广州市零售商业中心消费活动时变模式及其影响因素

doi:10.13249/j.cnki.sgs.2018.01.003

地理科学 ›› 2018, Vol. 38 ›› Issue (1): 20-30.doi: 10.13249/j.cnki.sgs.2018.01.003

广州市零售商业中心消费活动时变模式及其影响因素

傅辰昊¹(), 周素红^2,³(), 闫小培², 柳林^2,^3,⁴

1.合肥工业大学建筑与艺术学院,安徽合肥 230601
2.中山大学地理科学与规划学院,广东广州 510275
3.广东省城市化与地理环境空间模拟重点实验室,广东广州 510275
4.辛辛那提大学地理系,美国辛辛那提 OH 45221-0131

收稿日期:2017-04-18 修回日期:2017-07-17 出版日期:2018-01-10 发布日期:2018-01-10
作者简介:
作者简介：傅辰昊（1988-）,男,安徽蚌埠人,博士,讲师,主要从事城市地理学、商业地理学、时空间行为和城乡规划研究。E-mail:fch1988822@126.com
基金资助:
国家自然科学基金优秀青年基金项目（41522104）;国家自然科学基金重点项目(41531178);广东省自然科学基金项目（2017A030313228,2014A030312040）资助

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

Chenhao Fu¹(), Suhong Zhou^2,³(), Xiaopei Yan², Lin Liu^2,^3,⁴

1.College of Architecture and Art, Hefei University of Technology, Hefei 230601, Anhui, China
2.School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, Guangdong, China
3.Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, Guangdong, China
4.Department of Geography, University of Cincinnati, Cincinnati, Hamilton OH 45221-0131, USA

Received:2017-04-18 Revised:2017-07-17 Online:2018-01-10 Published:2018-01-10
Supported by:
Excellent Young Scholars Program of National Natural Science Foundation of China (41522104);Key Program of National Natural Science Foundation of China (41531178);Guangdong Natural Science Foundation (2017A030313228, 2014A030312040)

摘要/Abstract

摘要：

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。

关键词: 零售商业中心, 消费活动, 多项Logistic回归, 广州

Abstract:

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.

Key words: retailing center, consuming activity, multiple logistic regression model, Guangzhou

中图分类号:

F129.9

傅辰昊, 周素红, 闫小培, 柳林. 广州市零售商业中心消费活动时变模式及其影响因素[J]. 地理科学, 2018, 38(1): 20-30.

Chenhao Fu, Suhong Zhou, Xiaopei Yan, Lin Liu. Temporal Variation Patterns and Influencing Factors of Consuming Activity in Retailing Centers: A Case Study of Guangzhou, China[J]. SCIENTIA GEOGRAPHICA SINICA, 2018, 38(1): 20-30.

图/表 5

图1

表1

图2

图3

表2

变量似然比检验和回归结果"

