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Analysis of Regional Disparities of Location of Sports Facilities and Sports Education Using Scientific Satellite Data
Shoichi Otomo
TL;DR
Sports-facility location research has lacked broad quantitative frameworks, while nightlight data offer a way to compare spatial patterns. This study combines nationwide GIS proximity analysis and satellite nightlight radiance, finding that private facilities cluster in bright urban centers while public facilities are more evenly distributed; high-light prefectures also account for a disproportionately large share of tournament qualifiers.
Problem
Sports-facility location research has largely used qualitative concepts or localized case studies, with broad quantitative frameworks for planning remaining underused.
Source: §1.1 · p. 2Method
The study combines nationwide GIS proximity calculations at 100-meter resolution with satellite nightlight radiance to compare facility distributions.
Source: §1.3 · p. 2Results
Private facilities cluster in high-radiance urban centers, public facilities are more evenly distributed, and high-NTL prefectures account for a disproportionately larger share of national tournament qualifiers.
Source: §5 · p. 13Takeaways & Limitations
The conclusion identifies structural accessibility barriers for youth athletes in low-radiance rural areas during early career formation.
Source: §5 · p. 13The account of early structured swimming instruction at private facilities is presented as an inference about athletes’ prior training, not directly measured evidence.
Source: §4.2 · p. 13
Abstract
from arXiv · showhide
This manuscript is an English translation and extended version of a paper originally published in Japanese (Otomo, 2024). With the advancement of information and communication technology (ICT) and data analysis techniques, handling massive datasets (big data) has become feasible in spatial information science. Consequently, infrastructure is being established to generate new utility value from spatial data. Furthermore, nationwide initiatives are actively promoting the use of open data - public information provided under terms allowing secondary usage. However, in developing regions and specific municipalities, local statistical data remain scarce, unreliable, or difficult to acquire. Additionally, long-standing concerns exist regarding the inherent limitations of standard macroeconomic indicators when performing cross-regional or international comparisons. Meanwhile, high-frequency satellite data has become widely accessible. Among various observational products, nightlight data has attracted significant attention due to its versatile applications. Generally, nightlight radiance correlates with urbanization. Prior work indicates that areas with larger populations and higher commercial development display higher radiance, whereas public infrastructure - such as sanitation systems and healthcare facilities - shows little correlation with nightlight intensity (Otomo, 2021). Building on these findings, this paper analyzes the relationship between nightlight data and the spatial distribution of sports facilities, the evolution of the fitness industry, and regional environmental disparities in sports access. The analysis confirms that the spatial distribution patterns of sports facilities differ significantly between the public and private sectors.
1 Introduction
The paper addresses the limited use of quantitative spatial analysis in sports-facility planning with a nationwide comparison using satellite nightlight data. It finds that private facilities cluster in bright urban cores, while public facilities are more dispersed.
1.1 Literature Review
- Earlier sports-facility location research largely relied on qualitative concepts or localized case studies, leaving broad quantitative frameworks for planning underused.Karube (2002) is cited as an example of the localized case-study approach.Source: §1.1 · p. 2
1.2 Nightlight Data Used
- DMSP/NOAA nightlight data are annual, radiance-calibrated composites at 30-arc-second resolution, which cannot distinguish closely co-located infrastructure using radiance alone.The stated limitation includes adjacent fields, ballparks, and swimming facilities within multi-sport complexes.Source: §1.2 · p. 2
1.3 Significance and Novelty of This Study
- The study analyzes 10,683 sports facilities nationwide using GIS proximity analysis at 100-meter resolution, extending beyond a single-prefecture study of 155 facilities.The authors describe this as higher precision than the 1-km grid intervals used in legacy literature.Source: §1.3 · p. 2
- Private facilities cluster in high-radiance urban cores, while public facilities are more dispersed across lower-radiance peripheral zones.The study combines high-resolution proximity calculations with satellite nightlight radiance to reveal these distinct patterns.Source: §1.3 · p. 2
2 Data Analysis of the Sports Industry
The section documents fitness-industry growth through 2019 and contrasts the geography of public and private sports facilities. Public facilities are distributed across municipalities, while private facilities cluster in populous cities and have shorter nearest-neighbor distances.
