Exploration of the Relationship Between the Population and Football Stadiums in Romania

Abstract

A sport is a specific human activity with deep cultural and social meanings that involves cooperation and competitiveness according to well-established rules. One of the most representative sports at the local and global levels is football thanks to the large number of people and resources involved in supporting it. The aim of the study is to explore the connection between communities and football stadiums at a spatial level (development region, county and cities). From a methodological point of view, multicriteria analysis (eight criteria, four for the population and four for football stadiums) was utilized to determine and define the various relationships between the population and football stadiums. The obtained results highlighted, at the spatial level, the existence of some non-correlations between the population and football stadiums. Thus, the existence of strong relationships at the level of development regions (75%), followed by those established at the level of cities (24.1) and counties (2.4), were identified. The strongest relationships were established in the Northwest, Central and South development regions; in Bihor County and in cities like Bucharest, Brașov, Constanta and Iași.

1. Introduction

Man is a biological being endowed with intelligence and competitive spirit. Intelligence is an attribute that has developed over time under the pressures and restrictions imposed by the characteristics of the natural environment, in which it has evolved as an integral part of it. The first obstacles that man had to overcome, with the help of intelligence and endosomatic (at first) and exosmotic (later) means, were those of securing his primary needs (food, shelter, rest, security, etc.). Once overcome, other needs appeared, including those of belonging, recognition, respect, etc. [1,2]. Their satisfaction imposed, on man and human collectivities, a balance between cooperation and competitiveness, thus shaping the social side of humanity. History has shown us that the lack of balance between cooperation and competitiveness always led to the emergence and manifestation of unfortunate phenomena and struggles both between individuals and between human groups. So, the entire history of humanity is a history of creativity (the cooperative side) and inter-human disputes (the competitive side). This aspect was also well known by the scholars and elites of the great Greek and Roman civilizations [3]. Thus, it is not by chance that the stadiums from this period acted as sports sanctuaries, reflecting the balance between cooperation and competitiveness. Cooperation was reflected in the construction of stadiums through the participation of the whole community, while competitiveness was found in sports competitions, where cities and different human communities faced each other through the participants in sports games.

Stadiums are evidence of the creation and mastery of human collectives. They date back to the period of Ancient Greece as structures specially designed to hold sports competitions [3]. Over time, they have undergone minor conceptual and structural mutations, while significant changes can be noted in terms of the construction techniques, materials and symbolism used [4].

Stadiums are spaces with architectural–cultural value and significant political, cultural and sporting dimensions that play a crucial influence in the structuring and systematization of cities, sites and urban landscapes [5,6,7]. The integration of football stadiums into urban environments highlights their role as key elements in urban planning, contributing not only to the cultural and social fabric of cities but also to their architectural identity and spatial organization. These structures function as focal points in urban landscapes, influencing the distribution of infrastructure and services, shaping community interactions and stimulating economic activities. Furthermore, football stadiums often become symbols of civic pride and local identity, representing a city’s ability to host major events and promoting urban regeneration by attracting investment and improving local amenities [8,9]. Conservation (with an emphasis on buildings, terrain and landscape) is a great challenge considering the dynamic comfort and security needs of spectators and athletes [6,10]. Building new stadiums is a real challenge for local communities, given the high costs of construction and maintenance, with significant implications for the spatially sustainable development of a human community. Now, more than ever, against the backdrop of increasing pressure on exhaustible resources, sustainable development through rational management between current needs and the need to conserve resources is emerging as a desideratum worthy of consideration, including in the construction of football stadiums.

In this context, this study aims to spatially analyze (at the level of the development region, county and cities) the relations between the population and football stadiums in Romania.

The research hypotheses focused on the following facts:

• The distribution of the population has a direct influence on the distribution of stadiums at a spatial level, in the sense that development regions, counties and cities with many inhabitants can better support the existence of stadiums due to the larger number of potential spectators.
• The characteristics of the population (age, gender and number of employees) are influencing factors in the spatial distribution of football stadiums, so that in developing regions, counties and cities with a young, active male population and with a larger number of employees, the population is more likely to spend time in football stadiums, thus better supporting their existence.

The research questions are as follows: Are there relationships between the population and football stadiums in Romania? If so, what kind of relations exist between the population and football stadiums in Romania? What are the levels of spatial approach at which these relationships are established?