商业中心类型	似然比检验			下午高峰型				傍晚高峰型
商业中心类型	卡方		df	P值	B	P值	Exp(B)	B	P值	Exp(B)
截距	0.000	0	-	-12.170	0.003	-		-10.021	0.002	-
在广州居住时间	8.880	2	0.012	-0.230	0.005***	0.795		-0.156	0.049**	0.856
住在老城、新城区	5.711	2	0.058	-0.651	0.077*	0.522		0.249	0.452	1.283
住在新城、外围区	4.905	2	0.086	0.813	0.048**	2.254		0.407	0.116	1.502
家庭月收入	15.557	2	0.000	-0.584	0.002***	0.558		0.130	0.350	1.139
单目的消费[否=0]	9.870	2	0.007	-1.390	0.005***	0.249		0.058	0.895	1.059
购物[否=0]	10.664	2	0.005	-1.191	0.002***	0.304		-0.226	0.527	0.798
休闲[否=0]	14.566	2	0.001	-1.447	0.000***	0.235		-0.299	0.405	0.741
用餐[否=0]	24.964	2	0.000	-1.569	0.000***	0.208		0.471	0.201	1.602
消费额	5.604	2	0.061	0.000	0.152	1.000		0.000	0.019**	1.000
多目的出行[否=0]	0.000	0	.	0.831	0.003***	2.296		0.209	0.345	1.232
自行车[否=0]	9.814	2	0.007	2.173	0.107	8.784		2.873	0.004***	17.695
摩托/电瓶车[否=0]	5.864	2	0.053	-1.648	0.081*	0.192		0.346	0.703	1.413
公交车[否=0]	5.920	2	0.052	-0.232	0.701	0.793		0.922	0.030**	2.515
地铁[否=0]	4.147	2	0.126	0.087	0.872	1.091		0.820	0.046**	2.269
出租车[否=0]	8.144	2	0.017	-0.901	0.261	0.406		1.692	0.027**	5.428
私家车[否=0]	11.602	2	0.003	-1.605	0.021**	0.201		0.876	0.124	2.402
出行耗时	19.513	2	0.000	-0.003	0.794	0.997		0.034	0.000***	1.035
出行距离1~5 km[否=0]	24.363	2	0.000	-1.649	0.000***	0.192		-0.895	0.001***	0.409
出行距离>5 km[否=0]	31.694	2	0.000	-2.189	0.000***	0.112		-0.924	0.003***	0.397
商业网点密度	83.485	2	0.000	-0.282	0.000***	0.754		0.327	0.000***	1.387
用地混合度	41.724	2	0.000	2.120	0.012**	8.328		-3.073	0.000***	0.046
公交站点密度	59.916	2	0.000	0.041	0.446	1.042		-0.240	0.000***	0.787
地铁站点密度	19.368	2	0.000	-1.333	0.000***	0.264		-0.525	0.021**	0.591
位于老城、新城区	239.958	2	0.000	3.345	0.000***	28.373		7.873	0.000***	2626.398
位于新城、外围区	42.282	2	0.000	17.199	.	29476555		-0.495	0.058*	0.610
到市中心距离	104.205	2	0.000	0.000	0.000***	1.000		0.000	0.000***	1.000

表2

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自变量		变量取值	变量分类
X₁	年龄	岁	连续
X₂	性别	1=男;2=女	二分类
X₃	在广州居住时间	年	连续
X₄	居住地区位	1=老城区;2=新城区;3=外围区	多分类
X₅	就业状况	1=无就业;2=临时工;3=全职	多分类
X₆	家庭月收入	1. 0.5万元以下;2. 0.5~1.0万元;3.1~3.0万元;4.3~5.0万元; 5.5~8.0万元;6.8万元以上	多分类
X₇	是否有人陪伴	0=无;1=有	二分类
X₈	消费结构	1=单目的消费;0=多目的消费	二分类
X₉	消费额	元	连续
X₁₀	消费时长	min	连续
X₁₁	出行路径类型	0=单一消费目的的出行;1=多目的出行	二分类
X₁₂	出行交通方式	1=步行;2=自行车;3=摩托车/电瓶车;4=公交;5=地铁; 6=出租车;7=私家车	多分类
X₁₃	出行耗时	min	连续
X₁₄	消费出行距离	消费者居住地与消费目的地之间的直线距离,单位（km）	连续
X₁₅	商业网点密度	周边500 m范围大型商业中心数量,单位（个）	连续
X₁₆	用地混合度	周边500 m范围居住用地、商业用地与其他用地的混合程度	连续
X₁₇	公交密度	周边500 m范围公交站点数量,单位（个）	连续
X₁₈	地铁密度	周边500 m范围地铁站点数量,单位（个）	连续
X₁₉	商业区位特征	1=老城区,2=新城区,3=外围区	多分类
X₂₀	到市中心距离	到新老CBD的最短距离,单位（km）	连续

广州市零售商业中心消费活动时变模式及其影响因素

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

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