Sport Facilities VIII

The Influence of Economic Factors in Urban Sports Facility Planning: A Study on Spanish Regions

PABLO BURILLO∗, ÁNGEL BARAJAS∗∗, LEONOR GALLARDO† & MARTA GARCÍA-TASCÓN‡

∗Sport Sciences Institute, Camilo José Cela University, Madrid, Spain, ∗∗Faculty of Business Administration and Tourism, University of Vigo, Ourense, Spain, †Faculty of Sport Sciences, University of Castilla-La

Mancha, Toledo, Spain, ‡Sport Facutly, Pablo de Olavide University, Seville, Spain

(Received November 2009; accepted July 2010)

ABSTRACT Sports infrastructure development signals a major economic development in regional and local areas. It has considered one of the main policies related to promoting public health, by various studies. However, economic factors also come into play in the design and location of sports facilities. Our research aims to examine whether the economic development in the regions in Spain helps promote sports infrastructure development, using a synthetic indicator. We also examine the potential relationships in the main economic indicators that determine the wealth and economic development of the regions. There is a significant relationship between the degree of economic development in each Spanish region and the level of sports infrastructure resources. Insufficient investment in sports infrastructure, leads to fewer opportunities for Physical Activity by the population. These results should serve to redirect building planning and sports management policies onto the right path, and also give food for thought.

1. Introduction

In urban sports planning, being aware of the current needs of the people is as important as

researching and forecasting the orientation of future new activities. By doing so, it will be

possible to build facilities of a multidisciplinary nature, which may bring about an increase

in participation in physical activities. Various studies have shown the potential of, and

influence exerted on the public’s physical activity (PA) when sports facilities are built

in strategic areas (Andrews et al., 2005; Bale, 2001). Sports infrastructure development

signals a major economic development in such areas (Giles-Corti & Donovan, 2002b;

Correspondence Address: Leonor Gallardo, Faculty of Sport Sciences. University of Castilla-La Mancha. Avda.

Carlos III, PO 45071, Toledo, Spain. Email: [email protected]

European Planning Studies Vol. 19, No. 10, October 2011

ISSN 0965-4313 Print/ISSN 1469-5944 Online/11/101755–19 # 2011 Taylor & Francis http://dx.doi.org/10.1080/09654313.2011.614385

Holman, 1997; Macintyre & Ellaway, 1998). However, such infrastructure development

tends to be linked to the local economic situation (either current or projected) of the area.

Government involvement in shaping the physical environment should be considered an

imperative in promoting public health, which must be complemented with health edu-

cation projects (Holman, 1997; Schmid et al., 1995). Creating active, accessible spaces

has increasingly been the goal in urban design and urban regeneration (Hoehner et al.,

2003).

Nineteenth-century urban planning is completely different from that of today. Today,

there are a large number of spaces set aside for leisure and recreation, such as walking

and cycling or for children’s play areas (Hoehner et al., 2003). However, for certain phys-

ical activities to meet people’s requirements, special areas are also needed (areas with

water or snow, for example), which are mostly associated with vast shopping centres in

the big cities.

It has also been observed that in areas with limited income levels, as well as those with

fewer facilities devoted to fitness and wellness, there is very little participation in PA

among residents. The authors of these studies attribute these results to the fact that in

these areas, there is less investment in community services, green spaces and sports and

recreational infrastructure (Diez-Roux et al., 2000; Ecob & Macintyre, 2000; Giles-

Corti & Donovan, 2002a, 2002b; Macintyre & Ellaway, 1998).

The number of sports and leisure facilities in Spain has experienced significant growth

in recent years (Gallardo, 2007), accompanied by active sports participation and PA in

general (Garcı́a-Ferrando, 2006). In 2005, Spain had 79,059 sports facilities (78,873

sports facilities, not counting Ceuta and Melilla). This figure shows an increase with

regard to the 1997 National Sports Facilities Census (CNID) of 66,352 facilities. In

1985, the CNID reported 48,723 facilities, or an increase of 61.62% over the last 20 years.

A large number of sports facilities are currently being built or in the planning stage,

mainly funded by public money and, with certain exceptions, private investment

(Coates & Humphreys, 2003). By contrast, the average public funding in the US is 65%

of the total cost of building new facilities, with an average public spending of 208

million dollars (Coates & Humphreys, 2003).

Thus, it is quite clear that sport has acquired economic significance, not only for its

ability to generate resources, but also because of the need for investment to promote

active sports participation. In other words, the economic implications of sports may be

studied from two perspectives: first, the assessment of the economic impact produced

by the organization of sporting events or the building of sports facilities in a particular

city or area and second, the investment in a particular area to promote active sports par-

ticipation. In the first case, sport is the cause of economic development and in the

second, a consequence.

