  Understanding urban dynamics, i.e., how the types and intensity of urban
residents' activities in the city change along with time, is of urgent demand
for building an efficient and livable city. Nonetheless, this is challenging
due to the expanding urban population and the complicated spatial distribution
of residents. In this paper, to reveal urban dynamics, we propose a novel
system UrbanRhythm to reveal the urban dynamics hidden in human mobility data.
UrbanRhythm addresses three questions: 1) What mobility feature should be used
to present residents' high-dimensional activities in the city? 2) What are
basic components of urban dynamics? 3) What are the long-term periodicity and
short-term regularity of urban dynamics? In UrbanRhythm, we extract staying,
leaving, arriving three attributes of mobility and use a image processing
method Saak transform to calculate the mobility distribution feature. For the
second question, several city states are identified by hierarchy clustering as
the basic components of urban dynamics, such as sleeping states and working
states. We further characterize the urban dynamics as the transform of city
states along time axis. For the third question, we directly observe the
long-term periodicity of urban dynamics from visualization. Then for the
short-term regularity, we design a novel motif analysis method to discovery
motifs as well as their hierarchy relationships. We evaluate our proposed
system on two real-life datesets and validate the results according to App
usage records. This study sheds light on urban dynamics hidden in human
mobility and can further pave the way for more complicated mobility behavior
modeling and deeper urban understanding.
