The spatial organization pattern of urban-rural integration in urban agglomerations in China: An agglomeration-diffusion analysis of the population and firms

He, Yao; Zhou, Guohua; Tang, Chengli; Fan, Shuguang; Guo, Xiashuang

doi:10.1016/j.habitatint.2019.04.003

Cited by 96 publications

(56 citation statements)

References 30 publications

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“…It stretches 230 km from east to west and 88 km from north to south. Overall covers 11,819 km 2 , and as of 2018 it had a population of 8,150,000 people (He et al 2019). Accurate forecasting of bioaerosol concentration is likely to improve implementation of public health policies need to effectively 'Make our skies blue again'.…”

Section: Methodsmentioning

confidence: 99%

An improved wavelet de-noising-based back propagation neural network model to forecast the bioaerosol concentration

Cheng

et al. 2020

Aerosol Science and Technology

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Detection, monitoring and estimation of bioaerosol concentration have become increasingly important for several reasons, particularly for environmental quality monitoring and public health concerns on these materials. Bioaerosol concentration is highly variable and noisy, influenced by many factors including meteorological parameters. Directly predicting bioaerosol concentration with noisy data gives erroneous results. As such, we developed a facile approach to forecast bioaerosol concentration using the Wavelet De-noising-based Back Propagation (WDBP) neural network model. We used meteorological data for Changsha gathered from 1 st November 2018 to 1 st April 2019 to demonstrate the efficiency of WDBP neural network in forecasting atmospheric bioaerosol concentration. The superiority of the new approach over the single Back Propagation (BP) neural network was also validated using real dataset. Overall, the performance of WDBP neural network was satisfactory, underlying potential practical application of the method in forecasting bioaerosol concentration. This work provides a prospective model, useful in monitoring environmental quality and atmospheric bio-threats to public health.

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Section: Methodsmentioning

confidence: 99%

An improved wavelet de-noising-based back propagation neural network model to forecast the bioaerosol concentration

Cheng

et al. 2020

Aerosol Science and Technology

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“…HL and LH indicate negative local spatial correlation and that the population density of grid i is much higher (or lower) than the surroundings. The nonsignificant unit indicates that the correlation between close units is not significant [44]. The HH relationship has significance for identifying the areas of population concentration.…”

Section: Moran Indexes and Spatial Agglomerationmentioning

confidence: 99%

Population Density and Spatial Patterns of Informal Settlements in Nairobi, Kenya

et al. 2020

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The widespread informal settlements in Nairobi have interested many researchers and urban policymakers. Reasonable planning of urban density is the key to sustainable development. By using the spatial population data of 2000, 2010, and 2020, this study aims to explore the changes in population density and spatial patterns of informal settlements in Nairobi. The result of spatial correlation analysis shows that the informal settlements are the centers of population growth and agglomeration and are mostly distributed in the belts of 4 and 8 km from Nairobi’s central business district (CBD). A series of population density models in Nairobi were examined; it showed that the correlation between population density and distance to CBD was positive within a 4 km area, while for areas outside 8 km, they were negatively related. The factors determining population density distribution are also discussed. We argue that where people choose to settle is a decision process between the expected benefits and the cost of living; the informal settlements around the 4-km belt in Nairobi has become the choice for most poor people. This paper ends with suggestions for urban planning and upgrading informal settlements. The findings will increase our understanding of urban population distribution in underdeveloped countries.

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“…The issue of agglomerations' development is also essential foe Chinese scientists who are considering a policy to optimize the development of Chinese urban agglomerations (He, Zhou, Tang, Fan, & Guo, 2019), issues of their sustainable development, design and operational management of agglomeration systems (Huang et al, 2020). There are articles with the research about the problems of agglomerations' development in Central and Eastern Europe, their main growth factors were foreign investment resources and clusters' creation (Starostina, Ustymenko, & Shevchenko, 2017).…”

Section: Problem Statementmentioning

confidence: 99%

Improvement Of The Development Management Process Of The Samara-Togliatti Agriculture

Домнина¹,

Artemyeva²

2020

European Proceedings of Social and Behavioural Sciences

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The spatial organization pattern of urban-rural integration in urban agglomerations in China: An agglomeration-diffusion analysis of the population and firms

Cited by 96 publications

References 30 publications

An improved wavelet de-noising-based back propagation neural network model to forecast the bioaerosol concentration

An improved wavelet de-noising-based back propagation neural network model to forecast the bioaerosol concentration

Population Density and Spatial Patterns of Informal Settlements in Nairobi, Kenya

Improvement Of The Development Management Process Of The Samara-Togliatti Agriculture

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