Mapping　large　volume　of　origin-destination　flow　data　(or　spatial　interactions)　has　long　been　a　challenging　problem　because　of　the　conflict　between　massive　location-to-location　connections　and　the　limited　map　space.　Current　approaches　for　flow　mapping　only　work　with　a　small　dataset　or　have　to　use　data　aggregation,　which　not　only　cause　a　significant　loss　of　information　but　may　also　produce　misleading　maps.　In　this　paper,　we　present　a　density-based　flow　map　generalization　approach　that　can　extract　flow　patterns　and　facilitate　the　analysis　and　visualization　of　big　origin-destination　flow　data　at　multiple　scales.　Unlike　existing　methods　that　generalize　flow　data　by　spatial　unit-based　aggregation,　our　new　flow　map　generalization　algorithm　is　based　on　flow　density　distribution.　To　demonstrate　the　approach　and　assess　its　effectiveness,　a　case　study　is　carried　out　to　map　829,039　taxi　trips　within　the　New　York　City.　With　parameter　settings,　the　proposed　method　can　discover　inherent　and　abstract　flow　patterns　at　different　map　scales　and　generalization　levels,　which　naturally　supports　interactive　and　multi-scale　flow　mapping.