2004-2008年北京城区商业网点空间分布与集聚特征

Spatial distribution and clustering of commercial network in Beijing during 2004-2008

本文以北京城区内的8个行政区作为研究对象,选取批发和零售业、住宿和餐饮业、居民服务与其他服务业作为具体的商业类别,利用北京第一次、第二次全国经济普查数据,采用核密度(Kernel)、标准差椭圆、Ripley'sK(r)函数相结合的GIS点模式分析方法,对比研究了2004年和2008年北京市商业网点分布与空间集聚特征。研究结果表明:①北京商业网点呈现相对集中分布态势,具有向心性并形成明显的集聚区,集聚中心主要分布在五环内,且在两次普查期间有所改变,商业网点空间偏向性差异明显;②以CBD、金融街、王府井、中关村、亚运村和奥运村等为代表的典型商圈对北京商业网点的布局影响十分显著,商业网点在典型商圈周围分布密度较高,呈现集聚中心状态;③北京商业网点Ripley's K(r)曲线随距离的变化总体呈现"先增后减"态势,其中受居民小区影响较大的居民服务与其他服务业网点两次普查期间变化剧烈,反映了居民由市中心向外扩散的过程。

Internal spatial characteristics of commerce in a city are always one of the research focuses in com- mercial geography. Based on data from the first and second nation-wide economic census in China, we studied the spatial distribution and clustering of commercial networks in Beijing in 2004 and 2008. The data were divid- ed into three parts： wholesale and retail, accommodation and catering industry, and residential services and other services. Commercial networks data included business name, address, industry classification, business type, in- come, staff, location code, and so on. Linking location code to business allowed us to obtain the spatial informa- tion of commercial networks, which is a basic approach of point pattern analysis in GIS. Based on the spatial characteristics of the commercial networks in Beijing, we chose kernel density, standard deviational ellipse and Ripley＇s K（r） function as the research methods and take 8 districts in Beijing as study areas. As widely used point pattern analysis approach for single scale, kernel density and standard deviational ellipse can show the dis- tribution characteristics of commercial networks from microscopic and macroscopic view respectively. Further- more, Ripley＇s K（r） function is a point pattern analysis method based on distance, which is often used to de- scribe multi-scale of spatial clustering phenomenon. Compared to 2004, distribution and clustering of the com- mercial networks have changed significantly in 2008. The findings are as follows. （1） The commercial network of Beijing presents concentrated distribution, and forms obvious concentration area and centrality. The concen- tration center of commercial network is mainly located within the fifth beltway of the city, and the location of concentration center has changed between 2004 and 2008. Moreover, there are significant differences in the spa- tial bias among the commercial networks in Beijing. （2） Typical business areas are mainly distributed in the con- centration areas of the commercial networks. In the result of kernel density, a highly concentrated area is distrib- uted mainly around a typical business area. Examples of typical business areas with great influences on the dis- tribution of commercial networks include CBD, Financial Street, Wangfujing Street, Zhongguancun, Olympic Village and Asian Games Village. （3） Choosing Tian＇anmen Square as the center point, the patterns of spatial clustering of wholesale and retail industry and accommodation and catering industry are similar, showing in- crease first and then decrease. Greatly influenced by the residential areas, residential services and other service industries have changed dramatically between the two censuses. For Ripley＇s K（r） function value in 2008, the concentration of resident services and other services industries has a lower peak value than that in 2004, reflect- ing the diffusion for the networks of resident services and other services industries alongside with relocation of the residents from the city center to outer areas.