The importance and relevance of this approach emerges from the significance of the stadiums for the local communities, from the architectural style adopted, the uniqueness and singularity of the buildings, the locations of the stadiums within the localities, the events with emotional charge generated by the performance of new sporting actions for the local communities that hosted them, whether they will still host them, etc. Thus, the stadiums represent an expression of the evolution of sports and their role in Romanian society over time. Establishing the relationship between the population and football stadiums is important for understanding the impact these structures have on the local economy, social cohesion and urban development. Thus, by attracting spectators and major events, football stadiums stimulate the economy while also contributing to strengthening the community identity and improving urban infrastructure. At the same time, this research provides insights into how the spatial distribution of football stadiums aligns with the dynamics of urban development, offering practical implications for improving urban planning strategies in Romania.

Literature Review

A sport is the expression of the spatio-temporal manifestation of human cooperation and competitiveness; at the same time, it can represent an element of identity for both the individual and the human community of which he/she is a part [11,12]. Through its ability to bring people together in shared experiences, sports serve as a platform for strengthening social cohesion and reinforcing cultural values, thereby contributing to the strengthening of the collective identity of communities in diverse spatial and temporal contexts [13,14].

Sports are part of our social and cultural fabric, with a social and commercial power that makes them a powerful force globally [15]. However, in the literature, with some exceptions, there has been a neglect of sport research, ignoring one of the central components of popular culture around the world [16]. However, the studies conducted attempted to answer a number of research questions, among which some stand out. The position of sports in a society seeks to answer a number of questions: Is a sport an end in itself? What is the essence of sports? Is playing an essential element in sports? Are sports a necessity or a luxury? What impact does politics have on sports [17]?

In sports, soccer is the most popular sport in the world [18,19,20,21], being easy to play and practiced in almost any circumstance. It is perceived differently from one actor (fan, player and referee) to another depending on their experiences [22]. Scientists perceive football according to the field they come from. Most of the specialized literature that we know deals with the technological aspect of football. All the studies that offer training go in this direction, with football being seen almost entirely as the technical and tactical abilities of an individual player and strategic cooperation skills within a team [22]. Frank and Steets (2010) talk, in their book, about the connection between the environment created in a stadium at a football match and about the masculine identity that the players of this game have [23]. In the last decades, with the increase in the number of practitioners in different sports branches, we can discuss the progressive replacement of religion with sports and how it has an immense power to exercise passion, to offer an emotional escape and to bind fraternal friendships. Sports have become an inevitable reality that connects the public environment with intimate experiences, giving the opportunity to research and understand all their dimensions. Thus, the evolution of modern sports in Europe and their links with politics, class and gender can be clearly observed [24].

Exploring the vast world of sports, we find numerous studies and research that say that sports have been imitated and assimilated, becoming symbols of national and cosmopolitan identities. Also, this field has become extremely important at the international level, having a primary role in shaping the post-millennial global culture [25,26,27].

Many of the published studies talk about the athlete’s identity, which is associated with the sport he/she practices. Many times, sports lead to conflicts in the community, to heated discussions and, often, to deep disappointments [28]. In Romania, the most popular sport is football, and it is governed by the FRF (Romanian Football Federation). In 2020, Romania had over 700 thousand football players, of which 300 thousand were registered and constant participants in a football competition. Of these, approx. 126 thousand participate in competitions organized under the auspices of the FRF, and over 171 thousand participate in grassroots competitions, i.e., amateur football. Also, in 2020, we had over 2500 senior and junior girls. A study from 2022 confirms the fact that Romania had, in that year, 75 Romanian footballers registered with clubs abroad [29].

In recent years, the interest in football in Romania has grown, addressing complex and complete topics such as the psychology of football, the anatomy of football, the sociology of football and topics related to injury prevention, recovery after injuries and problems related to recovery after treatment effort [30,31,32].

Also, an analysis of the specialized literature highlighted the roles of sports and sports infrastructure in urban development and regeneration [33,34,35,36]. In this context, stadiums can be seen as multifunctional entities, with multiple possibilities of use, being able to host specific sports activities, act as conference centers, provide accommodations and catering structures, shops, etc. [37,38,39,40]. Due to having multiple purposes (sporting, cultural, commercial, etc.), special attention has been paid to the reconstruction of these emblematic elements for the local community, being an integral part of urban renewal and regeneration [37,41,42,43]. Considering their relatively accessible locations, most of the stadiums represent a motivational factor of prime importance for tourism, with roles and functions in diversifying the local offerings, extending the length of stay, the economic efficiency of tourism, etc. [44,45,46].

