At　present,　population　mobility　for　the　purpose　of　tourism　has　become　a　popular　phenomenon.　As　it　becomes　easier　to　capture　big　data　on　the　tourist　digital　footprint,　it　is　possible　to　analyze　the　respective　regional　features　and　driving　forces　for　both　tourism　sources　and　destination　regions　at　a　macro　level.　Based　on　the　data　of　tourist　flows　to　Nanjing　on　five　short-period　national　holidays　in　China,　this　study　first　calculated　the　travel　rate　of　tourist　source　regions　(315　cities)　and　the　geographical　concentration　index　of　the　visited　attractions　(51　scenic　spots).　Then,　the　spatial　autocorrelation　metrics　index　was　used　to　analyze　the　global　autocorrelation　of　the　travel　rates　of　tourist　source　regions　and　the　geographical　concentration　index　of　the　tourist　destinations　on　five　short-term　national　holidays.　Finally,　a　heuristic　unsupervised　machine-learning　method　was　used　to　analyze　and　map　tourist　sources　and　visited　attractions　by　adopting　the　travel　rate　and　the　geographical　concentration　index　accordingly　as　regionalized　variables.　The　results　indicate　that　both　source　and　sink　regions　expressed　distinctive　regional　differentiation　patterns　in　the　corresponding　regional　variables.　This　study　method　provides　a　practical　tool　for　analyzing　regionalization　of　big　data　in　tourist　flows,　and　it　can　also　be　applied　to　other　origin-destination　(OD)　studies.