TY - JOUR
T1 - Quantitative Analysis of Urban Polycentric Interaction Using Nighttime Light Data
T2 - A Case Study of Shanghai, China
AU - Tu, Yue
AU - Chen, Zuoqi
AU - Wang, Congxiao
AU - Yu, Bailang
AU - Liu, Bingjie
N1 - Publisher Copyright:
© 2008-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - The urban polycentric structure is connected to the economy and enormously impacts socioeconomic development and policies. Unlike traffic data and big geographic data, remote sensing data have shown an accessible way to measure urban spatial interaction. However, most existing studies only focused on the interaction among cities rather than within cities. Meanwhile, the urban spatial interaction, which should be directional, was always expressed as an undirected graph. Therefore, this article developed a network-based radiation model using nighttime light remote sensing data and mapped a directed interaction network (inward and outward direction) among urban centers. Taking the region within the outer ring of Shanghai as an example, the taxi trajectory data were adopted to validate the result with the R2 of 0.61. We discovered that: the urban polycentric interaction network is dumbbell-shaped with an east-west development corridor crossing the main center and connecting two main urban center clusters. The in-strength and out-strength interaction of each urban center have a similar distribution. The urban centers with higher in-strength and out-strength are mainly concentrated toward the main center, especially in the east-west direction. At the urban center level, the total inward interaction is slightly higher than the total outward interaction of most urban centers. Spatially, an unbalanced distribution was found. In summary, our proposed method effectively indicates the urban polycentric interaction and is applicable to other regions since it requires no arbitrary parameters and the input data (e.g., nighttime light data) is readily available.
AB - The urban polycentric structure is connected to the economy and enormously impacts socioeconomic development and policies. Unlike traffic data and big geographic data, remote sensing data have shown an accessible way to measure urban spatial interaction. However, most existing studies only focused on the interaction among cities rather than within cities. Meanwhile, the urban spatial interaction, which should be directional, was always expressed as an undirected graph. Therefore, this article developed a network-based radiation model using nighttime light remote sensing data and mapped a directed interaction network (inward and outward direction) among urban centers. Taking the region within the outer ring of Shanghai as an example, the taxi trajectory data were adopted to validate the result with the R2 of 0.61. We discovered that: the urban polycentric interaction network is dumbbell-shaped with an east-west development corridor crossing the main center and connecting two main urban center clusters. The in-strength and out-strength interaction of each urban center have a similar distribution. The urban centers with higher in-strength and out-strength are mainly concentrated toward the main center, especially in the east-west direction. At the urban center level, the total inward interaction is slightly higher than the total outward interaction of most urban centers. Spatially, an unbalanced distribution was found. In summary, our proposed method effectively indicates the urban polycentric interaction and is applicable to other regions since it requires no arbitrary parameters and the input data (e.g., nighttime light data) is readily available.
KW - Adjusted radiation model
KW - NPP-VIIRS
KW - nighttime light data (NTL)
KW - urban centers
UR - https://www.scopus.com/pages/publications/85122104669
U2 - 10.1109/JSTARS.2021.3137167
DO - 10.1109/JSTARS.2021.3137167
M3 - 文章
AN - SCOPUS:85122104669
SN - 1939-1404
VL - 15
SP - 1114
EP - 1122
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
ER -