Our research aims to examine whether the economic development in the regions in

Spain helps promote sports infrastructure development, thereby promoting public health.

2. Theoretical Framework

Spain is a European Union Member State, with an area of 505,997 km2. It is comprised of

17 regions and two autonomous cities, which have administrative, executive and legisla-

tive powers to act on sports matters in their territory. Regional level governance is con-

ducted via the Regional Departments and Sports Directorates, the main bodies for

1756 P. Burilloa, Á. Barajasb, L. Gallardoc & M. Garcı́a-Tascónd

sports planning and building of facilities. While the national government is responsible for

sports management and supervision, particularly competitive sports, the local government

is responsible for planning and carrying out all the sports programming and services in

these facilities (Figure 1).

The devolution of powers has helped reduce the imbalances between regions, as well as

among provinces within the same region. In certain cases, however, these differences have

widened (Banús et al., 2003).

The Spanish Sports Act gives the responsibility of constantly updating the National

Sports Facilities Census (CNID) to the Higher Sports Council (Government of Spain) in

collaboration with the Regional Authorities. The CNID is designed to help authorities

in sports facility planning and decision-making. Sports facility planning by public auth-

orities should be considered one of the main policies related to promoting public health

(Gil et al., 2010; Pascual et al., 2007). However, economic factors also come into play

in the design and location of sports facilities in cities (Bale & Moen, 1995; Jones, 2001).

The economic impact point of view has been extensively studied, mainly from the per-

spective of professional sports and leisure (Baade & Matheson, 2008; Capó et al., 2007),

sports facilities (Baade & Dye, 1990; Coates, 2007; Crompton, 1995), and the organization

of sporting events (Gratton et al., 2005, 2006; Kasimati, 2003; Lee, 2001; Matheson, 2002;

Preuss, 2004, 2007; Ramı́rez et al., 2007).

However, there has been no detailed study on the relationship between the wealth of a

region and investment in sports. Andreff (2001) shows a close relationship between the

level of economic development of a country and the level of its sports development.

Figure 1. Structures of organized sport in Spain.

Influence of Economic Factors in Urban Sports Facility Planning 1757

Developing countries tend to have shortcomings in physical education and sports,

including the lack of funding and poorly equipped facilities, the out-migration of

local athletes to developed countries, and scant resources for hosting major sporting

events. In fact, evidence shows that the probability of a country winning medals in the

Olympic Games increases according to its gross domestic product (GDP) per capita

and its population.

Studies by Pascual et al. (2007) in Spain found that the number of sports facilities is

associated with the absolute wealth level of the region, but they found no relationship

with the distribution of income among the population. These authors show that areas

with few spaces devoted to well-being, and those in which fewer resources have been

invested for the creation of sports spaces and infrastructure, lead to poor development

and health maintenance among residents.

Many studies have been conducted to investigate the association between availability of

sports facilities and PA (Eyler et al., 2003; Giles-Corti & Donovan, 2003; Humpel et al.,

2004; Li et al., 2005; Pascual et al., 2007; Stahl et al., 2001; Van Lenthe et al., 2005;

Wendel-Vos et al., 2004). Several authors have found a significant positive relationship

between PA as well as social and environmental support (Di Lorenzo et al., 1998; Sallis &

Owen, 1998). Other studies have shown that the various characteristics of a neighbourhood

are related to the PA of its inhabitants (Ball et al., 2001; Browson et al., 2001; Diez-Roux

et al., 2000; Ecob & Macintyre, 2000; Giles-Corti & Donovan, 2002a; Macintyre &

Ellaway, 1998; Parks et al., 2003; Takano et al., 2002; Van Lenthe et al., 2005).

In some studies, there are low percentages of PA in less-favoured areas, where there are

problems in terms of safety, accessibility and distance from sports facilities (Booth et al.,

2001; Wilson et al. 2004). Various studies have shown that measures taken in the PA and

sporting milieu in small districts and towns have led to increased participation in PA

among the local population (Sallis et al., 1997; Stone et al., 1998).

Other studies found that less-favoured areas usually have poorly designed, low-

quality sports facilities (Van Lenthe et al., 2005); they also had less material resources

for participation in PA and sports (Estabrooks et al., 2003). Kamphuis et al. (2007)

found that the lack of equipment and materials could affect whether sporting activities

were properly carried out. While these authors did not consider it to be a determining

factor in the increase or the reduction of PA, the participants believed that it was a

major factor.

Van Lenthe et al. (2005) stated that the proximity of sports facilities figures remarkably

in the relationship between features of the urban environment and participation in PA. For

example, a positive link was found in various studies between the perception of the proxi-

mity of facilities for physical activities and sports (such as gymnasia) and other urban

spaces (such as parks and shops), and the probability of being physically active (Ball

et al., 2001; Booth et al., 2000; Brownson et al., 2001; Giles-Corti & Donovan, 2002a,

2002b).

