Drunk-driving　events　can　be　divided　into　drunk-driving　crashes　(DDCs)　and　non-crash　drunk-driving　events　(NDDEs).　Strong　spatial　heterogeneity,　which　consists　of　spatial　local　heterogeneity　and　spatial　stratified　heterogeneity,　exists　in　drunk-driving　events.　Therefore,　this　article　proposed　a　hybrid　method　with　LISA　and　GeoDetector　to　investigate　the　spatial　heterogeneity　of　drunk-driving　events.　First,　an　integrated　method　of　Getis-Ord　Gi*　and　Anselin　Local　Moran＇s　I,　which　belong　to　LISA,　was　applied　for　the　analysis　of　spatial　local　heterogeneity.　Then,　based　on　the　improved　GeoDetector　with　a　highest　q　value,　this　article　explored　the　main　factors　contributing　to　spatial　stratified　heterogeneity.　Results　show　that　DDCs　are　concentrated　in　the　urban　areas　and　the　sub-centres　located　in　the　east　of　suburban,　while　NDDEs　are　predominantly　found　in　the　urban　areas　and　the　sub-centres　located　in　the　northwest　and　southwest　of　suburban.　The　spatial　stratified　heterogeneity　analysis,　conducted　using　the　Factor　Detector,　reveals　the　pivotal　factors　influencing　the　spatial　distribution　of　DDCs.　Entertainment　venues,　alcohol　outlets,　and　population　density　exhibit　significant　impacts,　with　q-values　of　0.233,　0.227,　and　0.203,　respectively.　Similarly,　for　NDDEs,　the　most　influential　factor　is　the　density　of　entertainment　venues,　with　a　q-value　of　0.243.　When　exploring　the　interaction　effects　between　factors,　the　study　emphasises　that　the　interaction　between　population　density　and　alcohol　outlet　density　shows　the　highest　q-value　of　0.787.　This　highlights　a　substantial　impact　on　the　spatial　formation　of　drunk-driving　events.　These　findings　offer　valuable　insights　for　implementing　targeted　measures　to　improve　road　safety　and　reduce　the　occurrence　of　drunk-driving　events　in　Beijing.