Search

Redevelopment of industrial land is an essential component of urban regeneration in China, which has profound implications for promoting urban sustainable development. Therefore, quantitative evaluations of the spatial variation of industrial land transformation and its influencing factors are required. This study explores the spatial patterns of industrial land transformation using overlay analysis. Then, it establishes a multinomial logistic regression model to explore their influential factors by collecting empirical data, including of the industrial renewal projects from 2010 to 2018 in Shenzhen, China. The findings show that urban redevelopment practices have profoundly transformed industrial land use in the recent urban renewal process. Market actors implement industrial land redevelopment projects with policy guidance. These actors with diversified redevelopment goals have directly shaped the spatial outcomes of industrial land transformation. A small portion of industrial sites has been transformed into public facilities and open space as required by redevelopment policies. At the same time, the majority have been converted into residential and commercial spaces, as well as new industrial spaces for innovative industries under market orientation. Significant spatial differences in regeneration outcomes exist at the urban level. The regression results indicate that policy and planning regulations, and land ownership, are the critical factors affecting the transformation of urban industrial land into different types of land uses. A favorable plot location tends to promote the transition of industrial parcels for commercial functions, with less impact on other options. The impact of public demand is almost negligible. [ABSTRACT FROM AUTHOR]

The development of rail transit is expected to gather housing and jobs around metro stations to improve the urban spatial structure for sustainable development. However, evaluations of the effects of transit-oriented development (TOD) practices in Chinese cities remain limited. Based on 2019 mobile phone signaling data, this study analyzed the spatial relationship between metro station areas, urban population density, and employment density in Tianjin City, China. Kernel density estimation, the coverage rate of population and employment, and the envelope range density method for rail transit networks were used to describe the spatial relationships between rail transit and the distribution of population and employment. The concentric circle method was used to analyze the spatial variation of residential population density and employment density within the station surrounding areas. Multiple linear regressions, spatial autocorrelation analysis, and geographically weighted regression analysis were established to quantitatively evaluate the effects of metro stations on the residential population density and employment density around the stations. Results show that: (1) population and employment are significantly concentrated along the rail transit area; (2) metro stations within the suburban area, which are roughly 10 km away from the center, have the most significant aggregation effect on population and employment; and (3) employment density and the proportion of residential and commercial lands are the most influential factors in residential population density within the station's surrounding areas. These findings contribute to comprehensive spatial analysis on population and employment densities around metro stations and provide scientific support for sustainable urban development. Based on the research results, policy implications are proposed to better guide the coordinated development of rail transit and urban spatial structure. [ABSTRACT FROM AUTHOR]