中国地级及以上城市网络结构韧性测度

doi:10.11821/dlxb202106006

摘要/Abstract

摘要：

随着城市间关系趋向于网络化发展以及外部急性冲击和慢性压力的增加,城市网络结构韧性作为衡量区域韧性的重要手段,致力于评估城市网络系统在面对突发故障或扰动时,能够抵御、吸收和恢复原有网络特征和重要功能的能力。以中国346个地级及以上城市为研究对象,采用2017年百度指数、腾讯人口迁徙大数据等,在构建信息、交通、经济和综合城市联系网络的基础上,从城市节点和网络层面对其层级性和匹配性进行测度,并对中断场景下网络的传输性和多样性进行分析,进而针对各网络从不同视角提出网络结构优化策略和建议。结果表明：① 2017年中国346个地级及以上城市的信息、交通、经济和综合联系网络整体上呈现以“胡焕庸线”为界的“东密西疏”的分布格局,但空间结构各有特点。② 信息、交通和经济三大网络的层级性排名依次为经济>信息>交通网络。大城市及省会城市因其信息技术的快速创新与传播、产业经济的高度发展与集聚及交通基础设施的快速配备与完善在网络中具有更高的层级。③ 信息、交通、经济三大网络均具有异配性特征。信息网络中高权重节点与低权重节点间的跨区域交流机会更多,因而具有最高的异配韧性。④ 信息网络的传输与多样韧性稍高于经济网络,远高于交通网络。同一城市节点的故障或中断在影响网络传输性的同时,也会累及网络的多样性。

关键词: 城市网络, 网络结构韧性, 空间分析, 空间大数据, 中国

Abstract:

With the rapid development of information technology, the interweaving physical infrastructure and virtual urban networks show more abundant connotations. As one of the most effective indicators to measure regional resilience, urban network structure resilience focuses on the capacity of the urban network system to restore, maintain, or improve the original network characteristics and important functions when faced with external acute shock and chronic pressure. Therefore, evaluating the resilience of the urban network structure is of great significance for recognizing and understanding regional resilience. Taking 346 cities of China at the prefecture level and above as the research objects, this study constructed information, transportation, economic, and comprehensive connection networks based on the Baidu index, Tencent location services, and social statistics data. The resilience of the urban network was then measured and evaluated in four aspects: hierarchy, heterogeneity, transmissibility, and diversity. The last two properties are in interrupting simulation scenarios. Accordingly, the optimization strategies and suggestions for the network structure were put forward. Based on the results, the following conclusions can be drawn: (1) Although China's information, transportation, economic, and comprehensive networks among the 346 prefecture-level cities and above in 2017 presented a spatial pattern of "dense in the east and sparse in the west" with "Hu Huanyong Line" as the boundary, their spatial structures exhibit their own characteristics. (2) The hierarchy of information, transportation, and economic networks from strong to weak is in the following order: economic network > information network > transportation network. Large cities and provincial capital cities have a higher hierarchy in a network because of the rapid innovation and dissemination of information technology, the high development and agglomeration of industrial economy, and the rapid allocation and improvement of traffic infrastructures. (3) The three major networks of information, transportation, and economy are all heterogeneous. In the information network, there are more opportunities for cross-regional communication between high-weighted and low-weighted nodes, thereby exhibiting the highest heterogeneous resilience. (4) The transmissibility and diversity resilience of information network was slightly higher than those of the economic network and much higher than those of the transportation network. Failures or perturbations of a city have almost the same impact on network transmissibility and diversity. Cities that have a great impact on the resilience of China's urban network structure typically exhibit high centrality and control power. These cities not only have a higher level of economic development and a relatively sound transportation hub, but also exhibit superiority with regard to their geographical conditions and distribution of natural resources.

Key words: urban network, network structure resilience, spatial analysis, spatial big data, China

魏石梅, 潘竟虎. 中国地级及以上城市网络结构韧性测度[J]. 地理学报, 2021, 76(6): 1394-1407.

WEI Shimei, PAN Jinghu. Network structure resilience of cities at the prefecture level and above in China[J]. Acta Geographica Sinica, 2021, 76(6): 1394-1407.

