Urban network externalities, agglomeration economies and urban economic growth

Yin, Huang; Hong, Tao; Tao, Ma

doi:10.1016/j.cities.2020.102882

Cited by 125 publications

(57 citation statements)

References 58 publications

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“…Transportation has been one of the most important economic growth drivers as it fosters access to employment opportunities, services, and amenities, and contributes to a social network [1,2]. As highlighted in previous studies, the relationship between transportation and economies is statistically significant and positive [3,4].…”

Section: Introductionmentioning

confidence: 76%

“…In effect, the UA owns a complex, unique, and largescale transportation system connecting intra and inter-cities in a broader scope [35]. The transportation system is more advanced than that of counterparts regarding travel distance hierarchy, travel purpose diversity, and travel mode efficiency [1,23].…”

Section: Ua Transportationmentioning

confidence: 99%

“…Due to the pursuit of efficiency, more capital will be invested to construct a stronger transportation network in the core cities [7,8]. UA government policy should thus address the integral development of transportation networks relying on the improvement of core cities' transportation networks [1]. Considering the applicability of the field strength model in studying cities' radiation effect, this research exploited the transportation field strength model.…”

Section: Ua Transportation Fieldmentioning

confidence: 99%

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Radiation Effect of Urban Agglomeration’s Transportation Network: Evidence from Chengdu–Chongqing Urban Agglomeration, China

Yao

Xiao

et al. 2021

Land

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Recent years have seen the global proliferation and integration of transportation systems in urban agglomeration (UA), suggesting that transportation networks have become more prominent in the sustainable development of UA. Core cities play a radiating and driving role in affecting their adjacent cities to formulate transportation networks. Such a phenomenon is called the radiation effect of transportation networks and can be imaged using a field strength model as proposed in the study. The field strength model was verified using the Chengdu–Chongqing urban agglomeration (CCUA) as a case. Case data concerning transportation routes and traffic volume were collected for the past 20 years. The data analyses results indicate a relatively stable pattern of transportation networks in the UA. UA cities’ radiation effects follow the same compactness trend. The core cities’ radiation spheres go beyond their territories, and they can envelop the surrounding cities, highlighting the core cities’ dominance in the entire transportation network. Moreover, two development stages of UA transportation—focus and spillover—are also identified. This study contributes to the literature by providing an innovative quantitative method to detect the interaction between a city’s transportation system and peripheral cities or regions. The radiation effect of cities’ transportation systems should be considered in the UA transportation development plan, so as to meet the needs of spatial structure planning and coordinated development of the UA.

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Section: Introductionmentioning

confidence: 76%

Section: Ua Transportationmentioning

confidence: 99%

Section: Ua Transportation Fieldmentioning

confidence: 99%

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Radiation Effect of Urban Agglomeration’s Transportation Network: Evidence from Chengdu–Chongqing Urban Agglomeration, China

Yao

Xiao

et al. 2021

Land

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“…算。借鉴上述方法，种照辉等 [35] 在测度长江三角洲城市群的人口流动网络、企业组织网络和电子商务网络基础上，通过构造空间计量模型考察了不同维度城市网络背景下影响城市群经济增长主要因素的空间溢出效应，并发现基于不同视角的城市网络，对经济增长具有显著影响的主要要素的网络溢出效应存在着明显差异。Huang 等 [36] 基于中国 273…”

Section: 城市规模借用unclassified

Review on the urban network externalities

Cheng¹,

SU²

2021

Progress in Geography

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“…Using this approach to estimate changes in land use entails more than simply building a Markov model for each city. While each city does require its own independent Markov process, the interaction and influence between urban agglomerations must also be considered [42,43] because land-use changes in a given city may be driven by forces exerted by surrounding cities [44,45]. In addition, the process must also be calibrated to reflect a given city's level of urbanization and unique resource and environmental constraints, such as China's "red line" policy, which seeks to ensure ecological security and basic farmland protection, limits the growth of some cities [46,47].…”

Section: Introductionmentioning

confidence: 99%

Integrating Spatial Markov Chains and Geographically Weighted Regression-Based Cellular Automata to Simulate Urban Agglomeration Growth: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area

Zhao

Xie

Zhang

et al. 2021

Land

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Urban agglomeration is an important spatial organization mode in China’s attempts to attain an advanced (mature) stage of urbanization, and to understand its consequences, accurate simulation scenarios are needed. Compared to traditional urban growth simulations, which operate on the scale of a single city, urban agglomeration considers interactions among multiple cities. In this study, we combined a spatial Markov chain (SMC) (a quantitative composition module) with geographically weighted regression-based cellular automata (GWRCA) (a spatial allocation module) to predict urban growth in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), an internationally important urban agglomeration in southern China. The SMC method improves on the traditional Markov chain technique by taking into account the interaction and influence between each city to predict growth quantitatively, whereas the geographically weighted regression (GWR) gives an empirical estimate of urban growth suitability based on geospatial differentiation on the scale of an urban agglomeration. Using the SMC model to forecast growth in the GBA in the year 2050, our results indicated that the rate of smaller cities will increase, while that of larger cities will slow down. The coastal belt in the core areas of the GBA as well as the region’s peripheral cities are most likely to be areas of development by 2050, while established cities such as Shenzhen and Dongguan will no longer experience rapid expansion. Compared with traditional simulation models, the SMC-GWRCA was able to consider spatiotemporal interactions among cities when forecasting changes to a large region like the GBA. This study put forward a development scenario for the GBA for 2050 on the scale of an urban agglomeration to provide a more credible scenario for spatial planning. It also provided evidence in support of using integrated SMC-GWRCA models, which, we maintain, offer a more efficient approach for simulating urban agglomeration development than do traditional methods.

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Urban network externalities, agglomeration economies and urban economic growth

Cited by 125 publications

References 58 publications

Radiation Effect of Urban Agglomeration’s Transportation Network: Evidence from Chengdu–Chongqing Urban Agglomeration, China

Radiation Effect of Urban Agglomeration’s Transportation Network: Evidence from Chengdu–Chongqing Urban Agglomeration, China

Review on the urban network externalities

Integrating Spatial Markov Chains and Geographically Weighted Regression-Based Cellular Automata to Simulate Urban Agglomeration Growth: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area

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