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arXiv · 2609.31624

Analysis of Regional Disparities of Location of Sports Facilities and Sports Education Using Scientific Satellite Data

Shoichi Otomo
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Abstract

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.

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Shoichi Otomo. 2026-08-10. Analysis of Regional Disparities of Location of Sports Facilities and Sports Education Using Scientific Satellite Data. https://doi.org/10.20637/0002000295

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