广州市零售业态空间分异影响因素识别与驱动力研究

doi:10.12082/dqxxkx.2020.190353

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (6): 1228-1239.doi: 10.12082/dqxxkx.2020.190353

广州市零售业态空间分异影响因素识别与驱动力研究

吴康敏^1,^2,³, 王洋², 叶玉瑶^2,^*(), 张虹鸥²

1. 中国科学院广州地球化学研究所,广州 510640
2. 广州地理研究所,广州 510070
3. 中国科学院大学,北京 100049

收稿日期:2019-07-04 修回日期:2020-02-21 出版日期:2020-06-25 发布日期:2020-08-25
通讯作者: 叶玉瑶 E-mail:yeyuyao@gdas.ac.cn
作者简介:吴康敏（1991— ）,男,广东汕头人,博士生,研究方向为城市地理与创新地理。E-mail: kangmwu@163.com
基金资助:
国家自然科学基金项目(41671128);国家自然科学基金项目(41671130);国家自然科学基金项目(41871150);广东省科学院实施创新驱动发展能力建设专项(2016GDASRC-0101)

A Study on the Influencing Factors and Driving Forces of Spatial Differentiation of Retail Formats in Guangzhou

WU Kangmin^1,^2,³, WANG Yang², YE Yuyao^2,^*(), ZHANG Hongou²

1. Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
2. Guangzhou Institute of Geography, Guangzhou 510070, China
3. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2019-07-04 Revised:2020-02-21 Online:2020-06-25 Published:2020-08-25
Contact: YE Yuyao E-mail:yeyuyao@gdas.ac.cn
Supported by:
National Natural Science Foundation of China(41671128);National Natural Science Foundation of China(41671130);National Natural Science Foundation of China(41871150);GDAS' Project of Science and Technology Development(2016GDASRC-0101)

摘要/Abstract

摘要：

以广州市47 026个零售业网点为基本数据,通过梳理零售业空间分异的机制,构建包含人口密度、商务条件、公共交通便利性、业态丰富度与租金条件5个影响因子的零售业态空间分异影响因素评价体系,通过信息熵、核密度函数与空间回归模型分析零售业态的空间分异影响因素,对比不同城市圈层区位与不同零售业态集聚分异的因素差异。结果表明：① 需求、区位、竞争与成本构成了广州市零售业态空间分异的主要驱动力,同时,零售的景观分异也由于业态异质性与城市的空间异质性而存在驱动力分异;② 5个影响因素强度格局圈层差异明显,城市内圈层人口集聚度高,具备更好的公共交通便利性条件、商务条件与业态丰富度,同时也承受更高的地租;③ 人口密度是零售空间分异的核心要素,公共交通便利性条件、商务条件与业态丰富度对零售的集聚也有正向驱动作用,租金的影响较弱;不同圈层区位的零售空间分布与不同类型业态的空间分异的主要影响因素各不相同。

关键词: 零售业态, POI, 集聚分异, 影响因素, 驱动力, 广州市

Abstract:

Exploration of the spatial differentiation of the retail industry based on large-scale geospatial data is of great significance for urban development. In the recent years, POI data has become an important data source for studying urban dynamics. POI data abstracts retail stores as a point on the map, and the data are of wide coverage and high fineness. These advantages make the POI data an ideal dataset for micro- analysis of urban retail commercial structure and their spatial distribution mechanism. Based on the data of 47 026 retail outlets in Guangzhou, we explored the driving mechanism of the spatial differentiation of the retail formats. By building an indicator system, we investigated the factors potentially affecting the spatial differentiation of the retail industry, which include population density, business conditions, public transportation convenience, format richness, and rent. Based on information entropy, kernel density function, and spatial regression, we analyzed the main influencing factors of the retail differentiation. Further, we divided the retail outlets by different urban areas and different retail formats, and conducted spatial regression analysis based on the same influencing factors to compare the main driving factors of different retail formats. Results shows that: (1) Demand, location, competition, and cost were the main driving force of the spatial differentiation of retail industry. Furthermore, because of the heterogeneity of the retail formats and the spatial heterogeneity of the city, there was also heterogeneity in the driving mechanism of the spatial differentiation in the retail industry. (2) There were significant spatial differences of the influencing factors. The inner circle of the city had higher population density, better accessibility, better business conditions, and higher format richness, and also higher land rent. There was a significant spatial differentiation between the old city area and the suburbs. (3) Compared with the traditional OLS regression method, the spatial regression method revealed the spatial distribution mechanism of the retail industry more accurately. The spatial error model revealed significant heterogeneity in the factors that influence the spatial agglomeration of the retail industry. Population density was the core driving force of retail spatial differentiation. Public transport convenience, business conditions, and format richness also had a positive effect on retail agglomeration, while the impact of rent was weak. The main driving factors of different retail formats and outlets located in different urban circle were different. Population density was the core factor, while the influence of other factors showed significant differences.

