Many developed cities in the world put forward a spatial development strategy in their construction planning. Analyzing the development level of the urban spatial structure and the influence of driving factors has become a hot topic. Based on 709,232 points of interest data in Beijing, China, this paper integrates nighttime light data and population density data to select eight key driving factors from three perspectives: urban function configuration, economic activity intensity and population spatial distribution. Geodetector is used to optimize data discreteness and highlight the spatial heterogeneity of the development level. We use the technique for order of preference by similarity to ideal solution (TOPSIS) model improved with the entropy weight method to grade the spatial differentiation characteristics of the comprehensive development level. The driving factors and their effects on space are further discussed using Geodetector. The results are as follows: (1) The quartile method can achieve the optimal dispersion of all urban functions. The standard deviation can achieve the optimal dispersion of economic activity intensity and population spatial distribution; (2) A comparison with the “Beijing Urban Master Plan (2016–2035)”, finds that the optimized evaluation system can effectively reflect the spatial heterogeneity of urban spatial structure development. It verifies the rationality of the evaluation methods and factors; (3) The driving force of the population under single-factor driving is 0.8428. The dual-factor driving force with population participation ranges from 0.8992 to 0.9550. The results of the study are significant and reflect the prominent role of population on the development level of spatial structure in Beijing. This paper aims to provide a new idea for the study of the interior space planning of large inland cities.

This study examines the spatial distribution of grain yield in the Songnen Plain Agro-Pastoral Zone in Heilongjiang Province from 2015, 2017, 2019 and 2021, using Kriging interpolation as the primary method. Ordinary Kriging (exponential kernel/semivariogram, step = 13) achieved optimal accuracy (RMSE = 0.856), outperforming Co-Kriging. Incorporating all covariates lowered precision due to weak spatial autocorrelation in slope and aspect, while limiting covariates to elevation and soil type improved results. Spatial patterns revealed a southwest-to-northeast gradient. Over time, yields increased notably in the southwest and northern areas, with Wudalianchi rising by 259.71%, but declining locally, such as a 12.20% drop in Shuangcheng. Environmental factors like slope and soil showed spatially heterogeneous influences, interacting with policies and socioeconomic variables. The grain yield center shifted slightly northward. Geographically Weighted Regression (GWR) further validated these spatial patterns. These findings provide valuable insights into covariate selection and spatial drivers, supporting more precise agricultural planning and management in the region.

Coupling coordination degree (CCD) between persistent late-night radiance and population density may vary nonlinearly with urban functional density, yet linear and single-city analyses cannot distinguish shared from city-specific patterns. A method-controlled dual-city XGBoost-SHAP framework was applied to 14,215 H3 cells in Shanghai and 31,067 in Beijing using six point-of-interest density factors. XGBoost outperformed OLS, with random hold-out R² values of 0.899 and 0.918 vs. 0.617 and 0.626. Public service (X2) ranked first in both cities, accounting for 39.77% and 58.73% of total mean absolute SHAP magnitude. The secondary hierarchy diverged as follows: commercial finance (X3) ranked second in Shanghai at 27.91% and formed the strongest interaction with X2, whereas transport infrastructure (X6) ranked second in Beijing at 18.63% and formed the strongest interaction with X2. Nonlinear analysis identified reproducible negative-to-positive crossings for X2, X3, and X6, peak-type responses for X4 and X5, and no stable second saturation threshold. Spatial OOF, grid, rank, LOWESS, and residual checks supported the leading-factor contrast while showing scale sensitivity and residual spatial dependence. The results identify a common leading attribution alongside city-specific secondary, nonlinear, and spatial patterns within the two observed megacities.

Compound hot–humid events are placing increasing pressure on urban health and land-use governance as climate warming and urban expansion continue. This study examined urban built-up areas in 21 prefecture-level cities and prefectures of Sichuan Province for 2000, 2005, 2010, 2015, 2020, and 2024. Using the IPCC hazard–exposure–vulnerability framework, ERA5-Land, MODIS land-cover, GHSL population, and socioeconomic data were combined to construct the Humid Heat Hazard Index (HI), Population-Density Exposure Index (EI), Socioeconomic Vulnerability Index (VI), and Comprehensive Urban Heat–Humidity Risk Index (HHRI). Contribution decomposition, LISA, and standard deviation ellipse analysis were then used to examine risk changes and spatial patterns. The mean HHRI decreased from 0.42 to 0.24, but rebounds appeared in 2010 and 2024. HI varied strongly among years and drove short-term risk changes, EI declined continuously, and VI dropped from 0.87 to 0.30, making it the main factor behind long-term risk reduction. Higher-risk areas were mainly located in southern Sichuan, southeastern Sichuan, and the central-eastern Sichuan Basin. LISA results showed high-risk clusters in basin cities and low-risk clusters on the western Sichuan Plateau. The standard deviation ellipse remained mostly within the basin and followed the basin urban belt. Future adaptation should combine public-service improvement with heat warnings, cooling services, and emergency preparedness.

Talents with digital knowledge structure and practical ability are the "main force" to realize the construction of digital China. As the core element of subject education, the course is the specific form and effective grasp to achieve the goal of talent training. Based on this, this paper explores the design of the open course of the Geo-science 3D printing laboratory. This course allows students to immerge in the process of solving scientific research problems by integrating the current cutting-edge information technology with their major, and accumulate the knowledge, energy and practical skills needed to participate in the construction of digital China after graduation.