Are Chinese Cities Oversized?

In this article, the socioeconomic determinants on urban population in China are empirically investigated with a theoretical equilibrium model for city size. While much of the research on urban size focuses on the impact of agglomeration economies based on “optimal city size” theory, this model is eschewed in our research due to its theoretical paradox in the real world, and we turn instead toward an intermediate solution proposed by Camagni, Capello, and Caragliu. This equilibrium model is estimated on a sample of 111 prefectural cities in China with multiple regression and artificial neural networks. Empirical results have shown that the model explains the variance in the data very well, and all the determinants have significant impacts on Chinese city sizes. Although sample cities have reached their equilibrium sizes as a whole, there is substantially unbalanced distribution of population within the urban system, with a strong contingent of cities that are either squarely too large or too small.

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Agglomeration Economies: Population, Density, and Network Effects

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v2i10.2934 ◽

2011 ◽

Vol 2 (10) ◽

Author(s):

Thomas J. White

Keyword(s):

Population Density ◽

Least Squares ◽

Significant Role ◽

Network Effects ◽

Total Population ◽

Agglomeration Economies ◽

Ordinary Least Squares ◽

City Size ◽

Optimal City Size ◽

Benefits And Costs

This paper builds on the optimal city size literature by examining factors that influence location benefits and costs. Total population, population density, employment type, and networking are evaluated using ordinary least squares. Results indicate that population density may play a more significant role in predicting average location benefits and average location costs than population.

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Reflections on Paradigm Shift in Urban System Reconstruction

Chinese Journal of Urban and Environmental Studies ◽

10.1142/s2345748119500040 ◽

2019 ◽

Vol 07 (02) ◽

pp. 1950004

Author(s):

Jiahua PAN

Keyword(s):

Rural Areas ◽

Urban Population ◽

Urban System ◽

City Size ◽

Low Carbon ◽

Ecological Civilization ◽

Urban Hierarchy ◽

Industrial Civilization ◽

Urban And Rural Areas ◽

Natural Productivity

In the era of agricultural civilization, the city size and layout adapted to nature and natural productivity; while in the era of industrial civilization, the constraints of natural productivity were broken by technological means, and the increasing returns to scale have enabled the urban population size to exceed 10 million and the urban population density to exceed 10,000 people/km2. Under the paradigm of industrial civilization, the spatial agglomeration of resources is driven by economic rationality. Besides, China’s urban hierarchy has become a barrier and further strengthened the polarization trend of city size, resulting in an urban system in which the cities at higher administrative levels concentrate a lot of resources while suffering from prominent urban diseases, small- and medium-sized cities lack development vitality, and urban and rural areas are separated from each other. The historical experience that the flow of resource factors between urban and rural areas facilitates a relatively balanced spatial distribution of quality resources is worth learning. Under the paradigm of ecological civilization, it is important to harmonize humans with nature in the transformation and reconstruction by pursuing nature-based solutions, and build a low-carbon, resilient, and coordinated urban system.

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City size based scaling of the urban internal nodes layout

PLoS ONE ◽

10.1371/journal.pone.0250348 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0250348

Author(s):

Wenhan Feng ◽

Bayi Li ◽

Zebin Chen ◽

Peng Liu

Keyword(s):

Rural Areas ◽

High Speed ◽

Urban System ◽

Structural Features ◽

Internal Node ◽

City Size ◽

Urban Structure ◽

The Impact ◽

The Relationship ◽

The City

The size of a city is not only essential for depicting the scale of the urban system, but also crucial to support the prosperity, order, and high-speed developments. However, its relation to the underlying urban structure has not been empirically investigated in detail. To examine the impact of city size on the city structure and quantify structural features, in this study, a statistical analysis was performed based on network science and an interdisciplinary theoretical system. To obtain the statistical law of internal node layout, the urban system was regarded as a complete graph weighted by the Euclidean distance. The relationship between the urban internal nodes layout (points of interest data, Weibo check-in data, and central point of road intersection data) and the city size was established. The results confirmed the existence of statistical laws in the layout of urban spatial elements, and explored the relationship between the changes in urban node network structure and inequality. This study provided a new perspective of urban structure to understand the complexity of the city, and suggested an approach to adjust this structure to narrow down the gap between the urban and rural areas.

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The Impact of High-Speed Rail on the Spatial Structure of Chinese Urban Agglomerations

Built Environment ◽

10.2148/benv.46.3.379 ◽

2020 ◽

Vol 46 (3) ◽

pp. 379-397

Author(s):

Chunyang Wang

Keyword(s):

High Speed ◽

River Delta ◽

City Size ◽

High Speed Rail ◽

Local Conditions ◽

Urban Agglomerations ◽

Large Cities ◽

Spatial Systems ◽

Economic Concentration ◽

The Impact

This paper measures the spatial evolution of urban agglomerations to understand be er the impact of high-speed rail (HSR) construction, based on panel data from fi ve major urban agglomerations in China for the period 2004–2015. It is found that there are signi ficant regional diff erences of HSR impacts. The construction of HSR has promoted population and economic diff usion in two advanced urban agglomerations, namely the Yang e River Delta and Pearl River Delta, while promoting population and economic concentration in two relatively less advanced urban agglomerations, e.g. the middle reaches of the Yang e River and Chengdu–Chongqing. In terms of city size, HSR promotes the economic proliferation of large cities and the economic concentration of small and medium-sized cities along its routes. HSR networking has provided a new impetus for restructuring urban spatial systems. Every region should optimize the industrial division with strategic functions of urban agglomeration according to local conditions and accelerate the construction of inter-city intra-regional transport network to maximize the eff ects of high-speed rail across a large regional territory.

