Evidence map›Paper›PMID 40544153›Full record

ArticleScientific data2025

100-m resolution Age-Stratified Population Estimation from the 2020 China Census by Township (ASPECT).

Yang Ju, Ying Liang, Jinyu Kong, Xuelu Wang, Shicheng Wen, Huiyan Shang, Xize Wang

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

7 authors.

Yang JuSchool of Geography and Ocean Science, Nanjing University, Nanjing, China.ORCID 0000-0002-1947-7533
Ying LiangSchool of Geography and Ocean Science, Nanjing University, Nanjing, China.
Jinyu KongSchool of Architecture and Urban Planning, Nanjing University, Nanjing, China.
Xuelu WangDepartment of Architecture, National University of Singapore, Singapore, Singapore.
Shicheng WenSchool of Architecture and Urban Planning, Nanjing University, Nanjing, China.
Huiyan ShangSchool of Architecture and Urban Planning, Nanjing University, Nanjing, China.
Xize WangDepartment of Real Estate, National University of Singapore, Singapore, Singapore. wang.xize@nus.edu.sg.ORCID 0000-0002-4861-6002

Funding

National Natural Science Foundation of China (National Science Foundation of China) 42301238Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation) BK20230792
6 · The paper itself

Abstract

Gridded population datasets are instrumental for modeling the interactions between human and the environment at fine spatial scales. Many of these datasets are downscaled from source data of aggregated population counts by census units. Here, we introduce an Age-Stratified Population Estimation from the 2020 China Census by Township (ASPECT), estimating total population and population by age groups (0-14, 15-59, 60-64, ≥65 years old) at 100 m spatial resolution as of year 2020. ASPECT uses the updated source data from the most recent Census of year 2020, incorporating population counts and age structures from nearly all townships (n = 40,718) - the finest spatial unit for which the 2020 Census data are publicly available. Therefore, ASPECT likely provides improved accuracy in gridded population estimation when compared with datasets based on county-level data such as WorldPop. Furthermore, ASPECT presents greater spatial variations in the estimated population age structure than those from other existing datasets. These advantages of ASPECT allow for more accurate estimations on population exposure to environmental hazards and access to public services.

Indexed as

CensusesAdolescentAdultAgedChildChild, PreschoolChinaHumansInfantInfant, NewbornMiddle AgedYoung Adult

Identifiers

PMID40544153
PMCPMC12182578

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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.