of oie 00 1.00 1.00 15.0 015.0 01.0 01'. 0 000004 io oa oe 00 1.00 1. O 18. oc 018. O 01.0 01'. 0 00002 1o of ie 00 2.0 021. d 06 03. O 010 004 1o of is 00401.00 1.00 20.0 020.0 01.0 01.0 010 2 1o of ie 00401.0 1. O 40.0 040.0 02.0 02.0 020 05 0 of oe 004101.25 2.75 20.0 026.0 01.0 0?. 0 1020 5 00404.25 4.2 49.0 049.0 02.1 02.1 01 2 170O 01 18 2 1 401 02 06 00401.50 1.5 50.0 050.0 01.0 01.0 10106 104 00401.75 1.7 20.0020.0 01.0 01.0 01 101 7 001 00 08 004; 02.002.00+035.0 035.0 01.0 01.0 0 020 05 000 01 18 00400.75b3.75019.0 034.5 01.0 02.0010001.0 0 31 1000 02 08 0101 00 18 00400 0003.00000 023.0 00'. OCIO2.7 . 00400.38102.13016.2 017.7 00.2 O 1.25101' 1'0ý040305ý 3 21; 004012008 00401.00102.00+021.0 026.0 01.0 02.0002d01. '03010q 5 2701. O10i018 0040$: 0004.00000.0028.0 00 0 2.00101101020 03 34, 1801 0101018 00401.5002.001030.0 045.0 01.02. OgO11: 01 *qC)q 3 8,00 0201018 0W. 5 9004010do 18 00400: - 1. OOk*b0: - 025.4 01.00101; 1;0205! 002 00 1. d 6.504012.5 053.5 00.5104 5001j1; 02p4b404j 2 i oo40101018 00401.00101.001019. Od019.0 01.01: 060110204b201 2p 17,0040 1 01018 004,00.7 2.751019.00039.0 00.5do5.500ad01; 04o109 30 8011035008 004101.5 6.001017.5d039. goo i. OCj03.0O101; 00i10301101 2J 17x004010008 004101.50101.251035.00051.9g01., O003.000 (]01: 0 ip l:o2 701 Oi01 7,0040 14

E-4

Data File for Area 4- Swanston 59

70 ee5 1.25 21.45269.7 I- e, 14.2 .1l el 12 022 17 e5 12 2.25 1S. 6 229.6 3.0 03 112 7e e00 17 00 17 ee5 t. e . ee5 3.52 8.. 8 042.7 6" ee01 1 e1 4 23 017 12 1e 08 ee5 1. te 25.8 225.8 01. ee 1.2 011 1e2 3 "28 017 09 lees 8.25 e e42. e 1 e. e el 11 21 4 23 017 e9 12 05 P? 5 .1e,, ei. Fe ee5 1.22 28.20228.2 ei. ¢e 1. e2e 11 le1 3 45 018 12 2C e8 e05 4.22 12.3 246" e ei. ee12.2621 1 101 4 16 009 09 1c e8 225 1" Ee 18.2 e18" e ei. ee 1.2621 1 121 27 214 e9 , 14 03 ee5 3.75 35" e 246.7 eie ee 4.0 1 101 4 23 007 e9 12 08 225 1.75 g0+ e14. e 6$º0& 3.0261 1e1 S. 7e eel? 17 ee 1S 0e5 2.5e 3e" 'e'2041. E 01. ee 5" ee61e q, 44 46 e18 01 2e 08 ee5 1" ee 00.0 612.2 opePre 1"e 01e 12221 le7e 22e 17 ee 03 01 F 121 21 270 eee2 26 05 13 ee5 2.5e I ei. ee . 4.2 ee5 1.22 23.6 e23.2 ¢i. ee 1" FF61e level 263 ele 171022 18 ee5 1.25 68.2F216.2 01. ee03.22212 16 e1 12'56 Dee 2612 e8 2e5 1" ee 22. e e2e. e ei. eee 1" e elft 1 Q' 06 670 220 17 00 16 ee5 IN 521e. C? 21C. e ei. e F 1. ec ele1 01 210107¢ eee 17206 18 ee5 1.22 00.0 212. e 02-E ße1" e ¢16010 0221072 Foe 1722¢¢18 ee5 2.75 e7.3 , 229.3 00.2 23.2 e12216121Il6 27262¢9 17",00 018 ee5 3.75, ON P 026.5 6090 24. e ¢112 e else 22280017 02; e12ee8 Ees 1.75 e7.3 214.8 ee. 2. 03" ^c 21 1ý2 e1e 1ýe 276222¢'21 Teecc 18 ee5 2.25 15"e223. C ee. 2_ 01.2 21 EE e 01(2 070 e2E 1702 218 ße1 ee5 29 eE 22. e 226. ei ei. 0 22.0 01 00 e1e 2280¢1 I? 266 225 2.5 2 15.0 e17.2! el. e e2" egel e2 O1F e7 2020217! 22 218 e05 2. ee 12" epe12. ei 01. e el" eýe111Ieie e ej07Fee ie1weoee4

