central place practice shopping centre attractiveness jrcs_9_2002.pdf

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Journal of Retailing and Consumer Services 9 (2002) 185199

Central place practice: shopping centre attractiveness measures,hinterland boundaries and the UK retail hierarchy$

Charles Dennis*, David Marsland, Tony Cockett

School of Business & Management, Brunel University, Borough Road, Isleworth TW7 5DU, UK

Received 22 August 2000; received in revised form 21 February 2001; accepted 28 March 2001

Abstract

Christallers (Central Places in Southern Germany (translated by Baskin C (1966)), Prentice-Hall, Englewood Cliffs, NJ, 1933)

well-known and much criticised central place theory was based on classical, arguably unsustainable, economic assumptions such asthe uniformity of consumers and travel. Nevertheless, it has been claimed that the emergence of shopping areas in UK towns could

largely be explained in terms of central place principles (Retail Location: A Micro-Scale Perspective, Aldershot, Avebury, 1992).

Brown drew support from the example of the retail hierarchy of Cardiff (UK, Store Location and Store Assessment Research,

Chichester, Wiley, 1984): a town centre core radiating progressively further out with greater numbers of district centres,

neighbourhood centres and finally local centres. Christallers theory was based on rigid laws of distribution of central places and

laws of settlement which often determine[d] with astonishing exactness, the location of central places in southern Germany. Guy

considered that for useful application to UK retail, a more flexible interpretation was needed and that strict economic assumptions

could be relaxed in a more pragmatic approach. The classical approach fails to account for the positions and hinterland (or

catchment area) boundaries of modern out-of-town regional shopping centres. Except in defining the components of places at

various levels in the hierarchy, Christaller did not even consider the attractiveness of shopping areas in consumer choice. A number

of other authors have investigated various measures to define positions in the retail hierarchy. In the Cardiff example, Guy used

retail sales floor area as a surrogate measure. Systems have been proposed based on numbers and status of retail outlets (The New

Guide to Shopping Centres of Great Britain, Hillier Parker, London, 1991; Shopping Centres, Mintel, London, 1997; J. PropertyRes. 9 (1992) 122160; J. Property Res. 9 (1985) 122160). This paper evaluates the authors empirically based measurement system

for attractiveness that can be applied to out-of-town as well as in-town shopping centres. The approach adapts previous simple

systems based on retailer counts. These have been combined in attractiveness measurements applied to definitions of position in the

hierarchy. Results support the prediction of central place hinterland boundaries based on the authors attractiveness measures and

adaptation of (The Law of Gravitation, Knickerbocker Press, New York, 1931) Law. The data fit exemplar published empirical

data on shopping centre hinterlands more closely than do the commonly used drive-time isochrones. r 2002 Elsevier Science Ltd.

All rights reserved.

Keywords: Shopping centres; Central place; Hierarchy; Hinterlands; Catchment; Attractiveness

1. Introduction

Planned shopping centres comprise an essential part

of the UK economy, employing over three-quarters of a

million people and playing a key role in the investments

of pension funds (OXIRM, 1999; Davies et al., 1993).

In the context of this paper, a shopping centre is defined

as a planned retail development comprising at least

three shops, under one freehold, managed and marketed

as a unit (Guy, 1994a).

Modelling and predicting shopping centre hinterland

boundaries and positions in the retail hierarchy have

implications for developers and planners. Questions

might be explored, for example, as to whether there will

be sufficient business to support a new shopping centre

development. Issues include the extent to which existing

facilities will be affected by a particular development

and the number of new customers who might be

attracted if an existing shopping centre was to be

$An earlier version of this paper was presented to the 7th

International Conference on Recent Advances in Retailing and

Services Science, Eindhoven, EIRASS. (Dennis, C.E., Marsland, D.,

Cockett, W., 2000d. Central place theory revisited: the use of

attractiveness measures in predicting shopping centre hinterland

(catchment area) boundaries).

*Corresponding author. Tel.: +44-(0)208-891-0121; fax: +44-

(0)208-891-8291.

E-mail address: [email protected] (C. Dennis).

0969-6989/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved.

PII: S 0 9 6 9 - 6 9 8 9 ( 0 1 ) 0 0 0 2 1 - 2


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extended. Despite research effort, this remains an

inexact science. Some centres have been reported only

50% let 12 months after opening (Kirkup and Rafiq,

1993, 1994a, b). Theoretical approaches to modelling

shopping centre hinterlands have produced consider-

able controversy and models in which the fit of the

distance component must be adjusted by the FiddleFactor (Rogers, 1984, p. 320; drawing on Openshaw,

1973). Complex models are unpopular among

practitioners (Breheny, 1988).

