Land use simulations are critical in predicting the impact of land use change (LUC) on the Earth. Various assumptions and policies influence land use structure and are a key factor in decisions made by policymakers. Meanwhile, the spatial autocorrelation effect between land use types has rarely been considered in existing land use spatial simulation models, and the simulation accuracy needs to be further improved. Thus, in this study, the driving mechanisms of LUC are analyzed. The quantity demand and spatial distribution of land use are predicted under natural development (ND), economic development (ED), ecological protection (EP), and sustainability development (SD) scenarios in Zhengzhou based on the coupled Multi-Objective Programming (MOP) model and the Patch-generating Land Use Simulation model (PLUS) considering Spatial Autocorrelation (PLUS-SA). We conclude the following. (1) The land use type in Zhengzhou was mainly cultivated land, and 83.85% of the land for urban expansion was cultivated land from 2000 to 2020. The reduction in forest from 2010 to 2020 was less than that from 2000 to 2010 due to the implementation of the policy in which farmland is transformed back into forests. (2) The accuracy of PLUS-SA was better than that of the traditional PLUS and Future Land Use Simulation (FLUS) models, and its Kappa coefficient, overall accuracy, and FOM were 0.91, 0.95, and 0.29, respectively. (3) Natural factors (temperature, precipitation, and DEM) contributed significantly to the expansion of cultivated land, and the increase in forest, grass, and construction land was greatly affected by socioeconomic factors (population, GDP, and proximity to town). (4) The land use structure will be more in line with the current requirements for sustainable urban development in the SD scenario, and the economic and ecological benefits will increase by 0.75 × 104 billion CNY and 1.71 billion CNY, respectively, in 2035 compared with those in 2020. The PLUS-SA model we proposed had higher simulation accuracy in Zhengzhou Compared with the traditional PLUS and FLUS models, and our research framework can provide a basis for decision-makers to formulate sustainable land use development policies to achieve high-quality and sustainable urban development.

Understanding the spatial patterns of retail stores in urban areas contributes to effective urban planning and business administration. A variety of methods have been proposed in the scientific literature to identify the spatial patterns of retail stores. These methods invariably employ arbitrary grid cells or administrative units (e.g., census tracts) as the fundamental analysis units. As most urban retail stores are distributed along street networks, using area-based analysis units is subject to statistical biases and may obfuscate the spatial pattern to some extent. Using the street segment as the analysis unit, this paper derives the spatial patterns of retail stores by crawling points of interest (POI) data in Zhengzhou, a city in central China. Then, the paper performs the network-based kernel density estimation (NKDE) and employs several network metrics, including the global, local, and weighted closeness centrality. Additionally, the paper discusses the correlation between the NKDE value and the closeness centrality across different store types. Further analysis indicates that stores with a high correlation tend to be distributed in city centers and subnetwork centers. The comparison between NKDE and cell-based KDE shows that our proposed method can address potential statistical issues induced by the area-based unit analysis. Our finding can help stakeholders better understand the spatial patterns and trends of small business expansion in urban areas and provide strategies for sustainable planning and development.

A reasonable layout optimization strategy of rural residential areas can improve the quality of life of rural residents and promote rural revitalization. Evaluating the suitability of rural residential areas is the basis of layout optimization. Based on 1:100,000 land cover data and a digital elevation model (30 m) for the Henan Province, China, we used the minimum cumulative resistance model to evaluate the spatial distribution suitability of rural settlements in the Zhengzhou administrative area (abbreviated: Zhengzhou). Then, we used a weighted Voronoi diagram to determine the scope of influence of central villages and determined the direction of relocation for the “combined migration” rural residential areas. The study results support the following conclusions: (1) the comprehensive resistance value of rural residential areas in the Northeastern part of Zhengzhou is low and the suitability is high. However, the comprehensive resistance value of the Southwestern part is high and the suitability is low. (2) The study area can be divided into highly suitable areas, suitable areas, generally suitable areas, unsuitable areas, and extremely unsuitable areas. Unsuitable areas and extremely unsuitable areas accounted for 33.66% of the total area and included 662 rural residential areas. (3) The rural residential areas were divided into four types of optimization: urbanization, key development, controlled development, and combined migration. Based on an analysis of the characteristics of each type of rural residential area, we proposed corresponding optimization strategies. The results remedy the lack of layout optimization strategies for large-scale rural residential areas and can provide support for the optimization of the layout of rural residential areas in Zhengzhou. Furthermore, the research techniques may apply to other regions.

Deep neural networks have been widely used in remote sensing image segmentation. Nowadays, artificial intelligence methods are increasingly applied to remote sensing feature classification. Although convolutional neural networks (CNNs) are widely used for image segmentation tasks, their global feature extraction with increasing image samples is insufficient. Furthermore, Transformer is now being focused on computer vision. However, although Transformer can capture the global information of remote sensing images, it cannot adequately model the detailed information of image changes. To comprehensively compensate for the defects of CNNs and the Transformer network in feature extraction, this study proposes a semantic segmentation network with multi-feature fusion (SWINT-RESNet). This network combines the Transformer-extracted global and local features and those of CNNs to improve the accuracy of remote sensing image segmentation. The experiments show that the segmentation performance of SWINT-RESNet is superior for both small and medium sample remote sensing image datasets.

Depicting the spatiotemporal dynamics of vegetation cover in the Yellow River Basin (YRB) and delineating the influences of climate change and human activities on the dynamics have been of significant importance for understanding the surface earth systems in general and also for formulating ecological protection plans of the YRB in particular. This study uses the GIMMS NDVI dataset from 1982 to 2015 and the MOD13A1 NDVI dataset from 2000 to 2021 to explore the spatial and temporal characteristics of vegetation cover in the YRB for the period from 1982 to 2021 with an attempt to reveal the influencing factors. The spatial distribution and temporal variation characteristics of vegetation cover are analyzed by maximum value composite, Theil-Sen median trend analysis, and Mann–Kendall test. Combined with the mean annual temperature and annual precipitation in the same period, influencing factors of vegetation cover in the YRB are discussed by using binary linear regression analysis and residual analysis. Results show that: (1) the multi-year average NDVI values increase from the northwest to the southeast and that the annual mean values of the vegetation covers fluctuate relatively greatly along an increasing trend with a growth rate of 0.019/(10a). Understandably, the monthly mean NDVI values show a single-peak distribution pattern, with August being the peak time (0.4936). (2) 77.35% of the studied areas are characterized by exhibiting an increasing trend of vegetation cover during the study period (i.e., 1982–2021). (3) Vegetation cover of the YRB is affected by the combined effects of climate change and human activities, with human activities being more significant in the observed amelioration of vegetation cover.