E-5

Data File for Area 5- Waverley 60

ee6 1. ET 1.52 25.22 37. e '3"e 23 ell 09 15 04 e¢6 2.75 IN 75 3e" 02 3e" e 11.0 40 022 19 le 07 eft 2.22 1.22 e2.82 e8.1 X3.2 71' 012E 17 00 18 _. 026 1. ee 1. ee 32" ee 32. e 1. e 16 009 09 e5 e8 Fe6 1.0C 1. ee 15. e2 150 e 1.0 24 000 ¢'7 12 18 226 1. e0 1.25 25" CC 33.2 2. e 29 15 04 23 ,e17 266 1.25 1.25 160 eF 16.2 1.2 38 000 29 11 28 ee6 1* ee 1" 0e 32" CC 32.2 1.2 24 ;015 12 le is ee6 1.5e 1.52 570 ee 57. e 2. e 3 2218 1? 3e e8 206 2.2E 4 22 27. Ee 38-C 4. e 2fc0C16 179e1e e6 ee6 1. ee 1. M27- ee 37. e 2. e 2 0015 ¢6 ele e8 ee6 1. I 2.2 e6 41.0 e2 2215 Z9 015 . 14 006 2.75 2.75 3e. ee 32. e! 1.2 2 Qe14 02 e8 226 e. 5Q 6.56035" CC 35" ei C. 2 28 2017' ei e i2 e8 226 2.25 "2.7 42. ee 45.21 3. Q 018¢eI! Ie¢ 222 13 226 1.62 3.2 e28.36249.6. 5" e. e2 2018 ¢e 634 14 ee6 1.22 1.2 035.22235" el 1.2i ¢2 e2 2017 ¢1 L712 1s 226 1. eP 1"e 235.2 235. el 1. e' ¢E 228221 E1 e1 e18 226 1.5021.5 042. P 242. ?1 1.21 ¢ i, ¢ e2 0214, e1 61 668 226 1.25.230e e 032.21 2. ei £ 12 27 222 81 e? e18 ee6fa. g. ggi03.7 Q4;0. Q 17. el 6. el 264001 E1 2¢ e14

E-6

Data File for Area 6- Westburn 61

£271.5 3.5e 27" e6fr47"e e1. ee 2-12e 1 104 2 !p2¢ 016 04 14 28 !ce1.25 1.25 112 e5 2 023 017 04 1e 18 e¢7 2" P 2. ee 61.5Eý¢61. 2 4e 018 ¢7 4E e8 eel ", Eýf1.4eBF"4. Ple3P" ¢216ß"Q¬ 6see 11¢ e5 efý027.2Q21" ¢e 2. ee 1eIe1 eis e2e ¢5 leeea 227 e. 7E .2.25¢16. 1 1224 Lee ¢5 1 207 e07 1.25 1.25213.2013" P e1" e¢ 1-" ee . 1e2 e07 0 2" C00f0.0¬212" eQ., O0 1" ee 1 012201 1238 206 ¢5 12228 U77 1.5e 1.52022. F e2e" FE¢1"ee 1. ¢f 1 01¬: 02 ¢24 e¢¢ e7 2 ee8 PP 1" eF 1" PP 15" P 215" ¢QF1" PP 1" ¢¢P1¢¢1¢4F1 e22 ¢2¢ 0 ¢120¢8 e-75012 52622.0Pee. 5e E'"75611 1¢562 056 22e 01 1 e1s 607ee" 5e . 5. ¢E¢1 ¬121¢1 27¬¬¬ee 1 ee 018 ee !3lº. =Eý . 5" e2Q'00.2e225- ¬2701.0222.02022" M24- 2601" ee 1.250 ¬1¬522 Z723¢¢17 E ¬12218 ee e1" eP 2.2¢72¢" P P3_°" ¬221" ee 2.6621116 ¢ 22 ¢4 0022 e 21 006 . e2 21.25¢2.25¬54.261258.56jE1" ee 3" Ee61i1212 e2 024 222 e ¬18228 22 Q¢¢E 028 eF02.25¬6.56ele Ceje17" PF;P¢" 2` 2.2521(1¢1¬ P2ýfir. .¢ '22 ¢07le1" of 1.2¬025" C Fe¢1. P.EF1" e6F1' 212. e2 e2 eeee 19¢122¬6 22702.0e02.22241.221241.26102.22! 22.2601I¬10 2122 02 ee17 e el ees 227121.2221.2e012. ¬0'210" MIS ¢E 1" EE23EI 21e121121l22 f¢Ee 1820 2E8 PEfý1! I, E e! F2iP4 2212 1c 22¢18 een19 ee2" ¢ 0.28" Pe541.2ee1" ee¢29 ie2, , ¢¢TOP" 57ee. 56026.5eIe26.5FýFe" 2522" 25e1'fc?fiP ec^'¢ýif2fc; EEl4jfC¢4; elE? ¢18 e2Tel" FPle1.5¬le17. ¬2.222" ¢¢'¢1" ¢e, ¢2" efie 1: e11e Fcj¢2¢24eeeeýsfce7ý¢2fce¢8 ee7P2.5e; e0.52, ¢15" ee, e15" 261¬1" fF}¢1" eke3(cýe1, e e1; 61¢24eeecle18lee dees Fe; e48" ee; eI. 2! I 5:eII. e1! P4CPe3eESflP17e1"! F11'Eis FPTP1" Sefel" SP'E'48" Ile P07(ee. 5 ee" SEie12" 26¬12.2fie1" e ¢1" EE'E1 lElif: ýFlf 1224¬26¬ P9E1ýPf8 I¢y¢f! 22ýee" e ¢2.2_eee" ee, F6P" 2222.. ¢ e9.2 ¢31¢ ¢. 'F. 8'fC¢FFfcF9; E2gF¢8 20120.7 00.7tQ'15.46! 215.42'01" edel. e CA elede 'P1, E24¢¢ee2Pý¢1E1¢e6 2r 2e" 2 02.25¬2¢" 2efe2P" edee. 2520.2jQ'gIle lje2eM4P23¢el eese11iee8 eo'leI. Sept. SPie35. ce-e35" e? P1" PE;F1" ecieu1: e1; e5e1: eaeplee1rFe4elsee4

