Evaluating the performance of LBSM data to estimate the gross domestic product of China at multiple scales: A comparison with NPP-VIIRS nighttime light data

Abstract Regional economic development evaluation is essential for understanding social and environmental issues. Although the nighttime light (NTL) data have been proved to be effective in economic estimation, it cannot reflect the human activities that occur during the daytime. Recently, with the widespread use of smart mobile devices, the location based social media (LBSM) data are increasingly being used as a proxy for real-time human activities. However, little work was carried out to explore the potential of LBSM data in estimating economic development at different scales in China. This study filled this gap by evaluating the effectiveness of Tencent user density (TUD) data, a typical type of LBSM data in China, in Gross Domestic Product (GDP) modeling at the provincial, municipal, and county scales. In this study, we employed holiday and non-holiday TUD sample data to simulate the annual TUD data, and compared it with the new generation NTL data, NPP/VIIRS images. The results showed that although the simulated annual TUD data does not perform better than NPP/VIIRS-NTL data in provincial and municipal GDP estimation, it outperforms NPP/VIIRS-NTL data at the county scale. More importantly, the simulated annual TUD data are much more powerful and reliable than NPP/VIIRS-NTL data in underdeveloped areas with complex terrain, such as the Northwest and Southwest China, as well as in more developed areas with separation of work and housing, such as the North China and South China. This is mainly because TUD data can reduce the impact of natural factors such as terrain on data collection as well as reflect both daytime and nighttime human activities. This study confirmed that the LBSM-TUD data is a potential and promising data source for economic modeling in small scale areas of China, which will help to support China's regional economic evaluation.