Version 1
: Received: 1 December 2019 / Approved: 3 December 2019 / Online: 3 December 2019 (05:24:26 CET)
How to cite:
Xia, J.; Chen, P.-J.; Wang, J.-H.; Zhuang, J.; Cao, Z.-B.; Zhang, Q. Evaluation on Sports Facility Resource in Primary School Using a Combined Approach of Unsupervised Machine Learning: a Case Study of Shanghai, China. Preprints2019, 2019120015. https://doi.org/10.20944/preprints201912.0015.v1
Xia, J.; Chen, P.-J.; Wang, J.-H.; Zhuang, J.; Cao, Z.-B.; Zhang, Q. Evaluation on Sports Facility Resource in Primary School Using a Combined Approach of Unsupervised Machine Learning: a Case Study of Shanghai, China. Preprints 2019, 2019120015. https://doi.org/10.20944/preprints201912.0015.v1
Xia, J.; Chen, P.-J.; Wang, J.-H.; Zhuang, J.; Cao, Z.-B.; Zhang, Q. Evaluation on Sports Facility Resource in Primary School Using a Combined Approach of Unsupervised Machine Learning: a Case Study of Shanghai, China. Preprints2019, 2019120015. https://doi.org/10.20944/preprints201912.0015.v1
APA Style
Xia, J., Chen, P. J., Wang, J. H., Zhuang, J., Cao, Z. B., & Zhang, Q. (2019). Evaluation on Sports Facility Resource in Primary School Using a Combined Approach of Unsupervised Machine Learning: a Case Study of Shanghai, China. Preprints. https://doi.org/10.20944/preprints201912.0015.v1
Chicago/Turabian Style
Xia, J., Zhen-Bo Cao and Qiang Zhang. 2019 "Evaluation on Sports Facility Resource in Primary School Using a Combined Approach of Unsupervised Machine Learning: a Case Study of Shanghai, China" Preprints. https://doi.org/10.20944/preprints201912.0015.v1
Abstract
The aim of this study is (a) to develop, test, and employ a combined method of unsupervised machine learning to objectively assess the condition of sports facility in primary schools (PSSFC) and (b) examine the examine the geographical and typological association with PSSFC. Based on the Sixth National Sports Facility Census (NSFC), six PSSFC indicators (indoor and outdoor facility included) were selected as the measurements and decomposed by using the t-stochastic neighbor embedding (t-SNE). Thereafter, the Fuzzy C-mean (FCM) algorithm was used to cluster the same type of PSSFC with selecting the optimum numbers of evaluation level. Overall 845 primary schools in Shanghai, China were recruited and tested by this combined approach of unsupervised machine learning. In addition, the two-way analysis of covariance was used to examine the location and types of school associated with PSSFC variables in each level. The combined method was found to have acceptable reliability and good interpretability, differentiating PSSFC into five gradient levels. The characteristics of PSSFC differ by the location and school type of individual school. Our findings are conducive to the regionalized and personalized intervention and promotion on the children’s physical activity (PA) upon the practical situation of particular schools.
Keywords
school sports facility; assessment; t-sne; fuzzy c mean; unsupervised learning
Subject
Social Sciences, Tourism, Leisure, Sport and Hospitality
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