E-7

Data File for Area 7- St Peters 62

9,060 q. ae. e 41.0 2.0 4.0 11 1516 29 021 10 11: 0 5 -S logo 1. I t8. e 18.0 1.0 1.0 11 127 00 17 e 15 me 4.10 4.0 85.0 05.50 3.5 3.5 116363 02 095 2 018 e®a 1.00 2.5 50.0 75.00 1.0 2.0 216253 01 010 5 008 00 8 1.00 3.5 30.0 45.50 1.0 5.00 21 1414 27 01 012 1 015 me 1.25 1.7 05.4 20940 0.2 1.25 21 2322 56 00 010 1 018 008 6.2 6 0.00 M0 6.0 11 131 4031 00 01! 1 018 00 8 2.0 3.2 018.0 26.00 1.0 2.0 2121 2063 e1 017 0 Ali 008 1.0 1.0 037.0 37.00 1.0 1.0 21 12 10 07 00 017 0 018 008 3.0 3.0 024.0 24.00 2.0 2.0 3 102 0 07 000 017 0 818 008 1.0 1.0 030.10 30.00 1.0 1.0 210103 10 02 002 005 2 006 005 1.5 1.5 032.0 32.0 1.0 1.0 0210205 e es 002 Al 02 008 008 -: e 1.0 12.0 0 02.0 Mein 1e e5 002 01701 008 008 1.0 2* 0 01700 23.0 1.0 02.0 0 10101 10 07 000 01700 018 0®8 1.0 1.0 020* 0 20.0 1.0 01.0 0 10103 10 e4 001 00601 008 8A 81 1.0 1-0 030.0 30.0 0100 01.0 0210102 10 03 002 00702 005 00 6.0 Q2.0 000- 19 35.0 0.0 03.0 0210 070 0 03 001 00504 005 008 1.0 1.0 021.0 21.0 02.0 02.0 0210101 0 07 000 01 00 013 00 b. 0 X3.5 p00.0 33.0 0.0 04.0 02101050 0 03 001 00 03 015 goo 05.0 oßß. 0 34.5 d. 0 05.0 02101010 0 06 001 01 00 018 00 0.0. 05.7 00000 56.0 ße. 0 06.0 02; 1010 010 031001 00 040000 00 0.7 00.7 040.0 40.0 01.0 01.0 021 01j0 0 V030451001 q018032008 00 1.0 03.0 045.0 61.5 01.0 05.0 02 010 01ý040410020011020018 00 0.0 00.7 000.0 30-0 111108 01.0d02,0110 01'0205 0000001020008 00 goo, 02.0 00000 26.000.0 04.0002E0g0 gZ0207oe000e11002011 00 1 . 2! 03.2 018.0 34-0001-0 e3. oý0ýeseýeýgs8eeege0ýetaees

