Big Data Analysis on Spatial Distribution Features of College Students' Physical Literacy

• Jianying Li ^[1] ; Yulian Zhu ^[1] ; Liying Wang ^[1]
  1. [1] Hebei Normal University of Science and Technology

     Hebei Normal University of Science and Technology

     China

     
• Localización: Revista de psicología del deporte, ISSN-e 1988-5636, ISSN 1132-239X, Vol. 31, Nº 2, 2022, págs. 172-180
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • To effectively improve college students' physical literacy, it is required to conduct a thorough examination of the geographical distribution characteristics of their physical literacy. Only a few domestic researchers have examined the system for measuring and evaluating physical literacy, and they have been unable to develop a cohesive study framework. Additionally, little research has been conducted on the spatial distribution characteristics of college students' physical literacy. As a result, this article performs a large-scale analysis of the spatial distribution characteristics of college students' physical literacy. To begin, certain established evaluation indices for college students' physical literacy were enhanced using spatiotemporal data on college students' physical literacy, and an evaluation index system (EIS) for college students' physical literacy was built. Following that, the technique for ranking preferences according to their similarity to the ideal solution (TOPSIS) was used to compare college students' physical literacy, and the assessment flow was described. The analytic hierarchy process (AHP) was used with the entropy value approach to optimise the index weights. Following that, the authors discussed the process of spatial distribution analysis and its application to college students' physical literacy. The standard deviational ellipse was used to determine the spatial distribution direction of college students' physical literacy. The average nearest neighbour was used to assess the degree of concentration or dispersion of spatial points, and the kernel density tool was used to analyse both the global features of the distribution space of college students' physical literacy and the structural elements of the distribution space of each. Finally, distributions of physical literacy among college students in various locations were reported, demonstrating the scientific quality of our analysis approach.