  In China, 260 million people migrate to cities to realize their urban dreams.
Despite that these migrants play an important role in the rapid urbanization
process, many of them fail to settle down and eventually leave the city. The
integration process of migrants thus raises an important issue for scholars and
policymakers.
  In this paper, we use Shanghai as an example to investigate migrants'
behavior in their first weeks and in particular, how their behavior relates to
early departure. Our dataset consists of a one-month complete dataset of 698
telecommunication logs between 54 million users, plus a novel and publicly
available housing price data for 18K real estates in Shanghai. We find that
migrants who end up leaving early tend to neither develop diverse connections
in their first weeks nor move around the city. Their active areas also have
higher housing prices than that of staying migrants. We formulate a churn
prediction problem to determine whether a migrant is going to leave based on
her behavior in the first few days. The prediction performance improves as we
include data from more days. Interestingly, when using the same features, the
classifier trained from only the first few days is already as good as the
classifier trained using full data, suggesting that the performance difference
mainly lies in the difference between features.
