Please wait a minute...
img

官方微信

遥感技术与应用  2021, Vol. 36 Issue (1): 237-246    DOI: 10.11873/j.issn.1004-0323.2021.1.0237
遥感应用     
一种基于POI大数据的城市核心区识别方法
康翔1(),潘剑君2(),朱燕香2,白浩然2,卢晓丽2
1.南京农业大学 公共管理学院,江苏 南京 210095
2.南京农业大学 资源与环境科学学院,江苏 南京 210095
A Method for Identifying the Urban Nuclei based on POI Big Data
Xiang Kang1(),Jianjun Pan2(),Yanxiang Zhu2,Haoran Bai2,Xiaoli Lu2
1.College of Public Administration,Nanjing Agricultural University,Nanjing 210095,China
2.College of Resources and Environmental Sciences,Nanjing Agricultural University,Nanjing 210095,China
 全文: PDF(6106 KB)   HTML
摘要:

城市核心区在城市功能的发挥中扮演着重要的角色,多核心式城市结构已成为一种重要的城市空间模式,但目前对于城市核心区识别研究却较为缺乏,快速而准确地提取城市核心区对于管理者进行精细的管理与规划有重要意义。研究提出一种基于兴趣点大数据(Point of Interest, POI)的城市核心区识别方法,结果发现应用该方法成功识别了案例城市的核心区,提供了其空间范围,对于城市核心区结构也有很好的探测效果。另外,通过相关检验证明了识别结果的合理性与可靠性。与传统研究不同的是,该方法可以提取城市核心区范围,并且方法简洁。研究结果表明:这种基于POI大数据的城市核心区识别方法能准确定位城市核心区位置,为日后城市规划与城市精细管理提供有价值的空间位置参考信息。

关键词: 城市核心区POI大数据城市多核结构城市规划    
Abstract:

Urban nuclei play a crucial role in conducting urban function, polycentric urban structure have become an important urban spatial model. However, there have few studies about the identification of urban nuclei. Rapid and precise extraction of urban nuclei is significative for urban management and planning. Our research introduced a method for identifying urban nuclei based on point of interest big data. Study indicated that our method successfully identified the urban nuclei in the case city, provided their spatial range, and also showed a fine detection effect on the structure of urban nuclei. In addition, the rationality and reliability of the results were checked through related tests. Different from traditional studies, our method can identify the boundary of urban nuclei, which is also convenience and simple. The results indicated that this urban nuclei identification method based on POI big data can accurately locate the location of urban nuclei, which might provide valuable spatial location reference information for urban planning and precise management in the future.

Key words: Urban nuclei    POI big data    Urban polycentric structure    Urban planning
收稿日期: 2019-09-18 出版日期: 2021-04-13
ZTFLH:  TP75  
基金资助: 江苏省高校优势学科建设工程资助项目
通讯作者: 潘剑君     E-mail: Kangxiang9523@163.com;Jpan@njau.edu.com
作者简介: 康翔(1995-),男,陕西宝鸡人,硕士研究生,主要从事城市遥感与GIS应用研究。E?mail:Kangxiang9523@163.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
康翔
潘剑君
朱燕香
白浩然
卢晓丽

引用本文:

康翔,潘剑君,朱燕香,白浩然,卢晓丽. 一种基于POI大数据的城市核心区识别方法[J]. 遥感技术与应用, 2021, 36(1): 237-246.