Results of qualitative studies in Australia showed that proximity and good access

to sports facilities were major factors in the use of these facilities and subsequent

participation in PA (Stahl et al., 2001). Nevertheless, improved access and proximity

called for higher investment in these sports facilities, either because of the higher

cost of building land (sports facilities in urban areas) or the deployment of transport

resources and facilities (car parking, buses, underground, local trains, etc.) to reach

them (sports facilities in outlying areas). Similarly, Sooman and Macintyre (1995)

1758 P. Burilloa, Á. Barajasb, L. Gallardoc & M. Garcı́a-Tascónd

reported a lower level of access and proximity to local sports facilities in less-

favoured areas.

Climate is another factor that has been studied in various countries, particularly in the

UK, US, and the Netherlands, given that lower activity in sports may be attributed to the

effects of climate. However, these countries tend to invest heavily in covered and climate-

controlled facilities, thereby reducing limitations owing to climate conditions (Kamphuis

et al., 2007).

Society is now calling for the design of new spaces in cities, such as walking or bike-

riding areas. This is directly related to the demand arising from other personal activities,

such as shopping or going for walks (Hoehner et al., 2003). However, sports authorities

employ two criteria in their planning: society’s needs and demands, and the size of the

investment (building, management, operation, etc.). Greater wealth should lead to

higher public investment in social and health policies. The higher availability of sports

facilities, plus sports programme planning, would help to develop these social policies,

thereby fostering healthier lifestyles and increasing people’s PA. Therefore, it seems

appropriate to study whether there is a relationship between a particular region’s economic

development and investment in sports facilities (given the distribution of each region’s

sports infrastructure).

In this study, we shall be ranking each Spanish region in terms of the state of its sports

infrastructure, by means of a synthetic indicator called the Sports Facility Synthetic Indi-

cator (SFSI), using data from the 2005 National Facilities Census. We shall be using 15

variables typical of sports facilities in the regions, chosen by a panel of experts. On the

basis of the review above, we shall examine the potential relationships in the main econ-

omic indicators that determine the wealth and economic development of the regions. It is

important to point out that, from the perspective of the regional comparative analysis, what

really matters is the production per inhabitant. There is no exact variable that includes the

complexity and multi-faceted nature of the concept of “wealth”, although there is a wide-

spread tendency to consider the GDP per capita (PPP) as the “principal measure of the

level of economic development”. We also considered complementing the study with

other economic and sporting indicators regarding people’s opportunity to take part in rec-

reational and sporting activities. The economic indicators were Registered unemployment

rate, Market share per capita, Tourism index per capita (TIpc), and Index of economic

activity per capita, plus a sporting indicator, the inhabitants’ PA.

3. Methodology

3.1. Objectives

The objectives of this study are as follows:

(1) To produce a ranking of the Spanish regions in terms of their sports infrastructure

resources.

(2) To discover whether there is any type of relationship between the economic indicators

and situation of each region and their sports infrastructures.

With regard to the second objective, we work with the following general hypothesis:

Influence of Economic Factors in Urban Sports Facility Planning 1759

H1: There is a relationship between the Regions’ economic development and the

number of sports facilities available.

3.2. Population

The sample comprises the total of sports facilities and spaces in the 2005 census, both

public and private, for collective use (individual/family facilities are not included), so

that it is not actually a sample, because it takes in all the sports infrastructures in the

17 Spanish regions under study.

3.3. Variables in the Study

3.3.1. Databases . 2005 National Sports Facilities Census (CNID-2005): A census conducted by the

Higher Sports Council, a Spanish Government body, which collects data from all

Spanish regions regarding sports infrastructure for collective use (www.csd.gob.es). . National Population Census: referring to the population count according to various ter-

ritorial disaggregates. In this case, we took as our reference the regional data for 2005

(INE [National Statistics Institute], 2006). . Territory of each Spanish Region: The area of each region was obtained from the data-

base on “Surface area by Region” measured in square kilometres, supplied by the

National Statistics Institute (www.ine.es). . Economic Yearbook for Spain (La Caixa, 2006): This includes a large number of vari-

ables and indicators of an economic nature, with the data broken down by regions, pro-

vinces and municipalities. We worked with figures for 2005.

3.2.2. Independent variables. In this study, the independent variables chosen were a

selection of economic indicators that were typical of, and significant to, the annual situ-

ation of each of Spain’s regions:

(1) GDP per capita (PPP): this is a value that aims to quantify a society’s disposable

material wealth. The PPP is an indicator used to assess whether the economic policies

applied to a population are positive. It is calculated as the total GDP divided by the

number of inhabitants; the GDP is the sum of all the goods and services produced

by a population or an economy during a certain period (usually 1 year). For this

study, we used the 2005 PPP figures for each region (La Caixa, 2006).

