期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
使用点评数据探测城市商业服务设施的发展规律 被引量:17
1
作者 蒋波涛 王艳东 叶信岳 《测绘学报》 EI CSCD 北大核心 2015年第9期1022-1028,共7页
大众点评网提供的商业设施及其满意度评价数据为城市商业设施的时空分布与发展规律研究提供了一个重要的信息源,它们来源于分布在道路两侧的商业设施。根据此特征,本文设计了一种基于道路网约束的反映商业服务设施与交通网络关系的密度... 大众点评网提供的商业设施及其满意度评价数据为城市商业设施的时空分布与发展规律研究提供了一个重要的信息源,它们来源于分布在道路两侧的商业设施。根据此特征,本文设计了一种基于道路网约束的反映商业服务设施与交通网络关系的密度计算方法,对点评数据中蕴含的设施空间分布、设施数量与其满意度之间关系进行了分析。它将商业设施在空间上的二维分布映射至一维的道路网上,更真实地反映了商业服务设施与所处交通环境的影响,揭示了商业服务设施位置、数量及其满意度之间的关系,为城市规划的定量化研究提供了数值依据。 展开更多
关键词 商业服务设施 道路网约束分析 城市规划定量评价 社交网络服务
下载PDF
Exploring Location Pattern of Commercial Stores in Shichahai, Beijing from a Street Centrality Perspective 被引量:3
2
作者 ZHANG Yuyang YANG Bowen +3 位作者 ZHANG Mengcai ZHANG Gong SONG Shanshan QI Ling 《Chinese Geographical Science》 SCIE CSCD 2019年第3期503-516,共14页
The location pattern of different commercial stores in Shichahai, a historic conservation area in Beijing, was investigated from a street centrality perspective. Many previous studies have investigated the relationshi... The location pattern of different commercial stores in Shichahai, a historic conservation area in Beijing, was investigated from a street centrality perspective. Many previous studies have investigated the relationship between street centrality and land use patterns or commercial activities at interurban or intraurban scales. We considered Shichahai in this study to determine if street centrality applied at the street scale and if the street network was the only factor influencing the selection of store location. First, the nearest neighbor index, nearest neighbor hierarchical spatial cluster(NNHSC), and kernel density estimation(KDE) methods were used to provide baseline spatial distributions of commercial stores. Second, urban network analysis(UNA) tools were used to measure the street centrality indices under two conditions, with and without the weighting of cultural relics calculated by a principal component analysis(PCA). Finally, both store locations and centrality values at nodes were transformed to one unit(raster pixel) for a correlation analysis.The results showed that three of the four store types were clustered and had their own hotspots that were mostly located in the eastern and central parts of city blocks. The most momentous findings were determined from the street centrality indices. Among the three store types with correlation coefficients above 0.5, all centrality indices with landmark weighting, except straightness, had higher correlations,with closeness with landmark weighting having the highest correlation, followed by betweenness with landmark weighting. Therefore,we statistically concluded that street centrality could apply at the street scale and that the street network was not the only factor that influenced store location pattern, with landmarks also playing a significant role. The results provide guidance in determining the selection strategy for stores in a historic conservation area. 展开更多
关键词 historic conservation area LOCATION selected behavior COMMERCIALIZATION LANDMARK influence urban network analysis tool street network REVIVAL
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部