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Deciphering the Spatial Structures of City Networks in the Economic Zone of the West Side of the Taiwan Strait Through the Lens of Functional and Innovation Networks
sustainability Article Deciphering the Spatial Structures of City Networks in the Economic Zone of the West Side of the Taiwan Strait through the Lens of Functional and Innovation Networks Yan Ma * and Feng Xue School of Architecture and Urban-Rural Planning, Fuzhou University, Fuzhou 350108, Fujian, China; [email protected] * Correspondence: [email protected] Received: 17 April 2019; Accepted: 21 May 2019; Published: 24 May 2019 Abstract: Globalization and the spread of information have made city networks more complex. The existing research on city network structures has usually focused on discussions of regional integration. With the development of interconnections among cities, however, the characterization of city network structures on a regional scale is limited in the ability to capture a network’s complexity. To improve this characterization, this study focused on network structures at both regional and local scales. Through the lens of function and innovation, we characterized the city network structure of the Economic Zone of the West Side of the Taiwan Strait through a social network analysis and a Fast Unfolding Community Detection algorithm. We found a significant imbalance in the innovation cooperation among cities in the region. When considering people flow, a multilevel spatial network structure had taken shape. Among cities with strong centrality, Xiamen, Fuzhou, and Whenzhou had a significant spillover effect, which meant the region was depolarizing. Quanzhou and Ganzhou had a significant siphon effect, which was unsustainable. Generally, urbanization in small and midsize cities was common. These findings provide support for government policy making. Keywords: city network; spatial organization; people flows; innovation network 1. -
World Bank Document
E289 Volume 3 PEOPLE'S REPUBLIC OF CHINA CHONGQING MUNICIPAL GOVERNMENT I HE WORLD BANK Public Disclosure Authorized Public Disclosure Authorized CHONGQING URBAN ENVIRONMENT PROJECT NEW COMPONENTS DESIGN REVIEW AND ADVISORY SERVICES Public Disclosure Authorized ENVIRONMENTAL ASSESSMENT VOLUME 1: WASTE WATER CONS L!DATEDEA AUGUST 2004 No. 23500321.R3.1 Public Disclosure Authorized TT IN COLLABORATION 0 INWITH ( SOGREAH I I i I I I I I I PEOPLE'S REPUBLIC OF CHINA CHONGQING MUNICIPAL MANAGEMENT OFFICE OF THE ROGREAH WORLD BANK'S CAPITAL 1 __ ____ __ __ ___-_ CCONS0 N5 U L I A\NN I S UTILIZATION CHONGQING URBAN ENVIRONMENT PROJECT NEW COMPONENTS CONSOLIDATED EA FOR WASTEWATER COMPONENTS IDENTIFICATION N°: 23500321.R3.1 DATE: AUGUST 2004 World Bank financed This document has been produced by SOGREAH Consultants as part of the Management Office Chongqing Urban Environment Project (CUEP 1) to the Chongqing Municipal of the World Bank's capital utilization. the Project Director This document has been prepared by the project team under the supervision of foilowing Quality Assurance Procedures of SOGREAH in compliance with IS09001. APPROVED BY DATE AUTHCP CHECKED Y (PROJECT INDEY PURPOSE OFMODIFICATION DIRECTOR) CISDI / GDM GDM B Second Issue 12/08/04 BYN Chongqing Project Management zli()cqdoc:P=v.cn 1 Office iiahui(cta.co.cn cmgpmo(dcta.cg.cn 2 The World Bank tzearlev(.worldbank.org 3 SOGREAH (SOGREAH France, alain.gueguen(.soqreah.fr, SOGREAH China) qmoysc!soQreah.com.cn CHONGQING MUNICIPALITY - THE WORLD BANK CHONGQING URBAN ENVIRONMENT PROJECT - NEW COMPONENTS CONSOLIDATED EA FOR WASTEWATER COMPONENTS CONTENTS INTRODUCTION ............. .. I 1.1. -
Beijing Subway Map