In this context, although there are numerous studies and research that have high-lighted the existing relationships between sports and populations [47,48,49], specifically between the population and football [50,51], there are no studies that highlight the links between the population and football stadiums viewed from the perspective of the population’s capacity to support these very expensive infrastructure elements. The methodology used in this study can also be used in other geographical regions with similar characteristics to Romania in order to design and build sports infrastructure, especially football stadiums, in close connection with the population and its defining characteristics.

2. Materials and Methods

The exploration of the spatial relationships between the population (4 variables) and the football stadiums (4 variables) at the spatial level (development region, county and city) involved the use of the multicriteria analysis method [52,53]. The limited number of variables for the analyzed aspects is a weak point of the study, but the need to cover the entire studied area, which is structured from 8 development regions, 42 counties and 66 cities where the football stadiums are located, was taken into account. Thus, in the present case, establishing and knowing the relationship between the population and football stadiums requires the use of four defining variables for the population (X1. size of the population; X2. gender of the population; X3. age of the population; X4. average number of employees), respectively, for football stadiums (Y1. number of football stadiums; Y2. capacity; Y3. UEFA category; Y4. age) (

Table 1. Sets of variables and types of data established for study analysis.

Criteria	Variables	Sub-Variables	Type of Data
X. Population	X1. Size of population	Total number of population members	Quantitative
	X2. Gender	Number of males in population	Quantitative
		Number of females in population	Quantitative
	X3. Population age	Number of population members aged 0–14	Quantitative
		Number of population members aged 15–64	Quantitative
		Number of population members aged over 65	Quantitative
	X4. Average number of employees	Average number of employees	Quantitative
Y. Football stadiums	Y1. Number of stadiums	Number of stadiums	Quantitative
	Y2. Capacity	Number of seats	Quantitative
	Y3. Elite category	The number of stadiums by elite category	Qualitative
	Y4. The age of the stadiums	Number of stadiums by year of building	Quantitative
		Number of stadiums by year of rehabilitation	Quantitative

). Obtaining the aggregate values for the population and football stadiums was achieved by using the min–max normalization method [54,55,56,57].

The methodology for carrying out the present study involved the following stages:

• The identification and processing of population information was conducted in 2024 from the databases of the National Institute of Statistics of Romania, regarding the 2021 Population and Housing Census in Romania [58] and TEMPO ONLINE, section FOM104D—The average number of employees per county and city [59]; those with reference to football stadiums were collected from the official websites of football stadiums and the list of football stadiums in Romania [60,61].

The population and football stadium criteria values have been rendered according to the matrix below [52,54,55,56]:

$$Z=\left[z_{ab}\right]=\left[\begin{array}{cccc}{z}_{11}& z_{12}& \cdots & z_{1n}\\ z_{21}& z_{22}& \mathrm{\dots }& z_{2n}\\ ⋮& ⋮& ⋮& ⋮\\ z_{r1}& z_{r2}& \cdots & z_{rn}\end{array}\right]$$

(1)

where each element z_ab represents the value of a specific variable (e.g., population size, gender distribution, etc.) for a reference unit (such as a development region, county or city). The r rows correspond to the reference units, while the nnn columns denote distinct types of variables collected for each reference unit.

2.

The data set, comprising 8 variables (4 related to the population and 4 associated with football stadiums), underwent a scaling process to standardize the data. The following formula was applied [54,55,56]:

C_ab = (z_ab − min z_ab)/(max z_ab − min z_ab)    z_b ϵ S,    c_ab = [0, …, 1]

(2)

Following the normalization process, the real values of the z_ab variables are transformed into values between 0 and 1, with the minimum–maximum normalization method thus ensuring the comparability of the studied variables and the implementation of the multicriteria analysis.

3.

The aggregate values for the population and football stadiums were obtained by using normalized values.

$$g_b={{\displaystyle \sum }}_{b=1}^n{c}_{ab} \left(a=1,\dots r\right)$$

(3)

The evaluation of the criterion by the value of the variable is carried out using the synthesis value G_a:

$$G_a={\displaystyle \frac{1}{n}}{{\displaystyle \sum }}_{b=1}^n{g}_a \left(a=1,\dots r\right), G_i=G_a є \left[0,\dots ,1\right]$$

(4)

where G_a represents the mean value for the a category, with a spanning from 1 to r, n denoting the total count of variables evaluated within each category and g_a corresponding to the specific value for the a-category.