2.1 Trends in the Market Size of the Fitness Industry
- Monthly users rose from approximately 9 million in January 2001 to over 22 million in July 2019, while sales rose from roughly 102,236 thousand USD to over 178,914 thousand USD.Employees increased from about 20,000 to a peak of over 45,000 during the same 2001–2019 period.Source: §2.1 · p. 3, Figure 2 · p. 3, Figure 1 · p. 3
2.2 Locations of Sports Facilities
- Public sports facilities are distributed across municipalities, whereas private facilities cluster in relatively populous cities such as Mito, Hitachi, and Tsukuba.The section attributes the comparison to the Statistics of Sports and Karube (2002).Source: §2.2 · p. 3, Figure 4 · p. 4
- Private facilities across Japan have shorter nearest-neighbor distances than public facilities, indicating denser concentration in specific locations.The section presents the nearest-public-facility distances in Figure 5.Source: §2.2 · p. 5, Figure 5 · p. 5
- The analysis asks whether sports facilities cluster like commercial services in bright areas or differ between public and private provision.Prior work cited in the section reports clustering of commercial stores in high-nightlight areas and relatively low nightlight correlation for highly public services (Otomo).Source: §2.2 · p. 5
3 Analysis and Discussion Using Nightlight Data
The section compares sports and swimming-facility locations with municipal nightlight intensity. Private sports and swimming facilities cluster more strongly in brighter areas, while public sports facilities are less concentrated there.
3.1 Distribution of Sports Facilities Based on Nightlight Intensity
- Facility locations were extracted from NTT’s i-Town Page and geocoded using the University of Tokyo’s Center for Spatial Information Science service.The section measures average nightlight intensity within each municipality.Source: §3.1 · pp. 5, 7
- Private sports and swimming facilities cluster densely in high-nightlight areas, while public sports facilities are also often in bright areas but show weaker clustering.The authors suggest public provision also considers factors beyond user convenience, leading to a more even distribution beyond high-light areas.Source: §3.1 · p. 7, Figure 8 · p. 8
- Private swimming facilities show a more pronounced tendency than general sports facilities to concentrate in higher-nightlight areas.The section maps public and private sports and swimming facilities in Figures 10–13.Source: §3.1 · p. 7, Figure 9 · p. 8
4 Data Analysis of Swimming Tournaments
In the 2021 swimming championships, leading prefectures were largely metropolitan at both school stages, while top-school representation shifted sharply toward private institutions in high school.
4.1 National Junior High School Swimming Championship
- Kanagawa, Tokyo, Saitama, Aichi, Osaka, and Chiba led junior-high tournament appearances, and all are metropolitan prefectures with high nightlight intensity and dense private swimming facilities.Source: §4.1 · p. 10
- Some junior high schools accounted for disproportionately many tournament appearances despite athletic recruitment generally not existing at that level.Source: §4.1 · p. 10, Figure 15 · p. 11
4.2 National High School Swimming Championship
- Junior swimmers commonly entered multiple events, so figures 15–16 count appearances; high-school specialization likely accompanies increased participants per school despite some multi-event entries.Source: §4.1 · p. 10, Figure 16 · p. 11, §4.2 · p. 12
- The authors suggest private-facility training may explain public junior-school concentrations, and infer elite high-school recruitment may reflect structured private instruction from elementary years or earlier.Source: §4.1 · pp. 10, 12, §4.2 · pp. 12–13
- Osaka, Tokyo, Kanagawa, Aichi, and Saitama led high-school appearances, with a nearly identical set of top prefectures to the junior-high championship.Source: §4.2 · p. 12, Figure 17 · p. 12
- All top 20 high schools were private, versus only 5 private among the 27 top junior high schools including ties; just one public school appeared among 34 top-30 high schools.The high-school comparison reflects a setting where athletic recruitment is generally used, unlike at junior high schools.Source: Figure 15 · p. 11, §4.2 · p. 12, Figure 18 · p. 13
5 Conclusion
The nationwide analysis finds distinct spatial patterns for public and private sports facilities and relates private-facility density to nighttime light. It identifies sports-access disparities for youth and proposes additional Earth-observation data for future work.
5 Conclusion
- Private sports facilities cluster in high-radiance urban centers, while public facilities are distributed more evenly across regions; nighttime light correlates strongly with private-facility density.Source: §5 · p. 13
- The authors report that youth athletes in low-radiance rural areas face structural access barriers, as swimming facilities and tournament qualifiers are concentrated in highly lit metropolitan prefectures.Source: §5 · p. 13
- Future work will combine snowfall and precipitation data for winter-sport location dynamics with vegetation metrics to study outdoor facilities, green spaces, and localized cultural capital.Source: §5 · p. 13