  As the ongoing rapid urbanization takes place with an ever-increasing speed,
fully modeling urban dynamics becomes more and more challenging, but also a
necessity for socioeconomic development. It is challenging because human
activities and constructions are ubiquitous; urban landscape and life content
change anywhere and anytime. It's crucial due to the fact that only up-to-date
urban dynamics can enable governors to optimize their city planning strategy
and help individuals organize their daily lives in a more efficient way.
Previous geographic topic model based methods attempt to solve this problem but
suffer from high computational cost and memory consumption, limiting their
scalability to city level applications. Also, strong prior assumptions make
such models fail to capture certain patterns by nature.
  To bridge the gap, we propose Macross, a metapath guided embedding approach
to jointly model location, time and text information. Given a dataset of
geo-tagged social media posts, we extract and aggregate location and time and
construct a heterogeneous information network using the aggregated space and
time. Metapath2vec based approach is used to construct vector representations
for times, locations and frequent words such that co-occurrence pairs of nodes
are closer in latent space. The vector representations will be used to infer
related time, locations or keywords for a user query. Experiments done on
enormous datasets show our model can generate comparable if not better quality
query results compared to state of the art models and outperform some
cutting-edge models for activity recovery and classification.
