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Urban Dreams of Migrants: A Case Study of Migrant Integration in Shanghai

机译:中国城市梦想:上海移民融合的案例研究

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摘要

Unprecedented human mobility has driven the rapid urbanization around the world. In China, the fraction of population dwelling in cities increased from 17.9% to 52.6% between 1978 and 2012. Such large-scale migration poses challenges for policymakers and important questions for researchers. To investigate the process of migrant integration, we employ a one-month complete dataset of telecommunication metadata in Shanghai with 54 million users and 698 million call logs. We find systematic differences between locals and migrants in their mobile communication networks and geographical locations. For instance, migrants have more diverse contacts and move around the city with a larger radius than locals after they settle down. By distinguishing new migrants (who recently moved to Shanghai) from settled migrants (who have been in Shanghai for a while), we demonstrate the integration process of new migrants in their first three weeks. Moreover, we formulate classification problems to predict whether a person is a migrant. Our classifier is able to achieve an F1-score of 0.82 when distinguishing settled migrants from locals, but it remains challenging to identify new migrants because of class imbalance. This classification setup holds promise for identifying new migrants who will successfully integrate into locals (new migrants that misclassifled as locals).
机译:前所未有的人类流动使世界各地的城市化迅速推动。在中国,城市的人口居住的一部分从1978年和2012年之间的人口住所增加到9.9%至52.6%。这种大规模移民对政策制定者和研究人员的重要问题构成了挑战。为了调查移民整合的过程,我们在上海雇用了一个月的电信元数据数据集,拥有5400万用户和6.98亿次电话日志。我们在移动通信网络和地理位置中发现当地人和移民之间的系统差异。例如,移民在他们安定下来之后,移民在城市周围移动而不是当地人。通过将新移民(最近搬到上海)的新移民(曾经在上海搬到了一段时间),我们展示了前三周新移民的整合过程。此外,我们制定分类问题,以预测一个人是否是移民。当区分当地人的定居移民时,我们的分类器能够实现0.82的F1分数,但由于类别不平衡,识别新移民仍然挑战。此分类设置拥有承担识别将成功集成到当地人(作为当地人被错误分类的新移民)的新移民。

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