This paper introduces and tests the authors empiri-

cally based measurement system for the attractiveness of

shopping centresFin- and out-of-townFwith a view to

modelling and predicting hinterlands and positions in

the hierarchy. The paper proceeds as follows. Firstly, the

implications of central place theory and the retail

hierarchy are briefly outlined. The development of the

authors attractiveness model follows and the model is

used, in case study examples, to define steps in the retail

hierarchy, based on shoppers choice behaviour. Scalesproposed by other researchers are examined and it is

demonstrated that such scales can be modified in the

light of the empirical research. A unified attractiveness

scale is proposed and demonstrated to have utility in

predicting hinterland boundaries, consistent with the

principles of central place theory. Finally, the authors

consider whether, when changes in attractiveness are

considered, central place theory modelling can become

dynamic. In discussing the results, indicators are drawn

for possible changes that might be anticipated in the UK

retail and population structure.

2. Central place theory

Central place theory is based on classical economic

assumptions such as uniformity of consumers and

travel, a theory described by Brown (1992, p. 40) as an

elegant andymuch maligned conceptualisation. Based

on the work of the German geographer, Christaller

(1933) and economist Losch (1940), the theory was,

according to OBrien and Harris (1991), widely

accepted by the planning profession as a model of retail

organisation. OBrien and Harris considered thatcentral place theory, for example, explains why London

[UK] contain[ed] the major fashion houses and top

stores, and why small places such as Durham [UK] [did]

not have department stores.

The theory has been criticised in recent decades and

dismissed as more elegant than practical by OBrien and

Harris, who drew support from Dawson (1979, p. 190):

Whilst the theory serves to describe and, in part,

explain locational patterns developed prior to the

1960s, it can no longer be used as anyexplanation of

present patterns or planning future.

According to Dawson, central place modelling was

flawed because of the complex nature of retailing and

scrambled merchandise mixes. Despite acknowledged

drawbacks, in this paper, the authors revisit and adapt

central place ideas in modelling hinterland boundaries

and positions in the retail hierarchy.

In Christallers terminology, goods were described interms of threshold (the population needed to make

supply worthwhile) and range (distance from source of

supply beyond which demand falls to zero). According

to Brown:

Expensive and infrequently purchased [compa-

rison] goodsyhave higher thresholds and ranges

than inexpensive, everyday [convenience] purcha-

sesyThere will be a large number of purveyors of

[convenience] goods and relatively few sellers of

[comparison] merchandise.

By combining the concepts of threshold and range,

hinterlands are defined in which comparison goods areonly supplied from populous central places whilst

convenience goods are sold locally. Economic modelling

based on these assumptions produces maps of catch-

ment hinterlands in terms of interlocking and over-

lapping areas (Fig. 1).

Brown points out that:

The spatial arrangement of shopping facilities in the

majority of British cities is explicable in terms of

central place principles. Typically, this comprises a

[comparison] retailing core, a series of [convenience]

shopping districts surrounding the core but closer to

it than the edge of the city, and a greater number of

shopping districts in the inner than the outer parts of

the urban area.

Effectively summing up researchers ambivalent ap-

proaches to central place theory, Tang and Ingene

(2000) drew attention to essential assumptions that

cannot be sustained in reality, for example the homo-

geneity of households and purchasing demands. Never-

theless, in calibrating their shopping model (using the

example of Shanghai), they observed that the retail

hierarchy predicted by the theory was actually observed

in practice.

3. The retail hierarchy

The distinction between higher order comparison and

lower order convenience goods has been observed in

empirical data, illustrated for example in Fig. 2.

Christaller (1933, p. 58) observed that:

We always find great numbers of central places of a

lower order, i.e. lesser importance and smaller size.

Beside them, we find a considerable number of

C. Dennis et al. / Journal of Retailing and Consumer Services 9 (2002) 185199186


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central places that have a somewhat greater impor-

tance, a still smaller number of places of a higher

order, and only very seldom, places of the highest

orderyThe greater a town is, the smaller is the

numberyin its respective category.

Analogous to Reillys (1929, 1931) approach to

measuring attractiveness, Christaller based his hierarch-

ical system on population numbers. The (then) standard

German classification defined five categories, from the

county town of 20005000 to the metropolis of

1,000,000+population. Despite having apparently uti-

lised these classifications, Christaller claimed to base his

definitions of the hierarchy on the classification of goods

on offer. For any particular good, the outer limit of its

range is defined by the distance that shoppers will travel

to obtain that good. Beyond a certain distance they may

purchase from another place, or may not purchase at all.

The inner limit is determined by the minimum salesyto

make the offering pay. Christaller claimed to classify

Fig. 1. Central place theoryFillustration of demand areas. (a) Hypothetical demand cones. (b) Hierarchy. Notes: (i) Different types and orders of

goods supplied from different levels of places will have different ranges and thresholds and thus sizes of market areas. A nested hierarchy will be

produced. (ii) This pattern is known as a K 3 hierarchy. TheKvalue is determined by the number of lower order centres served by the next higher

level of place. The numbers of centres follows a geometrical progression, proportional to 1, 3, 9 and so on according to the number of levels. Sources:

(a) Brown (1992); originally sourced from Davies (1976). (b) OBrien and Harris (1991).

C. Dennis et al. / Journal of Retailing and Consumer Services 9 (2002) 185199 187


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towns into categories based on levels of goods, but it is

not clear to the authors how the boundaries between the

categories were set. Nevertheless, in southern Germany,

especially for the plainsywhere there are no natural

barriers, the theory often determine[d] with astonishing

exactness, the locations of central places (pp. 190191).

