基于百度热力图的中国多中心城市分析
China Polycentric Cities Based on Baidu Heatmap
李 娟
清华大学建筑学院 博士研究生
摘要: 以市民借助互联网的活动为出发点重新定义城市中心，采用百度热力图数据以自下而上的方式识别全国658个城市的城市中心，其中69个城市表现出多中心性。基于识别出的多中心城市，进一步研究中国城市多中心发展的一般规律。依据城市中心的数量，将多中心城市划分为起步型多中心城市、成长型多中心城市以及成熟型多中心城市3类；分析了城市中心面积、城市中心之间的平均距离、城市活动强度等，以此考察中国多中心城市的多中心特征。其中，中国大城市表现出明显的多中心性，而小城市尤其是县级市的中心发育极其滞后；各城市中心面积的差距悬殊，但总体上都有层级化发展的趋势；随着多中心城市由起步到成熟，中心间的沟通距离会逐渐增大，但中心对于城市活力的带动作用也比较明显。回归分析的结果表明，就业人数和人均GDP与城市中心的形成与发展显著相关。最后，从中心培育的重要性、中心网络效率以及中心识别方式3方面给出了建议。
Abstract: This paper redefines urban center based on the activities which are carried out through Internet, and identifies all urban centers of
658 cities utilizing Baidu heatmap. We take the new method of recognizing urban centers as a bottom-up pattern which will assist the
traditional top-down method. Among 658 cities, there are 69 polycentric cities; and we focus on them to explore the general law of
Chinese polycentric cities. All polycentric cities are classified into three categories according to the number of urban centers, which
are primary polycentric city, growing polycentric city, and mature polycentric city. We further analyze areas, average distance and
activity intensity of all polycentric cities on the basis of these three categories. According to our analysis, Chinese big cities perform
significant polycentric city, while development of small cities (especially county-level city) are extremely lagging. Disparities among
all polycentric cities in areas of centers are huge; Generally, they all tend to develop a hierarchical structure. As the polycentric cities
keep developing from primary level to mature level, the communication distance will increase gradually, but the improvement of centers
to city dynamic is also remarkable. At last, the regression analysis indicates that the number of employment and GDP per capita have
significant correlation with the formation and development of urban centers. Accordingly, we provide three suggestions for Chinese
cities regarding to the importance of developing center, the efficiency of centers’ network, and the new method of identifying centers.
关键词：城市空间结构、多中心、百度热力图、人群聚集
Keyword: Urban spatial structure，Polycentric，Baidu heatmap，Human aggregation
中图分类号：中图分类号TU981
文献标识码: 文献标识码A
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