Identifying and ranking the key drivers of grassland conversion at the county level is crucial for developing targeted policies and improving protection efficiency. However, this process faces methodological challenges because of spatial and temporal variability. Partial order theory offers a robust framework for addressing these complexities. This study applies partial order theory (POT) combined with the Hasse diagram technique (HDT) to analyze grassland conversion in the Hohhot–Baotou–Ordos region during two time periods (2000–2010 and 2010–2020). First, patterns of grassland transformation are quantified, and the dominant driving factors of grassland conversion out (GCO) are identified and ranked, highlighting regional differences and temporal shifts. By integrating POT and HDT, this study offers a novel approach to handling complex, nonlinear, and hierarchical relationships among multiple drivers. The results provide scientific insight and policy recommendations for region-specific grassland management and sustainable land-use planning. The results show that (1) transitions between grasslands and other land-use types became more frequent across the two periods. Specifically, the rates of grassland conversion out and conversion increased from 2.1% and 3.5% during the period 2000–2010 to 4.7% and 4.8% during the period 2010–2020, respectively. (2) Urbanization was the primary driver of grassland conversion in 11 and 10 of the 18 counties during the first and second periods, respectively, followed by factors related to weather variables. (3) In the future, the eastern region of the study area needs to prioritize mitigating the impacts of urban development, while the western region should focus on enhancing ecological construction projects. This study recommends adopting region-specific ecological protection and economic strategies for balanced outcomes in conservation and development.

Rapid urbanization fundamentally transforms how residents perceive and interact with built environments, yet the dynamic relationships between these evolving perceptions and street vitality remain inadequately understood. As cities undergo rapid transformation, traditional assumptions about fixed perception–vitality relationships may no longer hold, necessitating a deeper understanding of how these relationships evolve over time and space. This study aims to investigate how multiple dimensions of built environment perception influence street vitality and how these relationships evolve spatially and temporally in rapidly urbanizing contexts. We developed a multi-level interpretative framework combining Multi-scale Geographically Weighted Regression (MGWR) with machine-learning-based SHAP analysis to analyze multi-source data from Hohhot, China, spanning 2019–2023. Our approach examined four key perception dimensions—comfort, safety, convenience, and pleasure—and their impacts on street vitality patterns during a period of intensive urban development. The analysis reveals three major findings: first, perception–vitality relationships evolved from highly heterogeneous spatial patterns toward increasing homogenization over time, suggesting urban development standardization effects driven by rapid urbanization processes. Second, several perception dimensions underwent significant transformations, with safety perception shifting from negative to positive influence and convenience perception displaying complex nonlinear threshold effects as urban infrastructure matured. Third, the relative importance of perception dimensions changed over time, reflecting evolving urban priorities and resident expectations shaped by urbanization experiences. These findings demonstrate that perception–vitality relationships are dynamic rather than static, challenging assumptions about fixed environmental effects in urban planning. The study provides empirical evidence for implementing adaptive, context-sensitive urban interventions that acknowledge both spatial heterogeneity and temporal evolution, offering valuable insights for enhancing street vitality in rapidly urbanizing environments worldwide.

Street vitality is crucial for sustainable urban development, yet current understanding of how built environment perceptions influence vitality remains limited by global statistical approaches and the lack of interpretable frameworks for analyzing spatial heterogeneity. This study proposes a novel multi-level interpretative framework combining Multiscale Geographically Weighted Regression (MGWR) with SHAP values to examine spatial variations in perception-vitality relationships. Using multi-source data from Hohhot, China, including mobile phone signals, POI data, and street view imagery, we analyzed how four dimensions of environmental perception influence street vitality across different urban contexts. The analysis reveals significant spatial heterogeneity in perception-vitality relationships, with varying effects across urban locations. Pleasure perception shows the strongest positive influence (SHAP values 0.072-0.103), while convenience perception exhibits an unexpected inverse U-shaped relationship with vitality. The machine learning approach (R² = 0.421) outperforms traditional methods in capturing nonlinear effects and complex interactions. The findings demonstrate the importance of considering both spatial heterogeneity and nonlinear relationships in understanding street vitality, suggesting the need for context-sensitive approaches to urban design and planning interventions.