地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (6): 969-978.doi: 10.12082/dqxxkx.2021.200408

• 地球信息科学理论与方法 • 上一篇    下一篇

空间同位模式支持下城市服务业关联发现及特征分析

胡添1,2,3(), 刘涛1,2,3,*(), 杜萍1,2,3, 余贝贝1,2,3, 张萌生1,2,3   

  1. 1.兰州交通大学 测绘与地理信息学院,兰州 730070
    2.地理国情监测技术应用国家地方联合工程研究中心,兰州 730070
    3.甘肃省地理国情监测工程实验室, 兰州 730070
  • 收稿日期:2021-07-29 修回日期:2021-11-04 出版日期:2021-06-25 发布日期:2021-08-25
  • 通讯作者: 刘涛
  • 作者简介:胡 添(1995— ),男,吉林敦化人,硕士生,主要从事城市数据挖掘研究。E-mail: 1316898338@qq.com
  • 基金资助:
    国家重点研发计划课题项目(2016YFC0803106);国家自然科学基金项目(41761088);兰州交通大学优秀平台支持(201806)

Correlation Discovery and Feature Analysis of Urban Service Industry Supported by Spatial Co-location Model

HU Tian1,2,3(), LIU Tao1,2,3,*(), DU Ping1,2,3, YU Beibei1,2,3, ZHANG Mengsheng1,2,3   

  1. 1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
    2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
    3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
  • Received:2021-07-29 Revised:2021-11-04 Online:2021-06-25 Published:2021-08-25
  • Contact: LIU Tao
  • Supported by:
    National Key Research and Development Program of China(2016YFC0803106);National Natural Science Foundation of China(41761088);Lanzhou Jiaotong University Excellent Platform (LZJTU EP)(201806)

摘要:

空间同位模式分析是数据挖掘中一种常见的方法,可有效挖掘城市设施在空间位置上的关联特征,进而发现城市设施的分布规律。本文基于POI数据同位模式挖掘用来获取城市服务业空间关联结构:首先,通过邻近实例获取、同位候选模式存储与筛选,得到城市服务业二阶同位模式;然后,据此构造产业空间关联图,得到产业间的关联结构;最后,分别构造了产业空间关联图密度和产业空间关联显著指数,用来衡量城市服务业空间关联的紧密程度和整体关联的显著程度。本文选取成都、兰州、郑州、沈阳、上海与深圳为试验区,实验结果表明:不同城市服务业的空间关联结构存在共性与特殊性,整体上,餐饮、购物等与居民日常生活相关的服务业易与其他服务业产生空间强相关,这几类服务业内部空间集聚明显;成都与沈阳的服务业整体表现空间关联度高且紧密,兰州其次,上海与深圳的服务业则整体表现空间关联较弱,郑州的服务业空间关联较紧密但强度较低。

关键词: 空间同位模式, 数据挖掘, 城市服务业, Voronoi图, 产业空间关联图密度, 产业空间关联显著指数

Abstract:

Spatial co-location mode analysis has been commonly used in data mining, which can be used to characterize the correlation between different urban service facilities and further quantify the distribution pattern of urban service industry. In this paper, a co-location pattern mining method with POI data is proposed to obtain the spatial correlation of urban service industry. Firstly, through the acquisition of neighboring instances, and selection and storage of homology candidate patterns, the second-order homology pattern of urban service industry can be obtained; Secondly, the industrial spatial correlation map is constructed to obtain the correlation structure between industries. Finally, the industrial spatial correlation graph density and spatial correlation significance are constructed to measure the tightness of urban service industry relationship. We select Chengdu, Lanzhou, Zhengzhou, Shenyang, Shanghai, and Shenzhen as experimental areas. The results show that there are both similarities and differences in the spatial correlation of urban service industry in different cities. Generally, service industries such as catering and shopping which are related to daily life have a strong spatial correlation with other service industries. These types of service industry are often spatially clustered. The administrative department has a weak spatial correlation with other service industries and often occupies a separate functional area. Based on the results of the co-location pattern mining for each city, we find that the co-location pattern between teahouses and residential areas is strong in Chengdu, which indicates a unique "tea culture". In Shanghai, foreign restaurants and leisure places show a co-location pattern, which indicates the internationalization characteristic of Shanghai. Both Chengdu and Shenyang show the strongest spatial correlation of service industry which is highly mixed. The spatial correlation of service industry in Lanzhou is moderate. While Shanghai and Shenzhen show the weakest spatial correlation of service industry. These two cities have a high-level economic development and show separated industrial zones. Zhengzhou also has a weak spatial correlation because of its "multi-center, group-like" structure. This paper uses the spatial co-location model to characterize the spatial correlation of the urban service industry, which can be used as references for future urban planning.

Key words: spatial co-location mode, data mining, urban services, voronoi algorithm, industrial spatial correlation graph density, industry spatial association significant index