  Currently, the global situation of COVID-19 is aggravating, pressingly
calling for efficient control and prevention measures. Understanding spreading
pattern of COVID-19 has been widely recognized as a vital step for implementing
non-pharmaceutical measures. Previous studies investigated such an issue in
large-scale (e.g., inter-country or inter-state) scenarios while urban
spreading pattern still remains an open issue. Here, we fill this gap by
leveraging the trajectory data of 197,808 smartphone users (including 17,808
anonymous confirmed cases) in 9 cities in China. We find a universal spreading
pattern in all cities: the spatial distribution of confirmed cases follows a
power-law-like model and the spreading centroid is time-invariant. Moreover, we
reveal that human mobility in a city drives the spatialtemporal spreading
process: long average travelling distance results in a high growth rate of
spreading radius and wide spatial diffusion of confirmed cases. With such
insight, we adopt Kendall model to simulate urban spreading of COVID-19 that
can well fit the real spreading process. Our results unveil the underlying
mechanism behind the spatial-temporal urban evolution of COVID-19, and can be
used to evaluate the performance of mobility restriction policies implemented
by many governments and to estimate the evolving spreading situation of
COVID-19.
