Abstract
Traditional census data are ill-suited for uncovering the true population patterns and underlying social and economic dynamics in China as the census relies on information of population with registered household status. A large number of migrant workers are registered rural residents but spend most of a year working in cities that are hundreds or even thousands of miles away. It is termed annual spatial mismatch'' here for the separation of registered residence and workplace in China, in contrast to spatial mismatch'' that is known in the urban commuting literature in the west but on a daily basis. Big geo-data, such as the mobile app data, afford us a rare opportunity to examine this unique phenomenon. Specifically, this research uses a mobile app dataset of two epochs, i.e., prior to and during the Chinese Spring Festival, to capture the population patterns before and after the migrant workers return home, respectively. The difference between them reflects distinctive roles of an area plays in labor market, termed source-sink areas''. A GIS-automated regionalization method is used to delineate China into hierarchical source-sink'' areas, characterizing various urbanization levels or distinctive roles in labor market. The study demonstrates the value of using human mobility data in urban and regional analysis on issues that were previously infeasible, especially in study areas without reliable data.
Yi Zhang
Associate professor
