SpaGRID: A spatial grid framework for high dimensional medical databases

Abstract

The outgrowth of technology in geographical databases has enhanced the growth of spatial databases, to deal with such enlarging databases scientists are laying down enormous efforts that can efficiently process these databases. Spatial data mining techniques has been collaboratively applied to extract implicit knowledge from spatial as well as non-spatial attributes. These techniques are efficiently applied in several fields such as healthcare, environmental, marketing and remote sensing databases to improve planning and decision making process. In this paper, we have designed and implemented SpaGRID framework for detection of spatial clusters. The framework has unprecedented efficiency to extract implicit knowledge of spatial data, due to its accessibility to handle and discover hidden patterns from spatial databases. We have also illustrated the usage of spatial variations among the United States men with prevalence of prostate cancer disease. The data of age group was taken from (15-65+) years in this group prostate cancers were examined and several stages of disease diagnosis was taken into account. The population of data was characterized by white, black and others were too small to be taken into account. Numerous challenges were encountered due to complexity of spatial datasets hence being resolved by certain statistical measures. The approach is to discover knowledge from spatial databases and design different aspects of knowledge discovery process from spatial databases. © 2012 Springer-Verlag.