A Model for Spatial Outlier Detection Based on Weighted Neighborhood Relationship

TL;DR

This paper introduces a weighted neighborhood model for spatial outlier detection that considers distance, cost, and connectivity, improving the identification of inconsistent objects in spatial data.

Contribution

It redefines spatial neighborhood relationships by incorporating weights based on key parameters, enhancing outlier detection in GIS applications.

Findings

Effective detection of spatial outliers in GIS data

Model adaptable to polygonal objects

Applied successfully in a literacy project in Fayoum

Abstract

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.