  Understanding the spatio-temporal dynamics of cities is in the heart of many
applications including urban planning, zoning, and real-estate construction. So
far, much of our understanding about urban dynamics came from traditional
surveys conducted by persons or by leveraging mobile data in the form of Call
Detailed Records. However, the high financial and human cost associated with
these methods make the data availability very limited. In this paper, we
investigate the use of large scale and publicly available user contributed
content, in the form of social media posts to understand the urban dynamics of
cities. We build activity time series for different cities, and different
neighborhoods within the same city to identify the different dynamic patterns
taking place. Next, we conduct a cluster analysis on the time series to
understand the spatial distribution of patterns in the city. Our instantiation
for the two cities of London and Doha shows the effectiveness of our method.
