Extraction of hotspot blocks in catering and retail industry and clustering analysis of spatial distribution features
Aiming at the problem of multi-dimensional attribute feature fusion processing of location data in the hot block extraction method of urban catering and retail industry,this paper proposes to introduce non-location attribute feature weight coefficient to perform hotspot block cluster analysis.In addition,a weighted density clustering model is established based on the traditional density cluster analysis method.This paper empirically studies the urban hot spots of some service industries in Wuhan City during the sample period by using Sina Weibo network check-in data as samples,and analyzes the influencing factors of the spatial distribution of urban service industries.The study found that urban catering and retail industry showed a relatively obvious spatial agglomeration on the whole,the regional distribution of urban hot spots was relatively balanced,and the development of the industry was easily affected by the pulling effect of city circle expansion.Compared with the traditional cluster analysis method,the research results verify that the weighted density clustering algorithm has obvious optimization effect on the extraction of urban hot spots,and can improve the high heat of the extracted urban hot spots.
urban hotspot blockspatial feature of urbanlocation dataDBSCAN
万方数据
维普
