Spatial Pattern Characteristics and Optimization of Urban High-quality Leisure Venues
The supply of high-quality leisure places is not only an important indicator of livability in cities,but also an important engine for the stable development of urban economy.The article takes 14231 leisure places listed on Dianping APP in Shanghai as research samples,and uses ArcGIS spatial analysis to reveal the clustering level and spatial distribution pattern of high-quality leisure venues in Shanghai.It constructs the convenience evaluation index system to evaluate the convenience of the spatial pattern of leisure places in Shanghai,and explores the formation factors and mechanisms of its formation have also been d.It's found that:1)High-quality leisure venues in Shanghai generally exhibit a spatial pattern of"multi center agglomeration and axial extension",with high-density agglomeration areas in the center of the main urban areas of Shanghai forming"connected clusters"and"isolated islands",while low-density axial agglomeration areas are formed in other peripheral urban areas.2)The types of leisure venues in Shanghai are unevenly clustered,with different types of leisure venues clustering in descending order:food and catering type,sports and fitness type,cultural and leisure type,shopping mall type,and characteristic park type.There are significant differences in the clustering patterns,with food and catering type and sports and fitness type forming a"block type"clustering,characteristic park type and shopping mall type forming a"multi center type"clustering,and cultural and leisure type forming a"single center type"clustering.3)The convenience level of high-quality leisure venues in Shanghai is relatively low,with significant regional differences.High-convenience areas are concentrated in the central area of Shanghai,showing an overall spatial distribution feature of"high in the middle and low in the surrounding area".Finally,it proposes some targeted strategies for optimizing the spatial pattern of high-quality leisure venues.
万方数据
维普
