There is potential in statistical modeling for location-referenced spatial data; Geographic information systems (GIS) software is growing more and more accessible. As such, scientific datasets containing geocoded locations are often found. Spatial data often manifests over regions as collections of data when applied to public health. There is however a possibility of using disease mapping and spatial survival analysis when studying areal datasets. When researching a particular illness by region, the highest and lowest of its death and occurrence rates are brought forth, and studied for variability. This is done through disease mapping. This strategy also studies neighboring regions and their shared environmental, cultural and demographic impacts for the presence of spatial clusters. On the other hand, the design and following study of geographically-based time-to-event information is called spatial survival analysis. The method continuously observes a subject up until a certain event. It is also used as a means of evaluating clusters in survival datasets collected across regions.