Rapid　urbanization　in　China　has　prompted　plenty　of　urban　facilities　to　be　constructed　with　the　expectation　of　harmonizing　with　the　rapid　growth　of　urban　population.　However,　regarding　the　spatial　interactions　produced　by　cross-area　human　mobility,　the　diversity　and　variability　of　residents＇　trip　requirements　inevitably　cause　the　deviations　of　the　real　interaction　patterns　from　the　optimal　status　determined　by　the　current　allocation　of　urban　facilities.　To　maximize　the　utility　of　urban　facility　allocation,　we　designed　a　bipartite　network-based　approach　to　explore　anomalous　spatial　interaction　patterns　within　cities.　First,　considering　the　potential　area　attractiveness,　a　weighted　origin-destination　bipartite　network　was　constructed　to　structure　the　spatial　interactions　between　traffic　analysis　zones.　Then,　a　branch　and　bound　(BnB)　based　augmenting　path　algorithm　was　proposed　to　optimize　the　distribution　of　spatial　interactions,　which　can　maximize　the　urban　population　carrying　capabilities.　Finally,　anomalous　interaction　patterns　causing　both　overload　and　underload　were　detected　through　comparisons　between　the　actual　and　optimal　spatial　interaction　distribution.　The　experimental　results　show　that　the　two　types　of　anomalous　interaction　patterns　have　significantly　different　spatial　distribution　characteristics.　Through　further　analyzing　the　relationships　between　the　two　types　of　anomalous　interaction　patterns　and　urban　evolution　process,　this　study　can　also　provide　targeted　decision　supports　for　the　accommodating　of　urban　facility　allocations　to　the　distributions　of　resident　trips　in　space.