Evidence map›Paper›PMID 35010489›Full record

ArticleInternational journal of environmental research and public health2021

Spatiotemporal Evolution and Spatial Network Analysis of the Urban Ecological Carrying Capacity in the Yellow River Basin.

Ke Liu, Shiwen Yang, Qian Zhou, Yurong Qiao

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Ke LiuSchool of Economics and Management, Zhengzhou University of Light Industry, Zhengzhou 450001, China.
Shiwen YangSchool of Economics and Management, Zhengzhou University of Light Industry, Zhengzhou 450001, China.
Qian ZhouEconomics School, Zhongnan University of Economics and Law, Wuhan 430073, China.ORCID 0000-0002-2319-1095
Yurong QiaoSchool of Economics and Management, Zhengzhou University of Light Industry, Zhengzhou 450001, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Based on the panel data of 82 cities in the Yellow River Basin (YRB) during 2008-2017, this paper calculated the urban ecological carrying capacity (UECC) index by means of the entropy method, drew a spatiotemporal evolution map using ArcGIS10.3 software, used a spatial cold-hot spot model to explore the spatial characteristics of the UECC index, and used the revised gravity model to construct the spatial network of the UECC. In addition, through social network analysis, we obtained the spatial network correlation characteristics of the UECC of 82 cities in the YRB. The study found the following: (1) The UECC index of the cities in the YRB increased steadily, and showed strong non-stationarity in space. The cold and hot spot patterns both changed greatly. Overall, the changes of the hot and cold spots were very significant. (2) The spatial correlation and linkage effects of the UECC in the YRB were not significant. The central cities with higher point centrality and closeness centrality showed the same spatial distribution, and most of them are located in the midstream and downstream of the YRB. The central cities in the midstream and downstream of the YRB had high betweenness centrality, and stood in the center of the association network. (3) The four plates in the spatial correlation network of the UECC in the YRB all showed their advantages and functions. The first plate was the net spillover plate, which was principally allocated in the upstream and midstream of the YRB. The second plate was the broker plate, which was principally located in the midstream and downstream of the YRB, and a few cities in the upper reaches. The third plate was the net inflow plate, which was distributed sporadically in the upstream and downstream of the YRB. The fourth plate was the broker plate, which was scattered in upstream, midstream, and downstream of the YRB. Therefore, it is necessary to shorten the gap of and promote the improvement of the UECC in the YRB.

Indexed as

Conservation of Natural ResourcesRiversChinaCitiesSpatial AnalysisUrbanizationecological carrying capacity (ECC)spatial networkspatiotemporal evolutionYellow River Basin (YRB)

Identifiers

PMID35010489
PMCPMC8751185

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.