Spatiotemporal Pattern of Air Turbulence Risks with QAR Flight Big Data

Objectives Air turbulence is one of the major safety risks during civil aviation flight. Explore the spatial distribution pattern of great significance for avoiding risky areas and enhancing flight safety.

Methods We use quick access recorder (QAR) big data of China's civil aviation industry from 2017 to 2019 to detect the nationwide air turbulence events, and conduct exploratory analysis in the spatiotemporal distributions and patterns via kernel density estimation and spatiotemporal visualization techniques. In addition, we use spatial statistical techniques, including spatial autocorrelation, hot spot analysis, and geographically weighted principal component analysis (GWPCA), to explore the spatial patterns of air turbulence events.

Results The results show that these events occur frequently in Tibet, central and southeastern regions of China. In particular, the event densities are highly correlated with local terrains, e.g. step I and step II regions, where the air turbulence events occur frequently. In the fine-grained scale, we adapt GWPCA to qualitatively analyze the spatial heterogeneities in the relationships between air turbulence and relative parameters. The surface elevation differences show significant impacts in the southeast coastal area, while the inertial vertical velocity tends to be the principle factor in Guangxi and Yunnan provinces.

Conclusions This study provides theoretical and practical supports in improving the risk management and safety insurance of the civil aviation industry.