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Volume 40, Issue 1 (2025)
GeoRes 2025, 40(1): 63-72 |
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Received: 2025/03/16 | Accepted: 2025/04/23 | Published: 2025/04/27
Received: 2025/03/16 | Accepted: 2025/04/23 | Published: 2025/04/27
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Akbari M. Application of the Seca Method in Estimating the Spatial Distribution of Sports Facilities in Mashhad Metropolis. GeoRes 2025; 40 (1) :63-72
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Authors
M. Akbari *
Department of Geography and Urban Planning, Faculty of Literature and Humanities, Yasouj University, Yasouj, Iran
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Background
Rapid urbanization and population growth have increased the need for the development of infrastructure such as sports facilities to enhance public health and well-being.
Previous Studies
Esmaeilzadeh et al. (2019) have demonstrated in their study that the distribution of sports facilities in Mashhad is unequal. Similarly, Saraei et al. (2020) point to the dispersed distribution of sports services in the city of Isfahan and the resulting social inequality. Shen et al. (2020) also state that in developing countries, few studies have been conducted on the provision of sports facilities. Additionally, Xiao and Wang (2022), in a study in Fuzhou, China, highlight the weak spatial alignment between population and facilities. Liu et al. (2022) have reported inequality in access to sports facilities between central and peripheral areas in Harbin. Kozma et al. (2022), in Hungary, have found that the distribution of facilities is strongly influenced by the size of settlements. Testa et al. (2023), have through an analysis of social perceptions of the impacts of sports spaces, emphasized the importance of these facilities in promoting healthy lifestyles and social participation. These studies underscore the importance of equitable design and distribution of sports facilities in enhancing health, social justice, and community cohesion.
Aim(s)
The aim of this article is to analyze the spatial pattern of indicators across the thirteen districts of the metropolis of Mashhad by applying weighting through the SECA technique and utilizing them within the MIRKA method.
Research Type
The present study is applied in nature and was conducted using the SECA and MIRKA techniques.
Research Society, Place, and Time
The present study was conducted in 2021 in the metropolis of Mashhad, located in Razavi Khorasan Province, Iran. The statistical population consisted of existing sports facilities across thirteen urban districts of Mashhad, which were analyzed using statistical data and multi-criteria analysis techniques.
Sampling Method and Number
Since all thirteen districts of the metropolis of Mashhad were examined, the research method employed was a full census, with the sample size equal to the 13 urban districts of Mashhad.
Used Devices & Materials
In this study, the required data were extracted from the 2021 Statistical Yearbook of the metropolis of Mashhad. For data analysis, Lingo software version 18.0.44 was used to implement the SECA (Spatial Equity and Compatibility Assessment) technique, and the MIRCA (Multi-criteria Integrated Ranking of City Areas) technique was employed to rank the districts. Therefore, the materials and tools used in this research included official urban statistical data, Lingo software, and multi-criteria decision-making analysis methods.
Findings by Text
In this study, the multi-criteria decision-making technique SECA (Spatial Equity and Compatibility Assessment) was employed to evaluate and rank the sports facilities across different districts of the metropolis of Mashhad in the year 2021. In the first step, a normalized matrix of sports facilities was developed (Table 1), consisting of eight indicators, including the number of outdoor sports facilities, women’s sports centers, the area of special stations for the disabled, and the number of fitness equipment units. Subsequently, by setting beta coefficients to 3, 4, and 5, the weights of the indicators were calculated (Table 2). Specifically, with β=3 as the reference value, the highest weight was assigned to the indicator “number of indoor sports facilities” (weight: 0.1514), and the lowest to “number of outdoor sports facilities” (weight: 0.1137).
Table 1. Normalized matsrix of sports facilities in the districts of Mashhad City in 2021
Table 2. Weight of sports facilities in the districts of Mashhad in 2021 using the SECA Technique
Using these weights, the final scores of Mashhad’s districts were calculated (Table 3, Figure 1). The results showed that District 2 received the highest score in all beta values, followed by District 9, while Thamen District received the lowest score. For further analysis, the MIRCA (Multi-criteria Integrated Ranking of City Areas) technique was applied, involving the calculation of preferences (PA=0.07692), the formation of a real evaluation matrix, and a total gap matrix (Table 4, Figure 2). These matrices revealed the gap between the current and the ideal conditions of sports facilities in the districts. Finally, by aggregating the total gap values, urban districts were ranked based on their priority for improving sports infrastructure.
