###### Abstract  
Understanding the dynamic spatial distribution and characteristics of fishing activities is crucial for fisheries management and sustainable development. In recent years, small pelagic fish and cephalopods in the Arabian Sea have become new targets for light purse seine fishing; however, there is a lack of publicly available reports. This study uses automatic identification system (AIS) data from January to May and October to December of 2021 to 2022 in the region between 58\({}^{\circ}\)-70\({}^{\circ}\) E and 10\({}^{\circ}\)-22\({}^{\circ}\) N to extract spatial distribution information through three methods. The results show that with a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), the spatial similarity index between the fishing ground information extracted in 2022 and catch data was consistently above 0.60, reaching 0.76 in March 2021 and 0.79 in November 2022, while the spatial similarity index in March 2022 exceeded 0.71. The spatial distribution of fishing effort and kernel density was similar to that of the fishing grounds, and the fishing intensity information exhibited the highest spatiotemporal similarity with commercial catch data, making it more suitable as a substitute for fishery data. Therefore, effective international cooperation and efficient joint management mechanisms for fishing vessels are needed to enhance the regulatory oversight of fishing vessels in this region. Integrating AIS data with other technological methods is crucial for more effective monitoring and management of fishing vessels. The findings presented in this paper provide both quantitative and qualitative scientific support for resource conservation and sustainable development in the region. AIS data; light purse seining; fishing effort; spatiotemporal distribution characteristics; Arabian High Seas +
Footnote †: journal: Applied sciences  
0000-0001-9900-0001-9900][PERSON]  
0000-0001-9900-0001-9900][PERSON]. [PERSON]  
0000-0001-9900-0001-9900][PERSON]. [PERSON]  
0000-0001-9900-0001-9900-0001-9900-0001-9900-0001-9900-0001-9900-0001-9900-0001-99000-0001-99000-0001-99000-0001-990001-990001-990001-990001-990001-990001-990001-90001-90001-990001-90001-990 mapping of the spatial distribution of fishing intensity [5; 6; 7]. The AIS is instrumental in assessing fishing pressure within fishing grounds [8] and analyzing the operating behavior of fishing vessels [9; 10]. As such, the AIS has emerged as a pivotal tool for fisheries resource management, fishing vessel regulation, and marine ecology protection. High-resolution fishing activity information can indirectly reflect the spatial distribution of fishery resources and fishing grounds to a considerable extent [11]. AIS data have been effectively utilized for extracting spatial information on fishing grounds. In essence, the aggregation range of vessel operation positions and fishing intensity is often defined as fishing grounds [12]. Various spatial methods have been employed for this purpose; for instance, [PERSON] et al. [13] utilized hotspot analysis to extract spatial information on fishing grounds in the North Pacific Ocean, while [PERSON] et al. [14] applied the kernel density method to derive information on bottom trawl fishing grounds in the western part of the Bay of Brest. These studies confirm that AIS data can be mined for spatiotemporal, high-precision fishing ground information. [PERSON] et al. [15] integrated two methods, kernel density estimation (KDE) and hotspot analysis (HSA), to extract the hotspot areas of fishing activities in the Bohai Sea of China by mapping high-precision fishing grounds. This study emphasized that the combination of KDE and HSA was more effective than the individual use of these methods. The existing methodologies examine the topological structure of spatial information and often involve arbitrarily defined spatial analysis scales while overlooking the crucial role of spatial information in providing reliable analytical data for the fisheries industry.