Spatial and temporal characteristics and correlation analysis of road networks and urban sprawl

被引:0
|
作者
Zhao, Guoliang [1 ]
Zheng, Xinqi [1 ]
Yuan, Zhiyuan [1 ]
Zhang, Lulu [1 ]
机构
[1] School of Information Engineering, China University of Geosciences, Beijing,100083, China
关键词
D O I
10.11975/j.issn.1002-6819.2015.12.030
中图分类号
学科分类号
摘要
Urban sprawl has become a widespread trend for developing countries. Road networks are an extremely important factor driving the expansion of urban land and are thus subject to special attention from academia. For the purpose of studying the relationship between road networks and urban sprawl based on multi-period remote sensing images and vector data of urban road networks, we took Beijing, New York, London and Chicago as the study areas, and firstly attained urban land use vector data through image interpretation with the aid of a remote sensing and GIS platform; and then utilized overlay analysis to extract the information on urban sprawl. A map of road network density was further generated and manufactured using the density analysis tool of ArcGIS. Finally, we conducted a spatial statistical analysis between road networks and urban sprawl and then systematically analyzed their distribution features. In addition, the Urban sprawl-road network density model was established by regression analysis used for fitting the relationship. The results proved that 1) in the last 3 decades, there had been a consistency in terms of urban land expansion features of the 4 cities. The area of urban land expansion was gradually reducing from the center outward, while the distribution of road network density overall was gradually thinner from the city center outward. There was a close association between urban land expansion and spatial density of road network. Due to different locations, there were some differences in spatial distribution for urban expansion. 2) The urban sprawl thresholds of Beijing, New York, London and Chicago were 1.89×104, 3.78×104, 5.70×104 and 6.47×104 km/km2, respectively, and urban expansion had an inverted U-shaped curve relationship with road networks when the road network density did not exceed the threshold. 3) The turning points for urban sprawl for Beijing, New York, London and Chicago were 3.3×103, 11.84×103, 16.86×103 and 21.40×103 km/km2, respectively, which indicated that urban expansion initially accelerated with the increasing of the density of road networks; however, after the turning point was reached, the expansion rate of urban decreased; and when the road density exceeded the threshold, urban areas would no longer expand. 4) The road network played a driving role for urban expansion, inducing urban land expansion. Thus, the construction process of urban transport systems required strict proof, and we should give a scientific definition to the capacity of transportation, the amount of road networks and the road network layout. The correlation between urban sprawl and road network features had an important reference for the future development of global developing cities. Understanding road network density offered some predictive effects for urban land expansion, allowed the avoidance of irregular expansion, and provided new ideas for addressing the inefficient utilization of land and other issues. The tendency for urban expansion varied with the degree of road network development in different locations, and exerting the law was conducive to scientific planning and rational use of the land in city and to the improvemente of land use efficiency. In short, the quantitative relationship between road networks and urban sprawl presented an inverted U-shaped pattern. A reasonable grasp of the turning point for urban expansion was worth pondering for other cities in the stage of rapid development in order to promote sustainable development and achieve smart growth in the future. Therefore, we should pay attention to the role of urban road network planning and promote urban's smart growth in order to achieve mutual promotion and coordinated development of urban land use and road network construction. ©, 2015, Chinese Society of Agricultural Engineering. All right reserved.
引用
收藏
页码:220 / 229
相关论文
共 50 条
  • [21] Role of Traffic Emission on Temporal and Spatial Characteristics of Pollutant Concentration on Urban Road Network: A Case of Beijing
    Wang, Zirui
    Zhou, Huixin
    Si, Yang
    Li, Yahui
    JOURNAL OF ADVANCED TRANSPORTATION, 2020, 2020
  • [22] Bayesian spatial correlation, heterogeneity and spillover effect modeling for speed mean and variance on urban road networks
    Zhou, Yue
    Jiang, Xinguo
    Fu, Chuanyun
    Liu, Haiyue
    Zhang, Guopeng
    ACCIDENT ANALYSIS AND PREVENTION, 2022, 174
  • [23] Spatial-Temporal Impacts of Urban Sprawl on Ecosystem Services: Implications for Urban Planning in the Process of Rapid Urbanization
    Li, Xiaoyan
    Suoerdahan, Gulinaer
    Shi, Zhenyu
    Xing, Zihan
    Ren, Yongxing
    Yang, Ran
    LAND, 2021, 10 (11)
  • [24] Toward a Better Understanding of Urban Sprawl: Linking Spatial Metrics and Landscape Networks Dynamics
    Hu, Tengyun
    Huang, Xiaochun
    Li, Xuecao
    Liang, Lu
    Xue, Fei
    COMPUTATIONAL URBAN PLANNING AND MANAGEMENT FOR SMART CITIES, 2019, : 163 - 178
  • [25] Spatial and Temporal Variation Characteristics of Urban Traffic Congestion Factors and Source Analysis
    Zhao X.-T.
    Hu L.-W.
    Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 2023, 23 (03): : 300 - 310
  • [26] Spatial and temporal population change in the Tehran Metropolitan Region and its consequences on urban decline and sprawl
    Talkhabi, Hamidreza
    Ghalehteimouri, Kamran Jafarpour
    Mehranjani, Mohammad Soleimani
    Zanganeh, Ahmad
    Karami, Tajeddin
    ECOLOGICAL INFORMATICS, 2022, 70
  • [27] Analyzing urban sprawl applying spatial autocorrelation techniques to multi-temporal satellite data
    Calamita, G.
    Lanorte, A.
    Lasaponara, R.
    Danese, M.
    Murgante, B.
    Nole, G.
    Casas, G. B. Las
    URBAN AND REGIONAL DATA MANAGEMENT, 2013, : 161 - 170
  • [28] Using Spatial Autocorrelation Techniques and Multi-temporal Satellite Data for Analyzing Urban Sprawl
    Nole, Gabriele
    Danese, Maria
    Murgante, Beniamino
    Lasaponara, Rosa
    Lanorte, Antonio
    COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2012, PT III, 2012, 7335 : 512 - 527
  • [29] Quantifying Urban Sprawl with Spatial Autocorrelation Techniques using Multi-Temporal Satellite Data
    Nole, Gabriele
    Lasaponara, Rosa
    Lanorte, Antonio
    Murgante, Beniamino
    INTERNATIONAL JOURNAL OF AGRICULTURAL AND ENVIRONMENTAL INFORMATION SYSTEMS, 2014, 5 (02) : 19 - 37
  • [30] Understanding structure of urban traffic network based on spatial-temporal correlation analysis
    Yang, Yanfang
    Jia, Limin
    Qin, Yong
    Han, Shixiu
    Dong, Honghui
    MODERN PHYSICS LETTERS B, 2017, 31 (22):