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遥感技术与应用  2021, Vol. 36 Issue (6): 1446-1456    DOI: 10.11873/j.issn.1004-0323.2021.6.1446
地理信息与遥感大数据     
基于多源地理大数据的城市空间结构研究
梁立锋(),谭本华,马咏珊,陈漾漾,刘秀娟()
岭南师范学院 地理科学学院,广东 湛江 524048
Research on Urban Spatial Structure based on Multi-Source Big Data
Lifeng Liang(),Benhua Tan,Yongshan Ma,Yangyang Chen,Xiujuan Liu()
College of Geographic Sciences,Lingnan Normal University,Zhanjiang 524048,China
 全文: PDF(7714 KB)   HTML
摘要:

以东莞市主城区为研究区,利用夜光遥感数据、POI数据与手机定位强度数据,采用核密度分析、数据格网化与双因素组合制图方法,获得3种数据空间耦合相同或相异区域,并比较其与城市空间结构的关系。研究表明,3种数据的空间分布趋势总体一致,部分区域出现空间耦合相异:①受交通、功能区与夜光遥感数据的“溢出”效应等因素影响,城市道路、商业区以及公共服务业集中区域,夜光遥感与POI耦合相异;在物流工业园、学校以及公园区域,夜光遥感与手机定位强度数据耦合相异。②职住地空间分布的差异,造成POI数据与手机定位强度数据空间耦合相异。公共服务与商务区的基础设施完善,POI密度高于手机定位强度数据密度;居住区人口集中分布,但基础设施建设相对薄弱,POI密度低于手机定位强度数据密度。

关键词: 夜光遥感POI手机定位强度数据空间耦合关系城市空间结构    
Abstract:

Combining with the kernel density analysis, the method for transforming data into regular grids and mapping double factors, we take the main urban area of Dongguan as study area, and use the nighttime light data, POI data and Lacation-based data in 2019 as data source, to obtain the same or different spatial coupling relationship of these three data and compare their relationship with the urban spatial structure. Research demonstrates that the overall spatial distribution trends of these three data types are generally consistent., but there are different couplings in partial areas: (1) Influenced by the factors such as traffic, functional areas, and “spillover” effect of nighttime light data, the coupling of nighttime light data and POI data are different in the roads, commercial districts, and public service areas in the urban scopes; the coupling of nighttime light data and Lacation-based data are different in logistics industrial parks, schools, and suburban parks. (2) The differences of the spatial distribution of job-housing places, causing the different spatial coupling of POI data and Lacation-based data. The public services and business districts where have complete infrastructure, the density of POI data is higher than Lacation-based data; the infrastructure construction of the residential areas is relatively weak, but the population distribution is concentrated, making the POI density lower than the Lacation-based data. The integration of these three types of spatial data can effectively reflect the spatial structure of the cities and the existing problems.

Key words: Nighttime light data    POI (Point of Interest) data    Lacation-based data    Spatial coupling    Urban spatial structure
收稿日期: 2020-11-06 出版日期: 2022-01-26
ZTFLH:  TP79  
基金资助: 广东省哲学社会科学规划项目(GD20XYJ04);广东省科技创新战略专项资金(pdjh2021b0315);广东省教育厅基金项目(2019 KTSCX089);广东省大学生创新创业训练计划项目(S202110579021);岭南师范学院人才专项(ZL1936);岭南师范学院校级教改课题联合资助
通讯作者: 刘秀娟     E-mail: 121436068@qq.com;544022065@qq.com
作者简介: 梁立锋(1978-),男,吉林长春人,博士,讲师,主要从事地理大数据分析与数据挖掘研究。E?mail: 121436068@qq.com
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引用本文:

梁立锋,谭本华,马咏珊,陈漾漾,刘秀娟. 基于多源地理大数据的城市空间结构研究[J]. 遥感技术与应用, 2021, 36(6): 1446-1456.

Lifeng Liang,Benhua Tan,Yongshan Ma,Yangyang Chen,Xiujuan Liu. Research on Urban Spatial Structure based on Multi-Source Big Data. Remote Sensing Technology and Application, 2021, 36(6): 1446-1456.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.6.1446        http://www.rsta.ac.cn/CN/Y2021/V36/I6/1446

图1  研究区概况图
房地产

公共

管理

交通

仓储

教育

金融

保险

居民

生活

科学

研究

批发

零售

水利

环境

文体

娱乐

信息

传输

医疗

卫生

住宿

餐饮

租赁

商务

南城1 3789781541 0688066 3181926 311378832806295 120584
莞城922662804682492 2752052 274195317512851 839489
东城1 7581 3163261 06656110 61222610 603419841988867 641602
万江7829933264642156 7411246 740278327603493 611312
表1  POI数据分类类别
图2  蜂窝图
POI数据密度等级夜间灯光亮度等级人口活动强度等级

<10

10~27

>27

<14

14~27

>27

<10

10~26

>26

表2  POI密度、夜间遥感灯光值和人口活动强度分级
图3  耦合关系图
图4  夜光遥感与POI非耦合区域图
图5  夜光遥感与手机定位强度数据非耦合区域
图6  POI与手机定位强度数据非耦合区域
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