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.