基于多源数据的武汉市多中心空间结构识别

doi:10.18306/dlkxjz.2019.11.003

摘要/Abstract

摘要：

研究城市不同要素空间集聚水平与层级差异、明确城市各职能中心分布,对城市多中心空间结构的发展引导和规划调控具有重要意义。论文分别采用兴趣点(points of interest, POI)、夜间灯光和路网3种数据,利用局部等值线树算法,识别武汉市多中心空间结构。研究表明：① 武汉不同城市要素的总体集聚水平不均衡,其中在汉阳集聚水平较低,在汉口和武昌集聚水平较高,特别是二环以内区域;② 武汉中央活动区发展比较均衡,内部各城市要素高度集聚,随着向外扩展,部分城市要素易于在局部集聚形成城市中心;③ 结合武汉圈层发展布局,城市中心沿环线的“商-住-工”职能分布模式逐步确立,其中内环以及二环区域已形成稳定的商服中心;二环附近综合组团内部出现了较具代表性的居住中心;三环外的沌口和武钢主导发展工业,是典型的工业中心。

关键词: 多中心空间结构, POI, 夜间灯光, 路网, 局部等值线树算法, 武汉

Abstract:

Examining the spatial agglomeration level and hierarchy differences of various urban elements and the distribution of diverse functional centers is significant for guiding the development and regulating the plans of the polycentric spatial structure of cities. City center is represented by various urban elements. It is difficult to fully identify and recognize the urban spatial structure by using a single data source. Thus, combining different types of data to deeply understand the status of urban internal development is necessary. This study used points of interest (POI), nighttime light, and road network data and adopted the local contour tree algorithm to identify the polycentric spatial structure in Wuhan City. The results show that: 1) The overall agglomeration level of various urban elements in the three towns of Wuhan is not balanced—the agglomeration level is higher in Hankou and Wuchang, and lower in Hanyang. 2) The development of the core area of Wuhan City is relatively mature. In the process of outward expansion, various urban elements tend to aggregate in local areas to form an urban center. 3) Considering the zonal development of Wuhan City, the functional distribution model of business-housing-industry corresponding to the ring roads of the city center was gradually established. The inner ring and the second ring regions have formed stable business service centers. Representative residential centers appeared in the multifunctional clusters near the second ring road. Similarly, Zhuankou and Wugang outside the third ring road led the development of industries, and are typical industrial centers.

Key words: polycentric spatial structure, POI, nighttime light, road network, local contour tree algorithm, Wuhan City

毛帅永, 焦利民, 许刚, 李泽慧. 基于多源数据的武汉市多中心空间结构识别[J]. 地理科学进展, 2019, 38(11): 1675-1683.

MAO Shuaiyong, JIAO Limin, XU Gang, LI Zehui. Identification of the polycentric spatial structure in Wuhan City based on multisource data[J]. PROGRESS IN GEOGRAPHY, 2019, 38(11): 1675-1683.

