Topographic relief is a key factor limiting population distribution and economic development in mountainous areas, especially in the transition zone from mountains to plains. Taking the southern Anhui mountainous area as an example, based on the digital elevation model (DEM) with a resolution of 30 m, we used ‘quadratic’ mean change-point analysis to calculate the optimal statistical unit, and then extracted the topographic relief. Taking the county as the unit of analysis, two indicators of population density and economic density were selected. Spatial statistics and correlation analysis were used to quantitatively analyze the impact of topographic relief on population and economy. Finally, the impact of slope and elevation was analyzed. The following results were obtained. (1) The topography of the study area was dominated by medium relief (200–500 m), followed by small relief (70–200 m), flat (0–30 m), and slight relief (30–70 m), and a small proportion of large relief (≥500 m). (2) The impact of topographic relief on population and economy was slightly stronger than that of slope and elevation. The impact on population distribution was stronger than that on economic development. The impact on primary industry was stronger than that on secondary and tertiary industries. (3) In the southern Anhui mountainous area, 72.35% of the population and 76.72% of GDP were distributed in the area with a topographic relief of 155 m or less, while the land area only accounted for 43.93%. The area with a topographic relief greater than 245 m accounted for 28.76%, but only 10.69% of the population, and only 8.34% of GDP. The population distribution and economic development were obviously concentrated in the low topographic relief area. However, the characteristics of high topographic relief not only hindered the agricultural mechanization and limited the development of the primary industry, but also had a significant impact on infrastructure development, investment, and industrial layout, thus weakening regional economic advantages. In the future, the economic level of these areas needs to be improved.

Regional logistic networks and linking urban clusters to boost high quality economic development are both key topics in sustainable development in China. In recent years, China has highlighted the significance of the logistics industry and urban cluster development, attaching practical importance to the connections between these two topics from a spatial perspective. This paper aims to discuss how regional logistic networks linking urban clusters improve economic development. This study constructs a logistic evaluation indicator system based on the multi-indicator data of 11 cities in the Guangdong-Hong Kong-Macao Greater Bay Area in 2020. This research employs the entropy-weighted-TOPSIS method to measure and rank the comprehensive logistics quality of each city in the economically linked logistic network of the Guangdong-Hong Kong-Macao Greater Bay Area. This study applies the modified gravity model to explore the logistics linkage and logistic network characteristics of each city in the Guangdong-Hong Kong-Macao Greater Bay Area. Finally, this research analyzes how the agglomeration ability of the central cities in the Guangdong-Hong Kong-Macao Greater Bay Area affects other cities from a spatial perspective. Furthermore, this spatial perspective investigates the agglomeration effect of the economic linkage logistic network through the social network analysis method. The results have the following three implications. (1) The logistic network has a high density, a stable overall structure with a strong agglomeration effect, and there is an increasingly mature logistic network development in the Guangdong-Hong Kong-Macao Greater Bay Area in the Bay Area. (2) Agglomeration is significant in the central cities of the Guangdong-Hong Kong-Macao Greater Bay Area including Hong Kong, Shenzhen, Guangzhou, and Macao. Nonetheless, insufficient peripheral cities have been cultivated. Therefore, the government should focus on strengthening the balance of urban development in the bay area and improving the logistics access among cities to break through the barriers of regional synergistic development. (3) The economic development of cities is highly correlated with the level of logistics links. Additionally, the economy is the cornerstone to promoting the high-quality development of the logistics industry. Moreover, the economy and logistics are inseparable, mutually promoted, and developed together.

The spatial coupling of population and economy is an important indicator that reflects regional differences and measures the coordination degree of industrial layouts and environments. To explore the spatial coupling of population and economic densities and the effect of topography in Anhui Province at a grid scale, a land-use impact model was used to spatialize socio-economic indicators in Anhui Province using 2020 county-level data. Geographical concentration and coupling index were used to quantify the spatial relationship between population and economic densities. Then the effects of topography were assessed. The results show the following: (1) the accuracy of the regression models for the individual counties is generally better than that of the models for the whole region. The population and economic densities predicted by the proposed model reflect actual conditions. (2) Topography has a stronger effect on population density and primary industry density than on secondary and tertiary industry density. Slope has the strongest effect on population and economic densities, followed by topographic relief and elevation. (3) A spatial correlation exists between topographic factors and population and economic densities. Although the spatial relationship between population and economic densities is predominantly of the balanced development type in regions with complex topography, population and economic densities are significantly lower in regions with complex rather than flat topography. In addition, economic development in the northern Anhui region, a flat region, lags behind population aggregation. Efforts should be made to improve the economic level of the southern and northern Anhui regions and the Dabie Mountain region in western Anhui.

