空间同位模式支持下城市服务业关联发现及特征分析

doi:10.12082/dqxxkx.2021.200408

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.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（1981— ）,男,湖北随州人,博士,教授,主要从事空间关系理论;GIS、RS应用与开发。 E-mail: ltaochina@foxmail.com
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. 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/Abstract

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

胡添, 刘涛, 杜萍, 余贝贝, 张萌生. 空间同位模式支持下城市服务业关联发现及特征分析[J]. 地球信息科学学报, 2021, 23(6): 969-978.DOI:10.12082/dqxxkx.2021.200408

HU Tian, LIU Tao, DU Ping, YU Beibei, ZHANG Mengsheng. Correlation Discovery and Feature Analysis of Urban Service Industry Supported by Spatial Co-location Model[J]. Journal of Geo-information Science, 2021, 23(6): 969-978.DOI:10.12082/dqxxkx.2021.200408

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一级分类	二级分类
餐饮住宿	宾馆酒店、茶馆、糕饼店、咖啡馆、快餐厅、奶茶店、外国餐厅、中餐厅
房地产业	楼宇、住宅区
公共管理	工商税务机构、公检法机构
金融保险	保险公司、财务公司、银行、证券公司
居民服务	便利店、超级市场、加油站、美容美发店、商场、药店、综合市场
科学教育	大学、科研机构、小学、幼儿园、中学
体育休闲	休闲场所、娱乐场所、运动场馆
信息科技	电信公司、网络科技
医疗健康	医院、诊所
运输存储	物流仓储场地、物流速递

城市	二阶空间同位规则	PI值
成都	{便利店}<=>{美容美发店}	0.89
	{住宅区}<=>{便利店}	0.88
	{诊所}<=>{便利店}	0.87
	{茶馆}<=>{住宅区}	0.84
	{宾馆酒店}<=>{快餐厅}	0.82
兰州	{大学}<=>{超级市场}	0.76
	{中餐厅}<=>{快餐厅}	0.74
	{药店}<=>{美容美发店}	0.74
	{小学}<=>{综合市场}	0.73
	{中餐厅}<=>{娱乐场所}	0.72
郑州	{中餐厅}<=>{快餐厅}	0.81
	{中餐厅}<=>{综合市场}	0.75
	{财务公司}<=>{证券公司}	0.75
	{宾馆酒店}<=>{快餐厅}	0.73
	{中餐厅}<=>{奶茶店}	0.72
沈阳	{快餐厅}<=>{便利店}	0.85
	{便利店}<=>{美容美发店}	0.83
	{娱乐场所}<=>{便利店}	0.81
	{便利店}<=>{药店}	0.81
	{便利店}<=>{住宅区}	0.80
上海	{中餐厅}<=>{快餐厅}	0.72
	{快餐厅}<=>{大学}	0.71
	{外国餐厅}<=>{休闲场所}	0.70
	{中餐厅}<=>{大学}	0.70
	{中餐厅}<=>{楼宇}	0.70
深圳	{宾馆酒店}<=>{快餐厅}	0.83
	{便利店}<=>{药店}	0.82
	{快餐厅}<=> 便利店}	0.81
	{奶茶店}<=>{快餐厅}	0.80
	{中餐厅}<=>{宾馆酒店}	0.80

空间同位模式支持下城市服务业关联发现及特征分析

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

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