Managing the human–nature relationship is key to facilitating the sustainable development of cities. The coupled coordination relationship between ecological civilization construction and urban green development and influence of spatio-temporal heterogeneity has been insufficiently studied. We used the coupled coordination degree model (CCDM) and spatio-temporal weighted model (GTWR) to analyze the relationship and heterogeneity between ecological civilization construction and UGD and ECC in each city in the Yangtze River Delta region from 2010 to 2019. The results show that: (1) UGD and ECC coordination levels fluctuated more from 2010 until 2019. There was a transition from lagging UGD and ECC to lagging ecological civilization construction and a decreasing degree of coupling coordination in the Yangtze River Delta region from east to west from near imbalance to primary coordination. (2) The Yangtze River Delta’s negative UGD and ECC effect was concentrated in northwest inland cities; the positive UGD and ECC effect was concentrated in southeast coastal cities. Thus, UGD and ECC and ecological civilization construction complement each other. This study provides a scientific basis for analyzing the coordination between ecological civilization construction and UGD and ECC and provides practical guidance for formulating and implementing urban high-quality development countermeasures.

Confronted with China’s burgeoning population and finite arable land resources, the enhancement of sustainable arable land efficiency is of paramount importance. This study, grounded in the United Nations Sustainable Development Goals (SDGs), introduces a robust framework for assessing sustainable arable land use. Utilizing the Sustainable Utilization of Arable Land (SUA) indicator system, the DGA–Super-SBM model, the Malmquist–Luenberger production index, and the TO–Fisher–OSM algorithm, we evaluated the efficiency of sustainable utilization of arable land (ESUA) in 52 prefecture-level cities within China’s major grain-producing regions of the Yellow and Huaihai Seas. We analyzed the cropland utilization patterns from 2010 to 2020, examining the influence of these patterns on sustainable utilization efficiency. Our findings indicate that between 2010 and 2020, the arable land usage in these regions exhibited minimal transformation, primarily shifting towards construction land and conversely from grassland and water systems. Notably, the ESUA of arable land demonstrated an upward trend, characterized by pronounced spatial clustering, enduring high efficiency in the northern regions, and a significant surge in the southern sectors. The declining ESUA (D-ESUA) trend was general but increased in half of the cities. The change in the center of gravity of ESUA correlated with the north–south movement of the proportion of cultivated land area, the turn-in rate, and the turn-out rate, yet moved in the opposite direction to that of cultivated land density and yield per unit area. Variables such as the replanting index, cropland density, yield per unit area, and cropland turn-in rate significantly affected ESUA. These findings offer a scientific basis and decision-making support for optimizing the utilization pattern of arable land and achieving a rational allocation of arable land resources.

Land use/land cover change (LUCC) analysis is a fundamental issue in regional and global geography that can accurately reflect the diversity of landscapes and detect the differences or changes on the earth’s surface. However, a very heavy computational load is often unavoidable, especially when processing multi-temporal land cover data with fine spatial resolution using more complicated procedures, which often takes a long time when performing the LUCC analysis over large areas. This paper employs a graph-based spatial decomposition that represents the computational loads as graph vertices and edges and then uses a balanced graph partitioning to decompose the LUCC analysis on spatial big data. For the decomposing tasks, a stream scheduling method is developed to exploit the parallelism in data moving, clipping, overlay analysis, area calculation and transition matrix building. Finally, a change analysis is performed on the land cover data from 2015 to 2016 in China, with each piece of temporal data containing approximately 260 million complex polygons. It took less than 6 h in a cluster with 15 workstations, which was an indispensable task that may surpass two weeks without any optimization.

Quantifying carbon sequestration in cultivated land ecosystems is essential for achieving carbon neutrality and ensuring food security, yet current models often fail to capture the complex interactions between crop phenology and environmental factors at regional scales. This paper proposed an improved CASA-CGC model that couples crop phenological parameters with photosynthetic physiological processes, enabling precise carbon sink accounting at the growth cycle scale of cultivated land ecosystems. Results indicate that the carbon sequestration capacity of cultivated land in the province significantly increased from 2010 to 2022, with an average increase of 163.04 g C m⁻², and the spatial pattern showed a centralized evolution characteristic. Model validation showed that the accuracy of the CASA-CGC model is significantly better than traditional methods. Compared with remote sensing inversion products and 93 ground measurement point data, the improved CASA-CGC model increased the R² by 0.155 and reduced the RMSE by 4.19 compared with the tr-CASA model. The innovative introduction of the GeoDetector model reveals that the nonlinear interaction between natural and human factors dominates the carbon sequestration process (accounting for 60%), with the interaction effect between altitude and cropping system configuration being the strongest (q = 0.312), confirming that humans can significantly amplify the potential of natural carbon sinks by optimizing cropping systems.

Currently, the core idea of the refined method of population spatial distribution is to establish a correlation between the population and auxiliary data at the administrative-unit level and, then, refine it to the grid unit. However, this method ignores the advantages of public population spatial distribution data. Given these problems, this study proposed a partition strategy using the natural break method at the grid-unit level, which adopts the population density to constrain the land class weight and redistributes the population under the dual constraints of land class and area weights. Accordingly, we used the dasymetric method to refine the population distribution data. The study established a partition model for public population spatial distribution data and auxiliary data at the grid-unit level and, then, refined it to smaller grid units. This method effectively utilizes the public population spatial distribution data and solves the problem of the dataset being not sufficiently accurate to describe small-scale regions and low resolutions. Taking the public WorldPop population spatial distribution dataset as an example, the results indicate that the proposed method has higher accuracy than other public datasets and can also describe the actual spatial distribution characteristics of the population accurately and intuitively. Simultaneously, this provides a new concept for research on population spatial distribution refinement methods.

Cultivated land concentration and contiguity, as a core element of agricultural modernization development, holds strategic significance for enhancing agricultural production efficiency and ensuring national food security. This study employs vector patches as research units and classifies spatial connections between patches into direct and indirect connections. We quantify six types of spatial relationships between patches using binary encoding, enabling precise delineation of concentrated contiguous cultivated land. A Patch Connectivity Index is proposed. Combined with the Patch Area Index and Patch Shape Index, an evaluation system for cultivated land concentration and contiguity is established. Using Suixi County as a case study, we investigate the spatiotemporal evolution of its cultivated land concentration and contiguity from 2019 to 2023. Overall, patch connectivity exhibits a “single-element dominant, multi-element complementary” structural pattern, while the evaluation grading of cultivated land concentration and contiguity follows a normal distribution. Between 2019 and 2023, the average patch area decreased while the average number of connections between patches increased, indicating significant improvement in cultivated land concentration and contiguity levels. By adjusting spatial relationships between patches, the effective integration and utilization of cultivated land resources can provide theoretical foundations and practical references for agricultural modernization development.