Mapping monthly population distribution and variation at 1-km resolution across China

ABSTRACT

Fine-grained inner-annual population data are instrumental in climate change response, resource allocation, and epidemic control. However, such data are currently scarce due to the lack of human-related indicators with both high temporal resolution and long-term coverage that can be used in the process of population spatialization. Here, we estimate monthly 1-km gridded population distribution across China in 2015 using time-series mobile phone positioning data. We construct a hybrid downscaling model to map the gridded population by incorporating random forest and area-to-point kriging. The estimated monthly population products appear to capture inner-annual population variations, especially during special periods, such as the festival, holiday, and short-term labor flow period, which are characterized by large-scale population movements. Additionally, compared with census data, the hybrid model-based results obtained exhibit higher consistency than popular global population products across all spatial extents. Our monthly 1-km data products for the population distribution across China in 2015 provide a credible dataset that can be employed in studies aimed at accurate population-dependent decisions.

KEYWORDS:

• Population distribution
• random forest
• area-to-point kriging
• China

Acknowledgments

We are grateful to Yuehong Chen and Binzhe Wang for comments and feedback in the development of this paper.

Data and codes availability statement

The monthly population distribution product, coupling with source data and codes that support the findings of this study are available with the identifiers at: https://doi.org/10.6084/m9.figshare.12319334

Disclosure statement

The authors declare no competing interests.

Additional information

Funding

This research was funded by the National Key Research and Development Program of China [2017YFB0503500], the National Natural Science Foundation of China [41971409, 41531174], and the Youth Innovation Promotion Association of the Chinese Academy of Sciences.

Notes on contributors

Zhifeng Cheng

Zhifeng Cheng is a master student of the University of Chinese Academy of Sciences and the State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS). His research is now focused on mapping social-economic dynamics with big data.

Jianghao Wang

Jianghao Wang is an associate professor in the State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS. His research interests concentrate on spatiotemporal data mining.

Yong Ge

Yong Ge is currently a Professor of the State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS. Her research is focused on spatiotemporal statistics.