Dockless　bike　sharing　plays　an　important　role　in　complementing　urban　transportation　systems　and　promoting　the　sustainable　development　of　cities　worldwide.　To　improve　system　operational　efficiency,　it　is　critical　to　study　the　spatiotemporal　patterns　of　dockless　bike　sharing　demand　as　well　as　factors　influencing　these　patterns.　Based　on　bicycle　trip　data　from　Mobike,　Point　of　Interest　(POI)　data　and　smart　card　data　in　Beijing,　we　built　a　spatially　embedded　network　and　implemented　the　Infomap　algorithm,　a　community　detection　method　to　uncover　the　usage　patterns.　Then,　the　Gradient　Boosting　Decision　Tree　(GBDT)　model　was　adopted　to　investigate　the　effect　of　the　built　environment　and　public　transit　services　by　controlling　the　temporal　variables.　The　spatiotemporal　distribution　shows　imbalanced　characteristics.　About　half　of　the　total　trips　occur　in　the　morning/evening　rush　hours　and　at　noon.　The　community　detection　results　further　reveal　a　polycentric　pattern　of　trip　demand　distribution　and　120　sub-regions　with　a　significant　difference　in　connection　strength　and　scale.　The　result　of　the　GBDT　model　indicates　that　factors　including　subway　ridership,　bus　ridership,　hour,　residence　density,　office　density　have　considerable　impacts　on　trip　demand,　contributing　about　62.6%　of　the　total　influence.　Factors　also　represent　complex　nonlinear　relationships　with　dockless　bike　sharing　usage.　The　effect　ranges　of　each　factor　were　identified,　it　indicates　rebalancing　schemes　could　be　changed　according　to　spatial　location.　These　findings　may　help　planners　and　policymakers　to　determine　the　reasonable　scale　of　bike　deployment　and　improve　the　efficiency　of　redistribution　in　local　regions　while　reducing　rebalance　costs.