Table 3. Scores of Mashhad metropolitan districts using the SECA technique in 2021
Figure 1. The weight of sports facilities in Mashhad urban districts in 2021 using the SECA technique
Table 4. Preference value, actual evaluation matrix, and total gap matrix of sports spaces in Mashhad districts in 2021
Figure 2. Score levels of Mashhad metropolitan areas using the SECA technique in 2021
In 2021, the analysis of access to sports facilities in the metropolis of Mashhad using SECA and MIRCA techniques revealed an unequal distribution of these facilities. District 2 received the highest scores in both methods, while Samen and District 1 had the lowest scores (Figure 3). Spatial clustering analysis also indicated a clustered distribution pattern, with a Z-score of -6.96 and a p-value of 0.0000001 (Figure 4). In the MIRCA model grouping analysis, District 2 was placed in the green group (highest access), while Districts 12, 8, 1, and Samen were classified in the red group (lowest access) (Figure 5).
Figure 3) Comparison of score levels of Mashhad metropolitan areas using SECA and MIRCA techniques in 2021
Figure 4. Multi-distance spatial cluster analysis of the distribution pattern of sports spaces in Mashhad metropolis in 2021
Figure 5. Group score analysis of the MIRKA model for sports facilities in the metropolis of Mashhad (2021-2022)
Main Comparisons to Similar Studies
The results of this study are consistent with several similar studies conducted both inside and outside the country, confirming similar patterns of inequality in the distribution of urban sports facilities. At the national level, the findings align with those of Esmaielzadeh et al. (2019), who also emphasized the unequal distribution of sports facilities across different areas of Mashhad and highlighted the necessity of implementing equitable planning in this regard. Additionally, the research by Saraei et al. (2020), conducted in the city of Isfahan, indicated an uneven dispersion of sports services and found no significant correlation between the distribution of sports services and the population density of neighborhoods, which is also similar to the present results in Mashhad. At the international level, the findings of this study correspond with those of Liu et al. (2022) in Harbin, China; they also pointed out spatial inequality in the distribution of urban sports facilities and stressed the need for a precise and fair evaluation of these spaces. This alignment among studies underscores the importance of spatial and statistical analyses in the fair management of urban sports infrastructure.
Suggestions
Considering Beta-3, Beta-4, and Beta-5 weights, Samen District, District 1, District 8, District 12, and District 3 are the top priorities for planning sports facilities and services in Mashhad Metropolis. It is recommended that special attention be given to these areas in future planning of service-oriented spaces in the city.
Conclusion
The 13 districts of Mashhad Metropolis show inequality in the distribution of sports facilities and spaces, indicating that these amenities have not been fairly distributed across the city.
Acknowledgments: Nothing declared by the author.
Ethical Approval: There is no ethical issue to report by the authors.
Conflict of Interest: Nothing declared by the author.
Authors' Contributions: Akbari M (First Author): Introduction Writer/Methodologist/Main Researcher/Statistical Analyst/Discussion Writer (100%)
Funding: Nothing declared by the author.
Rapid urbanization and population growth have increased the need for the development of infrastructure such as sports facilities to enhance public health and well-being.
Previous Studies
Esmaeilzadeh et al. (2019) have demonstrated in their study that the distribution of sports facilities in Mashhad is unequal. Similarly, Saraei et al. (2020) point to the dispersed distribution of sports services in the city of Isfahan and the resulting social inequality. Shen et al. (2020) also state that in developing countries, few studies have been conducted on the provision of sports facilities. Additionally, Xiao and Wang (2022), in a study in Fuzhou, China, highlight the weak spatial alignment between population and facilities. Liu et al. (2022) have reported inequality in access to sports facilities between central and peripheral areas in Harbin. Kozma et al. (2022), in Hungary, have found that the distribution of facilities is strongly influenced by the size of settlements. Testa et al. (2023), have through an analysis of social perceptions of the impacts of sports spaces, emphasized the importance of these facilities in promoting healthy lifestyles and social participation. These studies underscore the importance of equitable design and distribution of sports facilities in enhancing health, social justice, and community cohesion.
Aim(s)
The aim of this article is to analyze the spatial pattern of indicators across the thirteen districts of the metropolis of Mashhad by applying weighting through the SECA technique and utilizing them within the MIRKA method.
Research Type
The present study is applied in nature and was conducted using the SECA and MIRKA techniques.
Research Society, Place, and Time
The present study was conducted in 2021 in the metropolis of Mashhad, located in Razavi Khorasan Province, Iran. The statistical population consisted of existing sports facilities across thirteen urban districts of Mashhad, which were analyzed using statistical data and multi-criteria analysis techniques.
Sampling Method and Number
Since all thirteen districts of the metropolis of Mashhad were examined, the research method employed was a full census, with the sample size equal to the 13 urban districts of Mashhad.
Used Devices & Materials
In this study, the required data were extracted from the 2021 Statistical Yearbook of the metropolis of Mashhad. For data analysis, Lingo software version 18.0.44 was used to implement the SECA (Spatial Equity and Compatibility Assessment) technique, and the MIRCA (Multi-criteria Integrated Ranking of City Areas) technique was employed to rank the districts. Therefore, the materials and tools used in this research included official urban statistical data, Lingo software, and multi-criteria decision-making analysis methods.
