Understanding the impact of High Speed Railway on urban innovation performance from the perspective of agglomeration externalities and network externalities

Tang, Chenghui; Guan, Mingming; Dou, Jianmin

doi:10.1016/j.techsoc.2021.101760

Cited by 36 publications

(22 citation statements)

References 24 publications

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“…In contrast, other studies found that there is a strong spatial relevance between urban big data and NTL data within the urban city, which they used to modify the error generated by NTL data in the urban agglomeration boundary delineation with urban big data, contributing to a better delineation result (Liu et al, 2020). However, there are static factors in the real-world urban agglomeration space, such as city light brightness, the distribution of urban infrastructure, and the dynamic floating of elements between cities and clusters (CastelIs-Quintana, 2017;Tang et al, 2021). NTL data and current urban big data focus on the static space reflected in urban space rather than the dynamic floating.…”

Section: Discussionmentioning

confidence: 99%

Using Tencent User Location Data to Modify Night-Time Light Data for Delineating Urban Agglomeration Boundaries

Zhu

Chang

et al. 2022

Front. Environ. Sci.

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The study of urban agglomeration boundaries is helpful to understand the internal spatial structure of urban agglomeration, evaluate the development level of urban agglomeration, and thus, assist in the formulation of regional planning and policies. However, previous studies often used only static spatial elements to delineate the boundaries of urban agglomerations, ignoring the spatial connections within urban agglomerations. In this study, night-time light and Tencent user location data were evaluated separately and fused to delineate urban agglomeration boundaries from both static and dynamic spatial perspectives. Additionally, it has been shown in the study results that the accuracy of urban agglomeration boundary delineated by night-time light data is 84.90%, with Kappa coefficient as 0.6348. The accuracy delineated by Tencent user location data is 82.40%, with Kappa coefficient as 0.5637, while the accuracy delineated by data fusion is 92.70%, with Kappa coefficient as 0.7817. Therefore, it can be concluded that the fusion of night-time light and Tencent user location data had the highest accuracy in delineating urban agglomeration boundaries, which verified that the fusion of dynamic spatial elements on a single static spatial element can supplement the spatial connection of urban agglomeration. Our findings enrich the understanding of urban agglomerations, and the accurate delineation of urban agglomerations boundaries can aid urban agglomeration planning and management.

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Section: Discussionmentioning

confidence: 99%

Using Tencent User Location Data to Modify Night-Time Light Data for Delineating Urban Agglomeration Boundaries

Zhu

Chang

et al. 2022

Front. Environ. Sci.

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“…The improvement of highspeed connection infrastructure (such as aviation, high-speed rail, telecommunications and Internet, etc.) has strengthened the connections between cities, promoting intercity network relationships and externalities [1][2][3]. The space of flows proposed by Castells (1999) [4] has given birth to a network perspective on cities that describes the phenomenon of spatial connection in city networks.…”

Section: Introductionmentioning

confidence: 99%

“…The objectives of this paper are as follows: (1) To establish a framework by which to assess network structure resilience; (2) To reveal the spatial patterns and investigate the structural characteristics of China's urban information, transportation, and economic networks; (3) To present the structural resilience of urban networks under disruption scenarios in order to identify the dominant and vulnerable nodes affecting resilience; and (4) To propose suggestions for optimization and provide a reference for strengthening regional cooperation, resource allocation, and development planning.…”

Section: Introductionmentioning

confidence: 99%

Resilience of Urban Network Structure in China: The Perspective of Disruption

Wei

Pan

2021

IJGI

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In light of the long-term pressure and short-term impact of economic and technological globalization, regional and urban resilience has become an important issue in research. As a new organizational form of regional urban systems, the resilience of urban networks generated by flow space has emerged as a popular subject of research. By gathering 2017 data from the Baidu search index, the Tencent location service, and social statistics, this study constructs information, transportation, and economic networks among 344 cities in China to analyze the spatial patterns of urban networks and explore their structural characteristics from the perspectives of hierarchy and assortativity. Transmissibility and diversity were used to represent the resilience of the network structure in interruption scenarios (node failure and maximum load attack). The results show the following: The information, transportation, and economic networks of cities at the prefecture level and higher in China exhibit a dense pattern of spatial distribution in the east and a sparse pattern in the west; however, there are significant differences in terms of hierarchy and assortativity. The order of resilience of network transmissibility and diversity from strong to weak was information, economic, transportation. Transmissibility and diversity had nearly identical scores in response to the interruption of urban nodes. Moreover, a highly heterogeneous network was more likely to cause shocks to the network structure, owing to its cross-regional urban links in case of disturbance. We identified 12 dominant nodes and 93 vulnerable nodes that can help accurately determine the impetus behind network structure resilience. The capacity of regions for resistance and recovery can be improved by strengthening the construction of emergency systems and risk prevention mechanisms.

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“…Cities of different levels have great differences in economic scale, innovation factor agglomeration and innovation resource allocation efficiency, which may further lead to great differences in the effects of NEDC policy among different cities (Tang et al., 2021). We refer to the ‘ 2021 China Urban Business Charm Ranking ’ published by the New First‐tier Cities Research Institute, which classifies the city by the following five dimensions: the business resources, urban hub, urban agglomeration degrees activity, lifestyle diversity and future plasticity.…”

Section: Resultsmentioning

confidence: 99%

How does e‐commerce demonstration city improve urban innovation? Evidence from China

Cai

2023

Econ of Transit and Inst Chang

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This paper provides a feasible way to realize urban innovation through the e‐commerce pilot. Based on the data of 289 cities in China from 2002 to 2019, we examine the impact of the National E‐commerce Demonstration City pilot on urban innovation with a staggered difference‐in‐differences method. The results show that after becoming a pilot city of National E‐commerce Demonstration Cities (NEDC), the quantity and quality of urban innovation have improved. Further, NEDC policy plays a greater role in promoting innovation in economically agglomerated areas, first‐and third‐tier cities and areas with a higher degree of marketization. Finally, NEDC policy can affect the level of urban innovation by optimizing the allocation of innovation factors, improving the efficiency of credit resource allocation and forming the logistics scale effect.

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Understanding the impact of High Speed Railway on urban innovation performance from the perspective of agglomeration externalities and network externalities

Cited by 36 publications

References 24 publications

Using Tencent User Location Data to Modify Night-Time Light Data for Delineating Urban Agglomeration Boundaries

Using Tencent User Location Data to Modify Night-Time Light Data for Delineating Urban Agglomeration Boundaries

Resilience of Urban Network Structure in China: The Perspective of Disruption

How does e‐commerce demonstration city improve urban innovation? Evidence from China

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