Key words: retail format, POI, spatial differentiation, influencing factor, driving force, Guangzhou

吴康敏, 王洋, 叶玉瑶, 张虹鸥. 广州市零售业态空间分异影响因素识别与驱动力研究[J]. 地球信息科学学报, 2020, 22(6): 1228-1239.DOI:10.12082/dqxxkx.2020.190353

WU Kangmin, WANG Yang, YE Yuyao, ZHANG Hongou. A Study on the Influencing Factors and Driving Forces of Spatial Differentiation of Retail Formats in Guangzhou[J]. Journal of Geo-information Science, 2020, 22(6): 1228-1239.DOI:10.12082/dqxxkx.2020.190353

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评价因子	代表性指标	指标的计算过程	预期作用方向
人口密度	常住人口密度	常住人口/社区面积;其中社区面积中剔除了水系以及绿地等非建设用地面积	正向
公共交通便利性	地铁站点可达性	以各地铁站点为基础数据,以社区为基本评价单元,采用缓冲区赋分评价法得到公共交通便利性;位于地铁站点200 m（直线距离）范围内（9分）;位于地铁站点200~400 m范围内（7分）;位于地铁站点400~800 m范围内（5分）;位于地铁站点800~1500 m范围内（3分）;位于地铁站点1500 m范围外（1分）	正向
商务条件	办公点集聚空间评价	以300 m×300 m为基本研究单元,以研究区商务办公大厦、事业单位POI点等为基础数据计算办公点集聚核密度值	正向
业态丰富度	零售熵值	以300 m×300 m为基本研究单元,计算格网单元的零售网点熵值	正向
地价租金	二手房售价	以社区二手房均价与楼栋数为基础数据,以小区楼栋数占社区楼栋总数为小区均价权重,计算小区加权均价,计算社区中所有小区的加权均价的均值	负向

OLS					SLM				SEM
	系数	标准差	z统计值	p	系数	标准差	z统计值	p	系数	标准差	z统计值	p
常数项	144.8770^***	1.4166	102.2690	0.0000	12.4940^***	0.3013	41.4610	0.0000	29.3667^***	0.5876	49.9783	0.0000
人口密度	657.4760^***	17.9660	36.5956	0.0000	38.4481^***	3.3612	11.4386	0.0000	69.5201^***	5.5458	12.5357	0.0000
商务条件	5.5349^***	0.0356	155.5820	0.0000	0.3354^***	0.0082	40.7818	0.0000	4.3798^***	0.0380	115.2720	0.0000
公共交通便利性	5.1620^***	0.2582	19.9944	0.0000	0.2228^***	0.0482	4.6233	0.0000	1.8038^***	0.1225	14.7215	0.0000
业态丰度	-38.1272^***	0.7069	-53.9336	0.0000	-3.8552^***	0.1363	-28.2919	0.0000	7.3926^***	0.2717	27.2078	0.0000
租金条件	-2.8278^***	0.4429	-6.3848	0.0000	-0.1055	0.0824	-1.2802	0.2005	0.3857^**	0.1632	2.3638	0.0181
R-squared: 0.5155 似然估计:-286 235; AIC: 572 482					R-squared: 0.9832 似然估计:-213 619; AIC: 427 252				R-squared: 0.9903 似然估计:-202 927; AIC: 405865

