PROGRESS IN GEOGRAPHY

Spatiotemporal patterns and driving mechanisms of intra-city human mobility under extreme weather:A case study of Shanghai during Typhoon Bebinca

ZHANG Yan, LU Yuwen, ZHAI Guofang

PROGRESS IN GEOGRAPHY, 2026, 45(4): 882-896. DOI: 10.18306/dlkxjz.2026.04.015

Fig.5 Clustering and distribution of human mobility modes

Other figure/table from this article

Fig.1 The "weather-environment-behavior" analytical framework
Fig.2 The study area
Fig.3 The resilience curve of human mobility
Tab.1 Definition and formula of resilience indicators
Tab.2 Definition of variables and descriptive statistics
Fig.4 Time-series variation of mobility intensity and weather conditions
Fig.6 Spatial and frequency distribution of human mobility resilience indicator values
Tab.3 Results of global regression by the ordinary least squares (OLS) model
Tab.4 Results of partial regression by the geographically weighted regression (GWR) model
Fig.7 Positive and negative proportion of regression weights for different variables
Fig.8 Distribution of GWR coefficients for factors driving MI
Fig.9 Distribution of GWR coefficients for factors driving TPL