Evidence map›Paper›PMID 35371907›Full record

ArticleEPJ data science2022

Understanding China's urban system evolution from web search index data.

Hao Guo, Weiyu Zhang, Haode Du, Chaogui Kang, Yu Liu

Abstract read
In one paragraph

Article in EPJ data science, 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

5 authors.

Hao Guo *Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China.
Weiyu Zhang *Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China.
Haode Du *Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China.
Chaogui KangNational Engineering Research Center of Geographic Information System, China University of Geosciences (Wuhan), Wuhan, China.
Yu LiuInstitute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China.ORCID 0000-0002-0016-2902

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The spatial inequilibrium phenomenon is apparent during China's rapid urbanization in the past four decades. As the fertility rate decreases and the population ages, this phenomenon will become more critical. To accurately forecast the future economic development of China, it is necessary to quantify the attractiveness of individual cities. This study introduces web search data to quantify the attractiveness of cities with a fine spatial scale (prefecture-level city) and relatively long-term span (nine years). Results confirm that the estimated city attractiveness can unravel a city's capability to attract labor force, and suggest that tourism and health care functions of a city have a positive effect to the city's attractiveness. Additionally, China's north-south gap in economic development has been widened in the past decade, and 11 cities with nationwide influence have prosperous development potential. This study provides a new lens for predicting China's economic development, as well as its spatial patterns. Supplementary Information: The online version contains supplementary material available at 10.1140/epjds/s13688-022-00332-y.

Indexed as

ChinaCity attractivenessGravity modelParticle swarm optimizationSpatial inequilibriumWeb search

Identifiers

PMID35371907
PMCPMC8959800

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.