Comparisons between absolute levels of the GDP of the various territorial entities have

very little significance, in view of the vast differences in size and population between

them. This is why the GDP per capita (PPP) or per capita income is generally used.

(2) Registered unemployment: this variable, expressed as a percentage (%), includes the

number of registered unemployed in each municipality’s State Employment Public

Service, as a proportion of the population on the electoral roll as of 1 January 2006

(La Caixa, 2006).

1760 P. Burilloa, Á. Barajasb, L. Gallardoc & M. Garcı́a-Tascónd

(3) Per capita market share (pcMS): this is an index expressing the comparative consu-

mer capacity of municipalities during 2005 (La Caixa, 2006). The municipalities’

market share includes the consumer capacity of a municipality in terms of its purchas-

ing power. It is a suitable marker for assessing the quantity of products and services

that can be absorbed by the municipalities. We have used the market share values

of each region divided by the number of inhabitants.

(4) TIpc: this is a comparative indicator of the importance of tourism, with data for 2005.

It is calculated in terms of the economic activities tax for tourism activities (La Caixa,

2006). This tax depends on the category of tourist establishments, number of rooms

and occupation. It is an indicator of tourism activity resources. We have used the

tourism index values of each region divided by the number of inhabitants.

(5) Economic activities index per capita (EAIpc): this is a comparative indicator of econ-

omic activity in 2005. It is calculated in terms of the tax for all businesses (industry,

commerce and services) and professional economic activities (La Caixa, 2006). These

include all economic activities except agriculture (exempt from this tax). We have

used the economic activity index values of each region divided by the number of

inhabitants.

As well as the above economic indicators, we felt it would be useful to add the PA by

regions, included in the 2005 Spanish Sporting Habits Survey.

(6) PA: this refers to the population that plays sport or is involved in PA, be it one or

more sports/activity. Percentage distribution (%) of the population depending on

their active sports participation, by region of residence, in 2005 (Garcı́a-Ferrando,

2006).

3.3.3. Dependent variables. The study of the situation regarding sports facilities in the

regions of Spain is a complex topic and has been barely covered in the literature. A

meeting of a group of Experts was held, using the Group Discussion technique, to identify

the most suitable variables to explain and determine the situation of sports facilities in each

region (Figure 2).

The session of the group of experts was conducted as follows:

. Presentation of the components and objectives of the meeting.

. The meeting itself and discussion on the sports facility variables that might have an

influence on the population, and their weighting in the Synthetic Indicator. . Generation of conclusions, specification of the variables for the study and levels of

sports infrastructure grading in the regions, in terms of the overall Synthetic Indicator.

The result of this was the identification of 15 main-dependent variables for this

study, finally integrated into an overall synthetic indicator called the SFSI, and four

levels of ranking for the regions’ sports infrastructures, according to their results in

the overall synthetic indicator (Lower level, Lower-Middle level, Upper-Middle

level and Higher level). Following the group of experts, the variables were selected,

taking into account three factors that determine sports planning: population, quality,

and density.

Influence of Economic Factors in Urban Sports Facility Planning 1761

3.3.3.1. Variables referring to the population factor

(1) Number of facilities per inhabitant: Ratio of sports facilities/number of inhabitants in

each region.

(2) Number of sport spaces per inhabitant: Ratio of sports spaces/number of inhabitants in

each region.

(3) Sports area per inhabitant: Ratio of number of square metres of conventional sport

spaces/number of inhabitants in each region.

(4) Indoor swimming pools per inhabitant: Ratio of number of indoor swimming pools/

number of inhabitants in each region.

(5) Indoor water area per inhabitant: Ratio of number of square metres of water area in

indoor swimming pools/number of inhabitants in each region.

3.3.3.2. Variables referring to the quality factor

(6) Average age of facilities built in the last 30 years: Average age of facilities built from

1975 onwards in each region.

(7) Percentage of indoor sport spaces: The CNID-2005 catalogues conventional sport

spaces as being either open or closed (in other words, indoor). This variable has

been obtained from the number of indoor sport spaces as a percentage of the total

number of sport spaces in each region.

(8) Number of changing rooms per facility: Ratio of changing rooms/number of sports

facilities in each region.

(9) Percentage of playing surface in good condition: The CNID-2005 catalogues playing

surfaces in four categories: good, average, bad and unfit for use. In this study, only

surfaces in “good condition” were chosen for this variable. The number of surfaces

Figure 2. Group experts.

1762 P. Burilloa, Á. Barajasb, L. Gallardoc & M. Garcı́a-Tascónd

in good condition was then calculated as a percentage of the total number of playing

surfaces in each region.

(10) Auxiliary services per facility: Ratio of auxiliary services/number of sports facilities

in each region.