核心圈层					内圈层				外圈层
	系数	标准差	z统计值	p	系数	标准差	z统计值	p	系数	标准差	z统计值	p
常数项	106.8000^***	2.4841	42.9930	0.0000	42.6064^***	1.2464	34.1839	0.0000	19.3990^***	0.5358	36.2056	0.0000
人口密度	28.1051^***	7.5107	3.7420	0.0002	59.0739^***	10.8797	5.4298	0.0000	129.6830^***	12.7430	10.1767	0.0000
商务条件	2.8623^***	0.0613	46.6851	0.0000	5.6570^***	0.0847	66.8221	0.0000	3.4267^***	0.0751	45.6056	0.0000
公共交通便利性	-0.0587	0.2147	-0.2732	0.7847	0.7091^***	0.2087	3.3971	0.0007	0.8652^***	0.1832	4.7223	0.0000
业态丰度	2.2551^***	0.5978	3.7726	0.0002	8.2634^***	0.4151	19.9054	0.0000	7.7564^***	0.3042	25.4949	0.0000
租金条件	-2.3389^***	0.2440	-9.5844	0.0000	0.2213	0.3276	0.6756	0.4993	-0.1765	0.2628	-0.6715	0.5019
LAMBDA	0.9849^***	0.0006	1647.3400	0.0000	0.9768^***	0.0006	1651.7700	0.0000	0.9618^***	0.0010	993.3640	0.0000
R-squared：0.9897; AIC：133 755					R-squared：0.9835; AIC：182 900				R-squared： 0.9829; AIC：82 114

	便利店				超市				购物商场
	系数	标准差	t统计值	p	系数	标准差	t统计值	p	系数	标准差	t统计值	p
常数项	1.5604^***	0.2959	5.2744	0.0000	2.4298^***	0.1607	15.1206	0.0000	8.1382^***	0.1967	41.3738	0.0000
人口密度	55.6831^***	3.0559	18.2216	0.0000	11.0070^***	1.8800	5.8548	0.0000	6.9395^*	3.8914	1.7833	0.0745
商务条件	0.7780^***	0.0100	77.8535	0.0000	0.0137^***	0.0048	2.8385	0.0045	0.0159	0.0108	1.4705	0.1414
公共交通便利性	1.0040^***	0.0554	18.1375	0.0000	-0.0003	0.0281	-0.0091	0.9927	-0.0795	0.0522	-1.5222	0.1280
业态丰度	2.6217^***	0.1440	18.2006	0.0000	1.0691^***	0.0875	12.2247	0.0000	1.6330^***	0.1233	13.2476	0.0000
租金条件	0.1598^*	0.0824	1.9384	0.0526	-0.1993^***	0.0463	-4.3016	0.0000	-0.8529^***	0.0878	-9.7107	0.0000
LAMBDA	0.7867^***	0.0047	165.9330	0.0000	0.5383^***	0.0237	22.7231	0.0000	0.7342^***	0.0071	102.8850	0.0000
	R-squared:0.9303; AICs: 38 557				R-squared: 0.3597; AICs: 6059				R-squared: 0.6936; AICs:22 347
	专业店				食杂店
	系数	标准差	t统计值	p	系数	标准差	t统计值	p
常数项	37.6027^***	0.7209	52.1630	0.0000	-2.0150^***	0.4382	-4.5980	0.0000
人口密度	16.9848^***	3.3555	5.0618	0.0000	51.7967^***	3.8062	13.6084	0.0000
商务条件	0.0280^***	0.0072	3.9022	0.0001	0.5583^***	0.0113	49.5961	0.0000
公共交通便利性	5.9755^***	0.1776	33.6397	0.0000	0.8755^***	0.0725	12.0809	0.0000
业态丰度	0.5161^***	0.1643	3.1422	0.0017	2.2431^***	0.2212	10.1418	0.0000
租金条件	0.2104^**	0.0858	2.4520	0.0142	0.6456^***	0.1081	5.9716	0.0000
LAMBDA	0.9784	0.0004	2474.7900	0.0000	0.8171	0.0050	162.7250	0.0000
R-squared: 0.9861; AICs: 279 199				R-squared: 0.9272; AICs: 25 531

广州市零售业态空间分异影响因素识别与驱动力研究

A Study on the Influencing Factors and Driving Forces of Spatial Differentiation of Retail Formats in Guangzhou

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