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Shanghai has experienced a rapid process of urbanization and urban expansion, which increases travel costs and limits job accessibility for the economically disadvantaged population. This paper investigates the jobs-housing imbalance problem in Shanghai at the subdistrict-level (census-level) and reaches the following conclusions. First, the jobs-housing imbalance shows a ring pattern and is evident mainly in the suburban areas and periphery of the Shanghai metropolitan area because job opportunities are highly concentrated while residential areas are sprawling. Second, structural factors such as high housing prices and sprawling development significantly contribute to the jobs-housing imbalance. Third, regional planning policies such as development zones contribute to jobs-housing imbalance due to the specialized industrial structure and limited availability of housing. However, geographically weighted regression reveals the development zones in the traditional Pudong district are exceptional insofar as government policy has created spatial heterogeneity there. In addition, the multilevel model used in this study suggests regions with jobs-housing imbalance usually have well-connected streets, and this represents the local government’s efforts to reduce excessive commuting times created by jobs-housing imbalance. 

This paper studies the relationships between network capabilities and innovation development in the context of two types of innovation networks: scientific knowledge networks (SKN) and technological knowledge networks (TKN). Focusing on two types of network capabilities, namely acquisition capability and control capability, the paper uses spatial regime models to compare the impacts of multiple factors on different spatial regimes. The main conclusions are the following. First, as regards SKN, the political-administrative hierarchy has shaped the spatial evolution of acquisition capacity, forming a pattern consisting of three dominant cities (Beijing, Shanghai, Nanjing), three subsidiary cities (Guangzhou, Hangzhou, Wuhan), and multiple lesser centers (Tianjin, Chengdu, Xi’an). Moreover, high control capability cities are mainly clustered in the coastal areas, specifically, one monocentric city (Beijing) and two polycentric metropolises (Shanghai, Wuhan). Second, for TKN, cities with high acquisition and control also are mainly found in coastal areas, with Shanghai and Beijing dominating network capabilities. The model’s analysis confirms the positive effect of network capabilities on innovation development, especially in scientific knowledge networks, and the driver for regional innovation development appears to have shifted from global pipeline (globalization) to local buzz (localized talents). This paper concludes with suggestions regarding network capabilities’ potential to reduce regional inequality and achieve sustainable development of regional economies.