Spatial　Clustering　with　Obstacles　Constraints　(SCOC)　has　been　a　new　topic　in　Spatial　Data　Mining　(SDM).　Spatial　Obstructed　Distance　(SOD)　is　the　key　to　SCOC.　The　obstacles　constraint　is　generally　ignored　in　computing　distance　between　two　points,　and　it　leads　to　the　clustering　result　which　is　of　no　value,　so　obstructed　distance　has　a　great　effect　upon　clustering　result.　In　this　paper,　we　propose　a　novel　Spatial　Obstructed　Distance　using　Quantum-Behaved　Particle　Swarm　Optimization　(QPSO)　based　on　Grid　model　to　obtain　obstructed　distance,　which　is　named　QPGSOD.　The　experimental　results　show　that　QPGSOD　is　effective,　and　it　can　not　only　give　attention　to　higher　local　constringency　speed　and　stronger　global　optimum　search.　©　2009　IEEE.