Measuring the vibrancy of urban neighborhoods using mobile phone data with an improved PageRank algorithm

被引:20
|
作者
Jia, Chen [1 ,2 ]
Du, Yunyan [3 ]
Wang, Siying [1 ]
Bai, Tianyang [1 ]
Fei, Teng [1 ]
机构
[1] Wuhan Univ, Sch Resources & Environm Sci, Wuhan 430079, Hubei, Peoples R China
[2] Peking Univ, Sch Earth & Space Sci, Beijing, Peoples R China
[3] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
关键词
LAND-USE; PATTERNS; SYSTEMS;
D O I
10.1111/tgis.12515
中图分类号
P9 [自然地理学]; K9 [地理];
学科分类号
0705 ; 070501 ;
摘要
A neighborhood is a shared space for spatial interaction. The vibrancy of a neighborhood represents a synergy between the people, activities, and values in a place, which increases community vitality and spurs economic opportunity. It can be investigated both qualitatively and quantitatively. However, there are technical challenges involved in accurately charting vibrancy. With the recent advances in communication technology and the prevalence of location-aware devices such as mobile phones, individual trajectories can be collected and analyzed on a large scale. In previous research, the weights of the vibrancies corresponding to different trajectories were not differentiated. In this study, an improved PageRank algorithm using a weighted bipartite graph is proposed to measure the vibrancy of an urban neighborhood from a different perspective, which highlights the differences between vibrancies arising from different types of citizens. This method connects the land much more closely with human activities and provides a new perspective on, and guidance for, urban resource allocation and urban planning.
引用
收藏
页码:241 / 258
页数:18
相关论文
共 50 条
  • [31] Mobile phone data to describe urban practices: An overview in the literature
    [J]. SpringerBriefs Appl. Sci. Technol., (13-25):
  • [32] Identifying the Urban Transportation Corridor Based on Mobile Phone Data
    Wang, Yanwei
    Li, Zhiheng
    Li, Li
    Wang, Shuofeng
    Yu, Juntang
    Ke, Ruimin
    [J]. 2015 IEEE FIRST INTERNATIONAL SMART CITIES CONFERENCE (ISC2), 2015,
  • [33] Urban Traffic Commuting Analysis Based on Mobile Phone Data
    Dong, Honghui
    Ding, Xiaoqing
    MingchaoWu
    Shi, Yan
    Jia, Limin
    Qin, Yong
    Chu, Lianyu
    [J]. 2014 IEEE 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2014, : 611 - 616
  • [34] Improved centrality measure based on the adapted PageRank algorithm for urban transportation multiplex networks
    Li, Zhitao
    Tang, Jinjun
    Zhao, Chuyun
    Gao, Fan
    [J]. CHAOS SOLITONS & FRACTALS, 2023, 167
  • [35] Mobile phone component object detection algorithm based on improved SSD
    Huang, Zhe
    Yin, Zhenyu
    Ma, Yue
    Fan, Chao
    Chai, Anying
    [J]. PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE OF INFORMATION AND COMMUNICATION TECHNOLOGY, 2021, 183 : 107 - 114
  • [36] Measuring and monitoring construction dust using mobile phone cameras
    Abe, Reiko
    Sekhar, Ch Ravi
    Sasaki, S.
    Shivananda, K. R.
    Shinji, M.
    [J]. CURRENT SCIENCE, 2013, 104 (07): : 817 - 821
  • [37] Measuring Tree Diameter with Photogrammetry Using Mobile Phone Cameras
    Ahamed, Aakash
    Foye, John
    Poudel, Sanjok
    Trieschman, Erich
    Fike, John
    [J]. FORESTS, 2023, 14 (10):
  • [38] Measuring mobility inequalities of favela residents based on mobile phone data
    Rodrigues, Andre Leite
    Giannotti, Mariana
    Barboza, Matheus H. C. Cunha
    Alves, Bianca Bianchi
    [J]. HABITAT INTERNATIONAL, 2021, 110
  • [39] A Multi-City Urban Population Mobility Study Using Mobile Phone Traffic Data
    Gariazzo, Claudio
    Pelliccioni, Armando
    [J]. APPLIED SPATIAL ANALYSIS AND POLICY, 2019, 12 (04) : 753 - 771
  • [40] Delineating urban park catchment areas using mobile phone data: A case study of Tokyo
    Guan, ChengHe
    Song, Jihoon
    Keith, Michael
    Akiyama, Yuki
    Shibasaki, Ryosuke
    Sato, Taisei
    [J]. COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, 2020, 81