Gentrification, as a term introduced into China 20 years ago, has now become a topical word in scholarly discussion. This paper for the first time used the software of multiple pieces to analyze literature on gentrification between 1996 and 2017 in China based on bibliometrics, aiming to get the overview of the study, identify and expound the research themes, and analyze their evolution. It showed that the study on gentrification had entered into an exploratory stage with fluctuation from the early germination stage; gentrification research mainly concentrated on two disciplines, namely Geography and Urban and Rural Planning; the top 10 influential authors were identified and collaborative research teams leading the gentrification research had initially been formed. The themes of gentrification research in China were decided by visual analysis method, including urban renewal and dynamic mechanism of gentrification, evaluation and response to the effects of gentrification, new types of gentrification, and historical and cultural heritage conservation and creative industry, on which deeper content and information were described in detail. In terms of research themes evolution analysis, the results showed that the gentrification research in China had experienced the shift from initial concept and literature introduction to current empirical research and theory construction. There are significant signs showing the future trends of gentrification will move to the construction of a theoretical system of gentrification with Chinese characteristics, gentrification consequences evaluation and urban policies, new types of gentrification, gentrification driven by cultural consumption and authenticity protection of gentrification-stricken historical and cultural heritages, application of new technology to gentrification research, and relationship between shantytown renovation and gentrification in China. As to this paper, as far as the authors know, it is the first comprehensive systematic summary within this field based on bibliometric analysis.

Equal compulsory education is an important way to realize social and spatial equality, while the uneven allocation of educational resources in different regions and groups results in inequality of opportunity and solidification of social strata. Traditional research conducted on the basis of fixed search range ignores the special institutional background of Chinese school district system. In this paper, an improved Gaussian two‐step floating catchment area model is developed taking into consideration the school district system, while the bivariate local spatial analysis method and geographically weighted regression model are employed to study the social and spatial differentiation of compulsory education accessibility and its capitalization effects in Hangzhou. Results show that (1) the improved Gaussian two‐step floating catchment area model is more in line with the national condition of China’s “nearby schooling” policy; (2) the accessibility of compulsory schools in Hangzhou shows an obvious core‐periphery typology, and the aggregation effect of primary school accessibility is more significant than that of secondary schools; (3) compared to groups with high socioeconomic status, vulnerable groups are highly disadvantaged in terms of access to educational services; (4) spatial heterogeneity exists in education capitalization, and the areas where education accessibility has the strongest impact on housing prices are in the central city with rich high‐quality educational resources; (5) high‐quality educational resources, high‐priced communities, clusters of high socioeconomic status groups, and communities enjoying high‐level education accessibility are highly consistent in all spaces, which is the spatial expression of educational inequality. The research on Hangzhou, a regional central city, provides a theoretical basis and technical support for the humanistic shift in the allocation of educational resources.

The relationship between residential patterns and socioeconomic statuses highlights the complex interactions between the economic regime, welfare system, and neighborhood effects, which are crucial in urban inequality studies. With the diversification of the housing demand and supply system, the traditional analysis conducted separately from the ethnic or spatial segregation perspective fails to capture the rising inequalities and changing socio-spatial context. Taking Nanjing as an example, based on a multi-source database including the housing price, residential environmental quality, surrounding support facilities, and mobile phone user portrait data, this paper proposed a modified method for discovering the coupling relationship between residential patterns and socioeconomic statuses. It is found that socioeconomic status contributes to residential spatial aggregation and that the relationship between social and spatial dimensions of residential differentiation is tightly coupled and related. The lower socioeconomic strata were displaced to the periphery and the older urban core, while affluent inhabitants were more likely to settle voluntarily in segregated enclaves to isolate themselves from the general population through more flexible housing options. The heterogeneity of the urban socioeconomic dimension is primarily affected by consumption and occupational status, while housing prices mainly determine the divergence of spatial distribution.

The pattern, process, and mechanism of residential heterogeneity vary significantly with different geographical scales. However, most traditional methods ignore the checkboard and modifiable areal unit problem (MAUP), which may cover up the complexity and hierarchy of social space. Taking Hangzhou city as an example, a multiscalar method was proposed based on the information entropy theory to estimate residential heterogeneity and its scale sensitivity. Based on the sixth population census of Hangzhou and the housing price database of 6,536 residential districts from 2008 to 2018, we explore the scale effect and dynamic characteristics of residential heterogeneity. The results of spatial simulation and geostatistical analysis based on Python Spatial Analysis Library (PySAL) module show that the multiscalar algorithm better presents the real segregation pattern than traditional method, which is one of the new models and technologies in urban geography complex system. Exploring residential heterogeneity through multiscalar lens provides an important basis for the gradual and refined urban renewal.