An extract from Christallers mapping is reproduced inFig. 3, indicating striking regularity in the distribution

of places and hierarchy. Christaller, though, tested his

predictions against the distribution of population (and

telephone connections) but did not report doing so

against the availability of the specific goods that are

supposed to form the basis of the hierarchy. Other

authors have, however, reported confirmatory evidence.

Clark (1982) reported the applicability to market centres

in southwest Iowa (USA); once again, an area where the

map can be studied relatively free of the confusing effect

of natural barriers. On the other hand, the authors

question whether the (static basis of) central place

theory (i) explains why the biggest shopping centre in

the world is located in a sparsely populated province of

a sparsely populated country (West Edmonton Mall,

Alberta, CanadaFFinn, 2000); or (ii) accounts for the

shopping centre with the worlds highest number of

visitorsFover 40 millionFbeing located in a state of

only 5 million inhabitants (Mall of America, MOA,

Minnesota, USAFFeinberg et al., 2000). It is the

authors contention that the key to an understanding of

the locations of central places is dynamic. For example,

if entrepreneurs build shopping centres of sufficient

attractiveness (which Finn and Feinberg have found to

underlie the successes of West Edmonton and MOA,

respectively), shoppers will come despite travelling long

distances.

Rather than defining hierarchies based on nominal

measures such as population or type of goods, a more

rigorous approach is based on attractiveness. Fig. 4

illustrates, for example, the retail hierarchy of Cardiff,UK, based on the use of m2 selling area as the proxy

measure. From the town centre core the hierarchy

radiates progressively further out with greater numbers

of district centres, neighbourhood centres and finally

local centres. An analogous pattern can be demon-

strated to surround even a regional shopping centre such

as the Harlequin centre (Watford, UK), where local

centres survive and even thrive within a kilometre of the

shopping centre. Lakeside (Thurrock, UK) has another

shopping centre (in Romford) within the inner band of

its catchment area (20 km/15 min drive). In order to be

viable, those centres lower in the hierarchy need to

include a retailer mix appropriate to their position; in

Romford, satisfying the shoppers who spend less per

visit but come three times a week (the owners, quoted

in Retail Week, 6 June 2000).

Cardiff comprised the basis of a long-term investiga-

tion into retail change. Guy (1999, p. 458, referring to

his 1996 study) observed that the development of a few

large, new food stores coincided with the closure of

several smalleryshops. Guy described these changes as

the outcome of a general process of concentration,

disproving any static nature of the retail hierarchy. This

point was further illustrated in a report of the impact of

Fig. 2. Travel time decay curves, comparing convenience and shopping (comparison) goods. This (US) example indicated shopping frequencies for

three regional and three sub-regional shopping centres. Symbols represent the number of trips to those centres for customers at various travel times.

Sources: Jones and Simmons (1990, p. 40), based on Young (1975).



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the Merry Hill regional shopping centre (UK) on the

adjacent town of Dudley. On completion of the

shopping centre in 1989, a number of major retailers

closed their stores and in effect moved them to Merry

Hill. Many other shops were closed and the premises

were reoccupied by low quality and discount stores

(Guy, 1999).

In the UK, retail areas have grown in a haphazard

manner, from origins as markets in the centres of towns

and in suburban areas through conversions of other

types of property (Guy, 1998). Patterns are not as

regular as in, for example, southern Germany or

southwest Iowa. Guy criticised central place theory as

relying on the notion that consumers would tend to buy

the goods required at the nearest available location, an

assumption:

Clearly incorrect for many shopping trips since other

determinants of shopping success are also impor-

tantyConsumer choice of shopping destination

reflects several qualities, such as variety of goods,

price of goods, cleanliness, spaciousness and security

of the centre, and quality and quantity of car parking,

for example. Central place theory, which relies mainly

on distance as a choice criterion, becomes inadequate

in this situation.

Guy, though, goes on to review dimensions of retail

classification systems including goods sold, trip purpose,

size of store, store ownership, catchment area, physical

form, function, location, development history and type.

Guy pointed out that shoppers make their choices of

shopping centre destination from an evoked set with

Fig. 3. The distribution of towns as central places in southern Germany.Source: Adapted from Christaller, 1933, p. 225.



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respect to a particular combination of location and trip

purpose, suggesting a hierarchical classification based

on trip purpose and size.

An example is the system used in the UK which sums

the numbers of non-food shops in a centre, ranking

centres in order of attractiveness (Reynolds and Schiller,

1992; Schiller and Jarrett, 1985). Reynolds and Schiller

examined the cumulative frequency of scores, fitting the

divisions in the hierarchy to the gaps between size

clusters where possible. The hierarchy classifications

illustrate the increases in numbers of centres at the lower

levels but cannot be observed to follow the strictgeometrical progression of central place theory. The

six levels were:

Multiple branch

(Goad) score

Number of

centres

National 186 1

Metropolitan 93132 6

Major regional 3574 99

Minor regional 2534 60

Major district 624 290

Minor district 25 370

In this paper, the authors have attempted a pre-

liminary exploration of the use of retail attractiveness

measures in defining positions in the hierarchies and

hinterland boundaries for shopping centres and towns.

