Work

The Prediction of Finely-Grained Spatiotemporal Relative Human Population Density Distributions in China

Year: 2020

Type: article

Abstract: China's transportation industry has been experiencing huge changes and the travelling frequency of citizens becomes higher and higher and more and more diversified in a short time period. The analysis and deep research on the short-term change of population density in the city-level spatial resolution are worthy of further exploration. In this study, we first used two linear regression models to build relationships between the 2010 census density, predicted 2020 census density and the Tencent density respectively to test the usability of Tencent positioning data. The Pearson's correlation coefficients r 0.58 (p2) values of ConvLSTM model is higher than 0.9, while the other ones are about 0.78. Comparing the two results in both time and space concludes that: the evaluation results reflected by RMSE and R2showed that the two models are both suitable for the prediction of Tencent density distribution in finely-grained time and space. Nevertheless, the predicted density correlated much better with the tested data at temporal and spatial scales when using ConvLSTM compared to SARIMA, and the capability of prediction in space by ConvLSTM model is more stable.

Source: IEEE Access

DOI: 10.1109/access.2020.3027824

Authors: Zhi Zheng, Guangyuan Zhang

Institutions: National University of Singapore, Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research, University of Chinese Academy of Sciences, Queen Mary University of London

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,750