Big data analysis on the spatial networks of urban agglomeration

Fang, Chuanglin; Yu, Xiaohua; Zhang, Xiaoling; Fang, Jiawen; Liu, Haimeng

doi:10.1016/j.cities.2020.102735

Cited by 86 publications

(27 citation statements)

References 33 publications

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“…The technical concerns for developing smart cities appear in a variety of research about better-informed communication systems, at local (Keyvan-Ekbatani et al, 2012) or global scale (Graham, 1999). Massive data extracted from smart phones traces, exchanges in social networks collected from the web (as Twitter, Facebook or Weibo, see for instance Fang et al 2020) also are promising researches but now often lack of enough substance in the attributes they inform for being used efficiently as proxies for measuring interurban interactions. Interestingly enough, many lessons about the importance and variety of urban interactions may be taken from the gigantic experiment of their drastic reduction worldwide and at all geographical scales during the recent epidemics of Covid19.…”

Section: Towards Multi-level Urban Networkmentioning

confidence: 99%

From networks of cities to systems of cities

Pumain¹

2021

Handbook of Cities and Networks

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This chapter elaborates on a shift in urban theories from a static and structural view of "networks of cities" in a territory toward a systemic conceptualization and dynamic analysis of interdependencies and co-evolution in complex systems. The major concepts and methodological issues that were used in the process of theory building are reviewed during three major stages in the history of the science of cities, including examples taken mainly from the literature in Europe and United States. In a first step, the role of communication networks in the historical evolution of cities is intuitively described and the first measurements of cities spacing, size and functions lead to the construction of formal theories of networks of cities according to static models as central place theory and rank-size distribution. In a second step the dynamics of city networks is inferred from statistical analyses and cross sectional comparisons of the attributes of large numbers of cities at different points in time leading to constructing an evolutionary theory of systems of cities and including inter-urban communication and spatial diffusion of innovation. In a third step the growing availability of urban data, especially about urban interactions, and the increased power of computing algorithms enable to fully explain this evolutionary theory of systems of cities by including features of complex systems as bifurcations, fractals or scaling laws as well as theories of social sciences about networks, innovation and social change. This comprehensive theory is both exemplified on a huge variety of empirical data and tested through dedicated simulation models.

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Section: Towards Multi-level Urban Networkmentioning

confidence: 99%

From networks of cities to systems of cities

Pumain¹

2021

Handbook of Cities and Networks

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“…In order to further strengthen resource reallocation between regions, China has formulated a grand strategy for a national high-speed rail (HSR) system connecting 250 cities with a total mileage of 45,000 km by 2030. This process indicates that the construction of HSR to enhance the non-local connections of cities has become an unstoppable trend [ 5 , 6 ]. Hence, in-depth discussion the impact and mechanisms of HSR on ULUE has become an important concern.…”

Section: Introductionmentioning

confidence: 99%

Change in Urban Land Use Efficiency in China: Does the High-Speed Rail Make a Difference?

Qiao

Huang

2021

IJERPH

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High-speed rail (HSR) increases the non-local connections in cities and plays an essential role in urban land use efficiency. This paper uses a multi-period difference-in-difference model and a threshold model based on sample data that cover 284 Chinese cities from 2003–2018 to investigate the impact of HSR on urban land use efficiency. The results show that there is a 0.021 increase in urban land use efficiency after opening the HSR. The number of HSR stations and routes can increase the urban land use efficiency by 0.004 and 0.013, respectively. Compared with the cities in the East, the midwestern ones are more vulnerable to the impact of HSR. In particular, the positive impact of the number of HSR stations on the urban land use efficiency in cities with an urban population density exceeding 795 person/km2 is two times larger than cities with an urban population density of less than 795 person/km2. In addition, the impact of the number of HSR routes on urban land use efficiency in cities with an urban population density of less than 1003 person/km2 is five times larger than that of cities with an urban population density exceeding 1003 person/km2.

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“…e POLYNET project measured the degree of cohesion of urban agglomerations in Europe by analyzing daily commuting flows [15]. e intensity, direction, network pattern, and hierarchical structure of the connections between cities have been discussed [16][17][18][19]. e measurement methods of urban connection intensity mainly include the gravity model [20,21], urban flow intensity model [22], regional accessibility [23], and social network analysis [24,25].…”

Section: Introductionmentioning

confidence: 99%

Exploring Coupling Relationship between Urban Connection and High‐quality Development Using the Case of Lanzhou‐Xining Urban Agglomeration

et al. 2021

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Based on socioeconomic statistical data, transport data, and network big data, the urban connection index (UCI) was constructed in terms of industry, transportation, information, and innovation, and the high-quality development index (HDI) was established from five aspects: innovation, coordination, green development, openness, and sharing. Taking Lanzhou-Xining urban agglomeration as a case, the urban connection intensity and high-quality development level were measured to analyze the relationship between them. From 2012 to 2018, the UCI and HDI of each city showed different degrees of growth. Note that there exist significant regional differences, with Lanzhou and Xining having the largest difference. The biggest gap among cities lies in the innovative connection intensity. Moreover, urban external connections are closely related to high-quality development, especially innovation and green development. For every 1% increase in industrial and transport connection, the HDI will increase by 0.317% and 0.159%, respectively. This study provides a methodological reference for measuring urban connectivity and provides decision support for high-quality development in China and other countries.

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Big data analysis on the spatial networks of urban agglomeration

Cited by 86 publications

References 33 publications

From networks of cities to systems of cities

From networks of cities to systems of cities

Change in Urban Land Use Efficiency in China: Does the High-Speed Rail Make a Difference?

Exploring Coupling Relationship between Urban Connection and High‐quality Development Using the Case of Lanzhou‐Xining Urban Agglomeration

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