  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 one of the most
popular spatial data mining techniques which is used to detect spatial objects
whose non-spatial attributes values are extremely different from those of their
neighboring objects. Analyzing the behavior of these objects may produce an
interesting knowledge, which has an effective role in the decision-making
process. In this thesis, a new definition for the spatial neighborhood
relationship by is proposed considering the weights of the most effective
parameters of neighboring objects in a given spatial dataset. The spatial
parameters taken into our consideration are; distance, cost, and number of
direct connections between neighboring objects. A new model to detect spatial
outliers is also presented based on the new definition of the spatial
neighborhood relationship. This model is adapted to be applied to polygonal
objects. The proposed model is applied to an existing project for supporting
literacy in Fayoum governorate in Arab Republic of Egypt (ARE).
