One of the popular research areas in clinical decision supporting system (CDSS) is Spatial and temporal (ST) data mining. The basic concept of ST concerns about two combined dimensions of analyzing: time and space. For prediction of disease outbreak, we attempt to locate any potential uninfected by the predicted virus prevalence. A popular ST-clustering software called "SaTScan" works by predicting the next likely infested areas by considering the history records of infested zones and the radius of the zone. However, it is argued that using radius as a spatial measure suits large and perhaps evenly populated area. In urban city, the population density is relatively high and uneven. In this paper, we present a novel algorithm, by following the concept of SaTScan, but in consideration of spatial information in relation to local populations and full demographic information in proximity (e.g. that of a street or a cluster of buildings). This higher resolution of ST data mining has an advantage of precision and applicability in some very compact urban cities. For proving the concept a computer simulation model is presented that is based on empirical but anonymized and processed data.