Mixed land use is recognized as a crucial strategy for enhancing urban vitality and promoting urban renewal. While existing research has mainly focused on measuring mixed land use along single dimensions, there remains a notable gap in studies that explore multidimensional measurements and their impacts at the block scale. This study introduces a multidimensional mixed-degree index based on diverse data sources such as POI and land use status to comprehensively assess mixed land use levels at the block scale in Xi’an’s central urban area. Additionally, a multiple linear regression model is applied to analyze how the built environment influences mixed land use. Findings reveal that mixed land use at the block scale can be objectively evaluated across three dimensions: quantity, distance, and attribute. In Xi’an, mixed land use demonstrates a spatial distribution characterized by core agglomeration and concentric decline. The study highlights that block area and road network density significantly influence mixed land use, with block area negatively impacting it the most and road network density positively affecting it secondarily. These insights provide valuable guidance for optimizing land use practices and promoting high-quality urban development.

Primary health care (PHC) facilities play a significant role in constructing a “people-oriented city” to promote sustainable urban development. However, existing studies exhibit gaps in the spatial-performance evaluation of PHC facilities at the block scale and in identifying spatial association types between facilities and the population. Therefore, we examined the elderly population, who rely heavily on PHC facilities, and developed a spatial-performance evaluation model for PHC facilities at the block scale using the Ga2SFCA method and the bivariate spatial autocorrelation method. The results revealed an evident concentric pattern and spatial mismatch between the accessibility of facilities and the elderly population. Facilities in the central area were inadequate due to the excessive density of the elderly population, whereas medical services in suburban areas were unsustainable due to poor accessibility. From a spatial-justice perspective, the spatial-performance evaluation at the block scale can identify spatial correlation types and distribution characteristics between PHC facilities and the elderly population.

The evolution of industrial land layout is a significant feature of changes in urban land use types during rapid urbanization. However, the theoretical studies investigating the evolution mechanism behind industrial land are scarce. This study developed a dynamic theoretical framework examining the relationship between government and market forces. Furthermore, this study examined the determinants of industrial land evolution in Xi’an from 1994 to 2019 by using a logistic regression model. The results showed that industrial land area increased from 55.02 km² to 126.63 km² from 1994 to 2013 but later rapidly reduced to 106.61 km² in 2019. The spatial distribution of industrial land exhibited significant deindustrialization and suburbanization during park-orientated agglomeration and showed a shift from large-area small-patch to multi-center large-patch agglomeration. Factors such as development zone policies, land prices, agglomeration benefits, land supply, and transportation infrastructure are key determinants of suburbanization, while land prices and agglomeration benefits are key determinants of deindustrialization. This study reveals the mechanisms driving industrial land evolution and offers guidance in improving the rationality of urban land layout and achieving industry–city integration.

The built environment is critical in fostering vibrant cities, and evaluating urban vitality (UV) and its interplay with the built environment is essential for building people-oriented cities. Most existing studies have overlooked the nonlinear relationship, threshold effects, and spatial heterogeneity of the built environment on UV. Taking the Xi’an central urban area as a case study, this study constructs a block-scale UV evaluation index system and a built environment indicator system based on multi-source data. Then, the nonlinear effects of built environment factors on UV are explored via the Geographically Weighted Random Forest (GWRF) and SHapley Additive exPlanations (SHAP) models. The findings show that the spatial distribution of UV exhibits a core-periphery structure and transportation dependence characteristics. Among various built environment factors, floor area ratio (FAR) and mixed land use (MLU) are identified as key drivers. UV exhibits an S-shaped curve with FAR and diminishing returns with MLU. Spatial heterogeneity indicates that the polycentric UV spatial pattern as a pivotal model for improving urban quality and efficiency stage. This study provides empirical foundations for the refined governance and high-quality development of UV.

With the global economic restructuring and the consequent population mobility, urban shrinkage has become a common phenomenon. The Qinling–Daba Mountains, a zone with a key ecological function in China, have long experienced population decline and functional degradation. Clarifying the dynamics and influencing factors of urban shrinkage plays a vital role in supporting the sustainable development of the region. This study, using permanent resident population growth rates and nighttime light data, classified cities in the region into four spatial patterns: expansion–growth, intensive growth, expansion–shrinkage, and intensive shrinkage. It further examined the spatial characteristics of shrinkage across four periods (2005–2010, 2010–2015, 2015–2020, and 2020–2022). A Geographically and Temporally Weighted Regression (GTWR) model was applied to examine core influencing factors and their spatiotemporal heterogeneity. The results indicated the following: (1) The dominant pattern of urban shrinkage in the Qinling–Daba Mountains shifted from expansion–growth to expansion–shrinkage, highlighting the paradox of population decline alongside continued spatial expansion. (2) Three critical indicators significantly influenced urban shrinkage: the number of students enrolled in general secondary schools (X₅), the per capita disposable income of urban residents (X₇), and the number of commercial and residential service facilities (X₁₂), with their effects exhibiting significant spatiotemporal heterogeneity. Temporally, X₁₂ was the most influential factor in 2005 and 2010, while in 2015, 2020, and 2022, X₅ and X₇ became the dominant factors. Spatially, X₇ significantly affected both eastern and western areas; X₅’s influence was most pronounced in the west; and X₁₂ had the greatest impact in the east. This study explored the patterns and underlying drivers of urban shrinkage in underdeveloped areas, aiming to inform sustainable development practices in regions facing comparable challenges.

Assessing city–industry integration levels is a critical diagnostic approach for promoting sustainable urban development. However, existing evaluation frameworks are mainly based on overlaying the level of development of individual systems and rely on statistical data, lacking analysis of spatial attributes. This study addresses these gaps by constructing an “industry–city–population” (I–C–P) evaluation system based on the interaction mechanisms among industry (I), city (C), and population (P), viewed through the lens of spatial correlation. Focusing on Xixian New Area and using 2022 sectional data, the study applies the CRITIC method to calculate the overall level of city–industry integration and the interaction levels across different dimensions in the district, and the Entropy Method (EM) is used to validate the results. The findings indicate the following: (1) The overall level of city–industry integration in Xixian New Area remains relatively low, with Fengdong and Fengxi significantly outperforming the other three new cities. (2) The interactions between “P–I” and “C–P” exhibit lower levels compared to the “I–C” interactions. Additionally, the spatial characteristics of the dimensional levels reveal both variability and consistency. The integrated indicator system, incorporating both spatial big data and traditional statistical data, significantly expands the data sources and dimensions for evaluating city–industry integration, which helps to provide a reference for the assessment of the potential for high-quality sustainable development in the new district and other regions.