Evidence map›Paper›PMID 36605233›Full record

ArticleFrontiers in public health2022

Spatio-temporal evolution of the resilience of Chinese border cities.

Lili Sui, Fei Peng, Siyu Wu

Abstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lili SuiSchool of Geography, Liaoning Normal University, Dalian, China.
Fei PengCenter for Studies of Marine Economy and Sustainable Development, Liaoning Normal University, Dalian, China.
Siyu WuCenter for Studies of Marine Economy and Sustainable Development, Liaoning Normal University, Dalian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In China, border cities are developing in the direction of trade, investment, tourism, and regional diversification and becoming crucial for the national opening-up strategy and inter-regional exchange. In this study, we construct a comprehensive system for measuring and evaluating the resilience of border cities in China that also reveals the spatial and temporal characteristics of resilience. Three representative sample zones (Northeast, Northwest, and Southwest) are selected within the three major regions of China to analyze the regional differences in border city resilience and propose targeted coping strategies. The findings of this study are as follows. First, the spatial distribution of resilience in Chinese border cities varies significantly, with the overall resilience decreasing in the following order: Northeast China > Southwest China > Northwest China > North China > Tibetan China. Higher resilience of border cities is predominantly related to better economic foundations and advantages in border trade. Second, the resilience of China's border cities has increased significantly over the past decade, with highly resilient border cities concentrated in the northeastern part of China, the northern part of Xinjiang, and Guangxi Province. Moreover, high resilience generally spreads to surrounding low-resilience cities over time. Third, the spatial distribution and development trends of resilience levels differ among the three sample zones. Therefore, it is crucial to improve urban resilience according to the regional characteristics of each border city and its specific developmental stage.

Indexed as

East Asian PeopleInvestmentsChinaCitiesHumansborder citiesChina border regionChinese prefecture-level citiesregional differencesspatio-temporal patternurban resilience

Identifiers

PMID36605233
PMCPMC9807667

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.