4.

The classification of development regions, counties and cities into value groups according to the synthetic index related to the population and football stadiums was achieved following the determination of the constant value z [46,54,55]:

$$\mathrm{M}\left(G_a\right)=\max G_a-\min G_a \mathrm{t}={\displaystyle \frac{\mathrm{M}\left(G_a\right)}{4}}$$

(5)

Group 1: G_a ϵ (max{z_ab} − t, max{z_ab}]—the highest level (G_a ϵ (0, 0.25]);
Group 2: G_a ϵ (max{z_ab} − 2t, max{z_ab} − t]—an average level (G_a ϵ (0.26, 0.5]);
Group 3: G_a ϵ (max{z_ab} − 3t, max{z_ab} − 2t]—a small level (G_a ϵ (0.51, 0.75]);
Group 4: G_a ϵ [min{z_ab}, max{z_ab} − 3t]—a very low level (G_a ϵ ([0.76, 1]).

Here, M(G_a) represents the range G_a values, max (G_a) is the maximum value within the G_a data set, and min (G_a) is the minimum value within the G_a data set.

5.

The relationship index (I) between the population and football stadiums is calculated [43,44].

The cartographic depiction of the relationship between the two indicators was achieved by visualizing the distribution of the relationship index values, determined using the coefficients attributed to each reference unit (such as development region, county and locality). Thus, the relationship index (I) between the population and football stadiums is a normalized value, given by the following equation:

$$\mathrm{I}= {\displaystyle \frac{CoefP-CoefF}{ CoefP+CoefF}}, \mathrm{where}\mathrm{-1}\le \mathrm{I}\le 1$$

(6)

I = index of the relationship between the population and football stadiums;

CoefP = coefficient given to the population;

CoefF = coefficient given to football stadiums.

6.

We established the constant value t to classify development regions, counties and localities into value groups according to the type of relationship between the population and football stadiums [41,42,43].

The spatial analysis of the relationship index assumed its classification into 4 classes, calculated based on the interval between

$$\mathrm{M}\left(G_a\right)=\max G_a-\min G_a \mathrm{t}={\displaystyle \frac{\mathrm{M}\left(G_a\right)}{4}}$$

(7)

Group 1: G_a ϵ (max{z_ab} − t, max{z_ab}]—weak positive relationship (G_a ϵ (0.5, 1]);      
Group 2: G_a ϵ (max{z_ab} − 2t, max{z_ab} − t]—strong positive relationship (G_a ϵ (0, 0.5]);  
Group 3: G_a ϵ (max{z_ab} − 3t, max{z_ab} − 2t]—negative strong relationship (G_a ϵ (−0.5, 0]);
Group 4: G_a ϵ [min{z_ab}, max{z_ab} − 3t]—negative weak relationship (G_a ϵ ([−1, −0.5]).   

Here, M(G_a) is the range of the quantity G_a, max(G_a) is the maximum value in the set of G_a, and min(G_a) represents the minimum value in the set of G_a.

The software used for data processing and analysis at the spatial level (development region, county and city) was Excel (Microsoft Office 365) and ArgGis 10.8.

3. Results

3.1. Romania’s Population

The population is one of the most representative factors with a determining role in terms of the spatial distribution of football stadiums from a quantitative and qualitative point of view. The quantitative characteristics are represented by the number of structures, the allocated area and the capacity or the number of seats, while the qualitative ones refer to the degree of comfort and equipment, the year of construction and the year of rehabilitation. In this context, the knowledge of the spatial distribution of the population is an essential component that should be reflected in the way the infrastructure is distributed in general and that is specific to sport in particular, in this case being the distribution of football stadiums.

In order to explore the relationship between the population and football stadiums at a spatial level (development region, county and locality), the following defining information about the population was used: namely the total number of population members; population gender (number of male population members; number of female population members); age of the population (the number of the population members aged 0–14; the number of the population members aged 15–64; the number of the population aged members over 65) and the average number of employees.