A description of the empirical measurement system for

shopping centre attractiveness developed by the authors

is as follows. Empirical measurements have been

compared with retailer count systems and a unified

scale has been proposed. In a further step, the

attractiveness scale has been utilised in defining posi-

tions in the retail hierarchy and predicting hinterland

boundaries, comparing with data not gathered by the

authors and not used in developing the model.

4. Propositions to be investigated

Based on the preceding discussion, two propositionson the development of central places and on consumers

shopping centre choice behaviour can be derived.

(P1) Population and retail provision tend to cluster

around central places defined on a matrix.

(P2) Hinterlands and the retail hierarchy follow the

attractiveness of shopping and town centres.

P1 represents Christallers classical approach,

whereas P2 follows the more recent classification

systems of (e.g.) Guy; Reynolds and Schiller. P1 and

P2 are not necessarily mutually exclusive, but if P2 is

accepted, a trend towards the redistribution of popula-tion around attractive shopping destinations might be

expected. Accordingly, this study was designed to test,

on an exploratory basis, the extent to which P1 and P2

fit available exemplar UK data.

5. Attractiveness

The use of attractiveness measurements in the context

of consumers choices of shopping centres has been

reported earlier (Dennis et al., 1999a). The study was

based on a questionnaire survey of 287 respondents atsix UK shopping centres varying in size from small in-

town sub-regional to large regional out-of-town centres.

A regional centre has a gross retail area of greater than

50,000 m2 and a sub-regional one 20,00050,000 m2

(based on Guy, 1994a, b; Marjanen, 1993; Reynolds,

1993). The study evaluated shoppers comparative

ratings of two shopping centres, one of them being the

centre where the interview took place. The alternative

centre was the one where they shopped most (or next

most after the interview centre) for non-food shopping.

The questionnaire instrument was based on the

attributes of image elements employed by McGoldrick

and Thompson (1992a, b) together with additional

constructs derived from analyses of preliminary un-

structured interviews. Respondents stated their percep-

tions of the importance of each of 38 attributes

(including those identified by Guy as figuring in

consumers choices of shopping destination, for exam-

ple, quality of stores, cleanliness and availability of

toilets, following Hackett and Foxall, 1994). Each

attribute was also rated for both the centre studied

and the alternative centre. Respondents estimated

perceived travel distance and time to both centres and

supplied details such as age, location of residence and

Fig. 4. The urban retail hierarchy of Cardiff (UK). Source: Brown

(1992); originally sourced from Guy (1984).



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occupation of the main earner in the household.

Examination of the characteristics of the sample

indicated the distribution of socio-economic groups,

age and sex reasonably representative of that anticipated

at UK shopping centres.

Further questions concerned typical perceived

monthly spend at each of the two centres. AsMcGoldrick and Thompson pointed out, much of the

variation in shoppers expenditure relates to factors such

as income or socio-economic groups, rather than travel

distance or attributes of the shopping centre. Following

this approach, the main dependent variable used in this

study was the individual relative spend. A value of 100

indicated all expenditure at the centre studied none at

the alternative centre. A value of 50 indicated half of the

expenditure at each centre. The same approach was used

to scale perceived travel distance and time producing the

variables individual relative travel distance and time.

The view of attractiveness taken by the authors is

that any product (such as a shopping centre) can beseen as a bundle of expected costs and rewards which

East (1997, p. 131) found was upheld by research. East

drew support from Westbrooks (1980) finding that an

overall measure of retail satisfaction correlated well with

a simple addition of the satisfactions and dissatisfactions

that customers experienced. In the authors methodol-

ogy, the measures of satisfaction and dissatisfaction

have been taken from the respondents ratings of the

shopping centre compared to their main alternative

centre (on 5-point semantic differential type scales).

These satisfactions for the individual attributes were

weighted, firstly by the importance of the attribute tothe respondent (also on a 5-point scale) and secondly by

the degree of association with the stated relative spend.

Once weighted, satisfactions were added, giving an

overall attractiveness measured value. The authors

combined the attractiveness measurements with the

relative travel time or distance variables, to derive

(statistically significant) models of individuals relative

spend. More detailed derivations of the attributes and

models have been reported elsewhere (Dennis et al.,

1999a, b, 2000a).

6. Steps in the retail hierarchy

The positions of steps in the retail hierarchy of

shopping centres were investigated by first defining what

constituted a substantial competitor in the empirical

survey. In this context, a substantial competitor was

defined as another shopping centre used by over 25% of

shoppers at the centre studied. To qualify as substantial,

a competitor must be attractive relative to the centre

studied. For example, considering centres featuring in

this empirical work, the Blue Rose Centre (not its real

name) has a competitor of 55% of the physical size

(m2 gross selling area) within its catchment. This

competitor was used as a main or alternative centre by

only 2% of the Blue Rose Centres customers and thus

cannot be considered as a substantial competitor.

Conversely, The White Water Centres substantial

competitor is 95% of the size of the White Water

Centre and used as a main or alternative centre by 40%of the White Water Centres customers.

