基于夜光遥感与POI数据空间耦合关系的南海港口城市空间结构研究
Urban Spatial Structure of Port City in South China Sea Based on Spatial Coupling between Nighttime Light Data and POI
查看参考文献24篇
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文摘
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南海港口城市研究是南海资源环境监测的重要内容,目前对于南海港口城市空间结构特别是港口在城市空间结构中的地位缺乏研究。夜光遥感数据和POI数据均为城市空间结构研究的重要数据源,但对于2种数据的空间耦合关系与集成应用研究存在不足。针对上述问题,本文选取南海港口城市典型代表的三亚市为研究区,以研究区2016年NPP-VIIRS夜光遥感数据和POI数据为数据源,利用叠加分析将夜光遥感数据和POI核密度分析结果数据分别网格化。然后,利用双因素组合制图法对两种数据的空间耦合关系进行可视化,分析空间耦合关系相异区域和城市空间结构的关系,并在此基础上探讨港口在三亚城市结构中的地位。研究表明,三亚市夜光遥感和POI数据的空间分布总体趋势相一致,空间耦合关系相近的区域占比达85.6%;夜光遥感和POI数据空间耦合关系在三亚市内存在一定量的相异区域,如新城区、经济开发区、城市边缘区和乡镇中心等,结合2种数据的特点可以更显著地表征这些区域的城市空间结构特征;三亚市作为重要的南海港口城市,其城市的中心区域与港口密切关联。本研究为港口城市空间结构研究提供了新视角。 |
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其他语种文摘
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Port city in South China Sea, the important transportation hub in South China Sea and the 21st Century Maritime Silk Road, is significant for research on resource and environment monitoring of South China Sea. However, there is a lack of research about urban spatial structure of port city in South China Sea, especially the role of the port in urban spatial structure. Both of nighttime light data and POI (Point of Interest) data are widely used in research on urban spatial structure as data source, but few research focus on the spatial coupling between nighttime light data and POI data, and the integrated application of two data sources. As an example on the spatial coupling between nighttime light data and POI data, we take Sanya City, a typical representative port city in South China Sea, as study area, and use NPP-VIIRS nighttime light data and POI data of study area in 2016 as data source. In addition, we use overlay analysis to transform both data, the nighttime light data and the processed POI data by kernel density method, into regular grids. Then we use the method for mapping double factors to discuss the spatial coupling relationship between both data, and analyze the relationship between areas with different spatial couplings and urban spatial structures, especially the ports. The results show that: (1) The nighttime light data and POI data have strong spatial coupling relationship, which indicates a significant consistency. The global trend of spatial distribution between both data in Sanya City is pretty similar, and 85.6% of total area have same spatial coupling. (2) The regions where spatial coupling between nighttime light data and POI data differs have some significant spatial characteristics of urban structures, such as the large-scale homogeneous regions, rural-urban fringe, suburbs, and township. POI data has few distributions in economic development zone, new urban districts and so on, but much distribution in suburbs, townships and so on. By contrast, nighttime light data can characterize urban construction significantly, but can't characterize the township. (3) Sanya City, as an important port city in South China Sea, shows a strong relationship between its urban centers and ports. All of three urban centers revolve around three main ports and spread from it in a ring or line form. This study provides a new perspective of the research ont urban spatial structure of port cities in South China Sea. |
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来源
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地球信息科学学报
,2018,20(6):854-861 【核心库】
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DOI
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10.12082/dqxxkx.2018.180020
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关键词
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夜光遥感
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POI
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空间耦合关系
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三亚市
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南海港口城市
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城市空间结构
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地址
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1.
南京大学地理与海洋科学学院, 南京, 210023
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中国南海研究协同创新中心, 中国南海研究协同创新中心, 南京, 210023
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1560-8999 |
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学科
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测绘学 |
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基金
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国家社会科学基金重大项目
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江苏省高校哲学社会科学研究重大项目
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文献收藏号
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CSCD:6258651
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参考文献 共
24
共2页
|
|
1.
石伟. 南沙岛礁及周边港口可达性评价模型研究.
地理学报,2014,69(10):1510-1520
|
被引
9
次
|
|
|
|
|
2.
齐庆华. 21世纪海上丝绸之路海洋环境的气候变化与风暴灾害风险探析.
海洋开发与管理,2017,34(5):67-75
|
被引
3
次
|
|
|
|
|
3.
朱国强. 南海周边国家近20年海岸线时空变化分析.
海洋通报,2015,34(5):481-490
|
被引
10
次
|
|
|
|
|
4.
胡文秋. 越南下龙市不同时期土地利用变化特征.
地球信息科学学报,2017,19(4):570-579
|
被引
1
次
|
|
|
|
|
5.
张虎. 南海区域港口国监督合作机制探析.
海南大学学报(人文社会科学版),2014,32(6):59-66
|
被引
1
次
|
|
|
|
|
6.
李德仁. 论夜光遥感数据挖掘.
测绘学报,2015,44(6):591-601
|
被引
101
次
|
|
|
|
|
7.
王利伟. 转型期京津冀城市群空间扩展格局及其动力机制——基于夜间灯光数据方法.
地理学报,2016,71(12):2155-2169
|
被引
37
次
|
|
|
|
|
8.
Xie Y. Updating urban extents with nighttime light imagery by using an object-based thresholding method.
Remote Sensing of Environment,2016,187:1-13
|
被引
6
次
|
|
|
|
|
9.
王旭. 基于夜间灯光和人口密度数据的京津冀GDP空间化对比.
地球信息科学学报,2016,18(7):969-976
|
被引
10
次
|
|
|
|
|
10.
Chen Y. Analyzing parcel-level relationships between urban land expansion and activity changes by integrating landsat and nighttime light data.
Remote Sensing,2017,9(2):164
|
被引
1
次
|
|
|
|
|
11.
李德仁. 基于夜光遥感影像的“一带一路”沿线国家城市发展时空格局分析.
武汉大学学报·信息科学版,2017,42(6):711-720
|
被引
56
次
|
|
|
|
|
12.
吴健生. 中国城市体系等级结构及其空间格局——基于DMSP/OLS夜间灯光数据的实证.
地理学报,2014,69(6):759-770
|
被引
60
次
|
|
|
|
|
13.
许泽宁. 基于电子地图兴趣点的城市建成区边界识别方法.
地理学报,2016,71(6):928-939
|
被引
108
次
|
|
|
|
|
14.
Li M. Revealing the relationship between spatio-temporal distribution of population and urban function with social media data.
Geojournal,2016,81(6):1-17
|
被引
3
次
|
|
|
|
|
15.
Jiang S. Mining point-of-interest data from social networks for urban land use classification and disaggregation.
Computers Environment & Urban Systems,2015,53:36-46
|
被引
24
次
|
|
|
|
|
16.
李国旗. 基于POI的北京物流业区位特征与分异机制.
地理学报,2017,72(6):1091-1103
|
被引
29
次
|
|
|
|
|
17.
李江苏. 基于POI数据的郑东新区服务业空间聚类研究.
地理研究,2018,37(1):145-157
|
被引
35
次
|
|
|
|
|
18.
Yue Y. Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy.
International Journal of Geographical Information Systems,2017,31(4):658-675
|
被引
6
次
|
|
|
|
|
19.
Jin X. Evaluating cities' vitality and identifying ghost cities in China with emerging geographical data.
Cities,2017,63:98-109
|
被引
13
次
|
|
|
|
|
20.
潘思东. 基于夜光遥感和小区POI的住宅发展与经济增长的空间耦合研究.
地球信息科学学报,2017,19(5):646-652
|
被引
11
次
|
|
|
|
|
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