Existing work in outlier detection emphasizes the deviation of non-spatial attribute not only in outlier detecting in statistical database but also in spatial outlier detecting in spatial database.However

both spatial and non-spatial attributes must be synthetically considered in many applications

such as image processing

position-based service.We defined outlier in respect of taking account of both spatial and non-spatial attributes and proposed a new density-based spatial outlier detecting approach with leapingly sampling(DBSODLS).Existing density-based outlier detection approaches must calculate neighborhoods of every object

which are time-consuming.This method makes the best of neighbor information that have been detected

leapingly selects the next object

but not every object

which reduces many neighborhood queries.Theoretical comparison shows this method is better than other density-based methods in efficiency

and the experimental results also show that the approach outperforms the existing density-based methods in efficiency.