As　increasing　volumes　of　urban　data　are　being　available,　new　opportunities　arise　for　data-driven　analysis　that　can　lead　to　improvements　in　the　lives　of　citizens　through　evidence-based　policies.　In　particular,　taxi　trip　is　an　important　urban　sensor　that　provides　unprecedented　insights　into　many　aspects　of　a　city,　from　economic　activity,　human　mobility　to　land　development.　However,　analyzing　these　data　presents　many　challenges,　e.g.,　sparse　data　for　fine-grained　patterns,　and　the　regularity　submerged　by　seemingly　random　data.　Inspired　by　above　challenges,　we　focus　on　Pick-Up　(PU)/Drop-Off　(DO)　points　from　taxi　trips,　and　propose　a　fine-grained　approach　to　unveil　a　set　of　low　spatio-temporal　patterns　from　the　regularity-discovered　intensity.　The　proposed　method　is　conceptually　simple　yet　efficient,　by　leveraging　point　process　to　handle　sparsity　of　points,　and　by　decomposing　point　intensities　into　the　low-rank　regularity　and　the　factorized　basis　patterns,　our　approach　enables　domain　experts　to　discover　patterns　that　are　previously　unattainable　for　them,　from　a　case　study　motivated　by　traffic　engineers.