Beijing Subway Map Ming Tombs North Changping Line Changping Xishankou 十三陵景区 昌平西山口 Changping Beishaowa 昌平 北邵洼 Changping Dongguan 昌平东关 Nanshao南邵 Daoxianghulu Yongfeng Shahe University Park Line 5 稻香湖路 永丰 沙河高教园 Bei'anhe Tiantongyuan North Nanfaxin Shimen Shunyi Line 16 北安河 Tundian Shahe沙河 天通苑北 南法信 石门 顺义 Wenyanglu Yongfeng South Fengbo 温阳路 屯佃 俸伯 Line 15 永丰南 Gonghuacheng Line 8 巩华城 Houshayu后沙峪 Xibeiwang西北旺 Yuzhilu Pingxifu Tiantongyuan 育知路 平西府 天通苑 Zhuxinzhuang Hualikan花梨坎 马连洼 朱辛庄 Malianwa Huilongguan Dongdajie Tiantongyuan South Life Science Park 回龙观东大街 China International Exhibition Center Huilongguan 天通苑南 Nongda'nanlu农大南路 生命科学园 Longze Line 13 Line 14 国展 龙泽 回龙观 Lishuiqiao Sunhe Huoying霍营 立水桥 Shan’gezhuang Terminal 2 Terminal 3 Xi’erqi西二旗 善各庄 孙河 T2航站楼 T3航站楼 Anheqiao North Line 4 Yuxin育新 Lishuiqiao South 安河桥北 Qinghe 立水桥南 Maquanying Beigongmen Yuanmingyuan Park Beiyuan Xiyuan 清河 Xixiaokou西小口 Beiyuanlu North 马泉营 北宫门 西苑 圆明园 South Gate of 北苑 Laiguangying来广营 Zhiwuyuan Shangdi Yongtaizhuang永泰庄 Forest Park 北苑路北 Cuigezhuang 植物园 上地 Lincuiqiao林萃桥 森林公园南门 Datunlu East Xiangshan East Gate of Peking University Qinghuadongluxikou Wangjing West Donghuqu东湖渠 崔各庄 香山 北京大学东门 清华东路西口 Anlilu安立路 大屯路东 Chapeng 望京西 Wan’an 茶棚 Western Suburban Line 万安 Zhongguancun Wudaokou Liudaokou Beishatan Olympic Green Guanzhuang Wangjing Wangjing East 中关村 五道口 六道口 北沙滩 奥林匹克公园 关庄 望京 望京东 Yiheyuanximen Line 15 Huixinxijie Beikou Olympic Sports Center 惠新西街北口 Futong阜通 颐和园西门 Haidian Huangzhuang Zhichunlu 奥体中心 Huixinxijie Nankou Shaoyaoju 海淀黄庄 知春路 惠新西街南口 芍药居 Beitucheng Wangjing South望京南 北土城 -
Supersized Cities China's 13 Megalopolises
TM Supersized cities China’s 13 megalopolises A report from the Economist Intelligence Unit www.eiu.com Supersized cities China’s 13 megalopolises China will see its number of megalopolises grow from three in 2000 to 13 in 2020. We analyse their varying stages of demographic development and the implications their expansion will have for several core sectors. The rise and decline of great cities past was largely based on their ability to draw the ambitious and the restless from other places. China’s cities are on the rise. Their growth has been fuelled both by the large-scale internal migration of those seeking better lives and by government initiatives encouraging the expansion of urban areas. The government hopes that the swelling urban populace will spend more in a more highly concentrated retail environment, thereby helping to rebalance the Chinese economy towards private consumption. Progress has been rapid. The country’s urbanisation rate surpassed 50% for the first time in 2011, up from a little over one-third just ten years earlier. Even though the growth of China’s total population will soon slow to a near standstill, the urban population is expected to continue expanding for at least another decade. China’s cities will continue to grow. Some cities have grown more rapidly than others. The metropolitan population of the southern city of Shenzhen, China’s poster child for the liberal economic reforms of the past 30 years, has nearly doubled since 2000. However, development has also spread through more of the country, and today the fastest-growing cities are no longer all on the eastern seaboard. -
A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data
Hindawi Complexity Volume 2018, Article ID 3412070, 14 pages https://doi.org/10.1155/2018/3412070 Research Article A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data Kai Lu ,1 Alireza Khani,2 and Baoming Han 1 1School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China 2Department of Civil, Environmental and Geo-Engineering, University of Minnesota, Minneapolis, MN 55455, USA Correspondence should be addressed to Kai Lu; [email protected] and Baoming Han; [email protected] Received 18 December 2017; Revised 2 June 2018; Accepted 15 July 2018; Published 28 August 2018 Academic Editor: Shuliang Wang Copyright © 2018 Kai Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Automatic fare collection (AFC) systems have been widely used all around the world which record rich data resources for researchers mining the passenger behavior and operation estimation. However, most transit systems are open systems for which only boarding information is recorded but the alighting information is missing. Because of the lack of trip information, validation of utility functions for passenger choices is difficult. To fill the research gaps, this study uses the AFC data from Beijing metro, which is a closed system and records both boarding information and alighting information. To estimate a more reasonable utility function for choice modeling, the study uses the trip chaining method to infer the actual destination of the trip. Based on the land use and passenger flow pattern, applying k-means clustering method, stations are classified into 7 categories. -