E-8

Data File for Area 8- Saughton 63

07 00 10 06 059 3. 00 7. :0 1119 1 2 : 5 028 1. A e3.00 01 0 0 056 00 10 06 of 2. 08 2:. A 28 0 028.0 1.0 1. 00 01 00 1. 50 2. 01 0 25. 0 030.0 1.0 02. 00 01 0 02 Al 01 11 009 1. 0 ß8ßr 15.0 2. 00 01 01 056 00 01 06 N 1. 80 1. 0 12. 5 012.5 1.0 01. 88 010 010 070 00 01 0 18 089 2. 5 0. 055.0 .8 5. 00 010 01 034 02 04 2 08 009 2. 00 3. 0 46. 0 053.0 2.0 3. 00 010 010 032 02 00 25 04 009 1. 50 1. 5 030. 0 030.0 1.0 01. 00 010 020 04 01 00700 10 00 1. 50 1" S 035. A 035.0 01.0 01. 00 020 030 04 01 00602 08 00 0. 63 0. 6 025. 0 025.0 0.25 00. 25 010 020 036 02 01 02 06 00 0. 25 09 25 02S* 0 025.0 00.2 00. 25 010 00 027 01 01 03 08 00 2. 00 3. 0 035. 0 042.0 01.0 02. 00 010 010 01 01 01 01 04 00 2. 75 0 025.0 6d: @ 2. 00 010.010 056 00 009i,01 08 ON 1. 00 1. 0 27. 0 027.0 01.0 01. 00 010 010 023 01 008j01 08 0A 2. 75 e. @ 17.0 . -e 3. 00 1101)01010 070 00 01700 18 00 0. 50 0" 30. 0 030.0 0.2 00. 25 0000 034 02 00904 08 00 1. 00 1. 0 018. 0 18.0 1.8 01. 25 0000 05 02 017; 01 18 011,00 0.18 00 1. 2 00. 020-0 .0 01. 00 1 01: 0 00 0700 00 1. 0 1. 0 037. 0 37.0 1.0 01. 00 Ij 020 00 039001 0070251018 00 01. 5 88. 0 15.0 d. 0 2. 00 I( 01; 0 010 056000I1 00801 Qý008 0.0 1. 00 1i 01i0 010 070000d017000018 00 . 0 1. A 00. A 12.0 00 0. 2 00. 25' 014. 30 ýia. 3 00.25 00. 25 181; 0ä01l0 025001 002019008 00 08. 2 00. 7 015. 0 20.5. 0.2 01. 25 t 0t; 0 0 029001 00401.5008 00 01. 0 03. 0 020. 0 25.0 1.0 02. 00 103.0 00 02 01 00901 013 0 3.0 03. 00 101; 0 00 02 8! 00401g008 00 2. 5 2. -5 41. 41.0 00 1. 0 75 14. 00 024.0 1.8 64. 00 1. "2840 2 11 J 0120121018 00 ole o 61. 0 14. 00 1114.0 1.0 e 1. 00 11611890110 7 004017180ä018

E-9

Data File for Area 9- Craigleith Hill 64

171E 01 "P2.5 252. F 1217. FP 1" IMM PF 21 11 S 33 F1 12 22 27 E'117 P. P 4.2 0.2251.22 F P. r' 4. QQ 2 1K' 14 51 223217 1F 28 01 Q2" PF 7.2 222. PG 255" FP it 2006.22 22 1121 1 2: 21 19 1P P$ 01 0e 1.51175.2 232. B0Q47" 5 1.2226" PP 22 11 2 ¢25 215 19 1F 18 21 em Pf 22" (ý 020. P 042.22 Fß"-0(e C7 2.22222 1'2221 5227 222 12 is 28 212 e'ro 4.2 202'. 2 2`°1" FF P"P F6.22211 1 Ip2221 5220 216FF212 28 E1 21.2 21. P 225" F 1725.2221.2221.22222 1122{220 2722222217P? F 23 21 172.28 22.38 EP9.22225. C 2" 2523.225221 1F4; 2 231 228 2F8t22 28 21 Q" 2 3" F QPQ" F ? 3F. 22 ß" 22 3.2 21 12222! 28 E242F 12 19 22 18 01 0000 1.4 EEP"P 241.22 P" 2' 3" P ¢22 1j23+21l22256 222 Q9 15 13 01022" 21.5 252.2 F6°" 20 1.2226.22221 22&22126O32 222217 45 13 01 23" P 23.2' 055. e"055.22122.62,62.222! F 1,24+223'2 £2221 22822 173 21 8.17 23.5 FF P" PF t226.3GPP" PQý 6" P F1; 1'FI'P2; F1, FIF56jFFFF '66212026 21202. P 2.7EPee. PCIF22^" FP1P" ? 23.2FF1Fý21jQ4i? 1lPýQ^c4FFFFIFQ2PQe6 21001.0221.5 232.62037.2601! FFk2.22211! 21l? 4;FIýF, FSEFFFfc21?, F2Q? f6 21 2.2 21.7_ 092" PFle 14.17+17.2QtQ 1.25 PI121F1, Q I 172? QG'18 P1EG'1" 5021. " 522E. FG1F25.26ýc 1.22221.2621'f01! F3'Q2P056i2FEF £8'012218 212; 86.17 1 2ýF'PQ" PPýP2S. err e" QQ 2. fee- 'F.E1; F2'F1PýF7F'PfiFF 1E(C1! iF28 PIPP2" 6222.20033.26232.622ýFl. FF 1- F&F2: C21; 62""'16 3218`26171CQ6212228 FlýQ1 FF+27F'P2F. FG;FGP" FFý41" P? F2.22221`1`? 1.F2'F 1'Fý?F23'EF17 F9FIG'FPS P121P1" F 5V'225. C PQýF1" PFie1.22201; 12IT 2:F2: G'4G22EG'I", VIFI1Olf 18 Plr'Ff. FFýfI. 211. FFF22" kr, PF0FFfrr 1 FP'QIIeIC1G IE'2C: f3FFFF`4'ICE CCTF4 F1E; PF. 5di22. °2'211. °22023.5020" 5 Fi23" s622F. F1F2F1F2F7f`EEEfc'F172c0c18 912162.52122.52! 222.2217222.222122. ECIE.2" F(c Fc0 F 11F2'FcF2.F7(G1FEf Fý? 17FG'fcF18 21g21. !de 517;225.65'225" C 2F! 01" F 01 1-0213622462*Q14P1PC 17Q1_ eideI" eq? 4" eC1F2r. 0 P"F63" 2251" FCie E" fFF1; geI: r'we1"PQFIFFF82F6F10FES PP" 2VIC2.2_GI'MLF4F1FOf 70ECECie 17CCClP11 P1GýPP. 2 P?. PP!P? E" IC`E1: " ` ,,