Xiang Kang,Jianjun Pan,Yanxiang Zhu,Haoran Bai,Xiaoli Lu. A Method for Identifying the Urban Nuclei based on POI Big Data. Remote Sensing Technology and Application, 2021, 36(1): 237-246.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.1.0237        http://www.rsta.ac.cn/CN/Y2021/V36/I1/237

图1  研究区及POI数据分布图
图2  城市核心区识别流程图
POI数据分类描述数量占比/%
购物娱乐类购物广场、商场、超市、便利店等71 26835.44%
餐饮服务类饭店、餐馆、饮食市场、美食城等49 27724.50%
金融服务类银行、ATM、保险公司、证券公司、财务公司等6 5933.28%
日常服务类水电气服务、生活服务等45 73922.75%
公共设施类应急场所、公共厕所、报刊亭等4 5952.29%
交通服务类地铁站、公交站、停车场等23 62111.75%
总计201 093100.00%
表1  城市功能POI数据
图3  等值线树构建、简化与城市核心区域识别
图4  不同搜索半径下餐饮服务核密度结果
图5  核密度集成结果
图6  城市核心区等值线
图7  核心区识别结果及空间分布(背景为Landsat 8真彩色合成影像)
规划核心区识别结果面积/km2所属行政区
市级中心新街口核心区A4.43秦淮区
河西核心区C3.31建邺区
城南核心区B4.20江宁区
市级副中心江北核心区I1.34浦口区
副城中心东山副城中心B4.20江宁区
仙林副城中心E1.26栖霞区
表2  城市主要核心区域
识别区域编号核心区面积/km2所属行政区
H弘阳广场核心区1.70浦口区
D凤凰山公园核心区3.53浦口区
G狮子桥核心区1.03鼓楼区
F夫子庙核心区1.39秦淮区
M板桥核心区1.18雨花台区
J春江路核心区1.03雨花台区
K河定桥核心区1.22江宁区
L百家湖核心区1.02江宁区
表3  城市其他核心区域
  图8部分核心区识别结果、夜间灯光影像、百度热力图比较
1 Chen Z, Yu B, Song W, et al. A New Approach for Detecting Urban Centers and Their Spatial Structure with Nighttime Light Remote Sensing[J]. IEEE Transactions on Geoscience and Remote Sensing,2017,55(11):6305-6319. doi:10.1109/TGRS.2017.2725917.
doi: 10.1109/TGRS.2017.2725917
2 Boarnet M G, Hong A, Santiago-Bartolomei R. Urban Spatial Structure, Employment Subcenters, and Freight Travel[J]. Journal of Transport Geography,2017,60:267-276. doi:10.1016/j.jtrangeo.2017.03.007.
doi: 10.1016/j.jtrangeo.2017.03.007
3 Sun Bindong, Tu Ting, Shi Wei, et al. Test on the Performance of Polycentric Spatial Structure as a Measure of Congestion Reduction in Megacities: The Case Study of Shanghai[J]. Urban Planning Forum, 2013(2):63-69.孙斌栋, 涂婷, 石巍, 等. 特大城市多中心空间结构的交通绩效检验——上海案例研究[J]. 城市规划学刊, 2013(2):63-69.
4 Wei Yaping, Zhao Min. Spatial Structure and Performance of Metropolis: Interpretation and Application of Polycentric Structure[J]. City Planning Review, 2006(4):9-16.韦亚平, 赵民. 都市区空间结构与绩效——多中心网络结构的解释与应用分析[J]. 城市规划, 2006(4):9-16.
5 Yang Ka. Population Distribution and Multicenter Measurement of Great Beijing[J]. China Population, Resources and Environment[J]. 2015,25(2):83-89.杨卡. 大北京人口分布格局与多中心性测度[J]. 中国人口·资源与环境, 2015,25(2):83-89.
6 Ma Xiuxin,Liu Yaolin,Liu Yanfang,et al. Exploring the Evolution of Morphological Polycentricity in Urban China from the Perspective of Temporal Heterogeneity[J]. Geographical Research, 2020,39(4):787-804.
6 马秀馨,刘耀林,刘艳芳,等. 时间异质性视角下对中国城市形态多中心性演化的探究[J]. 地理研究, 2020, 39(4): 787-804.
7 Adolphson M. Estimating a Polycentric Urban Structure. Case Study: Urban Changes in the Stockholm Region 1991~2004[J].Journal of Urban Planning and Development.2009,135(1):19-30. doi:10.1061/(ASCE)0733-9488(2009)135:1(19).