图/表 8

图1

图2

图3

图4

图5

图6

图7

图8

参考文献 34

[1]	Cai Jianming, Guo Hua, Wang Degen. Review on the resilient city research overseas. Progress in Geography, 2012,31(10):1245-1255.
	[ 蔡建明, 郭华, 汪德根. 国外弹性城市研究述评. 地理科学进展, 2012,31(10):1245-1255.]
[2]	Holling C S. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 1973,4(1):1-23. doi: 10.1146/annurev.es.04.110173.000245
[3]	Heinzlef C, Robert B, Hémond Y, et al. Operating urban resilience strategies to face climate change and associated risks: Some advances from theory to application in Canada and France. Cities, 2020,104:102762. DOI: 10.1016/j.cities.2020.102762. doi: 10.1016/j.cities.2020.102762
[4]	Shamsuddin S. Resilience resistance: The challenges and implications of urban resilience implementation. Cities, 2020,103:102763. DOI: 10.1016/j.cities.2020.102763. doi: 10.1016/j.cities.2020.102763 pmid: 32501356
[5]	Li Yan, Chen Wen, Sun Yang. New reflections on the analysis of regional resilience in geographical sciences from a relational-dynamic perspective. Geographical Research, 2019,38(7):1694-1704.
	[ 李艳, 陈雯, 孙阳. 关联演化视角下地理学区域韧性分析的新思考. 地理研究, 2019,38(7):1694-1704.]
[6]	Wu Y, Que W, Liu Y G, et al. Is resilience capacity index of Chinese region performing well? Evidence from 26 provinces. Ecological Indicators, 2020,112:106088. DOI: 10.1016/j.ecolind.2020.106088. doi: 10.1016/j.ecolind.2020.106088
[7]	Wei Ye, Xiu Chunliang. Study on the concept and analytical framework of city network resilience. Progress in Geography, 2020,39(3):488-502.
	[ 魏冶, 修春亮. 城市网络韧性的概念与分析框架探析. 地理科学进展, 2020,39(3):488-502.]
[8]	Peng Chong, Lin Yingzi, Gu Chaolin. Evaluation and optimization strategy of city network structural resilience in the middle reaches of Yangtze River. Geographical Research, 2018,37(6):1193-1207.
	[ 彭翀, 林樱子, 顾朝林. 长江中游城市网络结构韧性评估及其优化策略. 地理研究, 2018,37(6):1193-1207.]
[9]	Pan J H, Lai J B. Spatial pattern of population mobility among cities in China: Case study of the National Day plus Mid-Autumn Festival based on Tencent migration data. Cities, 2019,94:55-69. doi: 10.1016/j.cities.2019.05.022
[10]	Lin Yingzi. Research on evaluation of regional network's structural resilience and its spatial optimization: A case study of the urban agglomeration in the middle reaches of the Yangtze River[D]. Wuhan: Huazhong University of Science and Technology, 2017.
	[ 林樱子. 城市网络结构韧性评估及其优化策略研究: 以长江中游城市群为例[D]. 武汉: 华中科技大学, 2017.]
[11]	Shao Yiwen, Xu Jiang. Understanding urban resilience: A conceptual analysis based on integrated international literature review. Urban Planning International, 2015,30(2):48-54.
	[ 邵亦文, 徐江. 城市韧性:基于国际文献综述的概念解析. 国际城市规划, 2015,30(2):48-54.]
[12]	Chen C K, Xu L L, Zhao D Y, et al. A new model for describing the urban resilience considering adaptability, resistance and recovery. Safety Science, 2020,128:104756. DOI: 10.1016/j.ssci.2020.104756. doi: 10.1016/j.ssci.2020.104756
[13]	Mirti Chand A V. Place Based Approach to plan for Resilient Cities: A local government perspective. Procedia Engineering, 2018,212:157-164. doi: 10.1016/j.proeng.2018.01.021
[14]	Ribeiro P J G, Pena Jardim Gonçalves L A. Urban resilience: A conceptual framework. Sustainable Cities and Society, 2019,50:101625. DOI: 10.1016/j.scs.2019.101625. doi: 10.1016/j.scs.2019.101625
[15]	Feng X H, Xiu C L, Bai L M, et al. Comprehensive evaluation of urban resilience based on the perspective of landscape pattern: A case study of Shenyang city. Cities, 2020,104:102722. DOI: 10.1016/j.cities.2020.102722. doi: 10.1016/j.cities.2020.102722
[16]	Peng Chong, Chen Siyu, Wang Baoqiang. Analyzing city network's structural resilience under disruption scenarios: A case study of passenger transport network in the middle reaches of Yangtze River. Economic Geography, 2019,39(8):68-76.
	[ 彭翀, 陈思宇, 王宝强. 中断模拟下城市群网络结构韧性研究: 以长江中游城市群客运网络为例. 经济地理, 2019,39(8):68-76.]
[17]	Lai J B, Pan J H. China's City network structural characteristics based on population flow during spring festival travel rush: Empirical analysis of "tencent migration" big data. Journal of Urban Planning and Development, 2020,146(2):04020018. DOI: 10.1061/(ASCE)UP.1943-5444.0000581. doi: 10.1061/(ASCE)UP.1943-5444.0000581