Findings by Text
In this study, the multi-criteria decision-making technique SECA (Spatial Equity and Compatibility Assessment) was employed to evaluate and rank the sports facilities across different districts of the metropolis of Mashhad in the year 2021. In the first step, a normalized matrix of sports facilities was developed (Table 1), consisting of eight indicators, including the number of outdoor sports facilities, women’s sports centers, the area of special stations for the disabled, and the number of fitness equipment units. Subsequently, by setting beta coefficients to 3, 4, and 5, the weights of the indicators were calculated (Table 2). Specifically, with β=3 as the reference value, the highest weight was assigned to the indicator “number of indoor sports facilities” (weight: 0.1514), and the lowest to “number of outdoor sports facilities” (weight: 0.1137).
Table 1. Normalized matsrix of sports facilities in the districts of Mashhad City in 2021
Table 2. Weight of sports facilities in the districts of Mashhad in 2021 using the SECA Technique
Using these weights, the final scores of Mashhad’s districts were calculated (Table 3, Figure 1). The results showed that District 2 received the highest score in all beta values, followed by District 9, while Thamen District received the lowest score. For further analysis, the MIRCA (Multi-criteria Integrated Ranking of City Areas) technique was applied, involving the calculation of preferences (PA=0.07692), the formation of a real evaluation matrix, and a total gap matrix (Table 4, Figure 2). These matrices revealed the gap between the current and the ideal conditions of sports facilities in the districts. Finally, by aggregating the total gap values, urban districts were ranked based on their priority for improving sports infrastructure.
Table 3. Scores of Mashhad metropolitan districts using the SECA technique in 2021
Figure 1. The weight of sports facilities in Mashhad urban districts in 2021 using the SECA technique
Table 4. Preference value, actual evaluation matrix, and total gap matrix of sports spaces in Mashhad districts in 2021
Figure 2. Score levels of Mashhad metropolitan areas using the SECA technique in 2021
In 2021, the analysis of access to sports facilities in the metropolis of Mashhad using SECA and MIRCA techniques revealed an unequal distribution of these facilities. District 2 received the highest scores in both methods, while Samen and District 1 had the lowest scores (Figure 3). Spatial clustering analysis also indicated a clustered distribution pattern, with a Z-score of -6.96 and a p-value of 0.0000001 (Figure 4). In the MIRCA model grouping analysis, District 2 was placed in the green group (highest access), while Districts 12, 8, 1, and Samen were classified in the red group (lowest access) (Figure 5).
Figure 3) Comparison of score levels of Mashhad metropolitan areas using SECA and MIRCA techniques in 2021
Figure 4. Multi-distance spatial cluster analysis of the distribution pattern of sports spaces in Mashhad metropolis in 2021
Figure 5. Group score analysis of the MIRKA model for sports facilities in the metropolis of Mashhad (2021-2022)
Main Comparisons to Similar Studies
The results of this study are consistent with several similar studies conducted both inside and outside the country, confirming similar patterns of inequality in the distribution of urban sports facilities. At the national level, the findings align with those of Esmaielzadeh et al. (2019), who also emphasized the unequal distribution of sports facilities across different areas of Mashhad and highlighted the necessity of implementing equitable planning in this regard. Additionally, the research by Saraei et al. (2020), conducted in the city of Isfahan, indicated an uneven dispersion of sports services and found no significant correlation between the distribution of sports services and the population density of neighborhoods, which is also similar to the present results in Mashhad. At the international level, the findings of this study correspond with those of Liu et al. (2022) in Harbin, China; they also pointed out spatial inequality in the distribution of urban sports facilities and stressed the need for a precise and fair evaluation of these spaces. This alignment among studies underscores the importance of spatial and statistical analyses in the fair management of urban sports infrastructure.
Suggestions
Considering Beta-3, Beta-4, and Beta-5 weights, Samen District, District 1, District 8, District 12, and District 3 are the top priorities for planning sports facilities and services in Mashhad Metropolis. It is recommended that special attention be given to these areas in future planning of service-oriented spaces in the city.
Conclusion
The 13 districts of Mashhad Metropolis show inequality in the distribution of sports facilities and spaces, indicating that these amenities have not been fairly distributed across the city.
Acknowledgments: Nothing declared by the author.
Ethical Approval: There is no ethical issue to report by the authors.
Conflict of Interest: Nothing declared by the author.
Authors' Contributions: Akbari M (First Author): Introduction Writer/Methodologist/Main Researcher/Statistical Analyst/Discussion Writer (100%)
Funding: Nothing declared by the author.
Keywords:
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