National PM2.5 and NO2 Exposure Models for China Based on Land Use Regression, Satellite Measurements, and Universal Kriging
Science of the Total Environment 655 (2019) 423–433 Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging Hao Xu a,b, Matthew J. Bechle c,MengWangd,e, Adam A. Szpiro f,SverreVedale, Yuqi Bai a,b,⁎, Julian D. Marshall c,⁎⁎ a The Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China b Joint Center for Global Change Studies (JCGCS), Beijing 100875, China c Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, United States d Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, United States e Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, United States f Department of Biostatistics, University of Washington, Seattle, WA 98195, United States HIGHLIGHTS GRAPHICAL ABSTRACT • First high spatial resolution national LUR models for both NO2 and PM2.5 in China • Satellite data and kriging are comple- mentary in making predictions more ac- curate. • Variable selection models perform simi- lar or better than PLS models. • 1km2 resolution prediction maps will be publicly available for future research. article info abstract Article history: Outdoor air pollution is a major killer worldwide and the fourth largest contributor to the burden of disease in Received 27 July 2018 China. China is the most populous country in the world and also has the largest number of air pollution deaths Received in revised form 8 November 2018 per year, yet the spatial resolution of existing national air pollution estimates for China is generally relatively Accepted 8 November 2018 low. -
Organizing Bicycle Traffic in Moscow to Reduce Air Pollutant Concentrations
E3S Web of Conferences 164, 04007 (2020) https://doi.org/10.1051/e3sconf /202016404007 TPACEE-2019 Organizing bicycle traffic in Moscow to reduce air pollutant concentrations Igor Pryadko1,* 1 Moscow State University of Civil Engineering, 129337, 26 Yaroslavskoye sh., Moscow, Russia Abstract. The objective of this article is to assess the prospects for development of cycling as a mode of transport in major cities in Russia and worldwide. Towards this end, the author addresses bicycle traffic organization patterns in the cities of Europe, South Eastern Asia and South America. The methods, employed in this research project, include sociological data collection, or the polling of urban residents (residents of the Russian capital), the retrospective analysis of sources, including news articles, the comparative historical method and forecasting. In the article, the impact produced on the urban environment, namely, on the surface layers of the urban atmosphere, by the motor traffic is compared with the one produced by the bicycle traffic. The mission of this research project is to analyze development of cycling network routes, parking lots, and accompanying small architectural forms in Moscow. The author employs methods of environmental monitoring to assess the impact produced by the motor transport on the environmental situation in the city. The conclusion is that there is a need to develop the urban walking infrastructure, to expand the urban cycling network, and to convert to the biosphere compatible urban transport. 1 Introduction More often than not urban planners and architects face the challenge of limiting pollutant emissions caused by human economic activities and designing a clean and biosphere compatible urban space. -
Effects of Air Pollution Control Policies on PM2.5 Pollution Improvement In
