Integrated　and　deep-seated　applications　call　for　the　intelligentization　of　spatial　data　processing.　Spatial　data　mining　is　becoming　more　and　more　important　to　satisfy　practical　application　and　business　intelligence.　With　the　rapid　development　of　distributed　sharing　infrastructures　such　as　grid　and　web　services,　providing　knowledge　services　on　grid　environment　is　becoming　the　trend　of　knowledge　discovery　and　application　technologies.　This　paper　introduces　an　architecture　of　geographical　knowledge　grid　platform　based　on　globus　(GeoKS-Grid),　then　puts　forward　a　spatial　data　integration　scheme　by　the　combination　of　GML　and　WFS　specification,　finally,　taking　spatial　outliers　mining　based　on　MST　clustering　as　an　example　to　illustrates　the　design　and　realization　of　knowledge　service.　Experimentation　finds　that　it　is　feasible　and　practicable　to　extend　the　grid　environment　by　adding　more　and　more　distributed　high　performance　knowledge　discovery　services　implemented　based　on　data　grid　mechanisms　and　globus　toolkit,　to　meet　the　requirements　of　application.　For　the　application　in　real　system,　a　task-oriented　architecture　based　on　KSDM　is　proposed　and　an　implementation　of　knowledge　service　is　introduced.　©　2008　IEEE.