E-10

Data File for Area 10 - Pilton 65

P121.2 P2.2 262.6 e8 2.6 22. e moo 2P1e e8 PP e4 2218 22 E2 218 P1 22.5 P1. ° 223" P 223. P F1" P P2" E P1 2124612127 222221 EPF 18 e1 lee* 2! eee7 264.5 626.2c62.2_63.2 2112 F: F P. P`_F22 21 21ýF13 21 NO 5601.5 272. F 272. Q21.6m . 21 16 2816.2.23 ell 221 644227 e1 101.25elo 7_229" P 248" P 22.2623.2 2112 PPe 23 262122 212218 21121.2222" P 222. e 229.1 271212710222212 4.2221 e12 27222621 Eel 18 24 ET 62.261 FP 212212 21 121.252.8 222. P624.221.6262.2 21116 2P 61121" PP 2.3 224.62247.22I6N" PFIP-6" Pe1i1F1F4ý 223221 22 612218 611 02.75 co 75216. P de 1 6" PP!e2. PP22.2221j16112612_228221762 E1 21228 21122.5E 2 522183 01736,F18"is: F ýF1. F F1! 1F1upýýe2P4Eee22e1901Ee18 P_F^cFýFE16EPýE12; Q18 21121.02 1 Pee'16 "Q F1" EF1" eQe111}PqFSe 21 1 Pip, PP 2.622.2 2 12.2! 2223.2221l1iI61j24ýC' 16127222621622218 211: 21.7! C2. PP42" 22,244. PF'P2.ýiJP@. Peg",. PctýP11:P2F5ýFýP[: P2 2217, EP P12i218 ei IR 1* ee 4.517'1227" Fe!e34. Isel" PP'P3" eP'611, E1C612N:, e45; FCe18'r

E-11

Data for File for Area 11 - Camino 66

el 01.0e01: 0 222.2 ¢20.0 00.5 00.5e0190103 101238000 00 01 006 21 01.5 ¢20 e24" ¢ ¢27.2 02" e 03.0 01 10103 0 019 0009 009 01 006 01 03.2506.5 027" 2 051.6 01. e 03" 2 01 1e e3 0 0l 0029 00 01 018 01 01.22121.7 040.2 046.0 01.0 02.020110 e3 12 ¢5 220 01 02 228 01 bow-of 1" e 00Q`"-0 015" L6o"-0 04.0 0110 06 1050220016e 001 006 01 e" e003- 0e000- @025.7 00"-0 02. e ¢1101¢3 10202z001223¢5 202 00d02 013 ei 01.2¢. e 202e- e 022.0221. H el. 2¢011¢ 04 22101 018 01 01.25 1.25046- ¢¢046" ¢L7¢19 2 ¢i. 25¢110 05 2125102 001 00402 008 039.02039. e ¢2" ¢ 01 e1.5201.5 22" 2 ¢110103 2051221¢3'03 002 006j01 006 01 00.75 ¢" 75034- 00034o 2 01.0 21.0e01110104 00150241¢2 218 j5.75041" 01 02" e¢ e 064.98102.0! ¢7" ¢¢¢1l11¢1e6ý'9¢5l02 ¢¢1 00704 028 21 01.5¢03.5 1024" ¢¢¢44.201¢1.2 23.0el¢lý02 23,2 04j23 22240201 218 21 ei. 52'1" 501032.02032.00-i'¢1" 2 ¢1.0e01i1,01; 04; eg. e3ie18¢e09e24iel 018 el 01.2203.25225.521¢46.00101. Z e3" e¢el11e1ie2! 02,04i¢4 "e028¢17, '0eQj018 01 01.7504.0e0350 0¢1051.20; 2.1.2 25.251ýe1i71¢1105ý2,04'2 e¢17ýe¢4jele018 012¢1.50jý1.5¢ 35.0¢¢35.20.01.0 e1.0ee1,2i01¢4 1;¢2¢+2 2'¢¢012i¢1Q018 e1 02.00¢2.0eý39.021039" ¢002" ¢ 02. e 1!1; 0ý¢4; ¢2re2'e5 ee02je04215lee8 21202.0225.52022.02220" e2; 00" 22j24.2¢01'12124; 01: 23e65i0228ý! 217005Se07 2 i222.02 5.0ü63 " e8ý¢9 2.23102.00105.25 1'.12226; 2225227! 2026; 221.212: '218 ¢1222" 2223.00041.00; 051.02: 22.02(23. ¢¢; ¢1,22¢1-¢4¢2¢6224¢¢24,0¢4¢1¢'018 21221.2221.52232.22240" e221.22122" ¢221121052204023`2217'2242 1 4.e14 01201" 0e01.22232" 02232" ¢¢`:¢ 1. ¢¢'¢ 1.2201 22 1 24e224216.2209 229 210218 01201.0201.02 22" e0 22.001¢1" ¢¢'01" ¢¢F'1; 20125'¢202Q31'0¢1¢'E04¢2¢; e¢8 ¢12! 00.0222.00: 80.08134.0080.0@04" eeP 1!0101; 06je2, ¢210381eeeE1012,022e13 01202.00M2.00 37" 00237.02101. ¢¢ße1" ¢21211101;0 0 0203810022006022; 208 210I. 00101.0eý25. e0Ie25.0222.5202.5¢ß'i 1ý! 1!23ý01'021023l002¢I0262052e¢8

