A Model for Spatial Outlier Detection Based on Weighted Neighborhood Relationship
This is an incremental improvement for spatial data analysis in decision-making processes.
The authors tackled the problem of detecting spatial outliers by redefining neighborhood relationships with weighted parameters, and applied their model to a GIS literacy project in Fayoum governorate.
Spatial outliers are used to discover inconsistent objects producing implicit, hidden, and interesting knowledge, which has an effective role in decision-making process. In this paper, we propose a model to redefine the spatial neighborhood relationship by considering weights of the most effective parameters of neighboring objects in a given spatial data set. The spatial parameters, which are taken into our consideration, are distance, cost, and number of direct connections between neighboring objects. This model is adaptable to be applied on polygonal objects. The proposed model is applied to a GIS system supporting literacy project in Fayoum governorate.