The　computation　of　the　skyline　provides　a　mechanism　for　utilizing　multiple　location-based　criteria　to　identify　optimal　data　points.　However,　the　efficiency　of　these　computations　diminishes　and　becomes　more　challenging　as　the　input　data　expands.　This　study　presents　a　novel　algorithm　aimed　at　mitigating　this　challenge　by　harnessing　the　capabilities　of　Apache　Spark,　a　distributed　processing　platform,　for　conducting　area　skyline　computations.　The　proposed　algorithm　enhances　processing　speed　and　scalability.　In　particular,　our　algorithm　encompasses　three　key　phases:　the　computation　of　distances　between　data　points,　the　generation　of　distance　tuples,　and　the　execution　of　the　skyline　operators.　Notably,　the　second　phase　employs　a　local　partial　skyline　extraction　technique　to　minimize　the　volume　of　data　transmitted　from　each　executor　(a　parallel　processing　procedure)　to　the　driver　(a　central　processing　procedure).　Afterwards,　the　driver　processes　the　received　data　to　determine　the　final　skyline　and　creates　filters　to　exclude　irrelevant　points.　Extensive　experimentation　on　eight　datasets　reveals　that　our　algorithm　significantly　reduces　both　data　size　and　computation　time　required　for　area　skyline　computation.　©　2024　The　Authors