Thus, in 2021, according to the data obtained from the census, the population of Romania was 19,053,815 inhabitants, of which 9,808,275 were women (51.5%), and the remaining 9,245,540 were men (48.5%). Regarding the structure of the population by age group, a large share of the population between the ages of 15 and 64 (64.3%) was noted, followed by the population members over 65 (19.6%) and children aged under 14 years (16.1%). The average number of employees was 5,209,493 people, which represents 27.3% of the total population. After normalizing and quantifying the four criteria associated with the population (Table 1) in Romania, across development regions, counties and localities, a synthetic population value was derived, ranging from 0 to 1.

The analysis of the distribution of the synthetic population index at the level of development regions indicates the existence of the following hierarchy: South (0.91), Northeast (0.72) and Ilfov–Bucharest (0.51), followed by the Northwest (0.49), Center (0.39), Southeast (0.16), Southwest (0.15) and West (0) (Figure 1). The Southern Development Region recorded the highest synthetic population index (0.91), having a population of 3,269,318 inhabitants, of which 1,676,805 are women (51.3%) and 1,592,513 are men (48.7%). Regarding the structure by age categories, the share of the population between the ages of 15 and 64 (2,087,877, 63.9%) is noteworthy, followed by the population over 65 (691,654, 21.2%) and children aged between 0 and 14 years (489,787, 15%). The number of employees was 897,155 people (17.2% of the total). At the opposite pole with the lowest synthetic population index (0) was the Western Development Region. The Western Development Region has a population of 1,668,921 inhabitants, of which 862,567 are women (51.7%) and 806,354 are men (48.3%). Regarding the structure by age categories, the share of the population between the ages of 15 and 64 was the largest (1,079,186, 64.7%), followed by the population over 65 (337,784, 20.2%) and children aged between 0 and 14 years, (251,951, 15.1%). The number of employees was 391,000 people (7.5% of the total).

At the county level, the top synthetic index values for the population were observed in Bucharest (0.25), followed by Prahova (0.056) and Bihor (0.055) (Figure 1). The territorial administrative unit of Bucharest has a population of 1,716,961 inhabitants, of which 926,212 are women (53.9%) and 790,749 are men (46.1%). Regarding the structure by age categories, the share of the population between the ages of 15 and 64 was the largest (1,119,862, 65.2%), followed by the population over 65 (333,080, 19.4%) and children aged between 0 and 14 years (264,019, 15.4%). The number of employees was 988,103 people (19% of the total). Prahova County has a population of 695,119 inhabitants, of which 360,105 are women (51.8%) and 335,014 are men (48.2%). Regarding the structure by age categories, the share of the population between the ages of 15 and 64 (446,547, 64.2%) is noteworthy, followed by the population over 65 (148,400, 21.3%) and children aged between 0 and 14 years, (100,172, 14.4%). The number of employees was 248,860 people (4.8% of the total). Bihor County has a population of 551,297 inhabitants, of which 283,444 are women (51.4%) and 267,853 are men (48.6%). Regarding the structure by age categories, the share of the population between the ages of 15 and 64 was greatest (358,871, 65.1%), followed by the population over 65 (100,424, 18.2%) and children aged between 0 and 14 years, (92,002, 16.7%). The number of employees was 248,416 people (4.8% of the total).

Among the 54 localities analyzed that host football stadiums with a capacity exceeding 4000 seats, the highest synthetic population index values were recorded in the cities of Bucharest (1), Cluj-Napoca (0.16) and Constanța (0.14) (Figure 1). Cluj-Napoca has a population of 286,598 inhabitants, of which 151,830 are women (53.3%) and 134,768 are men (47.0%). Regarding the structure by age category, the share of the population between the ages of 15 and 64 was the largest (191,482, 66.8%), followed by the population over 65 (53,576, 18.7%) and children aged between 0 and 14 years, (41,540, 14.5%). The number of employees was 186,456 people (3.6% of the total). The municipality of Constanța has a population of 263,688 inhabitants, of which 141,370 are women (53.6%) and 122,318 are men (46.7%). Regarding the structure by age category, the share of the population between the ages of 15 and 64 (163,449, 62.0%) is the largest, followed by the population over 65 (60,845, 23.1%) and children aged between 0 and 14 years, (39,394, 14.9%). The number of employees was 122,371 people (2.3% of the total).

