留还是走：初期城市流动人口的流失预测
To Stay or to Leave: Churn Prediction for Urban Migrants in the Initial Period
摘要：
在中国，有2.6亿人迁移到城市，以实现他们的城市梦想。尽管这些流动人口在快速的城市化进程中发挥了重要作用，但他们中的许多人未能定居下来，最终离开了城市。因此，移徙者的融合进程对学者和决策者提出了一个重要问题。本文以上海市为例，考察了流动人口在最初几周的行为，特别是他们的行为与提前离开的关系。我们的数据集包括5400万用户之间的698个电信日志的一个月完整数据集，以及上海18K个房地产的新颖且公开的房价数据。我们发现，提前离开的移民往往既不会在最初几周建立多样化的联系，也不会在城市中四处走动。他们活跃的地区的房价也高于留在当地的移民。我们制定了一个流失预测问题，以确定是否一个移民是要离开她的行为在头几天的基础上。当我们包含更多天的数据时，预测性能会提高。有趣的是，当使用相同的特征时，仅从最初几天训练的分类器已经与使用完整数据训练的分类器一样好，这表明性能差异主要在于特征之间的差异。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