Traditional landslide susceptibility assessment (LSA) methods typically adopt a global modeling strategy, which struggles to account for the pronounced spatial heterogeneity arising from variations in topography, geology, and vegetation conditions within a region. Furthermore, model predictive performance is often undermined by feature redundancy. To address these limitations, this study focuses on the landslide disaster early-warning demonstration area in Honghe Prefecture, Yunnan Province. It proposes an ensemble learning model termed heterogeneity feature optimized stacking (HF-stacking), which integrates spatial heterogeneity partitioning (SHP) with feature selection to improve the scientific rigor of LSA. This method initially establishes an LSA system comprising 15 static landslide conditioning factors (LCFs) and two dynamic factors representing the average annual deformation rates derived from interferometric synthetic aperture radar (InSAR) technology. Based on landslide inventory data, an SHP method combining t-distributed stochastic neighbor embedding (t-SNE) and iterative self-organizing (ISO) clustering was developed to divide the study area into subregions. Within each subregion, a tailored feature selection strategy was applied to determine the optimal feature subset. The final LSA was performed using the stacking ensemble learning approach. The results show that the HF-stacking model achieved the best overall performance, with an average AUC of 95.90% across subregions, 4.23% higher than the traditional stacking model. Other evaluation metrics also demonstrated comprehensive improvements. This study confirms that constructing an SHP framework and implementing feature selection strategies can effectively reduce the impact of spatial heterogeneity and feature redundancy, thereby significantly enhancing the predictive performance of LSA models. The proposed method contributes to improving the reliability of regional landslide risk assessments.

In response to the shortage of theoretical support for negative sample selection and the imbalance between positive and negative samples in landslide susceptibility assessment (LSA), this study presents a dual optimization framework of sampling strategy and sample ratio based on the frequency ratio (FR) model and evaluates its performance in the Heishui River Basin using the random forest (RF) model. At the level of negative sample optimization, FR is used to quantify the spatial associations between landslides and environmental factors, delineate non-landslide zones as the source region for negative samples, and systematically compare the performance of five sampling strategies—random, buffer-zone, low-slope, low-density, and FR-optimized sampling. At the level of ratio adjustment, an innovative positive–negative sample ratio model based on the area ratio between susceptible and non-susceptible zones (1:1.3) is proposed, and four additional schemes (1:1, 1:2, 1:2.5, and 1:3) are designed for comparison. The results show that the FR-optimized sampling strategy provides the best discriminative performance, with AUC (area under the curve) = 0.992, Kappa = 0.964, accuracy = 0.982, and F1 score = 0.982, and achieves the highest mean metric value (0.980) and the lowest standard deviation (0.008). The 1:1.3 ratio scheme maintains the highest mean performance (0.982) and the lowest standard deviation (0.007), while yielding the optimal Kappa (0.968) and F1 score (0.982), confirming its advantages in classification balance and model stability. Spatial prediction results indicate that very high and high susceptibility zones are concentrated along both sides of the middle and lower reaches of the Heishui River Basin, occupying only 18.52% of the total area but containing 98.38% of the mapped landslide points, which strongly supports the reliability of the proposed model in identifying landslide risk hotspots. The proposed dual optimization framework not only enhances the robustness of susceptibility mapping but can also be directly applied to risk screening for territorial spatial planning and engineering development in the Heishui River Basin.

In response to issues in regional landslide risk evaluation (LRE), such as the over-reliance on administrative unit statistics for vulnerability characterization and the difficulty in capturing the fine-scale spatial heterogeneity of socio-economic factors, this study takes the landslide-prone southern Anhui mountainous area, China as a case study to establish a comprehensive “susceptibility-hazard-vulnerability-risk” evaluation framework. First, landslide susceptibility assessment (LSA) was conducted by coupling frequency ratio (FR) with random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). Through accuracy comparison, the FR-RF model was identified as the optimal one (AUC = 0.836). Subsequently, annual cumulative precipitation and rainfall erosivity factor were introduced, and the analytic hierarchy process (AHP) was applied to achieve a spatiotemporal landslide hazard assessment (LHA). Innovatively, based on land use data and multiple linear regression (MLR), population density and economic density by industry were spatialized at a 100 m × 100 m grid scale, and a spatial landslide vulnerability assessment (LVA) model was established. Finally, a regional landslide risk zoning map was generated through weighted overlay (hazard weight 0.6, vulnerability weight 0.4). The results show that high and very high hazard zones account for 38.74 % of the study area. Vulnerability exhibits significant spatial differentiation, with high and very high vulnerability zones covering only 3.83 %, primarily distributed in areas of intensive human activity. The comprehensive LRE reveals that high and very high risk zones occupy 34.67 % of the total area. Notably, 32.93 % of originally low-hazard areas were elevated to high risk due to the overlay of high vulnerability. By employing spatialization techniques for socio-economic data, this study achieves grid-level refined characterization of landslide vulnerability. It provides a methodological reference for regional LRE and offers practical implications for disaster risk management and territorial spatial planning in mountainous regions.