Using selling area as a proxy indicator of attractive-

ness, the defining point for a substantial competitor

(used by over 25% of a shopping centres customers) lies

somewhere in between above 55% and below 95% of

the size of the centre studied. A mid value of 75% has,

thus been adopted as the defining level of a substantial

competitor, based not on selling area but rather on the

more precise unified attractiveness scale reported below.

For a competitor to be counted as at the equivalent level

in the hierarchy to the centre studied, the competitor

must have an attractiveness of at least 75% of that of the

centre studied. It follows, therefore, that when twoshopping centres are compared, if the smaller one has an

attractiveness of less than 75% of the larger one, the

smaller is at a lower level in the hierarchy. Although

studies of many more centres are needed to confirm this

arbitrary-seeming definition, it is consistent with the

results of this study and those of other authors analysed

below. That is, a centre at a lower level can lie within the

catchment of a larger centre, whilst there will be a break

point in catchment boundary between two centres at

the same level.

7. Unified attractiveness scale

Mintel (1997) reported an attractiveness measurement

scale for shopping centres and towns, based on numbers

of retail outlets, scoring multiples higher than others and

certain specific named retailers higher still. In the light of

many considerations that are known to inform

shoppers choices of shopping destinations, measuring

shopping centre attractiveness, in such a manner,

initially appears trivial. Nevertheless, evidence in sup-

port can be inferred from at least two independent

sources. Firstly, Finn and Louviere (1996) investigated

all 17 regional and community shopping centres in

Edmonton, Canada. The fit of their regression models

was highly significant, with between 70% and 90% of

image item variance accounted for by the store tenant

variables such as the presence of particular major and

discount department stores. Other characteristics had

some significant effects, but the additional effect on

image was generally rather small. It was concluded that

specific anchor stores had a substantial impact on

consumers images of shopping centres, accounting for

most of the variation in centre patronage. Secondly,

Feinberg and associates (2000) investigated the



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prediction of (US) mall patronage from attraction scales

using stepwise logit regression. In every case, the rating

of specific stores that most attracted customers was

demonstrated to be the most significant variable.

Findings such as these, are understandable when it is

considered that the most attractive shopping centres will

be expected to attract the most successful and popularretailers.

In this study, the Mintel scale correlated well with

the empirical measurements from the questionnaire

surveys, R2 0:94 for Eq. (1):

A 0:978B; 1

whereA is the Mintel score and B the empirical Brunel

Index for attractiveness (from the survey-measured

weighted attractiveness summations, rescaled to use an

equivalent numerical scale to the Mintel scoreFFig. 5).

Corrections must be made, though, for the differences

between planned, covered shopping centre attractiveness

and the unplanned town centres, and in addition for the

effect that the surrounding town area has on shoppers

perceptions of the attractiveness of the centre. The

attractiveness measure was significantly associated with

shopping centre sales turnover and rental income

(R2

=0.99 and 0.98, respectively, reported more fullyelsewhere: Dennis et al., 2000b).

To estimate and plot hinterland boundaries, maps

indicating the locations of towns and shopping centres

along with their Mintel scores would be useful. Such

maps can be produced to order by data owner(s). The

Hillier Parkers New Guide to shopping centres of

Great Britain (Goad Plans/OXIRM, 1991, the Goad

Plan) is useful in this connection. The Goad Plan is

based on the numbers of branches of 113 specific

multiple retailers (Goad scoresFbased on one of the

examples referred to by Guy and designed to reflect the

presence of multiple comparison goods retailersFRey-

nolds and Schiller, 1992). An extract from the GoadPlan is reproduced in Fig. 6. The area selected deliber-

ately typifies a part of middle England relatively free

from natural barriers and having in consequence some

regularity in the distribution of towns. Below those

towns rated in the Goad hierarchy, there is a level of

smaller places, such that, in the Middle Ages, every

habitation was within a one-day return walk of a

market.

The authors have determined the Goad scores to be

valid measures of attractiveness for towns, correlating

with scales proposed by other research organisations

(such as Management Horizons, 1995: for 15 towns alsorated on the Goad Plan, R2 0:88; significant as

p 0:01). The Mintel scores, though, are valid for bothtowns and shopping centres. To facilitate data handling,

the Goad scores have been rescaled to the Mintel

equivalents.

Fig. 6. Towns and shopping centres in central England.Source: Extracted and adapted from Goad Plans/OXIRM, 1991.

Fig. 5. Mintel scores, A; of shopping centres vs. the Brunelmeasured attractiveness index, B; of the centres on the MintelscaleFcorrected for towns (intercept set to 0).



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Based on the best fit of available data, it has been

estimated that, for plotting hinterland boundaries, the

attractiveness of towns must be scaled down by a fixed

percentage compared to shopping centres. Validating

the combined scale as linear, estimates of sales values for

town and shopping centre combinations together with

out-of-town shopping centres correlated well with thecombined attractiveness scale (coefficient of determina-

tion, R2 0:92; significant at p 0:01). It must bestressed that the authors new empirical attractiveness

measures should be used whenever practicable. Never-

theless, the scale based on Goad and Mintel values,

adjusted for town centres vs. shopping centres, can be

utilised as alternative where the empirical measures are

not available.

