Urban　waterlogging　poses　a　severe　threat　to　lives　and　property　globally,　making　it　crucial　to　identify　the　distribution　of　urban　value　and　waterlogging　risk.　Previous　research　has　overlooked　the　heterogeneity　of　value　and　risk　in　spatial　distribution.　To　identify　the　overlay　effect　of　urban　land　value　and　risk,　this　study　employs　the　Entropy　Weighting　Method　(EM)　to　assess　urban　value,　Principal　Component　Analysis　(PCA)　to　determine　waterlogging　risk　and　key　areas　(RK),　local　Moran＇s　I　(SC)　to　identify　key　areas　(HK),　and　finally　Bivariate　local　Moran＇s　I　(DC)　to　comprehensively　evaluate　urban　value　and　waterlogging　risk　to　delineate　key　areas　(BH).　The　results　indicate　that　waterlogging　risk　is　primarily　influenced　by　proximity　to　water　systems　(PCA　coefficient:　0.567),　population　density　(0.550),　and　rainfall　(0.445).　There　is　a　positive　correlation　between　urban　value　and　waterlogging　risk,　with　a　global　Moran＇s　I　of　0.536,　indicating　that　areas　with　higher　urban　value　also　face　greater　waterlogging　risk.　The　DC　method　improved　identification　precision,　reducing　the　BH　area　by　6.42　and　3.51　km2　compared　to　RK　and　HK,　accounting　for　25.50　%　and　15.76　%　of　the　RK　and　HK　identified　areas,　respectively.　At　present,　rescue　resources　can　access　less　than　one-third　of　the　area　within　5　min,　but　with　the　DC　method,　during　the　centennial　rainfall　scenario,　the　accessibility　rate　within　5　min　for　the　BH　area　reaches　63　%,　and　all　BH　key　areas　can　be　covered　within　15　min.　This　study　provides　a　new　methodology　for　identifying　key　areas　of　waterlogging　disasters　and　can　be　used　to　enhance　urban　rescue　efficiency　and　the　precision　management　of　flood　disasters.