ArticleScientific data2024
Global urban and rural settlement dataset from 2000 to 2020.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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
5 citing papers in PubMed.
- City-level carbon emissions data in Southeast Asia from 2000 to 2020.Scientific data · 2026Article
- High-resolution gridded dataset of sectoral water pollution discharges in China from 2007 to 2022.Scientific data · 2026Article
- A 1 km datasets for urban-suburban-rural-natural continuous gradient landscapes in Southwest China.Scientific data · 2025Article
- A high-resolution global annual city and town boundaries dataset (2000-2022) derived from GLC_FCS30D product.Scientific data · 2025Article
- Global urban and rural settlement dataset from 2000 to 2020.Scientific data · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
Accurate mapping of global urban and rural settlements is crucial for understanding their distinct expansion patterns and ecological impacts. However, existing global datasets focus mainly on urban settlements and ignore the delineation of rural settlements. Therefore, this study proposed a framework for delineating between urban and rural settlements based on dynamic thresholds defined by area and light brightness and constructed the first global 100-meter resolution urban and rural settlements dataset (GURS) spanning from 2000 to 2020, integrating GHS-BUILT-S R2023A, NPP-VIIRS-like nighttime light, and OpenStreetMap data. An accuracy assessment of 44,474 independent samples showed that GURS achieved an overall accuracy of 91.22% with a kappa coefficient of 0.85, outperforming nine multi-scale reference datasets in delineating global urban and rural settlements. GURS offers deep insights into the dynamics of global settlements, facilitating urban-rural comparative studies on socio-economic characteristics, environmental impacts, and governance modes, thereby enhancing the sustainable management of settlements.
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.