E-12

Data File for Area 12 - Turnhouse 67

01 8.25 : 64051.5 AB'. 6.0 A808 83 e01 8 A3 @1 0280E 01.0 01.0 0000 02 001 00 03 08 20* 01.0 81.0 1000 02 001 01 01 05 01 2.3.0 15.2 024.2 01.0 03.0 10 000 05 002 01 01 18 01 03.3.0 15.0 015.2 01.0 01.0 00000 07 000 01 00 18 0! 01.3.001030.0 035.0 01.0 03.0 011 000 0a 001 A0 01 18 01 020: 00ts@1*0 2.0 010 010 0 07 000 01 00 is 01 1.50! 01.50'025.0 025.0 01.0 01.0 010 000 03 001 00 01 . 08 Al 01.00101.00ý25.0P1025.0ý01.0 01.0 0ý0 0 010 05 000 01101 08 01 81.50ý01.50o15.00,015.00101.00j01.0 I00 0107 000001100 03 01 8A. 50ýB2.50g17.00'022.0010l. 001A3.0 010 01010 02 0017101 00 18 01 1.001.50ý010.15; 020.3001.0602.0 01001j0 010 03 002700 01 08 01 B6.38p2.38l3 10.0fä032.00%00.25ii32.2501,001i0 020 02 001 600 0101088 01 01.50101.501025.000259 001101.0001.00010010 0102ý03 000000 0101008 01 01.50Iä2.00; 825.00027.00i01.001ý2* 0 3111010' 020 030010001013 01 01.50 2.25,016.50.021.0001.08P0 2.001010 02A 10ýj 07 0000f0 01 1.00102.50'030.00! 05 i. 0601 " 00i4.00101110206ä0105i01 0013i009A010 18 01 00.3304.38.007.00028.70'00.25105.25; 0 ij0O1030110402 0014,008j010 08 01 1.5011 1* 50-019.56042.50s01.00`05.00104100 1!0101j0602410020019105013 01 01.5004.00'020.00039.50! 01.00J04.00'l04001,03'01; 0302$ý001T012,012gA6 013{00.4 080.4 0.009.5 00 09.5 0':00.25IO0.25; 01: 10206,02,04,023'00 170 04012005 013! 01. "2501.75018.2P028.7001.0002.00'01001.03'01: 02070'000Qt017,000018 013.00.00j17.75"000.00076.85: 00.00? 10.000 1101 03'01'030400027010015008 013401 . 30p3.35; 018.00048.0001.0005.0002091.0201,03034.0024010.010818 013 0.501A1.00830.06040.00ý01.0002.00020101; 03; 01: 05'020oA016ý004l010g83 01300.0001.0 000.00" 42.00100.0002.000201010201.03'0450a 18010023001 01301-50104o&0922.9,54.00j01.0005.000$0! A L0310110710194 . 4010015013

E-13

Data File for Area 13 - Leith 68

014 1. 0 01. 25 25. 01 0 35 . 00 1. 0 2. 0 00 05 002 0 02 008 01 1. 0 05. 00 32. 8 00.6 16 1. 0 6. 0 0 55 3 001 00 01 005 01 0. 7 00. 75 30. 0 030. 00 1. 0 1. 00 010 12 07 000 01 00 018 014 1. 0 01. 00 40. 0 040. 00 1. 00 1. 00 00 00 04 001 00 03 009 014 1. 2 01. 25 40. 0 040. 00 1. 00 1. 00 00 0 03 2 001 00 01 018 014 1. 0 02. 00 20. 0 040. 00 1. 00 2. 00 010 00 03 001 00102 006 014 1. 5 01. 50 38. 0 038. 0 1. 25 1. 20 00 0 05 2 00 1 00101 018 014 1. 0 01. 75 20. 0 029. 5 0. 50 3. 50 0 06 0 05 02 001 00201 014 014 1. 25 01. 25 37. 5 037. 5 2. 00 2. 00 010 00 06 001 01 00 018 014 0. 5 01. 50 12. 0 024. 00 0. 25 1. 250 010 0103 6 000 01 01 008 01 2. 5 03. 50 65. 5 072. 5 3. 0 4. 00011 010 0203 04 001 1700 014 01 0. 7 02. 25 16. 7 032. 70 1. 0 03. 0 01 010 020 4 02 001 00201 818 014 1. 25 02. 00 23. 5 036. 0 1. 0 2. 0 011 010 0 02 05 000 00401 014 014 2. 25 02. 25 31. 8 031. 8 02. 00 2. 0 01f 010 01021 07 0 000 01700 018 014 lo 5 2. 50 20. 0 25. 24 1. 00 3. 0 01j 1 0 V0 010 05 000 00401 007 014 1. 0 1. 00 20. 0 20. 0 1. 00 1. 0 011 0110 0101 07 0000 01700 018 014 1. 0 3. 00 25. 00 40. 0 1. 0 4. 00011 01; 04 0102 056 1000 01700 018 5. 0 01 01,0 0101 7Qý000 01700 018 01 . 0 2. 00 0. 0 10. 7 0. 0 014 0. 75 2. 50 15. 0 38. 0 01. 0 4. 0 01! 1 01! 0 01'0 07 0000 01 00 018 014 1. 25 2. 75 44. 56 54. 56 01. 0 2. 0 01013 1 0 10 0203'j 03 001 01 01 018 014 0 3 50 00. 0 21. 5 0. 0 4 00 01 1 01; 0ý0202! 056 00001001101 016 . . : , 1 t 014 1. 00 1. 00 21. 00 21. 0 01. 00 1 001! i !0207ý0ý02r 056i00001,00g01 018 01400. 30 81. 30 04. 0 015. 5 00. 10 1. 1001; 1 01.090102! 070000001700 018 , 0131. 00 3.. 50 25. 00073. 75. 0 01. 00 6.. 0 ro1 1j1 0ä0 03106102710023001: 01 018 : 1 014x0. S 2 00 23. 0 0 01. 00 3 O 1 01{0 020 2ä001T001,01 018 101,10 0 014 2. 00 3. 50 40. 00 1052. 0 81. 00 2. 00ý111 A1'01 07 0000 017ý00 018

