Skyline　computations　are　a　way　of　finding　the　best　data　points　based　on　multiple　criteria　for　location-based　decision-making.　However,　as　the　input　data　grows　larger,　these　computations　become　slower　and　more　challenging.　To　address　this　issue,　we　propose　an　efficient　algorithm　that　uses　Apache　Spark,　a　platform　for　distributed　processing,　to　perform　area　skyline　computations　faster　and　more　salable.　Our　algorithm　consists　of　three　main　phases:　calculating　distances　between　data　points,　generating　distance　tuples,　and　computing　the　skyline.　In　the　second　phase,　we　apply　a　technique　called　local　partial　skyline　extraction,　which　reduces　the　amount　of　data　that　needs　to　be　sent　from　each　executor　(a　parallel　processing　unit)　to　the　driver　(a　central　processing　unit).　The　driver　then　computes　the　final　skyline　from　the　received　data　and　creates　filters　to　eliminate　irrelevant　points.　Our　experiments　show　that　our　algorithm　can　significantly　reduce　the　data　size　and　the　computation　time　of　the　area　skyline.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.