Space-Time Technology for Reconstructing Exposure in Cancer Epidemiology Studies

DESCRIPTION (provided by applicant): Space-Time Technology for Reconstructing Exposure in Cancer Epidemiology Studies. The overall objective of this project is to develop the first software tools for exposure reconstruction using space-time datasets. Despite the increased use of Geographic Information Systems (GIS) in exposure assessment research, spatio-temporally varying datasets, such as daily activity spaces, residential histories, and time-varying maps of environmental contaminants are poorly characterized in the GIS environment. While current state-of-the-art methods allow for integrating datasets that contain spatial or temporal variability, until now datasets exhibiting spatio-temporal variability have been largely unmanageable, and researchers have been forced to simplify the complicated nature of their datasets by reducing or eliminating the spatial or temporal dimension. BioMedware recently developed Space-Time Information Systems (STIS) technology, a time-enabled geographic approach that displays and analyzes spatio-temporal datasets. The specific aims of this project will build on a firm foundation of STIS technology and include (a) Conducting a requirements analysis to identify the optimal exposure reconstruction methods and functionality to incorporate in the software; (b) Developing algorithms and scientific approaches for space-time exposure reconstruction. These algorithms will include but not be limited to the following: successful import of space-time datasets from environmental models and global positioning system software, space-time joins across raster and vector datasets, exposure calculations in a spatio-temporally-resolved manner, and quantification of uncertainty in the exposure estimates using validation metrics; and (c) Developing and testing a user-friendly interactive software prototype for space-time exposure reconstruction. This prototype will allow health researchers to visualize, analyze, and integrate information across space-time datasets for exposure assessment and environmental epidemiology. This software will enable exposure scientists to generate exposure estimates incorporating temporal variability on the scale of minutes to decades. In addition, exposure will be calculated and displayed using alternative temporal orientations, including participant's age, calendar year, and the number of years prior to diagnosis/interview. These temporally-detailed exposure estimates are especially valuable for diseases with long induction periods, such as cancer, and will allow environmental epidemiologists and risk assessors to quantify, model, and investigate spatial and temporal relationships between exposure and disease in unprecedented detail.