We propose a measure, Spatial Local Outlier Measure (SLOM) which captures the local behaviour of datum in their spatial neighborhood. With the help of SLOM we are able to discern local spatial outliers which are usually missed by global techniques like "three standard deviations away from the mean". Furthermore the measure takes into account the local stability around a data point and supresses the reporting of outliers in highly unstable areas, where data is too heterogeneous and the notion of outliers is not meaningful. We prove several properties of SLOM and report experiments on synthetic and real data sets which show that our approach is novel and scalable to large data sets.