E-14

Data File for Area 14 - Craigleith 69

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E-15

Data File for Area 15 - Easter 70

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APPENDIX G 97

FACTOR 1

A'1 A- 2 X= 3

F + 0.85 I + 0.93 I + 0.94 B + 0.84 R + 0.89 R + 0.89 D + 0.63 K + 0.78 K + 0.78 S + 0.57 L + 0.75 L + 0.76 L + 0.43 D + 0.45 D + 0.41 K + 0.42 E + 0.36 E + 0.40 0 + 0.35 F + 0.33 N + 0.35 M + 0.24 N + 0.32 F + 0.29 R + 0.21 B + 0.26 0 + 0.27 E + 0.18 0 + 0.24 B + 0.26 Q + 0.05 M - 0.03 M + 0.02 I + 0.01 G - 0.06 G - 0.03 G - 0.00 Q - 0.08 S - 0.04 C - 0.02 C - 0.26 Q - 0.04 N - 0.67 *S - 0.30 C - 0.17 P - 0.69 P - 0.32 P - 0.31

G-1 A-1,2, Principle Components Analysis for all Variables for and 3 98

PCA - FACTOR 2

A- 1 X- 2 A- 3

I + 0.94 M + 0.93 M+0.88 R + 0.88 E + 0.77 Q+0.74 K + 0.78 0 + 0.68 E+0.71 L + 0.75 Q + 0.66 *S+0.63 D + 0.41 D + 0.48 0+0.62 E + 0.38 C + 0.34 D+0.48 N + 0.36 L + 0.29 K+0.31 F + 0.28 K + 0.28 L+0.30 B + '0.25 F + 0.27 F+0.25 0 + 0.25 *S + 0.16 C+0.24 S + 0.09 * I + 0.15 I+0.13 M - 0.00 B + 0.14 B+0.09 G - 0.02 G + 0.14 G+0.04 Q - 0.06 P - 0.08 P-0.14 C - 0.18 R - 0.09 R-0.20 P - 0.31 N - 0.35 N-0.41

G-1 (Contd. ) X Principle Components Analysis for all Variables for 1,2, and 3 99

PCA - FACTOR 3

a- 1 X- 2 x. 3

M + 0.87 B + 0.86 F + 0.85 Q + 0.76 F + 0.74 B + 0.84 E + 0.68 D + 0.50 D + 0.62 0 + 0.64 L + 0.44 *S + 0.43 *S + 0.35 K + 0.38 L + 0.40 L + 0.31 0 + 0.31 K + 0.38 K + 0.28 M + 0.18 0 + 0.34 F + 0.20 R + 0.17 R + 0.24 C + 0.18 E + 0.09 M + 0.20 I + 0.15 C + 0.07 E + 0.14 B + 0.04 Q + 0.07 G + 0.01

G - 0.06 *S + 0.06 I - 0.01. P - 0.13 I - 0.00 Q - 0.01 R - 0.18 G - 0.04 C - 0.04 N - 0.40 N - 0.65 N - 0.65 N - 0.40 P - 0.75 P - 0.66

NOTE: Factor (2) for 3

G-1 (Contd. )

Principle Components Analysis for all Variables for 1,2, and 3 100

PCA - FACTOR 4

,. a= 1 A- 2 1- 3

G + 0.90 G + 0.87 G + 0.90 C + 0.83 C + 0.70 C + 0.81 S + 0.44 * P + 0.48 P + 0.46 P + 0.44 N + 0.35 E + 0.30 N + 0.32 R + 0.24 N + 0.35 E + 0.36 E + 0.23 M + 0.26 M + 0.31 B + 0.23 B + 0.20 B + 0.21 M + 0.15 *S + 0.16 R + 0.12 F + 0.11 R + 0.15 0 + 0.08 0 + 0.02 0 + 0.05 F + 0.06 I - 0.14 F + 0.05 I - 0.11 *S - 0.14 I - 0.13 D - 0.17 D - 0.17 D - 0.20 K - 0.19 K - 0.26 K - 0.24 L - 0.26 L - 0.31 L - 0.29 Q - 0.36 Q - 0.56 Q - 0.42

