Related Experiment Video
Updated: Sep 8, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
100-m resolution Age-Stratified Population Estimation from the 2020 China Census by Township (ASPECT)
Yang Ju1, Ying Liang1, Jinyu Kong2
1School of Geography and Ocean Science, Nanjing University, Nanjing, China.
None:
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.
Related Concept Videos
Stratified Sampling Method
To choose a stratified sample, divide the population into groups called strata and then take a...
Sampling Plans
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Estimating Population Standard Deviation
Cluster Sampling Method
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the...
Estimating Population Mean with Known Standard Deviation
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...