8. Distance exponent

The principle of the gravitational theory of retail isthat shoppers are more likely to shop in a more

attractive town or shopping centre, but the attractive-

ness decreases with distance. Spend can be considered as

being positively related to some measure(s) of attrac-

tiveness and negatively related to some measures(s) of

unattractiveness or deterrence, such as distance. Reilly

(1929, 1931) used analogy with gravitation as the basis

of his law which forms the bases of many approaches

to retail location. The frequency with which residents

trade with a town is postulated to be directly propor-

tional to the attractiveness and inversely proportional to

some power of the distance that they travel. This inverseeffect of distance, though, need not be limited to its

square as in Reillys gravitational analogy. Rephrasing,

the distance exponent is not necessarily numerically

equal to2. Based on the questionnaire survey outlined

above, the authors have previously reported on varia-

tions in the distance exponent (Dennis et al., 1999b,

2000a). The distance exponent was demonstrated to be a

variable related to attractiveness. The authors contend

that an estimation of the distance exponent, d; can bepredicted from the Brunel Index of attractiveness, B;using a relationship derived from a linear regression

(SPSS):

d 0:0044B3:19: 2

The empirical data comprising the basis of the

relationship are illustrated in Fig. 7.

9. The hinterland boundary

Reillys law has been restated (Converse, 1949) to

estimate the break point between two towns which

separates the areas of dominance of each. As reported

elsewhere, the break point calculation can be modified

to incorporate varying distance exponents (Dennis et al.,

2000c). Martin (1982) demonstrated that Reillys law

break points could be used to define a notional

catchment area boundary, by calculating (and joining)

multiple break points with surrounding towns. Martin

compared the calculated break points around North-

ampton (England) and surrounding towns with a survey

data contour (from 1350 respondents) enclosing the area

from which 90% of Northamptons shoppers travelled.

Martins calculated break points for Northampton

produced a hinterland boundary in reasonable agree-

ment with survey results, illustrated in Fig. 8. This

provided a quick quantitative method of estimating the

catchment area, but with two disadvantages. Firstly,

there was only a single measure of attractivenessF

po-pulation. Population must be an inappropriate measure

for the attractiveness of the new town shopping centre of

Milton Keynes, which acts in effect in a similar way to

an out-of-town shopping centre for a large hinterland.

Secondly, the distance exponent was treated as a

constant although it has been demonstrated to be a

variable. The authors have reworked the break point

calculations using unified attractiveness scores together

with estimated distance exponents (derived from Fig. 7).

An intuitive restatement of the Reilly/Converse break

point prediction of the position of the break point

between towns a and b would be

distance from adistance from a to b

1 dbffiffiffiffiffiffi

Abp

=daffiffiffiffiffiffi

Aap ; 3

whereda and db are the distance exponents for towns a

and b, respectively, Aa and Ab their attractiveness

scores. Eq. (3) is, though, only a first approximation of a

relationship to which there is no straightforward

arithmetic solution. The full derivation has been

reported elsewhere (Dennis et al., 2000c) and space

limitations preclude inclusion here. In brief, the break

point derived from Eq. (2) must be multiplied by the

Fig. 7. Distance exponent, d; on visit rate per 1000 residents vs.Brunel measured attractiveness index of shopping centre plus

correction for town centre (where relevant) on the Mintel scale, B :



10/15

correction factor

d

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

Fa Fb

2F ;

r

where the values ofFa and Fb; respectively, are

Aa

Ddaaand

Ab

Ddab

:

Da and Db are the distances of the calculated break

point (Eq. (3)) from a and b, respectively, dthe higher of

the distance exponents andFthe lower of the Fa and Fbfunction values. In the authors experience, this simpli-

fied procedure has always estimated break points within

0.2 km of the theoretically optimum solution.

Martin defined the catchment area in what may seem

to be an arbitrary mannerFthe boundary which

enclosed 90% of the shoppers, pointing out that any

attempt to identify more than around 90% of catchment

area results in absurdities such as claiming that parts

ofyAmerica should be included. The authors have

investigated the use of values other than 90%. No other

value was more useful in modelling shopper spend. The

value of 90% has therefore been retained as a standard

(R2 value in predicting the effect of distance on

individual spend 0.85 when using the 90% value for

defining catchment, compared to 0.62 and 0.57 for 85%

and 95%, respectively).By substituting attractiveness for population, and

using distance exponent values estimated from attrac-

tiveness, the break point method has been adapted to

help predict catchment area for an out-of-town shop-

ping centre as well as for a town. The authors have

tested predictions against data not gathered by them and

not used in developing the models. There is a scarcity of

such data published in the public domain, but two

surveys mapping customer catchments have been used, a

town (Northampton, UKFMartin, 1982Fthe example

referred to above) and an out-of-town shopping centre

(Meadowhall, UKFHoward, 1993). Predicted hinter-

land boundaries have been compared with survey results

for these two areas, illustrated in Figs. 810.

