
TL;DR
This paper introduces a new spatial neighborhood definition and a model for detecting spatial outliers in GIS data, enhancing knowledge discovery by considering weighted parameters like distance and connectivity.
Contribution
It proposes a novel neighborhood relationship considering weights of key parameters and a new outlier detection model for polygonal spatial objects.
Findings
Effective outlier detection in GIS data demonstrated
Model applied successfully to literacy support project in Fayoum
Enhanced understanding of spatial correlations in data mining
Abstract
Intelligent geographic information system (IGIS) is one of the promising topics in GIS field. It aims at making GIS tools more sensitive for large volumes of data stored inside GIS systems by integrating GIS with other computer sciences such as Expert system (ES) Data Warehouse (DW), Decision Support System (DSS), or Knowledge Discovery Database (KDD). One of the main branches of IGIS is the Geographic Knowledge Discovery (GKD) which tries to discover the implicit knowledge in the spatial databases. The main difference between traditional KDD techniques and GKD techniques is hidden in the nature of spatial data sets. In other words in the traditional data set the values of each object are supposed to be independent from other objects in the same data set, whereas the spatial dataset tends to be highly correlated according to the first law of geography. The spatial outlier detection is…
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Taxonomy
TopicsData Management and Algorithms · Data Mining Algorithms and Applications · Geographic Information Systems Studies
