## 4 Discussion
### Research Innovations and Ideas for Improvement  
In this research, soil nutrient spatial autocorrelation was predominantly strong, influenced chiefly by natural factors like topography, soil type, parent material, and climate, promoting a trend toward the homogenization of soil nutrient variability. Geostatistical analysis revealed that soil pH is more prone to being influenced by stochastic factors, including human activities and fertilization practices, aligning with findings from other statistical studies [50]. Similarly, the variability in soil organic matter, total nitrogen, available nitrogen, and available phosphorus was affected by random factors such as human interventions, a perspective endorsed by numerous scholars; notably, available phosphorus exhibited a coefficient of variation much higher than that of other nutrients, echoing the comparisons made by [PERSON] [50; 51] among multiple nutrients. Its Moran's I index was also the highest, underscoring the tendency of available phosphorus to accumulate [52]. The block base ratio for total nitrogen, available nitrogen, and slow-release potassium was notably high, mirroring [PERSON] and others [53] observations with total nitrogen and available nitrogen, thus highlighting their vulnerability to stochastic factors like human activities. Conversely, the block base ratio for readily available potassium was the lowest, with research indicating that soil potassium primarily originates from the weathering of parent materials [54], suggesting that readily available potassium is subject to the combined effects of structural factors such as parent material and stochastic factors like human activities. From the data perspective, although there were significant deviations in this study's results compared to others, the overall patterns of nutrient differences remained consistent. This discrepancy could be attributed to the extensive number of sampling locations within this study area, where the spatial variability characteristics of area variables altered with the sampling point layout's scale. When sampling scales are markedly smaller than the natural distribution scales of elements like parent material and topography, spatial variability tends to lean towards being governed by stochastic factors, such as fertilization levels from human intervention [55]. This phenomenon has also been confirmed in studies involving high-density sampling [56; 57].  
Our study leverages high-density, randomly sampled data from Fengtai County, applying the block base ratio and the semivariogram's range to delineate spatial autocorrelation and its influence radius. The global Moran's I index was employed to gauge the strength and significance of spatial autocorrelation, while the variance coefficient of nutrient statistics was juxtaposed with research outcomes. Furthermore, Kriging interpolation and the local Moran's I index were utilized to generate maps depicting nutrient distribution and LISA cluster analysis, thereby methodically illustrating the spatial variability and interconnectedness of different soil nutrients within the study locale. Presently, the independent application of geostatistics or the Moran's I index predominates among scholars for exploring soil nutrient spatial variability, with combined methodological approaches being relatively unexplored. This thorough examination of soil nutrients in our research, coupled with a comparative analysis, aims to serve as a dependable framework for future investigations into the spatial variability of soil nutrients across various regions.