Drunk-driving　events　often　escalate　into　drunk-driving　crashes,　however,　the　contributing　factors　of　this　progression　remain　elusive.　To　mitigate　the　likelihood　of　crashes　stemming　from　drunk-driving　events,　this　paper　introduces　the　notion　of　＇the　severity　of　drunk-driving　event＇　and　examines　the　complex　relationship　between　the　severity　and　its　contributing　factors,　considering　spatiotemporal　heterogeneity.　The　study　utilizes　a　Geographically　and　Temporally　Weighted　Binary　Logistic　Regression　(GTWBLR)　model　to　conduct　spatiotemporal　analysis　based　on　police-reported　drunk-driving　events　in　Beijing,　China.　The　results　show　that　most　factors　passed　the　non-stationary　test,　indicating　their　effects　on　the　severity　of　drunk-driving　event　vary　significantly　across　different　spatial　and　temporal　domains.　Notably,　during　non-workday,　drunk-driving　events　in　northeast　of　Beijing　are　more　likely　to　escalate　into　crashes.　Furthermore,　severe　weather　during　winter　in　the　northwest　of　Beijing　is　associated　with　high　risk　of　drunk-driving　crashes.　Based　on　these　insights,　the　authorities　can　strengthen　drunk-driving　checks　in　the　northeast　region　of　Beijing,　particularly　during　non-workdays.　And　it　is　crucial　to　promptly　clear　accumulated　snow　on　the　roads　during　severe　winter　weather　to　improve　road　safety.　These　insights　and　recommendations　are　highly　valuable　for　reducing　the　risk　of　drunk-driving　crashes.