Analysis of urban spatial structure based on spatial coupling between POI and nighttime data: a case study of main urban region in Wuhan

doi:10.13474/j.cnki.11-2246.2020.0217

Abstract

Abstract: Urban points of interest (POI) and luminous remote sensing images can visually reflect the spatial distribution characteristics of urban social and economic entities, and play an important role in the study of urban spatial structure. Firstly, this paper selects the typical city representative of the middle reaches of the Yangtze River-Wuhan as the research area, selects the 2016 POI and NPP/VIIRS luminous remote sensing data of the study area as the basic research data, and uses the GIS analysis tool to carry out the spatial nuclear density analysis of the POI data. Then the POI nuclear density analysis results and NPP/VIIRS luminous remote sensing data are spatially meshed. Finally, the two-factor mapping and grid overlay analysis methods are used to explore the spatial coupling relationship between the two data, and further analysis is carried out the spatial structure of the city. The research shows that the spatial coupling of the POI data and the luminous remote sensing data in Wuhan is better overall, and the spatial coupling is consistent with the proportion of 82.15%. However, there are some differences in the spatial coupling between the two in the Yangtze River, such as the Qiaokou district. The remote sensing data and POI data of Hanyang district are mainly based on the low-medium spatial coupling mode, while the Qingshan district, Wuchang district and Hankou district are dominated by the medium-low spatial coupling mode. As one of the core cities of the central plains urban agglomeration, Wuhan's internal spatial structure is closely related to the development of the Yangtze River economic belt. Through the spatial coupling relationship between POI and luminous remote sensing data, the spatial structure characteristics of Wuhan spatial entity elements can be learn more deeply. This study can provide a new perspective for the study of the internal spatial structure of cities along the Yangtze River.

Key words: nighttime light data, POI, NPP/VIIRS, urban spatial structure, spatial coupling

CLC Number:

CHEN Bin, XU Shangzhao, ZHOU Yangyang, XU Fengliang, WANG Hongzhi. Analysis of urban spatial structure based on spatial coupling between POI and nighttime data: a case study of main urban region in Wuhan[J]. Bulletin of Surveying and Mapping, 2020, 0(7): 70-75.

References

[1] 王芳, 高晓路, 颜秉秋. 基于住宅价格的北京城市空间结构研究[J]. 地理科学进展, 2014, 33(10):1322-1331.
[2] 申庆喜, 李诚固, 刘倩. 基于服务设施布局视角的城市空间结构研究——以长春主城区为例[J]. 经济地理, 2017, 37(3):129-135.
[3] 郭洁, 吕永强, 沈体雁. 基于点模式分析的城市空间结构研究——以北京都市区为例[J].经济地理, 2015, 35(8):68-74,97.
[4] 祝明明, 罗静, 余文昌,等. 城市POI火灾风险评估与消防设施布局优化研究——以武汉市主城区为例[J].地域研究与开发,2018,37(4):86-91.
[5] 谷岩岩,焦利民,董婷,等.基于多源数据的城市功能区识别及相互作用分析[J].武汉大学学报(信息科学版),2018,43(7):1113-1121.
[6] 马学广, 李鲁奇. 中国城市网络化空间联系结构——基于银行网点数据的研究[J]. 地理科学进展, 2017, 36(4):393-403.
[7] 王珺,周均清.武汉城市圈空间结构演变研究[J].湖北大学学报(自然科学版),2007,29(3):316-320.
[8] 刘沁萍, 杨永春, 付冬暇, 等. 基于DMSP_OLS灯光数据的1992~2010年中国城市空间扩张研究[J]. 地理科学, 2014, 34(2):129-136.
[9] 王利伟, 冯长春. 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[J]. 地理学报, 2016, 71(12):2155-2169.
[10] 钟洋, 林爱文. 长江中游城市群空间格局演变及优化研究——基于DMSP/OLS夜间灯光数据的方法[J].水土保持研究, 2018, 25(6):298-305.
[11] 张超, 王春杨, 吕永强, 等. 长江经济带城市体系空间结构——基于夜间灯光数据的研究[J]. 城市发展研究, 2015, 22(3):19-27.
[12] 杨洋, 李雅静, 何春阳, 等. 环渤海地区三大城市群城市规模分布动态比较——基于1992-2012年夜间灯光数据的分析和透视[J]. 经济地理, 2016, 36(4):59-69.
[13] 杨任飞, 罗红霞, 周盛, 等. 夜间灯光数据驱动的成渝城市群空间形成过程重建及分析[J]. 地球信息科学学报, 2017, 19(5):653-661.
[14] 胡云锋, 赵冠华, 张千力. 基于夜间灯光与LUC数据的川渝地区人口空间化研究[J]. 地球信息科学学报, 2018, 20(1):68-78.
[15] 韩向娣, 周艺, 王世新, 等. 基于夜间灯光和土地利用数据的GDP空间化[J]. 遥感技术与应用, 2012, 27(3):396-405.
[16] 李宗光,胡德勇,李吉贺,等. 基于夜间灯光数据的连片特困区GDP估算及其空间化[J]. 国土资源遥感, 2016, 28(2):168-174.
[17] 柴子为, 王帅磊, 乔纪纲. 基于夜间灯光数据的珠三角地区镇级GDP估算[J]. 热带地理, 2015, 35(3):379-385.
[18] 陆晓蕙, 梁勇, 燕琴, 等. 城市开发区土地集约利用评价——以武汉市为例[J]. 测绘通报, 2018(3):71-75,88.
[19] SHI K, YU B, HUANG Y, et al. Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales:a comparison with DMSP-OLS data[J]. Remote Sensing, 2014, 6(2):1705-1724.
[20] 许泽宁, 高晓路. 基于电子地图兴趣点的城市建成区边界识别方法[J]. 地理学报, 2016, 71(6):928-939.
[21] LI M, SHEN Z, HAO X. Revealing the relationship between spatio-temporal distribution of population and urban function with social media data[J]. Geojournal, 2016,81(6):1-17.
[22] JIANG S, ALVES A, RODRIGUES F, et al. Mining point of interest data from social networks for urban land use classification and disaggregation[J]. Computers Environment & Urban Systems, 2015, 53:36-46.
[23] 王法辉. 基于GIS的数量方法与应用[M]. 姜世国, 滕骏华, 译. 北京:商务印书馆, 2009.
[24] 禹文豪, 艾廷华, 杨敏, 等. 利用核密度与空间自相关进行城市设施兴趣点分布热点探测[J]. 武汉大学学报(信息科学版), 2016, 41(2):221-227.
[25] 于丙辰, 陈刚. 基于腾讯区域热力图的庐山核心景区客流研究[J]. 国土与自然资源研究, 2017, 39(2):83-89.