For out-of-town regional shopping centres, break

point calculations in the main are less applicable than

for towns as such centres are usually not surrounded by

competitors attractive enough to figure at an equivalent

level in the retail hierarchy. Fig. 10 is an example of

catchment data for Meadowhall out-of-town shopping

centre, near Sheffield, England using data from Ho-

wards (1993) survey of 1244 respondents.

The authors break point calculations have been

based on estimated drive times from Automobile

Fig. 8. Catchment area of Northampton, comparing the theoretical assessment from the Reilly/Converse law with the survey result. Source:

Adapted from Martin (1982, p. 71).



11/15

Fig. 9. Catchment area of Northampton, comparing the theoretical assessment from the Reilly/Converse law vs. the survey result, with a plot based

on Brunel Index superimposed by the author (based on drive times and predicted distance exponents)Sources:The authors and adapted from Martin

(1982, p. 71).

Fig. 10. Catchment area of Meadowhall, comparing the theoretical assessment from predicted Brunel attractiveness, distance exponents and

population, plus the break points with Manchester (based on drive times) vs. the survey result. Sources:The author, adapted from Howard (1993,

p. 101); Census (1991) and Routemaster (B1998).



12/15

Associationdata (AA:Routemaster programme,B1998),

plotted via the major roads. For Northampton,

illustrated in Fig. 9, the radii of the predictions in the

directions of towns in the vicinity of the hinterland

boundary fit the survey result well, with a Hoel and

Jesson index of fit=0.82 (equivalent to the R2 valueF

Hoel and Jesson, 1982).In directions other than Manchester, the effect

of competitors is not relevant and predictions

were made using standard calculations based on

population, drive time (or distance) and estimated

distance exponents only. Populations (residents in

households from Census, 1991) in radial segments in

the direction of the main population centres have been

analysed in conjunction with the (Routemaster, B1998)

drive times together with the estimated distance

exponent. Fig. 10 illustrates the catchment boundary

calculated on this basis (for directions other than

Manchester) compared with the boundary enclosing

90% of respondents (estimated by the authors fromHowards respondent spots).

The predicted catchment for Meadowhall is less

extensive than that observed in the directions of

Bradford, Leeds, Nottingham and Derby but at York,

Scunthorpe and Lincoln is close to the survey result. In

the direction of Manchester, the catchment area

boundary has been calculated using break point

calculationsFand the fit is considerably closer to the

survey result than is the standard calculation.

Practitioners tend to favour the estimation of catch-

ment area boundaries from drive time isochrones. For

example, the authors have calculated that the isochronethat enclosed 90% of respondents in the Meadowhall

survey was at the 50 min drive time. This isochrone,

though, extended into the city of Manchester, consider-

ably beyond the observed catchment. The break point

method predicts the catchment boundary in the direc-

tion of Manchester more closely than does either the

standard calculation or the isochrone. The radii of the

predictions using the break point method again fitted

the survey result well. When combined with the standard

distance exponent approach for other radii directions,

catchment area was predicted with a Hoel and Jesson

index of fit=0.77.

10. Discussion and conclusions

In this paper, the authors have eschewed the rigid

laws of settlement which, according to Christaller,

determined the location of central places. Rather, the

dynamic nature of shopper behaviour has been demon-

strated with shoppers following the provision of

attractive shopping areas. Decisions of developers and

planners can lead to the building of large out-of-town

shopping centres that redefine the retail hierarchy. As

briefly alluded to above, Guy (1996, 1998, 1999) has

studied many examples, and concluded that a more

flexible interpretation of central place theory is needed

for useful application to UK retail. The authors share

the view that strict economic assumptions can be relaxed

in a more pragmatic approach.

On the one hand, the distribution of market townsin middle England, illustrated in Martins study of

shopping patterns around Northampton, supported

proposition P1. In that approach, population appeared

clustered around central places defined on a

matrix (classical central place theory), with hinterland

break points being defined by population (Reilly/

Converse). The authors, though, have demonstrated

an alternative scenario. In line with proposition P2,

hinterland catchment areas and the retail hierarchy

followed the attractiveness of shopping and town

centres, which in turn was closely related to the

provision of shops that shoppers considered attractive.

This proposition was demonstrated to hold not only fortraditional towns and shopping, but also for new out-of-

town shopping centresFthus, extrapolating beyond the

scope of P1.

The dynamic nature of shopping behaviour following

the provision of shops can be further illustrated by

comparing the famous, traditional English University

cities of Oxford and Cambridge. These are of similar

population size (and socio-demographic profile), but

Cambridge supports 15 more of the specified multiple

comparison goods retailers included in the Goad score

(27% higher attractiveness score). Reynolds and Schiller

(1992) suggested this to be due to Cambridges expan-sion in catchment draw resulting from the opening of

the Grafton Centre on the edge of the central area.

The results from this study indicate a correlation

between attractiveness score and sales turnover

(R2 0:99;as mentioned above and reported elsewhere:Dennis et al., 2000b). By modelling sales turnover on

this basis, it has been deduced that Cambridges

consumer spending is around a quarter more than that

of Oxford, directly resulting from the building of the

new shopping centre.

