Habitation environment suitability and population density patterns in China: A regionalization approach

Liu, Cuiling; Wang, Fahui; Xu, Yaping

doi:10.1111/grow.12283

Cited by 17 publications

(10 citation statements)

References 12 publications

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“…The findings in this study indicate that the distribution of researchers is uneven and is concentrated in a few megacities and provincial capitals because the economic factors and amenity factors are superior in these regions (Liu et al., 2019). Universities and research institutes, representing the supply of jobs for researchers, are also distributed mainly in these developed cities (Franklin, 2013).…”

Section: Discussionmentioning

confidence: 84%

Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression

Sachdeva

et al. 2020

Growth and Change

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We employ a Multiscale Geographically Weighted Regression (MGWR) model to examine the spatial variation of researchers in China in 2015 and its determinants. It is found that the distribution of researchers is driven by the economy in urban centers, public services, natural areas of recreation, urban consumption, and work‐related facilities. Results from the MGWR model conclusively identify significant spatial non‐stationarity in the determinants measuring scientific researchers' distribution. Other factors such as per capita GDP, the number of hospital beds per 10,000 inhabitants, and public financial expenditures influence the distribution of researchers more in the Western cities, and their effect decreases steadily from West to East. Air pollutants affect researchers negatively in the eastern part of the country, while the percentage of available green land is a strong driver in the central and western parts. The factor measuring the effect of the number of universities is seen to affect researchers more strongly in the northwestern and northeastern cities. Other findings further suggest that the economy and public services in cities have a consistent effect on the distribution of researchers across the country whereas, natural areas of recreation, work‐related facilities, and urban consumption have a significantly varying effect across cities.

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Section: Discussionmentioning

confidence: 84%

Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression

Sachdeva

et al. 2020

Growth and Change

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“…One may also apply other GIS-automated regionalization methods such as the Mixed-Level Regionalization (MLR) (Mu et al, 2015) to the same data and examine whether the results from this study can be further improved. Perhaps the most valuable and also challenging task is to identify underlying environmental-ecological forces that shape this division, and examine the consistencies and inconsistencies between them (Liu et al, 2019). The latter (i.e., inconsistencies between human settlement and physical environmental factors) may hold the key to possibility of overcoming the barrier-an elusive goal that has enticed generations of policy makers and researchers in China.…”

Section: Discussionmentioning

confidence: 99%

Analyzing Population Density Disparity in China with GIS-automated Regionalization: The Hu Line Revisited

Wang

Liu

2019

Chin. Geogr. Sci.

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The famous 'Hu Line', proposed by Hu Huanyong in 1935, divided China into two regions (southeast and northwest) of comparable area size but drastically different in population. However, the classic Hu Line was derived manually in absence of reliable census data and computational technologies of modern days. It has been subject to criticism of lack of scientific rigor and accuracy. This research uses a GIS-automated regionalization method, termed REDCAP (Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning), to reconstruct the demarcation line based on the 2010 county-level census data in China. The results show that the logarithmic transformation of population density is a better measure of attributive homogeneity in derived regions than density itself, and produces two regions of nearly identical area size and greater contrast in population. Specifically, the revised Hu Line by Hu Huanyong in 1990 had the southeast region with 94.4% of total population and 42.9% of total land, and our delineation line yields a southeast region with 97.4% population and 50.8% land. Therefore, the population density ratio of the two regions is 27.1 by our line, much higher than the ratio of 22.4 by the Hu Line, and thus outperforms the Hu Line in deriving regions of maximum density contrast with comparable area size. Furthermore, more regions are delineated to further advance our understanding of population distribution disparity in China.

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“…We refer to the literature (Chi, 2012;Kotavaara et al, 2012;Liu et al, 2019a;Wang et al, 2019) and combine the population distribution characteristics of the QTP (Liao and Sun, 2003a;b;Qi et al, 2020), and based on the accessibility and integrity of the data, The adaptability index of living environment (HESI), GDP per capita (GDP p ), location conditions (Loc), land use degree index (L aj ), and urbanization level (U level ) were selected as independent variables, and the annual rate of change of township population (r i ) was taken as the dependent variable. HESI (Liu et al, 2019a;b) indicates the comprehensive factors of natural environment such as climate and terrain. GDP per capita represents the level of economic development (Wang et al, 2019).…”

Section: Selection Of Influencing Factorsmentioning

confidence: 99%

“…The studies on population distribution and change in the QTP have focused on, for example, the spatial distribution of county-level population on the basis of population census data (Liao and Sun, 2003a;Zhang et al, 2004;, sustainable development of a population, resources and the environment (Cheng and Shen, 2000;Liao and Sun, 2003b), intensity of the impact of human activities (Li et al, 2018b), urbanization (Fan and Wang, 2005), village clusters (Li et al, 2018a), and ethnic distribution (Paik and Shawa, 2013). Studies have also discussed the stability of the QTP as the sparse population area northwest of the 'Hu Line', from the perspective of population distribution patterns and evolution in China (Bai et al, 2015;Yang et al, 2016;Liu et al, 2019a) and discussed the regional differentiation regularity of population in the QTP on the basis of the idea of the 'Hu Line' (Qi et al, 2020). The QTP is a vast region with prominent differences between its natural and socio-economic environments.…”

Section: Introductionmentioning

confidence: 99%

Regional Differentiation Regularity and Influencing Factors of Population Change in the Qinghai-Tibet Plateau, China

2021

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The Qinghai-Tibet Plateau (QTP) is the most unique region of human-land relations, and its main factor is population. By building a population and space dataset of the QTP at the township level from 1982 to 2017, this paper presents the pattern evolvement and regional distribution characteristics of township-level population in the QTP in detail for the first time. By using Geodetector method to analyze the influencing factors of township-level population change in the QTP, this paper provides scientific foundations for studying the Third Pole, ecological environment protection of the QTP, and human-land relations. The conclusions are as follows:1) The population spatial distribution is not balanced at the township level, presents the regional differentiation characteristics of 'dense in southeast and sparse in northwest' along the 'Qilian-Jilong Line', and demonstrates the phenomenon of population center of gravity moving to the hinterland of the plateau at an accelerated speed; 2) The township-level population develops in a decentralized trend in general, and the cold-and hot-spot distribution of population has prominent spatial distribution characteristics. The population hot spots are concentrated in the surrounding areas of Xining, Golmud, Hotan and Lijiang; 3) The population of the QTP is increasing, and the inter-annual change of township population has a relatively stable regional regularity; 4) The level of per capita income is the leading factor in the change of township population, and its effect intensity is increasing continuously. The relative effect intensity of urbanization level and location conditions on population change has decreased significantly after 2000. The adaptability of living environment, such as topography and climate, has little influence on population change.

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Habitation environment suitability and population density patterns in China: A regionalization approach

Cited by 17 publications

References 12 publications

Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression

Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression

Analyzing Population Density Disparity in China with GIS-automated Regionalization: The Hu Line Revisited

Regional Differentiation Regularity and Influencing Factors of Population Change in the Qinghai-Tibet Plateau, China

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