(11) Level of accessibility: Following the terms of the 1989 Executive Order regarding

minimum measures for accessibility to buildings (Executive Order 556/1989 of 19

May), we analysed the percentage of sports facilities built since the Order came

into force that complied with this accessibility regulation, for both sports participants

and spectators.

(12) Use of renewable energies: The CNID-2005 identifies as renewable energies solar

panels (thermal and photovoltaic), and hydraulic, biomass and wind energies. This

variable takes in the use of any of the above systems. It was obtained from the

ratio of sports facilities using renewable energy/total number of sports facilities in

each region.

3.3.3.3. Variables referring to the density factor

(13) Density of sports facilities per region area: Ratio of sports facilities/region area in

square kilometres.

(14) Density of sport spaces per region area: Ratio of sport spaces/region area in square

kilometres.

(15) Number of square metres of sport space per region area: Ratio of number of square

metres of conventional sports spaces/region area in square kilometres.

3.4. Descriptive and statistical analysis of the results

To discover the situation in each region we set up an SFSI using experts’ opinion on the

most typical variables of sports facility resources in a particular region. The values

obtained in each variable were standardized so that they could be added together,

thereby giving us a single measuring scale. Therefore a standardized scoring was used,

with a mean of zero and a standard deviation of one. Thus, in each variable there were

negative values (below the mean) and positive values (above the mean). Finally a weighted

summation of these variables was drawn up (each with a value of 6.667%, since the experts

considered that all the variables had the same level of overall importance), giving us the

SFSI, which reflects the state of all the sports facilities in the various regions. For data

treatment, we used the SPSS 15.0 for Windows statistics program and Excel 2003 for

Windows.

As recommended by the group of experts, four levels of sports infrastructure were estab-

lished, according to the score for each region in the SFSI:

. Upper level: SFSI with scores higher than 3. Excellent sports infrastructure resources

compared with other regions. . Upper-middle level: SFSI with scores between 0.01 and 3. Good sports infrastructure

resources for the population. . Lower-middle level: SFSI with scores between 0 and 23. Poor sports infrastructure

resources for the population.

Influence of Economic Factors in Urban Sports Facility Planning 1763

. Lower level: SFSI with scores lower than 23. Very poor sports infrastructure resources compared with the other regions.

Secondly, to check the association between the six economic/sporting indicators chosen

and the SFSI, we used Pearson’s correlation, with a value of p , 0.05; we carried out an analysis between all the variables and a regression analysis between the various economic

and sports infrastructure variables. Previously, we checked the normality of all the vari-

ables using the Kolmogorov–Smirnov sample test.

4. Results

This section is divided into two parts:

(1) First, we present the results obtained in the SFSI, revealing the sports infrastructure

scenario in each region, including their final ranking and level attained.

(2) We then show the relationships between the regions’ economic/sporting wealth in the

six variables chosen (PPP, Registered unemployment, pcMS, TIpc, EAIpc, TIpc, and

PA) and levels the regions obtained according to the SFSI.

4.1. Results of the SFSI

The results obtained in the SFSI show the situation of the regions with regard to their

sports infrastructures (Table 1).

The Balearic Islands is the region with the most favourable situation with regard to

sports infrastructures resources. It is followed by Catalonia and the Canary Islands, all

of them with values higher than 3, in the Upper Level.

At the next level, with positive values, are Regions with final positive values

(0–3).They obtain good results in general, albeit with negative values in some variables.

For example, Navarre is in fourth place, due to its first five variables (notably the popu-

lation factor); but in the subsequent variables, it has lower rankings, and therefore

needs to improve, particularly in the aspects of Quality and Density of infrastructures.

At the Lower-Middle Level (from 23 to 0) are regions with negative final values, even though they may have scored higher in some partial factor. A notable example is Madrid

which, while being the region with a high score in the Density factor variables, obtains

very low scores in the variables of the other two factors.

Finally, in the Lower level (final scores below 23) are the regions with the lowest total results, which although they may have scored well in some variable (Castilla-La Mancha

and Extremadura in Sports Area per inhabitant; the Community of Valencia in Auxiliary

Services and Changing Rooms per facility, and so on), still need to make investments and

work harder to catch up with the rest of the regions in Spain.

Figure 3 shows the 17 regions and their scores in the SFSI, providing a visual image of

their ranking.

