Zhuhai City geographical location map.

Currently, the core idea of the refined method of population spatial distribution is to establish a correlation between the population and auxiliary data at the administrative-unit level and, then, refine it to the grid unit. However, this method ignores the advantages of public population spatial distribution data. Given these problems, this study proposed a partition strategy using the natural break method at the grid-unit level, which adopts the population density to constrain the land class weight and redistributes the population under the dual constraints of land class and area weights. Accordingly, we used the dasymetric method to refine the population distribution data. The study established a partition model for public population spatial distribution data and auxiliary data at the grid-unit level and, then, refined it to smaller grid units. This method effectively utilizes the public population spatial distribution data and solves the problem of the dataset being not sufficiently accurate to describe small-scale regions and low resolutions. Taking the public WorldPop population spatial distribution dataset as an example, the results indicate that the proposed method has higher accuracy than other public datasets and can also describe the actual spatial distribution characteristics of the population accurately and intuitively. Simultaneously, this provides a new concept for research on population spatial distribution refinement methods.</div

Firm performance across health care industries.

A: Return on Equity (ROE), B: Profit Margin, C: Asset Utilization, D: Financial Leverage, E: ROE Volatility. Notes: Box plot lines represent the 25th percentile, median, and 75th percentile. Whiskers are 1.5 times the interquartile range. Return on Equity (ROE), Profit Margin (PM), Asset Utilization (ATO) and Financial Leverage (LEV) are calculated for each company in each fiscal year. The total sample is comprised of 6,106 company-years of data from 2010 to 2019. Return on Equity is net income divided by the average of beginning and ending book value of equity. Profit Margin is net income divided by revenue. Asset Utilization is revenue divided by the average of beginning and ending total assets. Financial Leverage is the average of beginning and ending total assets divided by the average of beginning and ending book value of equity. ROE Volatility (ROE_VOL) is the standard deviation of annual ROEs for each company during 2010–2019.</p

Refined population spatial distribution map of 25 m spatial resolution in Zhuhai City.

The small map is the result map of Zhuhai City, and the large map is that of the main area.</p

Characteristics of publicly traded companies in health care industries, in GICS order.

Characteristics of publicly traded companies in health care industries, in GICS order.</p

Comparison of population distribution within the grid.

Two groups of visual grid population distribution comparison maps of A and B are listed. The land types presented in the land-use type map are the categories visible in the current map.</p

Summary statistics.

Summary statistics.</p

Regression analysis results.

Regression analysis results.</p

Partition land-type weight percentage accumulation map.

Partition land-type weight percentage accumulation map.</p

Datasets and sources.

Currently, the core idea of the refined method of population spatial distribution is to establish a correlation between the population and auxiliary data at the administrative-unit level and, then, refine it to the grid unit. However, this method ignores the advantages of public population spatial distribution data. Given these problems, this study proposed a partition strategy using the natural break method at the grid-unit level, which adopts the population density to constrain the land class weight and redistributes the population under the dual constraints of land class and area weights. Accordingly, we used the dasymetric method to refine the population distribution data. The study established a partition model for public population spatial distribution data and auxiliary data at the grid-unit level and, then, refined it to smaller grid units. This method effectively utilizes the public population spatial distribution data and solves the problem of the dataset being not sufficiently accurate to describe small-scale regions and low resolutions. Taking the public WorldPop population spatial distribution dataset as an example, the results indicate that the proposed method has higher accuracy than other public datasets and can also describe the actual spatial distribution characteristics of the population accurately and intuitively. Simultaneously, this provides a new concept for research on population spatial distribution refinement methods.</div

Accuracy comparison between the refined results and WorldPop.

The abscissa represents the population of WorldPop, and the ordinate represents the population of the refinement result, in the township-level administrative unit scale.</p