大数据视角下的商业中心和热点区分布特征及其影响因素分析——以乌鲁木齐主城区为例

doi:10.18306/dlkxjz.2020.05.004

Abstract

Abstract:

The structure of commercial space is vital to the vitality of cities, therefore it is essential to quantitatively identify and analyze the distribution of different types of commercial sites so as to optimize the configuration of commercial resources and facilitate the orderly development of cities. Taking the main urban area of Urumqi City as the case study area, using 136975 business-related points of interest (POIs) including six types of businesses in 2018 and open street map (OSM) road network, and based on head/tail division rule, this study identified high-density commercial parcels and used kernel density estimation to estimate the core region of business activities. The Getis-Ord G ^* method was used to identify the overall and different types of commercial hot spot areas. Geographic detector analysis was performed to explore the determinants of overall and different types of commercial site distribution in Urumqi, and Pearson correlation coefficient matrix of commercial sites was established to estimate the impact of the combination and coordination of business forms on commercial space. The findings of this study suggest that the key features of high-density commercial parcel distribution are central-peripheral, separated by highways and internal loops; the number of high value parcels from the center to the peripheral area reduces progressively; and the distribution of the six types of commercial sites varies. Commercial zone presents multi-core distribution characteristics, the agglomeration characteristic is apparent in the urban center region, and the northern commercial agglomeration is gradually becoming obvious. There are six main commercial centers including Nanhu, Zhongshan Road, Youhao, Huizhan Center, Midong New Area, and Tieluju. Tuwu Expressway and Wukui Expressway together constitute the two axes of commercial hotspots. Hotspots of the six types of commercial sites can be divided into three spatial structures. Business and finance show a single-center distribution trend; accommodation and food & restraurant are of banded extension type; while services and shopping spots are of banded dual-core type. The primary determinants of the spatial distribution of commercial sites are: land price, agglomeration effect, and road network density. The influence of population and central accessibility is secondary; elevation has no significant effect. In particular, for business and financial services, land price and center accessibility are the main factors affecting the distribution. Accommodation and food & restraurant are affected by road network density. Shopping and services are significantly affected by population density. Business and finance sites, food & restraurant and shopping sites all have strong synergistic effects on the formation of urban commercial space, while others are not significance.

Key words: big data, commercial center, commercial hotspot region, geodetector method, main urban area of Urumqi City

CHEN Hongxing, YANG Degang, LI Jiangyue, WU Rongwei, HUO Jinwei. Distribution characteristics and influencing factors of commercial center and hotspots based on big data: A case of the main urban area of Urumqi City[J].PROGRESS IN GEOGRAPHY, 2020, 39(5): 738-750.

Figures/Tables 11

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References 37

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主类	亚类	占比/%
餐饮服务	糕饼店、外国餐厅、快餐店、冷饮店、甜品店、餐饮相关场所、饮茶馆、咖啡厅、茶艺馆	31.58
购物服务	便民商店、家居建材市场、特色商业街、服装鞋帽皮具店、专卖店、文化用品店、超市、购物相关场所、家电电子卖场、体育用品店、商场、花鸟鱼虫市场、综合市场、特殊买卖场所	38.22
住宿服务	宾馆、酒店、旅馆、招待所、住宿服务相关场所	2.86
金融保险服务	保险公司、财务公司、银行、ATM、证券公司、金融保险服务机构	1.76
商务服务	商务写字楼、商住两用楼宇	1.81
生活服务	旅行社、美容店、摄影冲印店、事务所、售票处、物流速递点、洗衣店、洗浴推拿、中介机构、药店、诊所、驾校、培训机构	23.76

类型	街区单元数量	单元密度均值	头部单元数量	头部单元占比/%
商务服务	4138	0.58	190	4.59
	190	12.62	63	33.15
	63	25.30	24	38.09
	24	37.93	10	41.00
住宿服务	4138	6.50	555	13.41
	555	47.60	173	31.17
	173	68.54	113	65.00
餐饮服务	4138	68.95	680	16.43
	680	397.94	232	34.11
	232	809.86	85	36.48
	85	1299.73	36	42.35
购物服务	4138	83.05	662	16.00
	662	489.71	205	30.96
	205	1091.94	78	38.04
金融服务	4138	4.10	541	13.07
	541	31.03	160	29.57
	160	73.59	52	32.50
	52	134.36	19	36.50
	19	180.22	8	42.10
生活服务	4138	39.96	670	16.19
	670	232.95	223	33.28
	223	471.24	102	45.74

变量属性	变量	探测因子	计算方法
自然因素	高程	X₁	街道单元内各高程栅格数据的均值作为街道的高程属性
	地形起伏度	X₂	街道单元的高程栅格数据的最高和最低值之差
社会因素	人口密度	X₃	街道单元人口数量/街道面积
	路网密度	X₄	街道单元路网长度/街道面积
	中心可达性	X₅	距离市政府所在地的欧氏距离
经济因素	地价	X₆	街道单元土地基准价格
	集聚效应	X₇	距离大型商圈的最近欧氏距离

探测因子	整体	商务服务	住宿服务	餐饮服务	金融服务	生活服务	购物服务
高程X₁	0.03^**	0.03	0.05	0.06	0.01	0.04	0.02
地形起伏度X₂	0.23^*	0.07^*	0.11	0.30	0.04	0.15	0.19
人口密度X₃	0.37^**	0.41	0.38	0.45^*	0.31	0.62^***	0.66^**
路网密度X₄	0.39^**	0.17	0.31	0.29^*	0.08	0.44^**	0.29
中心可达性X₅	0.28^**	0.67^***	0.12	0.16	0.33^***	0.07	0.22
地价X₆	0.50^*	0.53	0.44	0.21	0.55^**	0.21	0.34
集聚效应X₇	0.47^***	0.36	0.09	0.16^**	0.25	0.14	0.44^**

商业业态	餐饮服务	住宿服务	商务服务	购物服务	金融服务	生活服务
餐饮服务	1.000	0.608^***	0.474^***	0.822^***	0.565^***	0.842^***
住宿服务		1.000	0.457^***	0.527^***	0.430^***	0.604^***
商务服务			1.000	0.420^***	0.695^***	0.513^***
购物服务				1.000	0.558^***	0.777^***
金融服务					1.000	0.606^***
生活服务						1.000

Distribution characteristics and influencing factors of commercial center and hotspots based on big data: A case of the main urban area of Urumqi City

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