Speeding　has　been　acknowledged　as　a　critical　determinant　in　increasing　the　risk　of　crashes　and　their　resulting　injury　severities.　This　paper　employs　Global　Moran＇s　I　coefficient　and　local　Getis　-　Ord　G*　indexes　to　systematically　account　for　the　spatial　distribution　feature　of　speeding-related　crashes,　study　the　global　spatial　pattern　of　speeding-related　crashes,　and　identify　severe　crash　cluster　districts.　The　findings　demonstrate　that　severe　speeding-related　crashes　within　the　state　of　Pennsylvania　have　a　spatial　clustering　trend,　where　four　crash　datasets　are　extracted　from　four　hotspot　districts.　Two　log-likelihood　ratio　(LR)　tests　were　conducted　to　determine　whether　speeding-related　crashes　classified　by　hotspot　districts　should　be　modeled　separately.　The　results　suggest　that　separate　modeling　is　necessary.　To　capture　the　unobserved　heterogeneity,　four　correlated　random　parameter　order　models　with　heterogeneity　in　means　are　employed　to　explore　the　factors　contributing　to　crash　severity　involving　at　least　one　vehicle　speeding.