Effects of air pollution control policies on PM2.5 pollution improvement in China from 2005 to 2017: a satellite based perspective Zongwei Ma 1, Riyang Liu 1, Yang Liu 2, Jun Bi 1, 3, * 1 State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, 5 Nanjing University, Nanjing, Jiangsu, China 2 Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA 3 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, 10 Jiangsu, China * Correspondence to: Dr. Jun Bi, School of the Environment, Nanjing University, 163 Xianlin Avenue, Nanjing 210023, P. R. China. Tel.: +86 25 89681605. E–mail address: [email protected]. 15 1 ABSTRACT Understanding the effectiveness of air pollution control policies is important for future policy th making. China implemented strict air pollution control policies since 11 Five Year Plan (FYP). There is still a lack of overall evaluation of the effects of air pollution control policies on PM2.5 th 5 pollution improvement in China since 11 FYP. In this study, we aimed to assess the effects of air pollution control policies from 2005 to 2017 on PM2.5 from the view of satellite remote sensing. We used the satellite derived PM2.5 of 2005-2013 from one of our previous studies. For the data of 2014-2017, we developed a two-stage statistical model to retrieve satellite PM2.5 data using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth 10 (AOD), assimilated meteorology, and land use data. -
Fusing Observational, Satellite Remote Sensing and 3 Air Quality Model Simulated Data to Estimate 4 Spatiotemporal Variations of PM2.5 Exposure in 5 China
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 16 February 2017 doi:10.20944/preprints201702.0059.v1 Peer-reviewed version available at Remote Sens. 2017, 9, 221; doi:10.3390/rs9030221 1 Article 2 Fusing Observational, Satellite Remote Sensing and 3 Air Quality Model Simulated Data to Estimate 4 Spatiotemporal Variations of PM2.5 Exposure in 5 China 6 Tao Xue 1, Yixuan Zheng 1, Guannan Geng 1,2, Bo Zheng 2, Xujia Jiang 1, Qiang Zhang 1,3,*, Kebin 7 He 2,3 8 1 Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, 9 Tsinghua University, Beijing 100084, China; 10 2 State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, 11 Tsinghua University, Beijing 100084, China; 12 3 The Collaborative Innovation Center for Regional Environmental Quality, Beijing 100084, China; 13 * Correspondence: [email protected] Tel.: +86-10-62795090 14 15 16 17 Abstract: Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique 18 for exposure assessments in epidemiological studies of its health risks. Previous studies have 19 utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the 20 spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data 21 simultaneously. Through assembling techniques, including linear mixed effect regressions with a 22 spatial-varying coefficient, a maximum likelihood estimator and the spatiotemporal Kriging 23 together, we develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol 24 optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and 25 apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. -
Leapfrogging Or Stalling Out? Electric Vehicles in China
Leapfrogging or Stalling Out? Electric Vehicles in China Sabrina Howell Henry Lee Adam Heal 2015 RPP-2015-07 Regulatory Policy Program Mossavar-Rahmani Center for Business and Government Harvard Kennedy School 79 John F. Kennedy Street, Weil Hall Cambridge, MA 02138 CITATION This paper may be cited as: Howell, Sabrina, Henry Lee, and Adam Heal. 2015. “Leapfrogging or Stalling Out? Electric Vehicles in China.” Regulatory Policy Program Working Paper RPP-2015-07. Cambridge, MA: Mossavar-Rahmani Center for Business and Government, Harvard Kennedy School, Harvard University. Comments may be directed to the authors. REGULATORY POLICY PROGRAM The Regulatory Policy Program at the Mossavar-Rahmani Center for Business and Government serves as a catalyst and clearinghouse for the study of regulation across Harvard University. The program's objectives are to cross-pollinate research, spark new lines of inquiry, and increase the connection between theory and practice. Through seminars and symposia, working papers, and new media, RPP explores themes that cut across regulation in its various domains: market failures and the public policy case for government regulation; the efficacy and efficiency of various regulatory instruments; and the most effective ways to foster transparent and participatory regulatory processes. The views expressed in this paper are those of the authors and do not imply endorsement by the Regulatory Policy Program, the Mossavar-Rahmani Center for Business and Government, Harvard Kennedy School, or Harvard University. FOR FURTHER INFORMATION Further information on the Regulatory Policy Program can be obtained from the Program's executive director, Jennifer Nash, Mossavar-Rahmani Center for Business and Government, Weil Hall, Harvard Kennedy School, 79 JKF Street, Cambridge, MA 02138, telephone (617) 495-9379, telefax (617) 496-0063, email [email protected]. -
