Abstract
The spatial distribution of the population is uneven for various reasons, such as urban-rural differences and geographical conditions differences. As the basic element of the natural structure of the population, the age structure composition of populations also varies considerably across the world. Obtaining accurate and spatiotemporal population age structure maps is crucial for calculating population size at risk, analyzing populations mobility patterns, or calculating health and development indicators. During the past decades, many population maps in the form of administrative units and grids have been produced. However, these population maps are limited by the lack of information on the change of population distribution within a day and the age structure of the population. Urban functional regions (UFRs) are closely related to population mobility patterns, which can provide information about population variation intraday. Focusing on the area within the Beijing Fifth Ring Road, the political and economic center of Beijing, we showed how to use the temporal scaling factors obtained by analyzing the population survey sampling data and population dasymetric maps in different categories of UFRs to realize the intraday variation mapping of elderly individuals and children. The population dasymetric maps were generated on the basis of covariates related to population. In this article, 50 covariates were calculated from remote sensing data and geospatial data. However, not all covariates are associate with population distribution. In order to improve the accuracy of dasymetric maps and reduce the cost of mapping, it is necessary to select the optimal subset for the dasymetric model of elderly and children. The random forest recursive feature elimination (RF-RFE) algorithm was introduced to obtain the optimal subset of different age groups of people and generate the population dasymetric model in this article, as well as to screen out the optimal subset with 38 covariates and 26 covariates for the dasymetric models of the elderly and children, respectively. An accurate UFR identification method combining point of interest (POI) data and OpenStreetMap (OSM) road network data is also introduced in this article. The overall accuracy of the identification results of UFRs was 70.97%, which is quite accurate. The intraday variation maps of population age structure on weekdays and weekends were made within the Beijing Fifth Ring Road. Accuracy evaluation based on sampling data found that the overall accuracy was relatively high—R2 for each time period was higher than 0.5 and root mean square error (RMSE) was less than 0.05. On weekdays in particular, R2 for each time period was higher than 0.61 and RMSE was less than 0.02.
Highlights
The overall process can be divided into 2 parts, namely, dasymetric mapping of the population age structure and intraday variation mapping of the population age structure, in order to realize the transformation of population data from demographic data to spatial data and from spatial data to spatiotemporal data
Optimal subset covariate selection was driven by the recursive feature elimination (RFE) algorithm using countylevel demographic data and the average value of 50 covariates in each county
root mean square error (RMSE) was used as the indicator to select the optimal subset of the population dasymetric model
Summary
Traditional population maps are often generated by collecting population data and presenting them on the basis of certain geographical areas, which may have little or no connection to population distribution [10] This method leads to the modifiable areal unit problem (MAUP) [11], which means the results of spatial analysis can significantly impact by statistical bias [12]. Dasymetric mapping, which uses different kinds of covariates to redistribute demographic data from the administrative scale to a fine scale, has proven to be more effective in population mapping than other methods [28,29] These studies only focus on the population mapping and ignore mapping of demographic attributes (e.g., gender, age, and education level)
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