【摘要】 随着信息通讯技术的飞速发展,实体和虚拟商业空间在不同程度上发生变更与重构,以传统零售业为主的商业区空心化已有端倪。以北京市城六区为例,基于2012~2015年微博签到数据,辅以问卷调查、实地测量进行纠偏,使用核密度分析提取商业热点地区,使用因子分析计算城六区30个典型商业区的"规模—热度"得分,结合K-means聚类衡量其"空心化"程度并分类。结果显示:(1)商业热度整体呈现老城区缩减、郊区扩展的"扁平化"趋势,并导致若干大型商业空间的"空心化";(2)规模等级高的商业热点地区整体离散分布,规划的若干市级商业中心出现商业设施面积广阔、商业热度低的"空心化"现象;(3)30个典型商业区中,工体、西单等六个商业区发育较完善,暂未出现空心化,西直门、望京商业区有轻度空心化趋势,大钟寺、鲁谷等其余22个商业区空心化显著。通过对城市商业空间的空心化识别,以期为"城市双修"规划引导下的商业区活力再造提供参考。更多还原

【Abstract】 Since 1990 s,information and communication technologies( ICTs) have experienced a rapid development over the world.Thus information and communication devices have almost penetrated into each aspect of people’s daily life,becoming the necessities in the modern world. Under this advancement,spatial structure both in entity zones and virtual space have been markedly changed and reconfigured. Especially in the impact of online shopping,the problems such as usage status quo deviating from building scale and commercial vitality deviating from planning objectives have occurred in more and more urban commercial districts,which still can’t be systematically explained by the empirical studies due to the lack of data. But with the aid of Location-based service( LBS),ICTs helps to record people’ s real space-time activity,which has been one of the main sources of big data in recent research. Based on this understanding,this article tries to analyze the hollowing phenomenon in commercial space,using the LBS checking-in data from Sina micro-blog,as well as sampled data from questionnaires and field observations aimed to extend the representativeness of checking-in data. Meanwhile,a Kernel Density method is used to distinguish and extract commercial hot spots. And K-means clustering is used to measure the degree of hollowing out of 30 commercial districts as well as classify them.更多还原