ArticleScientific reports2025
Enhancing demarcation in regionalization in the eastern Qinghai-Xizang Plateau through geographically weighted.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The eastern margin of the Qinghai-Xizang Plateau, as a critical transition zone between the plateau and the Sichuan Basin, poses substantial challenges for geographic regionalization, primarily due to its intricate terrain and climatic heterogeneity. Traditional spatial clustering methods often struggle to balance spatial continuity and attribute similarity, suffering from subjectivity and inadequate representation of topographic complexity. This study proposes a novel mountainous geographic regionalization framework that integrates topographic and climatic characteristics, using Kangding county as a typical case. Principal Component Analysis (PCA) was employed to perform dimensionality reduction on multiple environmental variables and assign relative weights. A Gaussian-weighted function was further applied to adjust attribute distances to capture spatial non-stationarity, while the geographic distance weight was systematically optimized. The partitioning outcomes were evaluated using clustering quality indicators (Davies-Bouldin index, Silhouette index, Calinski-Harabasz index) and spatial autocorrelation indicators (Moran's I index, Moran's Z-score). Results indicated that when the number of clusters was set to five and the geographic distance weight was 0.5, the clustering algorithm optimized the trade-off between spatial continuity and attribute similarity (Davies-Bouldin index = 1.14, Silhouette index = 0.30, Calinski-Harabasz index = 25150.91, Moran's I = 0.97, Moran's Z-score = 292.28). Compared to the traditional K-means clustering, this approach enhanced intra-cluster similarity (Sil) by 259% and improved spatial continuity (Moran's I, Moran's Z-score) by approximately 44%. This method effectively addresses the challenge of coordinating spatial constraints with attribute heterogeneity in mountainous environmental zoning, in a county scale, providing an automated, data-driven approach for geographic partitioning in complex terrains. The findings offer valuable insights for mountain ecosystem management and regional geographic studies. Our study provides a set of effective methods of demarcation of regional boundaries based on raster data, offering important insights for ecological zoning management and regional studies in mountainous environments at a small scale.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.