The actions of platforms and their all-inclusive impact on place development is coined platform-based place making in this article. We use the actor-network theory to analyse a typical e-commerce platform-based place making, namely the emergence, development and upgrading of Taobao villages in China, and to explore the mechanisms of platform place making power. Our study shows that platforms ‘make’ places by platform–place interactions of progressively expanding the enrolment of intra-regional and extra-regional actors. In addition, our research advances the actor-network theory and its application and transcends the urban and production-side bias of economic geography.

Since China’s housing reform, increasing attention has been paid to public housing. While the quantity of public housing units is increasing, residents’ attachment to the community remains underexplored. This study evaluates the social dimension of public housing from the perspective of community attachment in Guangzhou, China. We use a mixed methods approach to examine what factors influence community attachment in both public and commodity housing communities. We first analyse quantitative data (N = 344) collected from four public housing and commodity housing communities in Guangzhou using confirmatory factor analysis, hierarchical regression, and moderation analysis. Results show that housing type has no significant effect on community attachment. Housing type moderates the relationship between community ties, perceived public services, and community attachment, whereas community ties and perceived public services are positively associated with community attachment only for commodity housing residents. Qualitative data from 21 semi-structured interviews are then used to explain the quantitative results. Our findings support the rationality of the public housing strategy in China from the perspective of community attachment. We also argue that community attachment must be analysed in a contextualized approach.

Urban agglomeration is an important spatial organization mode in China’s attempts to attain an advanced (mature) stage of urbanization, and to understand its consequences, accurate simulation scenarios are needed. Compared to traditional urban growth simulations, which operate on the scale of a single city, urban agglomeration considers interactions among multiple cities. In this study, we combined a spatial Markov chain (SMC) (a quantitative composition module) with geographically weighted regression-based cellular automata (GWRCA) (a spatial allocation module) to predict urban growth in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), an internationally important urban agglomeration in southern China. The SMC method improves on the traditional Markov chain technique by taking into account the interaction and influence between each city to predict growth quantitatively, whereas the geographically weighted regression (GWR) gives an empirical estimate of urban growth suitability based on geospatial differentiation on the scale of an urban agglomeration. Using the SMC model to forecast growth in the GBA in the year 2050, our results indicated that the rate of smaller cities will increase, while that of larger cities will slow down. The coastal belt in the core areas of the GBA as well as the region’s peripheral cities are most likely to be areas of development by 2050, while established cities such as Shenzhen and Dongguan will no longer experience rapid expansion. Compared with traditional simulation models, the SMC-GWRCA was able to consider spatiotemporal interactions among cities when forecasting changes to a large region like the GBA. This study put forward a development scenario for the GBA for 2050 on the scale of an urban agglomeration to provide a more credible scenario for spatial planning. It also provided evidence in support of using integrated SMC-GWRCA models, which, we maintain, offer a more efficient approach for simulating urban agglomeration development than do traditional methods.

In recent years, the strategic development and utilization of historical and cultural resources in historic urban areas to foster advanced cultural industries has become a pivotal approach for promoting urban renewal. However, there are significant differences between the urban development models of Western countries and the unique context of China, rendering it challenging to directly apply Western theories to explain China’s urban development. As one of the leading cities in both cultural industry growth and urban renewal in China, Guangzhou offers a valuable case for examining how cultural industries can effectively empower urban renewal. This paper aims to explore and analyze the mechanisms through which cultural industry development influences urban renewal in China, using Guangzhou as a case to illustrate the dynamic interactions between cultural industries and urban transformation. Furthermore, it proposes a contextualized model and strategy for the role of cultural industries in driving urban renewal, thereby providing evidence-based insights and practical recommendations for enhancing the contribution of cultural industries to urban development. This study suggests that Guangzhou should further advance this goal by strengthening policy support, promoting the digital cultural industry, strengthening infrastructure for cultural industry spaces, and developing distinctive cultural industry projects, thus facilitating sustainable and high-quality urban development.

Rapid urbanization has led to a series of environmental problems that are harmful to the physical and mental health of residents. Not only can increasing urban tree canopy (UTC) improve sustainable city development, but it can also effectively promote the quality of life for residents; however, the uneven spatial distribution of UTC can also bring about social justice problems. Currently, there are few studies related to the justice of UTC distribution in China. Based on this, the paper adopts object-oriented image classification technology to interpret and extract UTC data from satellite images, and it uses house price to explore the justice of the spatial distribution of UTC in the main urban area of Guangzhou from the perspective of environmental justice by ANOVA, Pearson correlation coefficient, and bivariate local spatial autocorrelation. The results show that: (1) There is a significant positive correlation between UTC and house price in the main urban area of Guangzhou, and there are regional differences in the distribution of UTC, with the UTC in the “very high” house price group being significantly higher than that in other groups. (2) The spatial clustering of UTC and house price in the main urban area of Guangzhou is found to be “low-low” and “high-high”; thus, it can be concluded that the spatial distribution of UTC in the main urban area of Guangzhou is uneven. This is an environmental injustice, as the areas with low UTC are spatially clustered in old residential areas, while the areas with high UTC are spatially clustered in commercial housing estates with high house prices. The study shows that urban tree planting should not only focus on quantitative improvement but also on equitable spatial layouts to promote social equity and justice thereby improving the urban ecological environment and promoting healthy urban development.

Housing reform since the 1990s has created a new sociospatial structure in Chinese cities. However, neighborhood deprivation remains one of the key challenges for urban housing policies. This study investigates the relationship among perceived neighborhood characteristics, deprivation, and attachment, based on a survey of 59 neighborhoods across Guangzhou, China. We adopt an objective approach to measure the index of multiple deprivation (IMD) on the neighborhood scale. The descriptive statistics indicate that while residents in deprived neighborhoods generally report lower level of perceived neighborhood physical environments and neighborhood attachment, their evaluations of neighborhood social environments are not necessarily lower. Results from the hierarchical linear modeling (HLM) show that perceived housing conditions, perceived neighborhood environment, neighborhood ties, and sense of security are significantly correlated with neighborhood attachment. Furthermore, the moderation analysis reveals that the effect of perceived housing conditions on neighborhood attachment is stronger in more deprived neighborhoods. We propose that residents’ subjective feelings, timely and direct measurement of IMD, and context-based strategies should be used in urban housing policies to reduce the negative impacts of neighborhood deprivation.