The Modeling of Attraction Characteristics Regarding Passenger Flow in Urban Rail Transit Network Based on Field Theory
RESEARCH ARTICLE The modeling of attraction characteristics regarding passenger flow in urban rail transit network based on field theory Man Li1,2, Yanhui Wang1,2,3*, Limin Jia1,2,3 1 State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China, 2 School of traffic and transportation, Beijing Jiaotong University, Beijing, China, 3 Beijing Research Center of Urban Traffic Information Sensing and Service Technology, Beijing Jiaotong University, Beijing, China * [email protected] a1111111111 a1111111111 Abstract a1111111111 a1111111111 Aimed at the complicated problems of attraction characteristics regarding passenger flow in a1111111111 urban rail transit network, the concept of the gravity field of passenger flow is proposed in this paper. We establish the computation methods of field strength and potential energy to reveal the potential attraction relationship among stations from the perspective of the collec- tion and distribution of passenger flow and the topology of network. As for the computation OPEN ACCESS methods of field strength, an optimum path concept is proposed to define betweenness cen- Citation: Li M, Wang Y, Jia L (2017) The modeling trality parameter. Regarding the computation of potential energy, Compound Simpson's of attraction characteristics regarding passenger Rule Formula is applied to get a solution to the function. Taking No. 10 Beijing Subway as a flow in urban rail transit network based on field theory. PLoS ONE 12(9): e0184131. https://doi. practical example, an analysis of simulation and verification is conducted, and the results org/10.1371/journal.pone.0184131 shows in the following ways. Firstly, the bigger field strength value between two stations is, Editor: Xiaolei Ma, Beihang University, CHINA the stronger passenger flow attraction is, and the greater probability of the formation of the largest passenger flow of section is. -
Does Industrial Agglomeration Promote Carbon Efficiency? a Spatial Econometric Analysis and Fractional-Order Grey Forecasting
Hindawi Journal of Mathematics Volume 2021, Article ID 5242414, 18 pages https://doi.org/10.1155/2021/5242414 Research Article Does Industrial Agglomeration Promote Carbon Efficiency? A Spatial Econometric Analysis and Fractional-Order Grey Forecasting Zuoren Sun 1 and Yi Liu 1,2 1Business School, Shandong University, No. 180 West of Wenhua Road, Weihai, Shandong 264209, China 2Tianjin Central Sub-branch, 'e People’s Bank of China, Economic and Technological Development Zone, No. 59 East of Xincheng Road, Tianjin 300450, China Correspondence should be addressed to Zuoren Sun; [email protected] Received 11 June 2021; Accepted 17 July 2021; Published 31 July 2021 Academic Editor: Lifeng Wu Copyright © 2021 Zuoren Sun and Yi Liu. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In theory, the industrial agglomeration is a double-edged sword as there are both positive and negative externalities. China’s cities, with great disparities on degrees of the industrial agglomeration, often face different energy and carbon dioxide emission problems, which raise the question whether the industrial agglomeration promotes or inhibits energy efficiency and carbon dioxide emission. )is paper explored the effects of the industrial agglomeration on carbon efficiency in China. Spatial econometric methods were implemented using panel data (2007–2016) of 285 cities above the prefecture level. )e results revealed that industrial agglomerations have significant impacts on the urban carbon efficiency with significant spatial spillover effects. )e agglomerations of the manufacturing and high-end productive service industries take positive effects on carbon efficiency while the low-end productive and living service industries take negative effects.