4.2. Results of the relationships between economic and sports facility variables

The values taken by the five variables that typify each Spanish region’s economic activity

in 2005, and the PA variable, are shown in Table 2:

1764 P. Burilloa, Á. Barajasb, L. Gallardoc & M. Garcı́a-Tascónd

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Influence of Economic Factors in Urban Sports Facility Planning 1765

Table 1. Results of the SFSI

Population factor Quality factor Density factor

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 SFSI’s scores

Balearic Islands 2.19 2.82 20.72 3.51 2.38 20.46 20.99 21.35 1.46 1.48 0.99 0.37 1.88 1.83 0.35 15.76 Catalonia 20.46 20.05 20.74 0.19 1.29 0.19 20.2 0.74 1.1 1.28 0.88 3.01 0.49 0.58 0.51 8.81 Canary Islands 0.14 0.3 20.6 20.53 20.8 0.13 20.45 0.32 0.74 1.12 0.08 0.66 1.3 1.16 1.08 4.64 Navarre 0.25 0.59 0.74 1.03 1.94 0.7 1.34 0.29 21.17 21.84 1.16 21.02 20.58 20.53 20.45 2.45 Country Basque 20.81 20.49 20.7 0.3 0.54 21.91 2.15 20.37 21.34 0.4 0.02 0.05 0.76 0.85 1.25 0.71 Asturias 20.45 20.59 20.53 20.29 20.1 0.46 1.26 20.02 0.54 0 0.29 0.99 20.41 20.45 20.43 0.29 La Rioja 0.42 0.6 0.39 20.25 20.32 1.6 20.17 20.35 0.83 20.09 20.34 20.49 20.56 20.53 20.56 0.19 Aragón 0.77 1.02 0.6 20.22 20.38 0.45 20.78 1.71 0.93 20.36 21.17 20.1 20.87 20.81 21.01 20.21 Galicia 20.26 20.65 0.4 20.09 0.01 20.33 1.57 1.6 21.59 0.22 0.09 20.51 20.42 20.52 20.1 20.58 Cantabria 0.76 0.05 20.07 20.08 0 20.43 0.21 21.73 1.14 21.03 1.28 21.02 0.02 20.25 20.13 21.24 Castilla y León 1.57 0.76 2.4 20.44 20.57 0.3 21.18 21.34 20.56 21.61 1.45 20.63 20.81 20.83 20.78 22.27 Madrid 21.43 21.23 21.36 20.4 20.29 22 0.26 21.07 20.61 20.49 20.55 20.96 2.29 2.37 2.87 22.61 Andalusia 20.67 20.82 20.51 20.38 20.8 1.68 20.89 20.56 0.57 0.9 20.48 0.82 20.54 20.57 20.52 22.79 Extremadura 0.23 20.06 1.54 20.78 21.36 0.67 20.61 0.4 21.21 20.24 0.49 20.23 20.92 20.89 20.9 23.88 Community of

Valencia 21.35 20.96 20.9 20.35 20.48 20.21 20.53 0.51 20.46 1.26 20.86 0.17 20.16 0.01 0.22 24.1

Castilla-La Mancha

0.39 20.06 0.91 20.66 20.56 0.1 20.84 1.01 0.32 20.91 21.22 20.34 20.92 20.9 21.01 24.67

Región de Murcia

21.29 21.22 20.83 20.56 20.5 20.95 20.16 0.21 20.68 20.12 22.13 20.78 20.56 20.52 20.41 210.49

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Comparing the degrees of development in sports facilities, we see that three of the

regions with the lowest level of economic development as measured by the PPP variable

are also at the foot of the table with regard to sports facility resources (Extremadura, Cas-

tilla-la Mancha and Murcia). Only Catalonia has the highest level of PPP and sports

resources, SFSI. Similarly, notably above the other regions in the TIpc are the Balearic

and Canary Islands, and they are also placed in the Upper level of the SFSI. As far as

Registered unemployment is concerned, we find widely divergent data among the

regions: The Canary Islands (Upper level SFSI) and Extremadura (Lower level SFSI)

are the regions with the highest unemployed population (6.9%), while the Balearic

Islands (Upper level SFSI), Aragon (Lower-Middle level SFSI), La Rioja (Upper-

Middle level SFSI), and Murcia (Lower level SFSI) have the lowest unemployment in

the country. The pcMS as well as the EAIpc are evenly distributed among the regions.

However, we note that it seems that the better the results of these two variables, the

higher the ranking in the SFSI. Finally, in the PA variable, if we exclude the Community

of Madrid, the best results are obtained from regions in the Upper-Middle or Upper levels

of the SFSI (Navarre, Catalonia, La Rioja, the Basque Country, Asturias, and so on).

