ArticleScientific reports2025
Spatiotemporal distribution patterns and influencing factors of marathon events: A case study of Shandong province, China.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Assessing the tourism impacts of urban marathon events in central China's historic cities: a residents' SEM analysis.Frontiers in sports and active living · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
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