地理科学进展 ›› 2020, Vol. 39 ›› Issue (5): 738-750.doi: 10.18306/dlkxjz.2020.05.004

• 研究论文 • 上一篇    下一篇

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

陈洪星1,2, 杨德刚1,*(), 李江月1,2, 武荣伟1,2, 霍金炜1   

  1. 1. 中国科学院新疆生态与地理研究所,荒漠与绿洲生态国家重点实验室,乌鲁木齐 830011
    2. 中国科学院大学,北京100049
  • 收稿日期:2019-04-26 修回日期:2019-07-26 出版日期:2020-05-28 发布日期:2020-07-28
  • 通讯作者: 杨德刚 E-mail:dgyang@ms.xjb.ac.cn
  • 作者简介:陈洪星(1994— ),男,汉族,山东德州人,硕士生,主要从事城市地理研究。E-mail:chenhongxing17@mails.ucas.ac.cn
  • 基金资助:
    资源与环境信息系统国家重点实验室开放基金(201619)

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

CHEN Hongxing1,2, YANG Degang1,*(), LI Jiangyue1,2, WU Rongwei1,2, HUO Jinwei1   

  1. 1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-04-26 Revised:2019-07-26 Online:2020-05-28 Published:2020-07-28
  • Contact: YANG Degang E-mail:dgyang@ms.xjb.ac.cn
  • Supported by:
    2016 Open Foundation of State Key Laboratory of Resources and Environmental Information System(201619)

摘要:

商业空间结构是城市经济活动的重要载体,识别商业中心和商业热点区以及探究其影响因素对于商业资源优化配置显得尤为必要,进而指导城市有序发展。论文以乌鲁木齐主城区为例,利用开放平台大数据兴趣点(point of interest, POI),采用地理学空间统计方法定量识别商业中心和商业热点区,对商业分布和空间集聚特征进行分类和解读,并利用地理探测器方法探寻其影响因素。主要结论如下:① 乌鲁木齐市商业高值区分布在吐乌大高速—和平渠沿线地带,大型商业中心主要有南湖商圈、中山路商圈、友好商圈、会展商圈、米东商圈、铁路局商圈。② 商业热点区呈现“T型”双轴分布,北部新城商业地带与南部传统商业地带共同构成乌鲁木齐市最具活力的商业地带;6类商业热点区的分布可归纳为3种类型,商务和金融服务类为单一点状型,住宿和餐饮服务类为带状延伸型,生活与购物服务类为带状双核型。③地价、集聚效应、路网密度等是影响商业宏观分布的主要因素,其次为人口密度和中心可达性,自然因素如高程、地形起伏度等对商业布局影响有限;各因素对不同类型商业业态的影响程度各异,如人口密度、路网密度对购物类影响较大,中心可达性和地价对于商务、金融类影响较大;就各业态类型网点间的关系而言,商务和金融类协同作用强,餐饮与购物类协同效应较强,共同影响城市商业空间。

关键词: 大数据, 商业中心, 热点区, 地理探测器, 乌鲁木齐主城区

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