## 4 Discussion
### Comparison of the Three Methods for Extracting Spatial Information and Spatial Similarity Analysis  
Spatial variations in the maps were evident due to methodological factors; however, the fishing grounds extracted by the three methods exhibited overall consistency, were primarily located near the Omani EEZ domain, and displayed characteristic patchy distributions. Notably, the spatial similarity index between the fishing ground information extracted by the FE methods and the catch data from actual production operations was the highest, while KDHSA showed the lowest similarity. This suggests that KDHSA introduced some divergence in the spatial characterization compared to the actual production operation, highlighting a nuanced difference. The spatial distribution of fishing intensity and the spatial distribution of production data for each month demonstrated the highest compatibility. In October 2021, the similarity index calculated by KDE was 0.46, potentially indicating a lack of catch due to unknown fishery resources in the Arabian Sea. This anomaly was likely associated with vessels engaging in prolonged salvage operations in that region.  
Theoretically, when analyzing fishing vessel track data, the possibility of dividing time and space at a fine granularity exists. However, the determination of appropriate spatial and temporal scales is crucial and should align with the specific objectives of the study. The average spatial similarity index of fishery data and fishing intensity information at different spatial resolutions indicated that a spatial scale of 0.2\({}^{\circ}\) achieved an average similarity index exceeding 0.7. Considering that the 0.25\({}^{\circ}\) spatial scale also consistently surpassed 0.7 for all the months, it was deemed more reasonable for statistical analyses in the Arabian Sea, striking a balance between spatial accuracy and similarity to fishery resources. Excessive spatial precision can pose challenges to fisheries management and analysis. Figure S6 illustrates the fishing effort intensity at a high resolution, 0.01\({}^{\circ}\)\(\times\) 0.01\({}^{\circ}\), displaying dense and discrete areas resembling scattered sands in densely packed regions. To mitigate this, a map of fishing intensity at a resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) was developed in this study. Additionally, the maps for the kernel density and hotspot analysis were generated at a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\), providing smoother raster outputs. This higher resolution contributed to more comprehensive and detailed results in the analysis.  
[PERSON] et al. [15] highlighted that KDHSA leverages the strengths of both KDE and HSA, integrating the distance decay effect of KDE [26] with the statistical index of HSA [27]. However, the hotspot map generated by KDHSA did not include information on coldspots, resulting in a lower spatial similarity index than that of the other methods. Spatially, the hotspots extracted by KDHSA could only reveal boundary information and lacked the representation of both high-density and low-density areas. This limitation hindered the ability to showcase the changing spatial distribution of the fishery, making KDHSA unsuitable for assessing fishery pressure. Despite exhibiting sharper boundaries, the fishery intensity map demonstrated the highest spatial similarity index and clearly illustrated the spatial distribution characteristics. The kernel density map, while displaying the spatial variability of the fishery with smooth boundaries, did not perform as well overall as the fishery intensity map.