School of Kinesiology, Shanghai University of Sport, Shanghai, China.
Institute of Urban Development and Management, Tongji University, Shanghai, China.
PLoS One. 2022 Apr 20;17(4):e0267009. doi: 10.1371/journal.pone.0267009. eCollection 2022.
Sports facilities have been acknowledged as one of the crucial environmental factors for children's physical education, physical fitness, and participation in physical activity. Finding a solution for the effective and objective evaluation of the condition of sports facilities in schools (SSFs) with the responding quantitative magnitude is an uncertain task. This paper describes the utilization of an unsupervised machine learning method to objectively evaluate the condition of sports facilities in primary school (PSSFC). The statistical data of 845 samples with nine PSSFC indicators (indoor and outdoor included) were collected from the Sixth National Sports Facility Census in mainland China (NSFC), an official nationwide quinquennial census. The Fuzzy C-means (FCM) algorithm was applied to cluster the samples in accordance with the similarity of PSSFC. The clustered data were visualized by using t-stochastic neighbor embedding (t-SNE). The statistics results showed that the application of t-SNE and FCM led to the acceptable performance of clustering SSFs data into three types with differences in PSSFC. The effects of school category, location factors, and the interaction on PSSFC were analyzed by two-way analysis of covariance, which indicated that regional PSSFC has geographical and typological characteristics: schools in the suburbs are superior to those in the inner city, schools with more grades of students are configured with better variety and larger size of sports facilities. In conclusion, we have developed a combinatorial machine learning clustering approach that is suitable for objective evaluation on PSSFC and indicates its characteristics.
体育设施被认为是儿童体育教育、体质和参与体育活动的关键环境因素之一。找到一种有效的、客观的方法来评估学校体育设施（SSFs）的状况，并给出相应的量化指标，是一项具有挑战性的任务。本文描述了一种无监督机器学习方法在小学体育设施条件（PSSFC）客观评估中的应用。利用中国第六次全国体育场地普查（NSFC）的统计数据，对全国范围内每五年进行一次的普查中 845 个样本的 9 项 PSSFC 指标（室内和室外）进行了分析。采用模糊 C 均值（FCM）算法对 PSSFC 相似的样本进行聚类。使用 t 随机邻域嵌入（t-SNE）对聚类数据进行可视化。统计结果表明，t-SNE 和 FCM 的应用使得将 SSFs 数据聚类为具有 PSSFC 差异的三种类型具有可接受的性能。通过双向方差分析，对学校类型、地理位置因素以及二者的交互作用对 PSSFC 的影响进行了分析，结果表明区域 PSSFC 具有地理和类型特征：郊区学校优于市区学校，学生年级较多的学校配置了更好种类和更大规模的体育设施。总之，我们开发了一种组合式机器学习聚类方法，适用于 PSSFC 的客观评估，并显示了其特点。
