There　is　an　increasing　interest　in　the　field　of　parallel　and　distributed　data　mining　in　grid　environment　over　the　past　decade.　As　an　important　branch　of　spatial　data　mining,　spatial　outlier　mining　can　be　used　to　find　out　some　interesting　and　unexpected　spatial　patterns　in　many　applications.　In　this　paper,　a　new　parallel　&　distributed　spatial　outlier　mining　algorithm　(PD-SOM)　is　proposed　to　simultaneously　detect　global　and　local　outliers　in　a　grid　environment.　PD-SOM　is　a　Delaunay　triangulation　(D-TIN)　based　approach,　which　was　encapsulated　and　deployed　in　a　distributed　platform　to　provide　parallel　and　distributed　spatial　outlier　mining　service.　Subsequently,　a　distributed　system　framework　for　PD-SOM　is　designed　on　top　of　a　geographical　knowledge　service　grid　(GeoKSGrid)　developed　by　our　research　group,　a　two-step　strategy　for　spatial　outlier　detection　is　put　forward　to　support　the　encapsulation　and　distributed　deployment　of　the　geographical　knowledge　service,　and　two　key　techniques　of　the　geographical　knowledge　service:　parallel　and　distributed　computing　of　Delaunay　triangulation　and　the　implementation　of　PD-SOM　algorithm　are　discussed.　Finally,　the　efficiency　of　the　spatial　outlier　mining　service　is　analyzed　in　theory,　the　practicality　is　confirmed　by　a　demonstrative　application　on　the　abnormality　analyzing　of　soil　geochemical　investigation　samples　from　Fujian　eastern　coastal　zone　area　in　China,　and　the　effectiveness　and　superiority　of　PD-SOM　in　a　balanced,　scalable　grid　environment　are　verified　through　the　comparison　with　the　popular　spatial　outlier　mining　algorithm　SLOM,　for　the　involvement　of　large　amount　of　computing　cores.