学术文献库

Mobility cross spatial units represents the embodiment of how people manage activities between locations along temporal sequences. Spatiotemporal pattern nevertheless interacts with the socioeconomic characteristics of respected origin (push factors) and destination (pull factors) which widely discussed in spatial interaction literature. Observing this dynamics at higher spatial resolution allows us to entangle multifaceted nature of city, its complexity as a system or network, and the way it shapes movement of people. This study explore the extent interconnected elements of urban system or urban networks, in parallel with the appearance of external shock namely COVID outbreak, may affect estimation of mobility flows. To improve predictive power, gravity model is extended to urban system model by augmenting the complexities of urban network based on micro-analytical approach (intra-city networks). Our findings reveals better performance of a more complex urban system model as to compared with gravity model. Here, we leverage stratification in mobility by specifying mobility flows with respect to income status of respected areas. The occurrence of COVID outbreak followed by lockdown measure increases intra-class mobility, indicating the coupling between socioeconomic distance and geographical distance. Flows between areas with similar economic ranges are more predictable than the one of different level. Furthermore, the presence of pull factors is more affluent than push factors in determining mobility regardless the severity of external shock.