Evidence map›Paper›PMID 42463690›Full record

ArticleScientific reports2026

Spatial distribution characteristics and protection strategies of National intangible cultural heritage in the Pearl river basin.

Ying Wang, Rong Yu, Jiaying Hu

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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.

Ying WangNanning Normal University, College of Art and Design, China Guangxi, Nanning, 530000, China.
Rong YuGuangxi Zhuang Autonomous Region Supervising Station for Radioactive Environment, China Guangxi, Nanning, 530022, China.
Jiaying HuNanning Normal University, College of Art and Design, China Guangxi, Nanning, 530000, China. hjy@nnnu.edu.cn.

Funding

National Social Science Fund of China (Western Project of Art) in 2022 - Research on the Transformation of Traditional Zhuang Crafts into Cultural IP 22EG211
6 · The paper itself

Abstract

While the spatial analysis of Intangible Cultural Heritage (ICH) is well-established at national and provincial scales, cross-jurisdictional river basins remain a critical research gap. This study investigates the spatial heterogeneity of 289 national-level ICH items within China's Pearl River Basin to inform regional conservation planning. Employing Kernel Density Estimation and the Geographical Detector, the analysis identifies distribution patterns and quantifies the influence of key geographical and socioeconomic factors. Results reveal a pronounced upstream-downstream gradient, with ICH concentration culminating in Guangdong Province (52.2%), and a significantly clustered distribution forming primary agglomerations in the Pearl River Delta and Chaoshan regions. The Geodetector analysis indicates that topography is the dominant factor shaping the spatial pattern ([Formula: see text]= 0.27), while its interaction with transportation exerts the strongest synergistic effect ([Formula: see text] = 0.393). These findings suggest that the geography of national-level ICH is shaped more by foundational, path-dependent landscape features than by contemporary macro-economic indicators. Consequently, effective conservation necessitates a differentiated framework that focuses on protecting cultural originality in upstream "source" areas while leveraging the industrial capacity of downstream "sink" areas for sustainable development.

Indexed as

Cultural IPInfluencing factorsIntangible cultural heritagePearl river basinSpatial distribution

Identifiers

PMID42463690
PMCPMC13376866

What OpenQuestion holds

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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.