After checking the normality of the variables via the Kolmogorov–Smirnov sample

test, we carried out Pearson’s correlations between the SFSI levels obtained by the

Spanish regions and the other independent variables (Table 3):

We note positive significant correlations between the degree of development in sports

facilities, SFSI, and the GDP per capita (R ¼ 0.541), the TIpc (R ¼ 0.585) and the PA of the population (R ¼ 0.516), for a significance of p , 0.05. Thus, we assert that the higher the PPP in a Spanish region, the better the sports infrastructure resources. Similarly,

Table 2. Independent variables ordered by SFSI

Regions SFSI PPP Unemployment

(%) pcMS TI pc EAIpc Physical

activity (%)

Balearic Islands 15.76 22,947 2.8 1104.03 6420.54 1011.58 37 Catalonia 8.81 24,858 3.5 1039.16 926.18 1255.85 43 Canary Islands 4.64 18,879 6.9 1022.84 3689.47 750.89 35 Navarre 2.45 26,489 3.6 1063.73 592.03 1759.02 45 Country Basque 0.71 26,515 3.7 949.21 381.99 1197.72 39 Asturias 0.29 18,533 5 973.56 645.17 852.75 38 La Rioja 0.19 22,548 3.1 1071.11 510.75 1075.49 40 Aragón 20.21 22,403 2.8 1061.49 601.97 1077.59 36 Galicia 20.58 16,870 5.9 981.89 506.94 840.7 33 Cantabria 21.24 20,554 4.1 974.31 919.22 779.92 33 Castilla y León 22.27 19,782 4.3 1037.88 558.9 930.32 34 Madrid 22.61 27,279 3.7 991.41 773.16 1250.52 43 Andalusia 22.79 16,100 5.5 956.89 1004.59 775.26 33 Extremadura 23.88 14,051 6.9 1043.26 300.43 682.34 29 Community of

Valencia 24.1 19,057 4.2 986.65 826.76 1000.51 37

Castilla-La Mancha

24.67 16,314 4.7 1012.99 293.16 748.75 30

Región de Murcia

210.49 17,322 3.1 951.4 446.99 804.26 34

Spain – 20,838 4.5 1013.05 1141.07 987.85 37

Influence of Economic Factors in Urban Sports Facility Planning 1767

Table 3. Pearson’s correlation between variables of the study

SFSI levels PPP Unem-ployment pcMS TI pc EAIpc

Physical activity

SFSI levels Pearson’s correlation 0.541(∗) 20.142 0.400 0.585(∗) 0.385 0.516(∗) Sig. (bilateral) 0.025 0.586 0.111 0.014 0.127 0.034 N 17 17 17 17 17 17

PPP Pearson’s correlation 0.541(∗) 20.657(∗∗) 0.252 0.110 0.854(∗∗) 0.833(∗∗) Sig. (bilateral) 0.025 0.004 0.330 0.675 0.000 0.000 N 17 17 17 17 17 17

Unemployment Pearson’s correlation 20.142 20.657(∗∗) 20.245 20.050 20.555(∗) 20.529(∗) Sig. (bilateral) 0.586 0.004 0.343 0.849 0.021 0.029 N 17 17 17 17 17 17

pcMS Pearson’s correlation 0.400 0.252 20.245 0.454 0.329 0.190 Sig. (bilateral) 0.111 0.330 0.343 0.067 0.198 0.465 N 17 17 17 17 17 17

TI pc Pearson’s correlation 0.585(∗) 0.110 20.050 0.454 20.069 0.027 Sig. (bilateral) 0.014 0.675 0.849 0.067 0.792 0.919 N 17 17 17 17 17 17

EAIpc Pearson’s correlation 0.385 0.854(∗∗) 20.555(∗) 0.329 20.069 0.852(∗∗) Sig. (bilateral) 0.127 0.000 0.021 0.198 0.792 0.000 N 17 17 17 17 17 17

Physical Pearson’s correlation 0.516(∗) 0.833(∗∗) 20.529(∗) 0.190 0.027 0.852(∗∗) Activity Sig. (bilateral) 0.034 0.000 0.029 0.465 0.919 0.000

N 17 17 17 17 17 17

∗The correlation is significant at 0.05 (bilateral). ∗∗The correlation is significant at 0.01 (bilateral).

1 7

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we state that the regions with a higher TIpc have better sports facilities, and that the higher

a population’s PA, the higher the SFSI of sports facilities.

The results also show strong correlations between the main economic wealth indicator

of a region, its PPP, and the independent variables PA, Registered unemployment and the

EAIpc, all with significance below 0.01. Although, it might seem obvious to point out that

the greater the production of economic wealth in a region, the higher its economic activi-

ties, we have not found these to be reflected in Spain economic indices, as is the case with

tourism economic activities (according to the Institute of Tourism Studies [2006], with

over 55.6 million non-resident tourists in 2005), or Market Share. However, as shown

by the correlation between the tourism index and the SFSI level, the opportunities that

tourism offers do seem to have been taken as maximized, with a higher development of

regional sporting infrastructures.

