王宇渠, 陈忠暖*, 黄晓冰, 林齐根. 地铁流动性与站点商圈商业集聚规模耦合关系研究[J]. 华南师范大学学报(自然科学版), 2015, 47(5): 134-139. doi: 10.6054/j.jscnun.2014.12.035
引用本文: 王宇渠, 陈忠暖*, 黄晓冰, 林齐根. 地铁流动性与站点商圈商业集聚规模耦合关系研究[J]. 华南师范大学学报(自然科学版), 2015, 47(5): 134-139. doi: 10.6054/j.jscnun.2014.12.035
Wang Yuqu, Chen Zhongnuan*, Huang Xiaobing, Lin Qigen. Relationship between Metro Flow and Business Agglomeration Scale on Station Site Business Circle:A Case Study of Guangzhou Based on Data of 〖STHZ〗15 Stations[J]. Journal of South China Normal University (Natural Science Edition), 2015, 47(5): 134-139. doi: 10.6054/j.jscnun.2014.12.035
Citation: Wang Yuqu, Chen Zhongnuan*, Huang Xiaobing, Lin Qigen. Relationship between Metro Flow and Business Agglomeration Scale on Station Site Business Circle:A Case Study of Guangzhou Based on Data of 〖STHZ〗15 Stations[J]. Journal of South China Normal University (Natural Science Edition), 2015, 47(5): 134-139. doi: 10.6054/j.jscnun.2014.12.035

地铁流动性与站点商圈商业集聚规模耦合关系研究

Relationship between Metro Flow and Business Agglomeration Scale on Station Site Business Circle:A Case Study of Guangzhou Based on Data of 〖STHZ〗15 Stations

  • 摘要: 轨道交通在城市内部产生巨量的流动性,而流动与商业之间存在密切关系.分析流动与商业集聚的关系,对未来地铁站点及附近商圈的规划具有重要意义.以广州为例,在15个地铁站点进行实地调查的基础上,通过地铁流动性的产生与转换,对地铁流动性因素进行划分.利用主成分回归,构建商业集聚的回归模型分析不同因子与商业集聚之间的关系.并进一步讨论了地铁流动因素、商业集聚的空间耦合.结果显示:(1)除站点类型、站点开通时间外,文中划分的与地铁流动性因素与站点商业集聚都存在相关关系;其中,客流与站点商业集聚相关性最强.(2)从地铁流动性因素对商业集聚影响的角度看,流动性因素主要通过2个因子(地铁客流因子、地面基础设施因子)影响站点商圈商业集聚规模.(3)地铁站点市场腹地、客流量、发展历史等共同构成了地铁客流因子,为站点商业带来客流.(4)站点街道长度、地面公交等因素构成地面基础设施因子,为客流影响站点商业提供了地面物质基础,这2个因子共同影响了地铁商业规模.构建的主成分回归模型综合考虑了各流动要素对站点商业集聚的影响,提高了拟合精度.

     

    Abstract: Urban rail transit brings dramatic change to the city's dramatic internal flow changes, which has a great impact on the urban commercial space agglomeration and reconstruction, as the development of urban commercial space depends on the flow. Taking Guangzhou for an example, based on the investigation of the site business circle (500 meters area around subway stations) of agglomeration and the variables related to the metro flow as the breakthrough point, the regularity between the commerce agglomeration and causal factors is analyzed in this paper. Through principal component regression, the relationship between commercial scale and metro liquidity factors such as agglomeration district hinterland market, site traffic, ground bus transfer lines is studied. Finally, the space coupling flow factors and subway commercial agglomeration is further discussed. Based on the above-mentioned, using principal component regression analysis method, it extracts two causal factors on metro stations commercial scale agglomeration, including the market capacity and infrastructure in the influencing factors: the market capacity and infrastructure. The result of building regression method shows that metro stations' hinterland market, traffic and development history (the history of development) determine the market capacity, bringing the quasi-consumers to site business circle. Site' street length, ground transportation and other factors constitute a ground infrastructure factors, providing the material basis for the subway commercial. Both of these two factors together determine the metro commercial scale.

     

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