G-1 (Contd. )

Principle Components Analysis for all Variables for - 1,2, and 3 101

PCA - FACTOR 5

A 71-1 X -2 -3

*S+0.90 (Spatial Accessibility) D+0.50 (Expenditure at Superstore) F+0.41 (Frequency at Superstore) 0+0.18 P+0.16 R+0.11 L+0.07 E+0.05 B-0.05 K-0.09 L 0.10 G-0.11 Q-0.12 I-0.18

N-0.23 (Mean Age of Household) C-0.53 (Duration of Total Shopping)

G-1 (Contd. ) 's1,2, Principle Components Analysis for all Variables for and 3 102

Eigenvalue Canonical Chi-Square D. F. Significance Correlation Level Coefficient a2.

1.0 1.0 9999.00 66 0 1.0 1.0 9999.00 50 0 1.0 1.0 9999.00 36 0 0.96 0.98 25.18 24 0.396 0.81 0.90 9.53 14 0.796 0.23 0.48 1.33 6 0.970

As1.

1.0 1.0 9999.00 66 0 1.0 1.0 9999.00 50 0 1.0 1.0 84.67 36 0 0.89 0.95 18.50 24 0.778 0.69 0.83 7.26 14 0.924 0.24 0.49 1.37 6 0.968

A .. 3. 1.0 1.0 9999.00 66 0 1.0 1.0 9999.00 50 0 1.0 1.0 83.21 36 0 0.85 0.92 14.49 24 0.935 0.51 0.71 5.15 14 0.984 0.28 0.53 1.62 6 0.951

G-2 X-1,2 Canonical Correlation Analysis for and 3 103

CONVAR (1) for :

x- 1 X- 2 X s 3

L - 3.14 L - 2.49 L + 3.35 P - 1.50 P - 1.33 *S + 0.93 N - 0.53 N - 0.41 R + 0.88 M - 0.17 M - 0.20 N + 0.45 *S + 0.09 *S - 0.16 Q + 0.42 Q + 0.22 I - 0.12 P + 0.06 J + 0.27 J - 0.09 0 + 0.01 I + 0.34 Q + 0.26 M - 0.36 0 + 0.54 0 + 0.26 I - 0.57 K + 0.67 K + 0.92 K - 1.33 R + 1.36 R + 1.30 J - 1.82

B + 0.99 B + 0.95 E + 0.98 D + 0.39 D + 0.31 B + 0.75

G + 0.25 G - 0.03 F + 0.03 F - 0.14 F - 0.08 G - 0.31 E - 0.34 C - 0.15 D - 0.50 C - 0.36 E - 0.28 C - 0.58

G-3

Canonical Variate Structures for - 1,2 and 3 104

CONVAR (2) for

- 1 A 2 A - 3

L - 4.94 L + 5.38 L + 4.04 N - 1.01 P + 1.22 P + 1.08 P - 0.97 N + 1.06 N + 0.93 0 - 0.47 0 + 0.36 K + 0.53 Q - 0.15 Q + 0.13 0 + 0.50 *S - 0.05 K - 0.03 Q - 0.05 M + 0.35 M - 0.1 M - 0.21 R + 0.91 R - 1.05 *S - 0.34 I + 1.75 I - 1.88 J - 1. a2 J + 1.93 J - 2.01 I - 1.67

G + 1.40 G - 1.45 G - 1.40 B + 0.75 B - 0.81 B - 1.22 E - 0.05 E - 0.19 E - 0.40 F - 0.36 F + 0.37 F + 0.38 D - 0.65 D + 0.41 D + 0.96 C - 1.29 C + 1.28 C + 1.71

G-3 (Contd. ) Canonical Variate Structures for X-1,2 and 3 105

CONVAR (3) for :

A= 1 A= 2 A - 3

L - 4.15 L + 1.30 L + 5.74 0 + 0.93 R + 0.90 P + 1.73 *S + 0.86 Q + 0.50 *S + 0.85 Q + 0.63 P + 0.50 N + 0.74 R + 0.43 K + 0.16 Q + 0.14

N + 0.37 M - 0.02 M - 0.02 I - 0.13 N - 0.11 I - 0.26 M - 0.32 0 - 0.57 R - 0.49 0 - 0.84 S - 0.89 0 - 1.02 J - 1.54 I - 0.92 J - 1.47 K - 1.94 J - 1.12 K - 2.38

E + 2.02 E + 1.64 E + 1.64 B + 1.13 B + 1.39 F + 0.05

G + 0.05 F - 0.10 G - 0.02 F - 0.13 F - 0.14 B - 0.08 C - 1.68 C - 1.29 C - 1.58 D - 2.22 D - 2.56 D - 1.58

G-3 (Contd. )

Canonical Variate Structures for X-1,2 and 3 106

APPENDIX H BEST COPY AVAILABLE

Variable print quality 107.

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