To　facilitate　efficient　and　effective　city　management,　it　is　important　for　urban　authorities　to　understand　the　regular　functionalities　of　urban　areas　and　the　irregular　crowd　dynamics　moving　around　the　city.　However,　existing　methods　relying　on　manual　surveys　and　statistics　usually　cost　substantial　time　and　labor,　hindering　the　fine-grain　characterization　of　urban　structures　and　the　in-depth　understanding　of　crowd　dynamics.　In　this　paper,　we　leverage　large-scale　mobility　data　collected　from　vehicle　GPS　devices　to　analyze　the　dynamics　of　crowd　movement　in　different　urban　areas　in　a　low-cost　and　automatic　manner.　We　extract　the　regular　crowd　movement　patterns　in　different　areas,　detect　the　abnormal　crowd　movement　flow　peaks,　and　then　interpret　the　influences　of　different　types　of　urban　events.　More　specifically,　we　first　divide　the　city　into　fine-grained　geographic　regions　and　cluster　them　according　to　the　similarity　of　crowd　movement　characteristics.　Second,　we　detect　anomaly　traffic　flow　for　each　cluster　area,　interpret　urban　events　for　each　abnormal　flow　point,　and　correlate　urban　events　to　the　interpretation　results.　Finally,　we　determine　the　scope　of　urban　events　and　use　visualization　techniques　to　demonstrate　the　impact　of　different　types　of　urban　events.　We　leverage　the　large-scale　real-world　datasets　from　Xiamen　City　for　evaluation.　Experimental　results　validate　the　effectiveness　of　our　method,　and　several　case　studies　in　Xiamen　are　conducted.　©　2019,　Springer　Nature　Switzerland　AG.