Typological analysis based on the synthetic population index revealed distinct patterns at different levels of analysis. At the level of development regions, four types of regions were identified: regions with a very high value (between 0.76 and 1; one region, 12.5%), regions with a high value (between 0.76 and 1; two regions, 25.0%), regions with a low value (between 0.26 and 0.5; two regions, 25.0%) and regions with a very low value (between 0.0 and 0.25; three regions, 37.5%). At the county level, only one category was identified, consisting of counties with a very low value (between 0.0 and 0.25; 42 counties, 100%). In contrast, at the locality level, two categories of cities were identified: those with a very high value (between 0.76 and 1; Bucharest, 2.4%) and those with a very low value (between 0.0 and 0.25; 53 localities, 97.6%) (Figure 2).

3.2. Football Stadiums in Romania

Romania is represented, in terms of infrastructure focused on the practice of football, by the existence of 66 stadiums with a capacity of over 4000 seats each, totaling 878,816 seats, located in 54 localities in 40 counties (Figure 3). Among the analyzed stadiums, the oldest is the Miercurea Ciuc Stadium, built in 1904. Except for this one, in Romania, the first football stadiums appeared immediately after the creation of Great Romania (from 1918), so the first sports units of this type were built in 1920 (in the cities of Galați and Bucharest), followed by those in 1921 (Hunedoara), 1924 (Slatina and Oradea), etc.

The newest stadiums were completed in 2020 (Francisc von Neuman in Arad), 2021 (Steaua, Rapid Arena and Arcul de Triumf stadiums in Bucharest and Sepsi in Sfantu-Gheorghe) and 2023 (Eugen Popescu Stadium in Târgoviște), while another nine football stadiums are in the process of completion (Table 1).

The analysis by typological categories of the stadiums in Romania revealed the following situation: 45%, 26 units (stadiums with a renovation age of more than 10 years); 28%, 16 units (stadiums renovated in the last 10 years); 15%, 9 units (new stadiums, built in the last 10 years) and 12%, 7 units (older stadiums, not renovated) (Figure 3).

Going over the small number of such structures, compared to the population of Romania, which was 19,038,098 people on 1 January 2022 [58,59], and their needs, it can be seen that the share of functional, new or newly rehabilitated ones is small (43%, 25 units). However, in the detailed analysis of the stadiums renovated in the last 10 years, some non-functional aspects of them can be observed. One such example is the Iuliu Bodola stadium, built in 1924 and successively rehabilitated in 2004, 2007 and 2015.

To investigate the relationship between the population and football stadiums at a spatial level (development region, county and locality), the following defining information about football stadiums was used: namely the number of stadiums; the capacity of the stadiums; the elite category and the age of the stadiums (the year of construction, respectively, and the year of their rehabilitation).

After the normalization and quantification of the four criteria associated with football stadiums in Romania (Table 1), at the levels of development regions, counties and cities, a synthetic value for football stadiums was determined, ranging from 0 to 1.

The analysis of the distribution of the synthetic index for football stadiums at the development region level reveals the following hierarchy: Ilfov–Bucharest (0.94), South (0.78) and Southwest (0.71), followed by the Center (0.54), West (0.51), Southeast (0.438), Northwest (0.43) and Northeast (0.23) regions (Figure 4). The Ilfov–Bucharest Development Region registered the highest synthetic index regarding football stadiums (0.94), having 11 stadiums (16.7% of the total) with a capacity of 113,325 seats (12.9% of the total), while at the opposite pole, the lowest synthetic index was recorded in the Northeast Development Region (0.23), where there were 7 stadiums (10.6% of the total) with a capacity of 82,912 seats (9.4% of the total).

At the county level, the highest values of the synthetic index for football stadiums were observed in Bucharest (0.77), Arad (0.522) and Cluj (0.52), while the lowest values were recorded in the counties of Harghita (0.11) and Bistrița Năsăud (0.12) (Figure 4). Sălaj and Teleorman counties do not benefit from any football stadium with a capacity greater than 4000 seats; therefore, the synthetic value of football stadiums for these two counties was not calculated.

At the city level, the highest values of the synthetic index for football stadiums were recorded in Bucharest (0.77), Cluj-Napoca (0.61) and Arad (0.51), while the lowest values were recorded in the cities of Olt (0.13), Harghita (0.11) and Ialomița (0.05) (Figure 4).

