Detection of spatial outliers has been one of the hot issues in the field of spatial data mining and knowledge discovery. So far

the detection of spatial outliers is determined by spatial outlier factor in most of the existing methods

while geometrical distances among their corresponding spatial neighbor are ignored. In this case

these existing methods are inappropriative to the spatial inhomogeneous distribution. To overcome this limitation

this paper presents a new method for spatial outlier detection

named as spatial outlier measure method （SOM for short）. At first

some concepts related to the SOM are defined

such as the attribute gradient

the inverse distance weight and the degree of spatial outlier. The algorithm of the SOM is further presented. One can easily find that the new method considers the distances among the neighborhood and their effects on the attribute values of the target entities

and the degree of spatial outlier is used to check spatial outliers. Finally

a practical example is employed to demonstrate the validity of the method proposed in the paper

where the Cr concentration data of soil in a southern city of China are utilized.