doi: 10.1061/(ASCE)0733-9488(2009)135:1(19
8 Luo Zhendong, Zhu Chasong. Understanding Polycentricity by Configuration, Function and Governance[J]. Urban Planning International, 2008(1):85-88.罗震东, 朱查松. 解读多中心:形态、功能与治理[J]. 国际城市规划, 2008(1):85-88.
9 Giuliano G, Small K A. Subcenters in the Los-angeles Region[J]. Regional Science and Urban Economics, 1991, 21(2): 163-182.doi: 10.1016/0166-0462(91)90032-I.
doi: 10.1016/0166-0462(91)90032-I
10 Guo Danhuai, Zhang Mingke, Jia Nan, et al. Survey of Point-of-Interest Recommendation Research Fused with Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12):1890-1902.
10 郭旦怀,张鸣珂,贾楠,等. 融合深度学习技术的用户兴趣点推荐研究综述[J]. 武汉大学学报(信息科学版), 2020, 45(12): 1890-1902.
11 Hu T, Yang J, Li X, et al. Mapping Urban Land Use by Using Landsat Images and Open Social Data[J]. Remote Sensing, 2016,8:1512. doi: 10.3390/rs8020151.
doi: 10.3390/rs8020151
12 Chi Jiao, Jiao Limin, Dong Ting, et al. Quantitative Identification and Visualization of Urban Functional Area based on POI Data[J]. Journal of Geomatics, 2016,41(2):68-73.
12 池娇, 焦利民, 董婷, 等. 基于POI数据的城市功能区定量识别及其可视化[J]. 测绘地理信息, 2016,41(2):68-73.
13 Xue Bing, Xiao Xiao, Li Jingzhong, et al. POI-based Spatial Correction of the Residences and Retail Industry in Shenyang City[J]. Scientia Geographica Sinica, 2019,39(3):442-449.
13 薛冰, 肖骁, 李京忠, 等. 基于POI大数据的沈阳市住宅与零售业空间关联分析[J]. 地理科学, 2019,39(3):442-449.
14 Cui Zhenzhen, Huang Xiaochun, He Lianna, et al. Study on Urban Convenience Index based on POI Data[J]. Geomatics World, 2016,23(3):27-33.
14 崔真真, 黄晓春, 何莲娜, 等. 基于POI数据的城市生活便利度指数研究[J]. 地理信息世界, 2016,23(3):27-33.
15 Cao Fangjie, Xing Hanfa, Hou Dongyang, et al. Research on Identification and Spatial Patterns of Commercial Centers in Beijing based on POI Data[J]. Geomatics World, 2019,26(1):66-71.
15 曹芳洁, 邢汉发, 侯东阳, 等. 基于POI数据的北京市商业中心识别与空间格局探究[J]. 地理信息世界, 2019,26(1):66-71.
16 Hao Feilong, Wang Shijun, Feng Zhangxian, et al. Spatial Pattern and Its Industrial Distribution of Commercial Space in Changchun based on POI Data[J].Geographical Research,2018,37(2):366-378.
16 浩飞龙, 王士君, 冯章献, 等. 基于POI数据的长春市商业空间格局及行业分布[J]. 地理研究, 2018,37(2):366-378.
17 Zhang Hong,Xu Shan,Gong Enhui. The Calculation of Urban Wasteful Commuting Concerning Real-time Traffic Information——A Case Study of Chengdu[J].Geomatics and Information Science of Wuhan University,2020. doi:10.13203/j.whugis 20190363.
doi: 10.13203/j.whugis 20190363
张红,徐珊,龚恩慧.顾及实时路况的城市浪费性通勤测算——以成都市为例[J]. 武汉大学学报(信息科学版), 2020.
doi: 10.13203/j.whugis20190363网络首发论文
18 Li Qiang, Zheng Xinqi, Chao Yi. Research on Function Identification and Distribution Characteristics of Wuhan Supported by Big Data[J]. Science of Surveying and Mapping, 2020, 45(5):119-125.
