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author:

Tu, Yue (Tu, Yue.) [1] | Wang, Congxiao (Wang, Congxiao.) [2] | Yu, Bailang (Yu, Bailang.) [3] | Chen, Zuoqi (Chen, Zuoqi.) [4] (Scholars:陈佐旗) | Zhang, Tinglin (Zhang, Tinglin.) [5]

Indexed by:

EI Scopus SCIE

Abstract:

Urban spatial interaction serves as an indicative measure for estimating the intensity and character of interurban linkages and relationships. The previous studies have utilized intercity relational data (e.g., population migration (PM), goods trade, and information exchange) to build up urban connections directly. Besides, the nighttime light (NTL) data have also been adopted to simulate a dynamic urban intercity flow. However, the relevant studies have not clearly defined the urban spatial interaction based on the NTL data. To answer this question, we used trial-and-error testing to define the NTL-based interaction. First, five traditional urban interactions were selected as the potential definitions, including PM, transfer of innovation, information flow (IF), financial flow, and urban composite interaction (CI). Second, as usual, the NTL-based urban interaction, named the NTL interaction (NTLI) index, was simulated based on the NPP-VIIRS-like NTL data and the radiation model. Taking the Yangtze River Delta urban agglomerations (YRDUAs) as an example, we found that the NTL-based urban interaction is more like the PM at the urban agglomeration scale and the provincial scale with R-2 of 0.71 and 0.59, respectively. In addition to this, the NTLI index has a weak correlation with the transfer of patent (TP) index, IF index, economic interaction (EI) index, and CI index. To sum up, the interaction network from NTL data can be an adequate proxy of urban population interaction, rather than the knowledge network or economic network. This study provides a new thought for urban network simulation and urban population mobility research.

Keyword:

Economics Indexes Nighttime light (NTL) data NTL interaction (NTLI) index Patents Sociology Statistics Urban areas urban spatial interaction Web and internet services Yangtze River Delta urban agglomerations (YRDUAs)

Community:

  • [ 1 ] [Tu, Yue]East China Normal Univ, Key Lab Geog Informat Sci, Minist Educ, Shanghai 200241, Peoples R China
  • [ 2 ] [Wang, Congxiao]East China Normal Univ, Key Lab Geog Informat Sci, Minist Educ, Shanghai 200241, Peoples R China
  • [ 3 ] [Yu, Bailang]East China Normal Univ, Key Lab Geog Informat Sci, Minist Educ, Shanghai 200241, Peoples R China
  • [ 4 ] [Tu, Yue]East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R China
  • [ 5 ] [Wang, Congxiao]East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R China
  • [ 6 ] [Yu, Bailang]East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R China
  • [ 7 ] [Chen, Zuoqi]Fuzhou Univ, Acad Digital China, Natl & Local Joint Engn Res Ctr Satellite Geospati, Key Lab Spatial Data Min & Informat Sharing,Minist, Fuzhou 350108, Peoples R China
  • [ 8 ] [Zhang, Tinglin]East China Normal Univ, Res Ctr China Adm Div, Sch Geog Sci, Future City Lab, Shanghai 200241, Peoples R China

Reprint 's Address:

  • [Wang, Congxiao]East China Normal Univ, Key Lab Geog Informat Sci, Minist Educ, Shanghai 200241, Peoples R China

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Source :

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

ISSN: 1545-598X

Year: 2024

Volume: 21

4 . 0 0 0

JCR@2023

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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