Three-dimensional Visualization, Interactive Analysis and Contextual Mapping of Space-time Cancer Data

The increase in computational power and storage capacity of computers, combined with the growing availability of geocoded data, has increased dramatically the amount of information processed in health studies, making it difficult to understand, to explore, and to discover interesting patterns within the data. The major difficulty in the analysis of health outcomes is that the patterns observed reflect the influence of a complex constellation of demographic, social, economic, cultural and environmental factors that likely change through time and space, and interact with the different types and scales of places where people live. Thus, there is a large heterogeneity in the temporal and spatial scales of investigation, leading to the utilization of a wide range of statistical methods and visualization techniques in most studies of health outcomes.