On the other hand, the correlations between the SFSI level and other economic indi-

cators, such as the unemployment rate, pcMS, and the Economic Activities Index per

capita, are not significant. Noteworthy is the fact that the regional Unemployment rate

has a negative correlation with all the other variables, albeit significant only with the

PPP (p ¼ 0.004), the EAIpc (p ¼ 0.021), and PA (p ¼ 0.029). With regard to the population’s PA, as well as the significant relationship between the

SFSI and Unemployment, there is a significant correlation with the PPP (p ¼ 0.000), thereby suggesting that the greater the PPP wealth in a region, the more the population

engages in PA.

Nevertheless, in studying the significant correlations between the SFSI and PA and the

other economic variables, Pearson’s r has a moderate value. This degree of interdepen-

dence may be attributed to several factors. First, it can be claimed that leisure and

sports are secondary goods that are only consumed when basic necessities have been

covered. It would be expected that the wealthier regions had more sports facilities. In con-

trast, sports and the industry surrounding it are seen as a potential impulse for economic

development, particularly in a country closely linked to tourism such as Spain. Thus, the

poor regions also have incentives to invest in sports facilities. The two forces are opposed,

and thus it is no surprise that the degree of explanation is moderate.

5. Discussion

The National Sports Facilities Census is an extremely valuable tool for sports authorities

when they are making diagnoses and plans in their regions, as well as analysing more

thoroughly, situations pertaining to the sports system (economic, social and cultural evalu-

ations, among others). Public authorities will be able to create policies to promote

measures to redress the balance between regions, tailor the demand for PA and sport to

the use of sports spaces, and modernize and adapt sports facilities to current requirements.

However, the level of a region’s wealth has a decisive influence on its social priorities,

with investment primarily being made in health, social and employment integration,

and education programmes, and a reduced budget for “secondary” items related to

leisure, such as sports.

It has been shown that there is a wide variation in Spain with regard to sports facilities

and although the sports infrastructure has grown overall in recent years, there is still a pre-

dominance of some regions over others in this respect.

Influence of Economic Factors in Urban Sports Facility Planning 1769

When analysing the results of the partial factors, we see that a positive situation in one is

no guarantee of positive situations in the others, as is the case, for example, with Castilla y

León (in population) and Madrid (density), which score highly in the variables of one

factor, but fall short in the other two.

Insufficient investment in sports infrastructure (such as limited spaces and sports area

per inhabitant) leads to fewer opportunities for PA by the population. Also, the overcrowd-

ing of users in these facilities reduces the convenience in undertaking physical activities.

Sports facilities should be designed to provide the user with a pleasurable experience. The

use of renewable energies in sports infrastructure is also critical in sustainable develop-

ment. But all this calls for higher investment by the sports authorities.

As far as testing the hypothesis is concerned, we find that there is a significant relation-

ship between the degree of economic development in each Spanish region, analysed by the

GDP per capita, and the level of sports infrastructure resources (SFSI level). It has also

been shown that the greater the regions’ TIpc, the greater their sports infrastructure

resources. It seems clear that there are better sports infrastructures in the wealthier

regions and those with a higher tourism activity.

With regard to the sports variable, we also see that the better a region’s sports facilities,

the higher the percentage of the people’s PA. With sports infrastructure influencing the

population’s PA, greater sports investments will translate to improvements in the popu-

lation’s sporting and health habits.

We feel that changes in the regions’ physical surroundings (with improved sports infra-

structures) also need to be backed up simultaneously by educational changes. When these

changes are made together, they will produce much more satisfactory results in ensuring

healthier habits among the population than with individual measures (Stahl et al., 2001).

Other major aspects to assess are personal experiences undergone by clients of the sports

facilities, as well as the typical features of the sports facility in general, such as: cost,

appearance, comfort, users’ perception of the staff, and so on (Corti et al., 1996).

In the last decade, perspectives of sport have changed; no longer are sports facilities

expected to contribute to people’s health, they now have to address other aspects, such

as leisure. Many of these new sports facilities have special features and spaces, such as

luxury areas, VIP seating, swimming pools, restaurants, hotels, theme parks with attrac-

tions that make certain sports facilities genuine centres of leisure and enjoyment

(Coates & Humphreys, 2003).

To summarize, these results should serve not only to detect the vast imparity between

different regions, which has come about because of varying investment and management

with regard to sports infrastructures, but also to redirect building planning and sports man-

agement policies onto the right path, and also give food for thought. With the mutual col-

laboration of all the institutions, experts will be able to develop guidelines to serve as a

model for the design of cities and regions within a framework for promoting PA and

health (Hoehner et al., 2003).

This study has enabled us to offer an idea of the current state of the sports infrastructure

in each region of Spain, and its relationship with their economic development. Each zone’s

wealth has a decisive influence on the sports sector. The variables and indicators provide

an objective measurement of the shortfalls in each region with regard to the rest, and give

us an idea of what the Autonomous Governments should focus on in future sports facility

planning.

1770 P. Burilloa, Á. Barajasb, L. Gallardoc & M. Garcı́a-Tascónd

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