The typological analysis of the synthetic index for football stadiums revealed the following situations based on the level of analysis: At the development region level, four categories of regions were identified: regions with a very high value (between 0.76 and 1; two regions, 25.0%), regions with a high value (between 0.5 and 0.76; three regions, 37.5%), regions with a low value (between 0.26 and 0.5; two regions, 25.0%) and regions with a very low value (between 0.0 and 0.25; one region, 12.5%). Based on the synthetic values related to football stadiums at the county level, four types of counties were identified: those with a very good value (between 0.76 and 1; 1 county, 2.4%), those with a good value (between 0.75 and 0.5; 3 counties, 7.1%), those with a weak value (between 0.26 and 0.5; 10 counties, 23.8%) and those with a very weak value (between 0.0 and 0.25; 25 counties, 59.5%). A similar pattern emerged from the analysis of the synthetic values of football stadiums at the city level. The cities were categorized as follows: those with a very high value (between 0.76 and 1; 1 city, 1.9%), a high value (between 0.5 and 0.75; 3 cities, 5.6%), a low value (between 0.26 and 0.5; 10 cities, 18.5%) and a very low value (between 0.0 and 0.25; 40 cities, 74.1%) (Figure 5).

3.3. Relations Between the Population and Football Stadiums in Romania

Following the analysis of the relationship index between the population and football stadiums in Romania, at the spatial level (development region, county and locality), based on the eight analyzed criteria, the following types of relationships were identified:

(1) Weak negative relationships, characterized by index values ranging from [−1; −0.5], were identified in 2 development regions (25.0%), 39 counties (92.9%), and 41 cities (75.9%). This type of relationship indicates that the population values are either zero or lower than those associated with football stadiums (Figure 6 and Figure 7). For instance, in the West and Southwest Development Regions, the population values were 0 and 0.156484957, respectively, compared to the football stadium values of 0.510335409 and 0.713751832. Similarly, in Buzău County, a relationship index of −1 was observed, derived from the ratio of the population index (0.0) to the football stadium index (0.147281618). Regarding this type of relationship at the locality level, we mention Ilfov, Olt and Constanța where the population index values (0.000268894, 0.000622124 and 0.002269193) were lower than those related to football stadiums (0.231860021, 0.142361238 and 0.396021006).

(2) Strong negative relationships, with relationship index values ranging between −0.51 and 0, were observed in 3 development regions (37.5%), 1 county (2.4%), and 12 localities (22.2%) (Figure 6 and Figure 7). These relationships are characterized by population values being lower than the corresponding values for football stadiums. Thus, for the Ilfov–Bucharest, Southeast and Center Development Regions, the population values were 0.511295494, 0.161579611 and 0.395843817, while the specific values for football stadiums were 0.944710031, 0.438559322 and 0.542372881, respectively. A comparable situation was observed in Bihor County, characterized by a value for the relationship index of −0.461113854, resulting from the ratio of the population index (0.055941796) to the index of football stadiums (0.151678297). Regarding this type of relation at the locality level, we mention Brașov, Constanta and Iași, where the values of the population index (0.130803651, 0.145060331 and 0.144312265) were lower than those related to football stadiums (0.170756629, 0.169859653 and 0.191263739).

(3) Strong positive relationships, with relationship index values ranging between 0 and 0.5, were identified in three development regions (37.5%), zero counties (0.0%), and one locality (1.9%) (Figure 6 and Figure 7). In this case, the population values are higher than those specific to football stadiums. Thus, for the Northwest, Northwest and Southern Development Regions, the population values were 0.499256316, 0.724344256 and 0.915406286, while the specific values for football stadiums were 0.430548559, 0.230271026 and 0.781516587. A similar situation was also noted regarding the city of Bucharest, characterized by a relationship index with a value of 0.124118446 resulting from the ratio of the population index (1) to the index of football stadiums (0.779171943).

(4) Weakly positive relationships, characterized by relationship index values between 0.51 and 1, were not identified. This type of relationship is typically defined by football stadium values being equal to zero or lower than the corresponding population-specific values.