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参考文献 34

1	奥沙利文. 2013. 城市经济学 [M]. 周京奎, 译. 北京: 北京大学出版社.
	O'Sullivan A. 2013. Urban Economics. Translated by Zhou J K. Beijing, China: Peking University Press. ]
2	陈佐旗 . 2017. 基于多源夜间灯光遥感影像的多尺度城市空间形态结构分析[D]. 上海: 华东师范大学.
	Chen Z Q . 2017. A multiscale analysis on urban area and spatial structure based on nighttime light data. Shanghai, China: East China Normal University. ]
3	池娇, 焦利民, 董婷 , 等. 2016. 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, 41(2):68-73.
	Chi J, Jiao L M, Dong T , et al. 2016. Quantitative identification and visualization of urban functional areas based on POI data. Journal of Geomatics, 41(2):68-73. ]
4	谷岩岩, 焦利民, 董婷 , 等. 2018. 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), 4(7):1-9.
	Gu Y Y, Jiao L M, Dong T , et al. 2018. Identification and interaction analysis of urban functional areas based on multi-source data. Geomatics and Information Science of Wuhan University, 4(7):1-9. ]
5	谷一桢, 郑思齐, 曹洋 . 2009. 北京市就业中心的识别: 实证方法及应用[J]. 城市发展研究, 16(9):118-124.
	Gu Y Z, Zheng S Q, Cao Y . 2009. The identification of employment center in Beijing. Urban Development Studies, 16(9):118-124. ]
6	霍华德 . 1898. 明日的田园城市 [M]. 金经元, 译. 北京: 商务印书馆.
	Howard E. 1898. Garden cities of tomorrow. Translated by Jin J Y. Beijing, China: Commercial Press. ]
7	蒋丽, 吴缚龙 . 2009. 广州市就业次中心和多中心城市研究[J]. 城市规划学刊, ( 3):75-81.
	Jiang L, Wu F L . 2009. A study on Guangzhou's employment subcenters and polycentricity. Urban Planning Forum, ( 3):75-81. ]
8	蒋丽, 吴缚龙 . 2013. 2000-2010年广州人口空间分布变动与多中心城市空间结构演化测度[J]. 热带地理, 33(2):147-155.
	Jiang L, Wu F L . 2013. Guangzhou population spatial distribution and polycentricity spatial structure evolution. Tropical Geography, 33(2):147-155. ]
9	焦利民, 李泽慧, 许刚 , 等. 2017. 武汉市城市空间集聚要素的分布特征与模式[J]. 地理学报, 72(8):1432-1443. doi: 10.11821/dlxb201708008
	Jiao L M, Li Z H, Xu G , et al. 2017. The characteristics and patterns of spatially aggregated elements in urban areas of Wuhan. Acta Geographica Sinica, 72(8):1432-1443. ] doi: 10.11821/dlxb201708008
10	罗震东, 朱查松 . 2008. 解读多中心: 形态、功能与治理[J]. 国际城市规划, 23(1):85-88.
	Luo Z D, Zhu C S . 2008. Understanding polycentricity by configuration, function and governance. Urban Planning International, 23(1):85-88. ]
11	钮心毅, 丁亮, 宋小冬 . 2014. 基于手机数据识别上海中心城的城市空间结构[J]. 城市规划学刊, ( 6):61-67.
	Niu X Y, Ding L, Song X D . 2014. Understanding urban spatial structure of Shanghai central city based on mobile phone data. Urban Planning Forum, ( 6):61-67. ]
12	沙里宁 . 1943. 城市,它的生长、衰退和将来 [M]. 顾启源, 译.北京: 中国建筑工业出版社.
	Saarinen E. 1943. City, its development, decline and future. Translated by Gu Q Y. Beijing, China: China Building Industry Press. ]
13	施歌, 江南, 姚恋秋 , 等. 2017. 基于GIS和兴趣点(POI)数据的城市中心体系识别方法研究: 以上海市为例[J]. 现代测绘, 40(6):27-30.
	Shi G, Jiang N, Yao L Q , et al. 2017. Study on the identification of urban center system based on GIS and POI: A case study of Shanghai. Modern Surveying and Mapping, 40(6):27-30. ]
14	孙斌栋, 陈浩 . 2016. 城市空间结构对地价的影响: 基于多中心的视角[J]. 城市问题, ( 4):31-38.
	Sun B D, Chen H . 2016. The influence of urban spatial structure on land price is based on multi-center perspective. Urban Problems, ( 4):31-38. ]
15	孙斌栋, 丁嵩 . 2017. 多中心空间结构经济绩效的研究进展及启示[J]. 地理科学, 37(1):64-71.
	Sun B D, Ding S . 2017. Progress and implications for polycentric spatial structure's effect on economic performance. Scientia Geographica Sinica, 37(1):64-71. ]
16	孙斌栋, 潘鑫 . 2008. 城市空间结构对交通出行影响研究的进展: 单中心与多中心的论争[J]. 城市问题, ( 1):19-22.
	Sun B D, Pan Y . 2008. Research progress on the influence of urban spatial structure on traffic and travel: The debate between single center and multi-center. Urban Problems, ( 1):19-22. ]
17	孙铁山, 王兰兰, 李国平 . 2012. 北京都市区人口: 就业分布与空间结构演化[J]. 地理学报, 67(6):829-840. doi: 10.11821/xb201206010
	Sun T S, Wang L L, Li G P . 2012. Distributions of population and employment and evolution of spatial structures in the Beijing metropolitan area. Acta Geographica Sinica, 67(6):829-840. ] doi: 10.11821/xb201206010
18	孙铁山, 王兰兰, 李国平 . 2013. 北京都市区多中心空间结构特征与形成机制[J]. 城市规划, 37(7):28-32.