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Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-0015 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Malte Seemann ◽

Lennart Bargsten ◽

Alexander Schlaefer

Keyword(s):

Computed Tomography ◽

Neural Networks ◽

Deep Learning ◽

Medical Imaging ◽

Computed Tomography Angiography ◽

Data Augmentation ◽

Domain Adaptation ◽

Synthetic Image ◽

Wide Range ◽

The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

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IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

Sensors ◽

10.3390/s21030676 ◽

2021 ◽

Vol 21 (3) ◽

pp. 676

Author(s):

Andrej Zgank

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Markov Models ◽

Audio Signal ◽

Audio Signals ◽

Mel Frequency Cepstral Coefficients ◽

Animal Activity ◽

The Impact ◽

Acoustic Classification ◽

Swarm Activity

Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural networks IoT-based acoustic swarm classification is proposed in this paper. Audio recordings were obtained from the Open Source Beehive project. Mel-frequency cepstral coefficients features were extracted from the audio signal. The lossless WAV and lossy MP3 audio formats were compared for IoT-based solutions. An analysis was made of the impact of the deep neural network parameters on the classification results. The best overall classification accuracy with uncompressed audio was 94.09%, but MP3 compression degraded the DNN accuracy by over 10%. The evaluation of the proposed deep neural networks IoT-based bee activity acoustic classification showed improved results if compared to the previous hidden Markov models system.

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Quantitative Dynamics Effects of Belt and Road Economies Trade Using Structural Gravity and Neural Networks

SAGE Open ◽

10.1177/21582440211032662 ◽

2021 ◽

Vol 11 (3) ◽

pp. 215824402110326

Author(s):

Koffi Dumor ◽

Li Yao ◽

Jean-Paul Ainam ◽

Edem Koffi Amouzou ◽

Williams Ayivi

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Analysis ◽

Bilateral Trade ◽

Belt And Road Initiative ◽

Neural Network Analysis ◽

Analysis Techniques ◽

Belt And Road ◽

The Impact ◽

Trade Partners

Recent research suggests that China’s Belt and Road Initiative (BRI) would improve the bilateral trade between China and its partners. This article uses detailed bilateral export data from 1990 to 2017 to investigate the impact of China’s BRI on its trade partners using neural network analysis techniques and structural gravity model estimations. Our main findings suggest that the BRI countries would raise exports by a modest 5.053%. This indicates that export and network upgrades should be considered from economic and policy perspectives. The results also show that neural networks is more robust compared with structural gravity framework.

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The Tapio Decoupling Principle and Key Strategies for Changing Factors of Chinese Urban Carbon Footprint Based on Cloud Computing

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18042101 ◽

2021 ◽

Vol 18 (4) ◽

pp. 2101

Author(s):

Min Shang ◽

Ji Luo

Keyword(s):

Economic Growth ◽

Cloud Computing ◽

Carbon Footprint ◽

Driving Factors ◽

City Size ◽

Series Data ◽

Marginal Effect ◽

Low Carbon ◽

The Impact ◽

Xi’An City

The expansion of Xi’an City has caused the consumption of energy and land resources, leading to serious environmental pollution problems. For this purpose, this study was carried out to measure the carbon carrying capacity, net carbon footprint and net carbon footprint pressure index of Xi’an City, and to characterize the carbon sequestration capacity of Xi’an ecosystem, thereby laying a foundation for developing comprehensive and reasonable low-carbon development measures. This study expects to provide a reference for China to develop a low-carbon economy through Tapio decoupling principle. The decoupling relationship between CO2 and driving factors was explored through Tapio decoupling model. The time-series data was used to calculate the carbon footprint. The auto-encoder in deep learning technology was combined with the parallel algorithm in cloud computing. A general multilayer perceptron neural network realized by a parallel BP learning algorithm was proposed based on Map-Reduce on a cloud computing cluster. A partial least squares (PLS) regression model was constructed to analyze driving factors. The results show that in terms of city size, the variable importance in projection (VIP) output of the urbanization rate has a strong inhibitory effect on carbon footprint growth, and the VIP value of permanent population ranks the last; in terms of economic development, the impact of fixed asset investment and added value of the secondary industry on carbon footprint ranks third and fourth. As a result, the marginal effect of carbon footprint is greater than that of economic growth after economic growth reaches a certain stage, revealing that the driving forces and mechanisms can promote the growth of urban space.

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