Author(s): Goovaerts P
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. Despite the significant work accomplished in health data visualization and analysis this last decade, spatial and temporal data are still displayed in separate views and so current software do not capitalize on the human visual processing engine to extract knowledge from the spatial interconnectedness of information over time and geography. In addition, common approaches to disease mapping too often focus on the display of disease rates for political or administrative units and lack information on the local context of cancer burden, which is critical to facilitate the interpretation of these maps by local communities and engage their participation to prevention and control activities. This paper reviews common approaches for the space-time visualization of health data and explores solutions for the 3D interactive visualization of health outcomes in a combined time and geography space and contextualization of disease maps through the incorporation of familiar markers, such as highways, rivers or topographic details.