18 李强, 郑新奇, 晁怡. 大数据支持的武汉市功能识别与分布特征研究[J]. 测绘科学, 2020, 45(5):119-125.
19 Zhang Ling. Research on POI Classification Standard[J]. Bulletin of Surveying and Mapping, 2012(10):82-84.
19 张玲. POI的分类标准研究[J]. 测绘通报, 2012(10):82-84.
20 Ran Zhao, Zhou Guohua, Wu Jiamin, et al. Study on Spatial Pattern of Consumer Service Industry in Changsha based on POI Data[J]. Word Regional Studies, 2019,28(3):163-172.
20 冉钊,周国华,吴佳敏,等.基于POI数据的长沙市生活性服务业空间格局研究[J]. 世界地理究,2019,28(3):163-172.
21 Liu Ling, Li Gang, Yang Lan, et al. Spatial Distribution Characteristics and Influencing Factors of the Delivery Sites in Shenzhen[J]. Journal of Geo-information Science, 2019,21(8):1240-1253.
21 刘玲, 李钢, 杨兰,等. 深圳市快递自提点的空间分布特征与影响因素[J]. 地球信息科学学报, 2019,21(8):1240-1253.
22 Wu Kangmin, Zhang Hongou, Wang Yang, et al. Identify of the Multiple Types of Commercial Center in Guangzhou and Its Spatial Pattern[J]. Progress in Geography,2016,35(8): 963-974.
22 吴康敏,张虹鸥,王洋,等. 广州市多类型商业中心识别与空间模式[J].地理科学进展,2016,35(8):963-974.
23 Lu Min, Yang Liu, Wang Jinyin, et al. Application of Pointgroup Density Cartography based on Kernel Density Estimation[J]. Engineering of Surveying Mapping, 2017,26(4):70-74.
23 卢敏, 杨柳, 王金茵, 等. 基于核密度估计的点群密度制图应用研究[J]. 测绘工程, 2017,26(4):70-74.
24 Cai J, Huang B, Song Y. Using Multi-source Geospatial Big Data to Identify the Structure of Polycentric Cities[J]. Remote Sensing of Environment,2017,202:210-221.doi:10.1016/j.rse.2017.06.039.
doi: 10.1016/j.rse.2017.06.039
25 Wang C, Chen Z, Yang C, et al. Analyzing Parcel-level Relationships between Luojia 1-01 Nighttime Light Intensity and Artificial Surface Features Across Shanghai, China: A Comparison with NPP-VIIRS Data[J]. International Journal of Applied Earth Observation and Geoinformation, 2020,85:101989.doi:10.1016/j.jag.2019.101989.
doi: 10.1016/j.jag.2019.101989
26 Wu Kaihua,Li Chaokui,Liu Junjie,et al. Identification of Growth Boundary of Core Areas in an Urban Agglomeration based on Spatial Syntax Theory[J]. Geographical Research, 2020, 39(6):1418-1426.
26 武凯华,李朝奎,刘俊杰,等. 基于空间句法理论的城市群核心区发展边界识别[J]. 地理研究,2020, 39(6): 1418-1426.
27 Wang De, Li Dan, Fu Yingzi. Employment Space of Residential Quarters in Shanghai: An Exploration based on Mobile Signaling Data[J]. Acta Geographica Sinica, 2020, 75(8):1585-1602.
27 王德,李丹,傅英姿. 基于手机信令数据的上海市不同住宅区居民就业空间研究[J]. 地理学报, 2020, 75(8): 1585-1602.
28 Li Y, Sun Q, Ji X, et al. Defining the Boundaries of Urban Built-up Area based on Taxi Trajectories: A Case Study of Beijing[J]. Journal of Geovisualization and Spatial Analysis. 2020,4(1). doi:10.1007/s41651-020-00047-6.
doi: 10.1007/s41651-020-00047-6
[1] 吴健平,张 立. 卫星遥感技术在城市规划中的应用[J]. 遥感技术与应用, 2003, 18(1): 52-55.