  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.