4. Discussion

Sports, particularly football, are one of the fundamental interests of modern society, with significant impacts across social, cultural, political and other domains. This fact calls for and requires their analysis at a spatial level (development region, county and locality) through multicriteria methods that include both quantitative and qualitative variables. According to this idea, establishing and knowing the relationships between the population and football stadiums is a necessary activity, having a major impact on the judicious distribution of the resources needed to build the sports infrastructure. The working hypotheses from which the present approach started, according to which there are strong relations between the population and football stadiums, were partially confirmed only at the level of 6 development regions (75%), 1 county (2.4%) and 12 localities (24.1%). This is explained by the complexity and dynamics of the determinants that can influence the relationship between the population and football stadiums. Among the factors we mention are political, economic, cultural, social, etc. So, explaining the relationship between the population and football stadiums has proven to be a particularly interesting and difficult aspect to quantify. The difficulty of quantification comes from the lack of information with national coverage on the three levels of analysis studied (development region, county and city). Overcoming this obstacle could represent a desirable future that requires resources to be commensurate with the area studied and the accuracy and precision of the desired results to be obtained. With all these inconveniences and disadvantages that the study has, it represents a step forward in researching the relationships between the population and football stadiums.

Among the weak points of the research, it is noted that only large stadiums with a capacity of over 4000 seats were considered in the study. Another weakness was the low number of indicators due to the lack of data and information covering the three levels of analysis, both for the population and for football stadiums. The lack of data was the reason why, regarding the population, we used the data from the 2021 Population and Housing Census of Romania. Despite this, we mention the fact that the trends regarding the population do not change from one year to another, so the objective of the research did not suffer for this reason.

Over time, numerous studies have been carried out that used a working methodology like the one used in the present study, aiming at cultural heritage [62], urban functionality [63], social and economic development [52,64,65] and tourism [46,54,55,56,57]. Considering that football stadiums are points of convergence for a population that loves sports events, with a strong spatial impact, new future studies are needed to highlight: the mechanisms by which football stadiums attract the population from the territory; the motivations of the population’s participation in sports events; the perception of the population, decision makers and service providers towards sports, specifically football; determining factors; population attraction area; etc.

While the study provides significant insights into the relationship between the population and football stadiums, future research could benefit from incorporating a sensitivity analysis to assess the robustness of the findings. This would help identify how variations in input parameters or criteria weights might influence the results. Additionally, the exclusion of smaller stadiums, which often play a vital role in local communities, represents a limitation that could be addressed in future studies. Including such facilities may offer a more comprehensive understanding of the spatial distribution of sports infrastructure and its alignment with population characteristics. These enhancements would strengthen the methodology and provide more actionable insights for policymakers and urban planners.

5. Conclusions

Following the current research, it emerged that the relations between the population and football stadiums are complex and multidimensional, involving economic, social, cultural and urban aspects. Understanding and analyzing the spatial relationships between the population and football stadiums is essential for developing an effective approach to allocating financial resources for sports infrastructure. This approach considers the spatial distribution across development regions, counties and cities, aligning investments with the population size and defining characteristics such as the gender, age, purchasing power, leisure time and preferences.

The lack of information covering the three levels of analysis—development region, county and city—led to some limitation of the index of the relationship between the population and football stadiums in Romania, with it being defined by eight indicators: four for the population, four for football stadiums. However, the synthetic values of the population and football stadiums in Romania were calculated on three levels of spatial analysis, namely the development region, county and city. The analysis of the synthetic population values at the spatial level highlighted the following: the development regions of the South, Northeast and Ilfov–Bucharest; the counties of Bucharest, Prahova and Bihor and the cities of Bucharest, Cluj and Constanța. In contrast, the synthetic values for football stadiums were highest in the regions of Ilfov–Bucharest, South and Southwest; the counties of Bucharest, Cluj and Arad and the cities of Bucharest, Cluj-Napoca and Arad. Furthermore, based on the synthetic values of the population and football stadiums, three types of relationship between the population and football stadiums in Romania were established as follows: slightly negative relationships (2 development regions, 39 counties and 41 cities), relationships strongly negative (3 development regions, 1 county and 12 cities) and strongly positive relationships (3 development regions and 1 city). Weakly positive relationships were not identified. Also, in Teleorman and Sălaj counties, there were no relations between the population and football stadiums due to the total lack of football stadiums with a capacity of over 4000 seats.

Therefore, from the spatial analysis of the distribution of relations between the population and football stadiums in Romania, the existence of a normal situation (prevailing strong positive and negative relations) was found, which tests the research hypothesis but at the level of 6 development regions (75%), 12 localities (24.1%) and 1 county/district (Bihor County, 2.4%). The results of the study underline how the spatial distribution of football stadiums can guide urban planning decisions, contributing to the coherent and functional development of cities in Romania.