The　check-in　behaviors　of　users　are　ubiquitous　in　location-based　social　networks　in　urban　living.　Understanding　user　preferences　is　critical　to　improving　the　recommendation　services　of　social　platforms.　In　addition,　great　quality　of　recommendation　is　also　beneficial　to　sustainable　urban　living　since　the　user　can　easily　find　the　point　of　interest　(POI)　to　visit,　which　avoids　unnecessary　consumption,　such　as　a　longer　time　taken　for　searching　or　driving.　To　capture　user　preferences　from　their　check-in　behaviors,　advanced　methods　transform　historical　records　into　graph　structure　data　and　further　leverage　graph　deep　learning-based　techniques　to　learn　user　preferences.　Despite　their　effectiveness,　existing　graph　deep　learning-based　methods　are　limited　to　the　capture　of　the　deep　graph＇s　structural　information　due　to　inherent　limitations,　such　as　the　over-smoothing　problem　in　graph　neural　networks,　further　leading　to　suboptimal　performance.　To　address　the　above　issues,　we　propose　a　novel　method　built　on　Transformer　architecture　named　spatiotemporal　aware　transformer　(STAT)　via　a　novel　graphically　aware　attention　mechanism.　In　addition,　a　new　temporally　aware　sampling　strategy　is　developed　to　reduce　the　computational　cost　and　enable　STAT　to　deal　with　large　graphs.　Extensive　experiments　on　real-world　datasets　have　demonstrated　the　superiority　of　the　STAT　compared　to　state-of-the-art　POI　recommendation　methods.