	Sun T S, Wang L L, Li G P . 2013. Characteristics and formation mechanism of polycentric spatial structure in Beijing metropolitan area. Urban Planning Review, 37(7):28-32. ]
19	孙斌栋, 魏旭红 . 2014. 上海都市区就业: 人口空间结构演化特征[J]. 地理学报, 69(6):747-758. doi: 10.11821/dlxb201406003
	Sun B D, Wei X H . 2014. Spatial distribution and structure evolution of employment and population in Shanghai metropolitan area. Acta Geographica Sinica, 69(6):747-758. ] doi: 10.11821/dlxb201406003
20	王桂新, 魏星 . 2007. 上海从业劳动力空间分布变动分析[J]. 地理学报, 62(2):200-210. doi: 10.11821/xb200702009
	Wang G X, Wei X . 2007. Change of labor force spatial structure in Shanghai from 1996 to 2001. Acta Geographica Sinica, 62(2):200-210. ] doi: 10.11821/xb200702009
21	吴康敏, 张虹鸥, 王洋 , 等. 2016. 广州市多类型商业中心识别与空间模式[J]. 地理科学进展, 35(8):963-974.
	Wu K M, Zhang H O, Wang Y , et al. 2016. Identify of the multiple types of commercial center in Guangzhou and its spatial pattern. Progress in Geography, 35(8):963-974. ]
22	杨俊宴, 章飙, 史宜 . 2012. 城市中心体系发展的理论框架探索[J]. 城市规划学刊, ( 1):33-39.
	Yang J Y, Zhang B, Shi Y . 2012. The theoretical frameworks of urban center system development. Urban Planning Forum, ( 1):33-39. ]
23	张亮, 岳文泽, 刘勇 . 2017. 多中心城市空间结构的多维识别研究: 以杭州为例[J]. 经济地理, 37(6):67-75.
	Zhang L, Yue W Z, Liu Y . 2017. Multi-dimensional identification of multi-center urban spatial structure: A case study of Hangzhou. Journal of Economic Geography, 37(6):67-75. ]
24	周新刚, 乐阳, 叶嘉安 , 等. 2014. 动态数据空间分析的不确定性问题: 以城市中心识别为例[J]. 武汉大学学报(信息科学版), 39(6):701-705. doi: 10.13203/j.whugis20140074
	Zhou X G, Yue Y, Ye J A , et al. 2014. Uncertainty in spatial analysis of dynamic data: Identification city center. Geomatics and Information Science of Wuhan University, 39(6):701-705. ] doi: 10.13203/j.whugis20140074
25	Anas A, Arnott R, Kenneth A S . 1998. Urban spatial structure[J]. Journal of Economic Literature, 36(3):1426-1464. doi: 10.1111/1365-2656.13148 pmid: 31758695
26	Chen Z Q, Yu B L, Song W , et al. 2017. A new approach for detecting urban centers and their spatial structure with nighttime light remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 55(11):6305-6319. doi: 10.1109/TGRS.2017.2725917
27	Harris C D, Ullman E L . 1945. The nature of cities[J]. Annals of the Ameirican Academy of Political & Social Science, 242(4):7-17. doi: 10.2190/NS.24.3.c pmid: 25261022
28	Huang Q Y, David W. S. Wang . 2016. Activity patterns socioeconomic status and urban spatial structure what can social media data tell us[J]. International Journal of Geographical Information Science, 30(9):1973-1898.
29	Leslie T F . 2009. Identification and differentiation of urban centers in Phoenix through a multi-criteria kernel-density approach[J]. International Regional Science Review, 33(2):205-235. doi: 10.1177/0160017610365538
30	Liu X, Gong L, Gong Y , et al. 2015. Revealing travel patterns and city structure with taxi trip data[J]. Journal of Transport Geography, 43(5):78-90. doi: 10.1016/j.jtrangeo.2015.01.016
31	Lüscher P, Weibel R . 2013. Exploiting empirical knowledge for automatic delineation of city centers from large-scale topographic databases[J]. Computers, Environment and Urban Systems, 37(2):18-34. doi: 10.1016/j.compenvurbsys.2012.07.001
32	Sun Y, Fan H, Li M , et al. 2016. Identifying the city center using human travel flows generated from location-based social networking data[J]. Environment and Planning B: Planning and Design, 43(3):480-498. doi: 10.1177/0265813515617642
33	Yu W, Ai T, Shao S . 2015. The analysis and delimitation of central business district using network kernel density estimation[J]. Journal of Transport Geography, 45(3):32-47. doi: 10.1016/j.jtrangeo.2015.04.008
34	Zhu J, Sun Y . 2017. Building an urban spatial structure from urban land use data: An example using automated recognition of the city centre[J]. ISPRS International Journal of Geo-Information, 6(4):122. doi: 10.3390/ijgi6040122. doi: 10.3390/ijgi6040122

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