In　this　paper,　a　more　effective　Quantum　Particle　Swarm　Optimization　(QPSO)　method　for　Spatial　Clustering　with　Obstacles　Constraints　(SCOC)　is　presented.　In　the　process　of　doing　so,　we　first　proposed　a　novel　Spatial　Obstructed　Distance　using　QPSO　based　on　Grid　model　(QPGSOD)　to　obtain　obstructed　distance,　and　then　we　developed　a　new　QPKSCOC　based　on　QPSO　and　K-Medoids　to　cluster　spatial　data　with　obstacles　constraints.　The　contrastive　experiments　show　that　QPGSOD　is　effective,　and　QPKSCOC　can　not　only　give　attention　to　higher　local　constringency　speed　and　stronger　global　optimum　search,　but　also　get　down　to　the　obstacles　constraints　and　practicalities　of　spatial　clustering;　and　it　performs　better　than　Improved　K-Medoids　SCOC　(IKSCOC)　in　terms　of　quantization　error　and　has　higher　constringency　speed　than　Genetic　K-Medoids　SCOC.