广州市零售商业中心消费活动时变模式及其影响因素

利用2016年在广州典型大型零售商业中心进行的客流量监测和居民消费行为问卷数据,分析基于客流时间变化的广州大型零售商业中心消费活动时变模式,构建多项Logistic回归模型,从消费者社会经济属性、消费活动特征和商业中心建成环境3方面探讨该时变模式的影响因素。结果显示：① 广州不同的大型零售商业中心内消费活动存在明显的时变特征差异,可分成稳定型、下午高峰型、傍晚高峰型和不规则波动型4类时变模式;② 这种时变模式受到消费者社会经济属性、消费活动特征和商业建成环境等变量的影响。其中,消费者社会经济属性和消费活动特征影响有限且影响差异较大,以稳定型为参照组,在广州居住时间、居住区位和家庭月收入等社会经济属性,以及消费结构、出行路径、交通方式和出行距离等消费活动特征能区分稳定型和下午高峰型商业中心,但只有在广州居住时间和出行距离、交通方式3个变量能显著地解释稳定型和傍晚高峰型商业中心的差异;建成环境是影响零售商业中心时变模式的首要因素,商业网点密度、用地混合度、公交与地铁站点密度、商业中心区位特征、到市中心距离等变量均对各个类型商业中心的形成作用显著。期望为城市零售商业中心的分类和评价提供一个新思路,为预测商业中心可能的消费活动时变模式、消费者属性和活动特征提供理论依据,对城市商业规划和商业中心开发运营有一定的现实指导意义。

Temporal Variation Patterns and Influencing Factors of Consuming Activity in Retailing Centers: A Case Study of Guangzhou,China

Paying more attention to the space dimension, the studies on retailing center hierarchy seldom consider the temporal characteristics and patterns of consuming activities in retailing centers, and fail to reveal the influencing mechanism of temporal diversities of consuming activity and its impact on retailing centers deeply. Based on a survey data collected in 39 large retailing centers in Guangzhou and a multiple logistic regression model, this article attempts to explore the temporal patterns of consuming activity in different retail centers and their influencing factors. The results are shown as follows: 1) The time-varying characteristics of consuming activities in different large retail centers are different obviously, and the retailing centers can be divided into four patterns, such as ‘stable’,‘peak in the afternoon’, ‘peak in the evening’ and ‘irregular fluctuation’. 2) Both of the consumers’ social and economic attribute and the characteristics of consuming activities are limited and different effects on different patterns of retailing centers. In the one hand, living time, housing location, family monthly income, consuming structure, trip chain, mode of transportation and travel distance can distinguish the ‘stable’ retail center and the ‘peak in the afternoon’ retailing center. In the other hand, there are only three variables, such as living time, travel distance and mode of transportation, which can significantly explain differences between the ‘stable’ and the ‘peak in the evening’ retailing centers. Built environment is the primary factor forming the patterns of retailing centers. Density of commercial centers, mixed of land use, density of bus stations and subway stations, location and distance to downtown have significant effects on the formation of various patterns of retailing centers. This study provides a new way to classify and evaluate urban retailing centers, and the conclusions can be regarded as the theoretical basis to predict the potential consuming activities time-varying model, consumer attributes and activity characteristics. Last but not least, the conclusions of this article give advice to urban commercial planning and development of commercial center.