ENTERPRISE AI ANALYSIS
Dynamic Evolution and Frontier Trends in Outdoor Sports Risk Management Research: A Bibliometric Analysis Based on CiteSpace
This paper analyzes the dynamic evolution and frontier trends in outdoor sports risk management research using a bibliometric approach. It highlights the rapid development of outdoor sports, the increasing need for effective risk management, and the current research landscape in China. The study identifies key research stages, prominent authors and institutions, hot topics (e.g., risk assessment, safety risk, trail running), and future trends focusing on technological integration (AI, GIS) for predictive risk management.
Executive Impact & Strategic Imperatives
This analysis reveals critical findings and future directions that can strategically guide enterprises in the outdoor sports sector towards more robust risk management and innovation.
Key Insights at a Glance
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Articles Analyzed
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Avg. Papers/Year (2019-2025)
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Research Stages Identified
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Outdoor sports risk management research in China has seen continuous growth, with a rapid acceleration from 2019 onwards, driven by national policies.
335,000+
Outdoor Sports Enterprises in China (as of June 2025)
| Stage | Period | Characteristics |
|---|---|---|
| Preliminary Emergence | 2005-2010 |
|
| Foundational Exploration | 2011-2018 |
|
| Rapid Development | 2019-2025 |
|
Research authors form small, independent groups with limited global collaboration. Institutions are mainly universities, operating in isolation with weak overall correlation.
Author Collaboration Model
Limited Active Authors
→
Core Authors Lead Small Groups
→
Sparse Inter-Group Connections
→
Independent Research Dominates
→
Lack of Close Collaborative Ecosystem
0.0046
Network Density (CNKI Author Co-authorship)
Institutional Research Landscape
Research is predominantly university-led across China, with institutions like Beijing Sport University, Shenyang Sport University, and Shanghai University of Sport making significant contributions. However, most institutions operate in isolation, indicating a lack of cross-institutional and cross-disciplinary collaboration, leading to a disconnect between theory and practice.
Key Takeaway: Future efforts should focus on cross-institutional and cross-disciplinary collaboration to bridge the theory-practice gap and promote the application of theoretical knowledge.
Key hotspots include 'risk management', 'outdoor sports', 'risk assessment'. Recent trends integrate AI and GIS for predictive risk management, shifting from generalized to specific scenario-based research.
| Keyword | Frequency (TOP10) | Centrality (TOP10) |
|---|---|---|
| Risk Management | 63 | 0.27 |
| Outdoor Sports | 31 | 0.15 |
| Risk Assessment | 15 | 0.13 |
| Risk Identification | 14 | 0.13 |
| Safety Risk | 13 | 0.13 |
2.97
Highest Mutation Intensity for 'Outdoor Sports' (2012-2015)
Future Research Directions
Future research will focus on the application of new technologies like AI algorithms, GIS analysis, IoT monitoring, and Beidou positioning for enhanced risk identification and early warning. Scenario-based risk prediction models for specific sports (rock climbing, hiking, trail running) are critical, aiming to improve perception accuracy, data reliability, algorithm optimization, privacy protection, and interaction experiences.
Key Takeaway: Shift from post-event response to pre-event warning through technological empowerment.
Advanced ROI Calculator
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Annual Savings Potential
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Hours Reclaimed Annually
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Implementation Roadmap
Our phased approach ensures a smooth and effective integration of advanced risk management solutions tailored to your outdoor sports enterprise.
Phase 1: Foundation & Data Integration
Establish data pipelines from various outdoor activity sources, clean and preprocess data for risk management models, and integrate relevant policy frameworks.
Phase 2: Predictive Model Development
Train dedicated AI/ML models for scenario-based risk prediction across different sports (e.g., rock climbing, trail running), focusing on improving accuracy and reliability.
Phase 3: Real-time Monitoring & Alert Systems
Deploy IoT sensors and Beidou positioning for real-time data collection, integrate with GIS for spatial analysis, and develop early warning systems with interactive dashboards.
Phase 4: User Experience & Privacy Enhancement
Design intuitive interfaces for participants and organizers, ensure robust privacy protection protocols, and conduct pilot programs for feedback and iteration.
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