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<p>Marathon events play a crucial role in promoting the synergistic development of sports and tourism economies, significantly influencing the construction of a new “sports + events + tourism” development model and serving as a key driver of regional economic growth. This study analyzed POI data from marathon events in Shandong Province, China, from 2018 to 2024, using spatial analysis tools such as the nearest neighbor index, standard deviational ellipse, and kernel density estimation in geographic information systems to explore the spatiotemporal distribution patterns of marathon events. Additionally, the geographic detector method was employed to identify key factors influencing marathon event development. The results indicate: (1) Regionally, marathon events exhibit significant distribution disparities, with a dispersed spatial layout and a diffusion trend along the northeast-southwest axis, gradually shifting toward inland western areas. This forms a hierarchical spatial structure characterized by a multi-core cluster-based layout. (2) Marathon events are strongly influenced by five key factors: number of universities, GDP per capita, proportion of tertiary industry, policy intensity, and tourism revenue. Among these, policy intensity, number of universities, and proportion of tertiary industry are identified as dynamic factors, showing increasing influence over time. Road mileage, elevation, and river systems represent static factors, maintaining relatively stable effects. Meanwhile, population density, sports-related intangible cultural heritage, tourism revenue, and average temperature are potential factors, exhibiting fluctuating but growing impact trends. (3) Dual-factor interaction analysis reveals three types of interactions: independent, dual-factor enhancement, and nonlinear enhancement. Tourism revenue and road mileage exhibit no interaction. The core interaction factors are sports-related intangible cultural heritage and the number of universities, with the secondary core interaction being sports-related intangible cultural heritage and tourism revenue.</p>

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Spatiotemporal distribution patterns and influencing factors of marathon events: A case study of Shandong province, China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月12日

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  • Xinlong Zhao,
  • Yan Wang,
  • Junjin Chen

摘要

Marathon events play a crucial role in promoting the synergistic development of sports and tourism economies, significantly influencing the construction of a new “sports + events + tourism” development model and serving as a key driver of regional economic growth. This study analyzed POI data from marathon events in Shandong Province, China, from 2018 to 2024, using spatial analysis tools such as the nearest neighbor index, standard deviational ellipse, and kernel density estimation in geographic information systems to explore the spatiotemporal distribution patterns of marathon events. Additionally, the geographic detector method was employed to identify key factors influencing marathon event development. The results indicate: (1) Regionally, marathon events exhibit significant distribution disparities, with a dispersed spatial layout and a diffusion trend along the northeast-southwest axis, gradually shifting toward inland western areas. This forms a hierarchical spatial structure characterized by a multi-core cluster-based layout. (2) Marathon events are strongly influenced by five key factors: number of universities, GDP per capita, proportion of tertiary industry, policy intensity, and tourism revenue. Among these, policy intensity, number of universities, and proportion of tertiary industry are identified as dynamic factors, showing increasing influence over time. Road mileage, elevation, and river systems represent static factors, maintaining relatively stable effects. Meanwhile, population density, sports-related intangible cultural heritage, tourism revenue, and average temperature are potential factors, exhibiting fluctuating but growing impact trends. (3) Dual-factor interaction analysis reveals three types of interactions: independent, dual-factor enhancement, and nonlinear enhancement. Tourism revenue and road mileage exhibit no interaction. The core interaction factors are sports-related intangible cultural heritage and the number of universities, with the secondary core interaction being sports-related intangible cultural heritage and tourism revenue.