Unraveling the multi-scalar residential segregation and socio-spatial differentiation in China: A comparative study based on Nanjing and Hangzhou

Abstract

Residential segregation is a dual process of socio-spatial differentiation in residents and spatio-temporal heterogeneity in dwelling. However, most of the existing studies are established from the single perspective of urban residents based on demographic data, which is difficult to reveal the dynamics and complex spatial reconstruction within and between cities. With the characteristics of both stability and timeliness, the rapidly changing housing market is one of the processes and results of socio-spatial reconfiguration, and it is undoubtedly a better lens to observe residential segregation. This paper adopts methods such as multi-group segregation index, multi-scalar segregation profiles, and decomposition of segregation index, with Nanjing and Hangzhou as case cities, and establishes multi-scalar segregation profiles and comparative models based on three geographical scales of census tract, block and grid, and different residential types. A quantitative study was conducted on the degree and pattern of multi-scalar residential segregation in Nanjing and Hangzhou from 2009 to 2018. The paper found that the spatial segregation index is an improvement of the non-spatial segregation index. There are differences between Nanjing and Hangzhou in the evolution process of residential segregation. Nanjing has a higher degree of spatial differentiation as a whole, among which spatial components have a more significant impact.

Nonlinear Hierarchical Effects of Housing Prices and Built Environment Based on Multiscale Life Circle—A Case Study of Chengdu

“…Therefore, choosing to break through the existing life circle scale in planning policies (15-min), seeking the best scale that meets the needs of urban development and residential equity, such as based on individual behavior [2] and refining urban units [73], can lead to reasonable planning of living space. Moreover, it is important to grasp the scale of the built environment factors, capture their abrupt changes in the impact on housing prices in the local range, prevent the dramatic manifestation of housing price differences within a large area, and effectively alleviate the degree of residential differentiation in urban areas [33,74].…”

“…High-value residential space usually lays claim to a more favorable built environment and societal resources [31,32]. Therefore, housing price is not only an important indicator to measure the level of living space but also an important identifier of class polarization and residential differentiation [31,33]. The scale and speed of urban renewal cannot easily offset the degree of social differentiation, especially under the urban-suburban-rural residential space in metropolitan areas, where the differentiated landscape and class self-stratification phenomenon presented by the complex evolution of the urban housing system are becoming more and more serious [33,34].…”

“…Therefore, housing price is not only an important indicator to measure the level of living space but also an important identifier of class polarization and residential differentiation [31,33]. The scale and speed of urban renewal cannot easily offset the degree of social differentiation, especially under the urban-suburban-rural residential space in metropolitan areas, where the differentiated landscape and class self-stratification phenomenon presented by the complex evolution of the urban housing system are becoming more and more serious [33,34]. Therefore, exploration of the differentiation and formation mechanisms of residential space morphology across different levels, focusing on residential differentiation and considering multiple scales, has emerged as a prominent topic in the realms of urban spatial governance, urban system renewal, and rural geography [33,35].…”

“…Quantifying this influence necessitates the exclusion of the structural attributes of the dwelling itself. With the rise in exploratory spatial data analysis (ESDA), methods, such as geographically weighted regression (GWR) [36,42] and geographical detectors (GDs) [33,49], have been applied to the study of housing price impact mechanisms. Although their parameter optimization and fitting degree are better than those of previous multiple linear regression models, they still cannot escape the category of linear relationships, and they are inadequate in explaining the original variables and clarifying the impact mechanism.…”