A spatial multi-scale object to analyze road networks

被引:9
|
作者
Lagesse, C. [1 ]
Bordin, P. [2 ,3 ]
Douady, S. [1 ]
机构
[1] Univ Paris Diderot, Sorbonne Paris Cite, MSC, UMR 7057, Paris, France
[2] Univ Paris Est, Inst Rech Constructibilite, ESTP, Cachan, France
[3] Univ Paris Diderot, Sorbonne Paris Cite, IED, UMR 8236, Paris, France
关键词
road network; graph theory; spatial analysis; city modeling; morphogenesis;
D O I
10.1017/nws.2015.4
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
City road networks have been extensively studied for their social significance or to quantify their connections and centralities, but often their geographical origin is forgotten. This work focuses on the spatial-geographical and geometrical aspects of the road network skeleton. Following previous work, a multi-scale object, the way, is constructed, based only on the local geometry at road crossings. The best method to reconstruct significant elements is investigated. The results show that this object is geographically meaningful, with many particular characteristics. A new indicator, structurality, is introduced and compared with previous indicators, on the cities of Paris and Avignon. Structurality appears to be stable over the borders of the map sample, and is able to reveal the underlying coherence of the road network. This stability can be interpreted as coming from the particular way the network developed in time, and was later preserved. This link with the historical development of the cites, which deserves to be further studied, is exemplified in the cases of Villers-sur-Mer (France) and Manaus (Brazil). The construction method, the results, and their potential meaning are discussed in detail so that they can be used in various related disciplines, such as sociology, town planning, geomatics, and physics.
引用
收藏
页码:156 / 181
页数:26
相关论文
共 50 条
  • [41] A new Voronoi diagram-based approach for matching multi-scale road networks
    Jianhua Wu
    Yu Zhao
    Mengjuan Yu
    Xiaoxiang Zou
    Jiaqi Xiong
    Xiang Hu
    [J]. Journal of Geographical Systems, 2023, 25 : 265 - 289
  • [42] Spatial Index Technology for Multi-scale and Large Scale Spatial Data
    Liu, Yuanyuan
    Liu, Gang
    He, Zhenwen
    [J]. 2010 18TH INTERNATIONAL CONFERENCE ON GEOINFORMATICS, 2010,
  • [43] MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection
    Ma, Wenchi
    Wu, Yuanwei
    Wang, Zongbo
    Wang, Guanghui
    [J]. 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2018, : 2510 - 2515
  • [44] Attention to the Scale : Deep Multi-Scale Salient Object Detection
    Zhang, Jing
    Dai, Yuchao
    Li, Bo
    He, Mingyi
    [J]. 2017 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING - TECHNIQUES AND APPLICATIONS (DICTA), 2017, : 105 - 111
  • [45] Influenced Consensus for Multi-Scale Networks
    Foight, Dillon R.
    Mesbahi, Mehran
    [J]. 2019 AMERICAN CONTROL CONFERENCE (ACC), 2019, : 2753 - 2758
  • [46] Multi-Scale Behavior Learning for Multi-Object Tracking
    Liu Wancun
    Tang Wenyan
    Zhang Liguo
    Zhang Xiaolin
    Li Jiafu
    [J]. PROCEEDINGS FIRST INTERNATIONAL CONFERENCE ON ELECTRONICS INSTRUMENTATION & INFORMATION SYSTEMS (EIIS 2017), 2017, : 639 - 643
  • [47] USSC-YOLO: Enhanced Multi-Scale Road Crack Object Detection Algorithm for UAV Image
    Zhang, Yanxiang
    Lu, Yao
    Huo, Zijian
    Li, Jiale
    Sun, Yurong
    Huang, Hao
    [J]. SENSORS, 2024, 24 (17)
  • [48] A Multi-Scale Traffic Object Detection Algorithm for Road Scenes Based on Improved YOLOv5
    Li, Ang
    Sun, Shijie
    Zhang, Zhaoyang
    Feng, Mingtao
    Wu, Chengzhong
    Li, Wang
    [J]. ELECTRONICS, 2023, 12 (04)
  • [49] Improved YOLOv3 model with feature map cropping for multi-scale road object detection
    Shen, Lingzhi
    Tao, Hongfeng
    Ni, Yuanzhi
    Wang, Yue
    Stojanovic, Vladimir
    [J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2023, 34 (04)
  • [50] Salient object detection in hyperspectral imagery using multi-scale spectral-spatial gradient
    Zhang, Lei
    Zhang, Yanning
    Yan, Hangqi
    Gao, Yifan
    Wei, Wei
    [J]. NEUROCOMPUTING, 2018, 291 : 215 - 225