Now a day disease is of interest to the geographers for several reasons. Geography of disease is fully understood by explaining a variety of factors, such as, climate, culture, geology, socio-economic status, biology etc. There are number of events which are concentrated in some specific places. For example, traffic accidents, disease outbreaks, gentrification, crime etc. When a geographer analyses a particular disease, including prediction, a key element centers where is the disease outbreak concentrated. Disease occurrences are not distributed randomly through spaces. Their distribution is dense at some locations while sparse at others. The places where disease incidents are relatively densely distributed are called hotspots. In this study, the filariasis morbidity pattern for the study area is investigated and analyzed. Analyses of data are performed using the Spatial Statistics Hot Spot Analysis tool, a spatial statistical technique which uses the Getis-Ord Gi* algorithm. This type of analysis helps to identify statistically significant spatial clusters of high and low filariasis morbidity in the study area.