Urban planners are progressively recognizing the significant effects of the built environment and land use on PM_2.5 levels. However, in analyzing the drivers of PM_2.5 levels, researchers’ reliance on annual mean and seasonal means may overlook the monthly variations in PM_2.5 levels, potentially impeding accurate predictions during periods of high pollution. This study focuses on the area within the Sixth Ring Road of Beijing, China. It utilizes gridded monthly and annual mean PM_2.5 data from 2019 as the dependent variable. The research selects 33 independent variables from the perspectives of the built environment and land use. The Extreme Gradient Boosting (XGBoost) method is employed to reveal the driving impacts of the built environment and land use on PM_2.5 levels. To enhance the model accuracy and address the randomness in the division of training and testing sets, we conducted twenty comparisons for each month. We employed Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDP) to interpret the models’ results and analyze the interactions between the explanatory variables. The results indicate that models incorporating both the built environment and land use outperformed those that considered only a single aspect. Notably, in the test set for April, the R² value reached up to 0.78. Specifically, the fitting accuracy for high pollution months in February, April, and November is higher than the annual mean, while July shows the opposite trend. The coefficient of variation for the importance rankings of the seven key explanatory variables exceeds 30% for both monthly and annual means. Among these variables, building density exhibited the highest coefficient of variation, at 123%. Building density and parking lots density demonstrate strong explanatory power for most months and exhibit significant interactions with other variables. Land use factors such as wetlands fraction, croplands fraction, park and greenspace fraction, and forests fraction have significant driving effects during the summer and autumn seasons months. The research on time scales aims to more effectively reduce PM_2.5 levels, which is essential for developing refined urban planning strategies that foster healthier urban environments.

Incorporating the distance-decay effects of facility points into the analysis of metro ridership helps generate more precise and actionable strategies for station area renewal. The majority of existing studies, however, calculated the built environment variables based on the same pedestrian catchment areas (PCAs) of metro stations and failed to consider the impact of distance decay from POIs (points of interest) on the accuracy of metro station ridership regression models. The objective of this study is to determine which distance-decay function best improves the fit of the metro ridership regression model and investigate the effect of the built environment on ridership under the optimal distance-decay model. Based on the distribution density of metro stations in Beijing, the research area is divided into three zones with different PCAs. Built environment variables for all metro stations are aggregated according to the PCA scope. Various distance-decay functions are examined to determine how the accuracy of the Multi-scale Geographically Weighted Regression (MGWR) model is affected by built environment variables calculated from POI facilities (Gaussian distance decay, power distance decay, piecewise distance decay). Finally, under optimal distance decay, the MGWR model is used to investigate how the built environment influences metro ridership. The results show the following: (1) The Gaussian distance-decay function improves the goodness of fit of the regression model, resulting in an 11.25% increase in the R² value when compared to the model without a distance-decay function. (2) During morning peak hours, apartment and office density significantly impacts ridership. The proposed research framework is conducive to improving the accuracy of the metro station ridership regression model. Moreover, it facilitates the formulation of targeted strategies for the renewal of the built environment by government managers and planners.

Military settlements are an integral part of the military defense system of the Ming Great Wall, and the spatial layout of their constituent elements embodies the wisdom of ancient military geography. However, existing studies have predominantly focused on the macro-scale distribution of military settlements, with insufficient exploration of the spatial differentiation mechanisms of their micro-level constituent elements. Therefore, this study examines 61 military settlements in Miyun District, Beijing. Based on documentary research and field surveys, the types of constituent elements were systematically identified. This study employs kernel density analysis and the Optimal Parameters-based Geographical Detector (OPGD) model to explore their spatial patterns and driving mechanisms. The results show that (1) the constituent elements of military settlements collectively exhibit a spatial pattern of “one belt and three cores”, with pronounced spatial heterogeneity; (2) Fortress level, Military strength, and Distance to the Lu Fort are the core factors influencing the spatial differentiation of elements; and (3) when multiple factors interact, the interaction between Military strength and Distance to the Lu Fort demonstrates a significant nonlinear enhancement effect. This study reveals the spatial organizational logic of the Ming Great Wall military settlements at the micro-element level, providing a scientific basis for the graded protection and adaptive reuse of military settlements in Miyun District. Furthermore, the proposed analytical framework can also offer methodological insights for studies in other regions along the Great Wall.

The cultural landscape of Gubeikou, with distinct historical stratification and event-relatedness, bears unique value. Against the backdrop of increasingly prominent themes of cultural heritage development and transformation, research on Gubeikou’s cultural landscapes remains fragmented and lacking in depth. This research explores its evolution mechanism via historical events to fill gaps. This study takes Gubeikou Town as the research object, applies the text analysis method to sort and categorize 302 historical events, summarizes 12 event types, identifies 19 landscape elements, and constructs a data matrix based on co-occurrence frequencies. It performs clustering analysis on these using Principal Component Analysis (PCA) and Agglomerative Hierarchical Clustering (AHC), while integrating historical and geographical data. Findings: (1) The landscape evolution of Gubeikou can be divided into four main stages: the military embryonic period, the functional expansion period, the system maturity period, and the multi-element integration period. (2) The dynamic evolutionary trajectory of the correlation between its landscapes and events shows that the core factors affecting the evolution of cultural landscapes in each period not only maintain the dominance of military elements throughout the evolutionary process but also integrate diverse elements like economy, culture, and folk customs with social development, presenting the characteristics of composite evolution. (3) The landscape evolution is driven by the “primary–secondary synergy” dynamic structure composed of four types of activities: military–political, transportation, production–trade, and construction. It is the product of the coupling effect of political goals, social operation, and geographical conditions. This study provides a basis for the sustainable protection and utilization of Gubeikou, and also offers a reference for other regions.