Measuring mass displacement of urban renewal in Shenzhen,China: Using longitudinal mobile phone trajectory data

Abstract

A large-scale urban renewal programme in many Chinese cities has resulted in residential displacement, raising concerns about its negative consequences. However, quantitative evidence is scarce. Utilising mobile signalling data that records continuous individual movements, we devise a strategy for measuring mass displacement caused by urban renewals, where a large number of migrant tenants are forced to move at the same time. Focusing on multiple urban renewal projects in Shenzhen, a pioneer city in urban renewal practices in China, we estimate the effects of mass displacement on the living conditions of displaced residents using both a difference-in-differences approach and a machine learning approach. The results show that, compared with relocations unaffected by renewal, displaced residents relocated to areas with worse housing quality and poor access to urban amenities, and experienced longer commutes, the pattern of which is more severe for urban renewals in the central area of the city. The aggregate displacement indices derived from the support vector machine model indicate that 25% of the displaced experienced a worsening of living conditions following the relocation. Our findings suggest significant adverse consequences of mass displacement as a result of large-scale urban renewal.