The planning and development of sports infrastructure is a complex process that has a broad and long-term impact on health and well-being in communities. It involves many different stake- holders and usually requires significant public or private investments. Its framework is outlined by policies that define the general social goals of such development. To ensure the maximum alignment between the goals and the development activities, it is important to support the policy making process by high-quality information based on real-world data and presented in a clear and focused way. This work introduces a new pipeline of methods for processing and interpretation of data on physical activity and lifestyle in adolescents. The data is extracted from the Health Behaviour in School-aged Children (HBSC) study and analyzed by modern machine learning methods. We identify behavioural patterns associated with the presence and absence of a school sports ground in different sex and age groups of adolescent in the Czech Republic. The patterns are presented by concise graphical models that ease their use by stake- holders without expert knowledge in sociology, statistics, mathematical modelling, etc. They enable intuitive visual assessment of situation in different regions and highlight the specific similarities and differences among them. Together, the proposed methods contribute towards objective evidence-based policy making in sports management and development.

中文翻译：

---

与学校运动场相关的行为：为决策者提供的视觉信息

体育基础设施的规划和发展是一个复杂的过程，对社区的健康和福祉具有广泛和长期的影响。它涉及许多不同的利益相关者，通常需要大量的公共或私人投资。其框架由定义此类发展的总体社会目标的政策概述。为了确保目标和发展活动之间最大限度地保持一致，重要的是通过基于真实世界数据并以清晰和有针对性的方式呈现的高质量信息来支持政策制定过程。这项工作引入了一系列新的方法来处理和解释青少年身体活动和生活方式的数据。数据提取自学龄儿童健康行为 (HBSC) 研究，并通过现代机器学习方法进行分析。我们确定了捷克共和国不同性别和年龄组青少年与学校运动场存在和不存在相关的行为模式。这些模式以简洁的图形模型呈现，方便利益相关者在没有社会学、统计学、数学建模等专业知识的情况下使用。它们可以直观地视觉评估不同地区的情况，并突出它们之间的具体异同。总之，所提出的方法有助于体育管理和发展中客观的基于证据的政策制定。