A study on the design of residential areas incorporating physical education functions - enhancing community health education

Abstract

Traditional residential area design lacks the integration of physical education functions, making it difficult to meet the health needs of residents. With the enhancement of national health awareness, the integration of physical education functions into residential area design has become an important way to improve community health education. This paper proposes a residential layout optimization design method that integrates physical education functions, which aims to improve the level of community health education. The study extracts the spatial distribution characteristics of the main functional areas of residential areas through deep neural networks, establishes a residential area layout optimization design model, and solves the problem by using the improved adaptive variational particle swarm algorithm (IAMPSO). The experiments are carried out in nine areas of a city and verified under five planning scenarios, and the spatial compactness of urban residential land planned by the research method reaches more than 0.92, which is much higher than that of the planning method based on geospatial information system (0.73) and the planning method based on big data (0.77). Based on the data from the China Household Tracking Survey, the current status of the health level of Chinese residents was analyzed, and it was found that the health score of residents in the eastern region (5.52) was higher than that in the central (5.44) and western regions (5.26), and the health level of urban residents (5.6859) was significantly higher than that of rural residents (5.3143). The study shows that optimizing the layout of residential areas through the IAMPSO algorithm can effectively improve the land space utilization rate, and the integration of physical education functions can improve the level of health education in the community, which provides new ideas for the design of residential areas.