[18]	Zhen Feng, Wang Bo, Chen Yingxue. China's city network characteristics based on social network space: An empirical analysis of Sina Micro-blog. Acta Geographica Sinica, 2012,67(8):1031-1043.
	[ 甄峰, 王波, 陈映雪. 基于网络社会空间的中国城市网络特征: 以新浪微博为例. 地理学报, 2012,67(8):1031-1043.]
[19]	Jiang Daliang, Sun Ye, Ren Hang, et al. Analyses on the city network characteristics of middle Yangtze urban agglomeration based on Baidu index. Resources and Environment in the Yangtze Basin, 2015,24(10):1654-1664.
	[ 蒋大亮, 孙烨, 任航, 等. 基于百度指数的长江中游城市群城市网络特征研究. 长江流域资源与环境, 2015,24(10):1654-1664.]
[20]	Leng Bingrong, Yang Yongchun, Li Yingjie, et al. Spatial characteristics and complex analysis: A perspective from basic activities of urban networks in China. Acta Geographica Sinica, 2011,66(2):199-211.
	[ 冷炳荣, 杨永春, 李英杰, 等. 中国城市经济网络结构空间特征及其复杂性分析. 地理学报, 2011,66(2):199-211.]
[21]	Ye Lei, Duan Xuejun, Ou Xiangjun. The urban network structure of Jiangsu province based on the traffic and information flow. Scientia Geographica Sinica, 2015,35(10):1230-1237. doi: 10.13249/j.cnki.sgs.2015.010.1230
	[ 叶磊, 段学军, 欧向军. 基于交通信息流的江苏省流空间网络结构研究. 地理科学, 2015,35(10):1230-1237.]
[22]	Li X M, Xiao R B. Analyzing network topological characteristics of eco-industrial parks from the perspective of resilience: A case study. Ecological Indicators, 2017,74:403-413. doi: 10.1016/j.ecolind.2016.11.031
[23]	Rak J. K-Penalty: A novel approach to find κ-Disjoint paths with differentiated path costs. IEEE Communications Letters, 2010,14(4):354-356. doi: 10.1109/LCOMM.2010.04.091597
[24]	Zhang Y T, Shao C Q, He S B, et al. Resilience centrality in complex networks. Physical Review E, 2020,101(2):022304. DOI: 10.1103/PhysRevE.101.022304. doi: 10.1103/PhysRevE.101.022304
[25]	Boccaletti S, Latora V, Moreno Y, et al. Complex networks: Structure and dynamics. Physics Reports, 2006,424(4-5):175-308. doi: 10.1016/j.physrep.2005.10.009
[26]	Crespo J, Suire R, Vicente J. Lock-in or lock-out? How structural properties of knowledge networks affect regional resilience. Journal of Economic Geography, 2014,14(1):199-219. doi: 10.1093/jeg/lbt006
[27]	Newman M E J. Assortative mixing in networks. Physical Review Letters, 2002,89(20):208701. DOI: 10.1103/PhysRevLett.89.208701. doi: 10.1103/PhysRevLett.89.208701
[28]	Chen L, Gable G G, Hu H B. Communication and organizational social networks: A simulation model. Computational and Mathematical Organization Theory, 2013,19(4):460-479. doi: 10.1007/s10588-012-9131-0
[29]	Latora V, Marchiori M. A measure of centrality based on network efficiency. New Journal of Physics, 2007,9(6):188. DOI: 10.1088/1367-2630/9/6/188. doi: 10.1088/1367-2630/9/6/188
[30]	Sterbenz J P G, Çetinkaya E K, Hameed M A, et al. Evaluation of network resilience, survivability, and disruption tolerance: Analysis, topology generation, simulation, and experimentation. Telecommunication Systems, 2013,52(2):705-736.
[31]	Ip W H, Wang D W. Resilience and friability of transportation networks: Evaluation, analysis and optimization. IEEE Systems Journal, 2011,5(2):189-198. doi: 10.1109/JSYST.2010.2096670
[32]	Gao Lei. Research of effective information spreading on complex networks[D]. Chengdu: University of Electronic Science and Technology of China, 2017.
	[ 高磊. 复杂网络上的信息有效传播研究[D]. 成都: 电子科技大学, 2017.]
[33]	Zhao Ziyu, Wei Ye, Wang Shijun, et al. Measurement of directed alternative centricity and power of directed weighted urban network: A case of population flow network of China during "Chunyun" period. Geographical Research, 2017,36(4):647-660.
	[ 赵梓渝, 魏冶, 王士君, 等. 有向加权城市网络的转变中心性与控制力测度: 以中国春运人口流动网络为例. 地理研究, 2017,36(4):647-660.]
[34]	Ma H T, Fang C L, Lin S N, et al. Hierarchy, clusters, and spatial differences in Chinese inter-city networks constructed by scientific collaborators. Journal of Geographical Sciences, 2018,28(12):1793-1809.