In the examples of Northampton and Meadowhall, a

fit has been observed between the predicted break points

and the boundary containing 90% of shoppers. From

this, it can be concluded that there is an overlap of

catchment area boundariesFclose to 10%. Shoppers do

not necessarily shop at the nearest place that satisfies

their requirements for specific goods. Rather, they take

into account many aspects of shopping destinations and,

when considered on aggregate, distribute their (non-

food) shopping expenditure according to their assess-

ments of the attractivenesses of the destinations. Despite

the many other considerations in shoppers decisions of

where to shop, there nevertheless appears to be a close

association between assessments of attractiveness and



13/15

the numbers of shops, particularly those shops especially

desired by the consumers. This is understandable on the

basis that the most attractive shopping centres will

attract the most successful retailers. Centres under-

performing on attractiveness lack the big name and

designer stores.

The authors results have justified one aspect ofclassical central place theoryFthe nesting hierarchy of

hinterlands. For example, from Fig. 9, the town of

Market Harborough can be seen to lie within the

hinterland of Northampton. On the other hand, there is

a break point boundary between Milton Keynes and

Northampton; although Milton Keynes is closer to

Northampton than is Market Harborough. Milton

Keynes (and the other towns used in the break point

assessments) is on the equivalent hierarchy level

to Northampton, based on the rule of at least 75% of

the attractiveness score of Northampton. Market

Harborough, though, in common with a number of

smaller towns not used in the break points assessment,is below the 75% measure. The authors data (ongoing

study, to be reported later) indicate that the

typical resident of Market Harborough has some

comparison goods expenditure in that market town,

but substantially more in Northampton and/or Milton

Keynes. In the examples of both Northampton and

Meadowhall, the defining level of 75% of attractiveness

appears to have been effective in defining the level in the

hierarchy. Towns below 75% attractiveness do not

appear to affect the catchment area boundaries of the

larger centres.

The authors contend that classical central placetheory can be modified in the light of proposition P2,

taking as a basis for the provision of retail offerings,

which can be modified by planners and developers.

Thus, it can be forecast that movements of population

towards the new central places will follow develop-

ments of regional shopping centres. This effect has been

observed in the USA from as long ago as 1976 (Gosling

and Maitland, 1976; Lion, 1976). Young (1985) docu-

mented the US trend for residential and office develop-

ment around suburban growth poles centred on

regional, out-of-town shopping centres, sometimes

growing to minicities of 300,000500,000 residents

(for example, Brae, California). In these edge cities,

malls usually function as the village squares (Garreau,

1991). Des Rosiers and associates (1996, Canada)

reported a positive correlation between house prices

and proximity to shopping centres. In the UK, it is

understood that some of the best-known out-of-town

shopping centres (The Metro Centre and LakesideF

Mintel, 1997; BraeheadFLowe, 2000a) have attempted

to be reclassified for planning regulation purposes as in-

town, on the grounds that residential development has

been attracted to the area. According to Lowe (2000b),

UK shopping centres are becoming the new high

streets in a trend even more marked than in the US.

All of these indications support an essential tenet of

central place theory associating the number of popula-

tion and provision of services with the availability of

shops. Retail has been stated to form the heart of

virtually all [UK] towns and cities (Retail Week, 9 June

2000). That article went on to claim that the first step inurban regeneration is renovating shopping centres and

retail, citing the example of the transformation of Wood

Green Shopping City [that] created a focus for the north

London community and improved the surrounding

area. The authors work has demonstrated the link

between shoppers and retail attractiveness to be part of a

dynamic process in which planners and developers might

take the initiative in providing shops, leading to changes

in population, expenditure, residence patterns and

indeed bringing new life into run-down areas.

If the findings are confirmed by larger studies, there

may be a number of implications. Shopping centre

developers will be able to more accurately model thelikely catchment areas of new or extended centres, and

planners predict the impact on existing facilities with

more confidence. By progressing to a further stage of

modelling, residential developers and institutional len-

ders can benefit from improvements to the prediction of

house price changes. Planners will be able to model the

effects of regeneration projects in order to more

accurately assess required infrastructure improvements

and residential provision associated with retail and

shopping centre developments.

The work reported has been exploratory in nature.

Confirmatory studies of larger numbers of shoppingcentres and respondents are recommended. There are

limitations to central place theory but the authors

contend that aspects of central place practice, when

suitably modified to place the main focus on the retail

provision, are worthy of further consideration by

planners, developers and managers. The indications

from the results to date are that, once the strict

economic assumptions are relaxed, the principles can

be applied to both in-town and out-of-town shopping

centres, improving the prediction of hinterland catch-

ment area boundaries and defining positions of centres

in the retail hierarchy.

Acknowledgements

The authors thank Capital Shopping Centres PLC for

assistance with funding, Professor Peter McGoldrick of

UMIST for posing many searching questions on early

versions of the results, Professors Ross Davies of the

Oxford Institute of Retail Management, Clifford Guy of

the Cardiff University of Wales and Heli Marjanen of

Turku School of Business Administration for providing

much extra useful information.



14/15

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