With the continuous acceleration of China’s urbanization process, color steel plate, as a new type of building material, has been widely used in all kinds of temporary buildings and has become the spatial carrier of the specific development stage of urbanization. This study focuses on Lanzhou City as a case study to deeply analyze the spatiotemporal distribution and evolution of color steel plate buildings. Utilizing data extracted from Google imagery and GF-2 satellite images of the built-up areas in Lanzhou, spatial statistical and analytical methods such as centroid analysis, compactness index, and patch density are applied. Systematic analysis is conducted across different time periods and spatial scales to examine the evolution of indicators, including quantity, centroid distribution, spatial clustering, and distribution direction. The results show that from 2013 to 2021, the prevalence of color steel buildings in Lanzhou city initially increased and then decreased, and the number peaked in 2017, but there is a significant difference between distinct areas in the urban area. By quantitatively analyzing the spatial and temporal evolution characteristics of color steel plate buildings, this study reveals the important role it plays in promoting the urbanization process and provides a scientific basis for relevant planning decisions.

The urban fringe, a critical transitional zone between urban and rural areas, exhibits the most intense land-use conflicts and the most dynamic spatial restructuring. Its fragmentation not only undermines the efficiency of spatial land use but also threatens ecosystem stability, thereby posing a challenge to sustainable urban development. This study aims to examine the characteristics and future trends of spatial fragmentation within the urban fringe of Xi’an. Using multi-source datasets—including nighttime light imagery, land use data, and building vector data—we delineated the urban fringe boundary based on a light gradient threshold method. By combining landscape metrics with spatial overlay analysis, we assessed land use changes and spatial fragmentation patterns from 2000 to 2020 and projected land use conditions for 2030, 2040, and 2050. The results reveal that between 2000 and 2020, cultivated land became increasingly fragmented due to the expansion of construction land, while forest areas continued to expand under ecological restoration policies. Impervious surfaces exhibited infill aggregation. Rapid increases in nighttime light intensity were strongly correlated with heightened fragmentation (R² = 0.65, p < 0.01), confirming that urban expansion is the dominant driver of fragmentation. Projections indicate that the proportion of cultivated land will decline to 0.51, approaching a “critical warning threshold”.

Veterinary hospitals are increasingly important neighborhood services, but citywide evidence on their strategic allocation remains limited. Existing GIS-based suitability studies identify favorable areas, but do not determine which limited set of planning nodes to prioritise when existing supply is clustered and underserved areas require explicit protection. This study develops a transparent allocation framework for Beijing by integrating Random Forest (RF)-based observed clinic-location probability, E2SFCA-derived service-gap indicators, and a priority-constrained covering optimisation model. The RF output is interpreted as a proxy for location attractiveness, rather than a direct estimate of demand for veterinary healthcare. The optimisation then protects predefined priority areas and maximises weighted coverage under alternative facility budget scenarios. Additional analyses include a threshold-derived priority set, class-imbalance metrics, catchment-radius sensitivity, key-area-count sensitivity, systematic candidate/key-score coefficient perturbation, district-grouped spatial validation, raw versus calibrated RF allocation sensitivity, and pet-service endogeneity checks. Under the main 5 km catchment and P = 40 scenario, the model achieved complete priority-area coverage and weighted coverage of 0.656. Coverage increased further at higher budgets, so P = 40 is not regarded as a practical optimum, but rather as a diminishing-return scenario. At P = 20 and P = 40, priority-area coverage increased by 5.0 and 2.5 percentage points relative to MCLP, respectively, while aggregate weighted coverage remained comparable. The framework supports strategic planning priority analysis, not direct commercial site selection.