作者: Jaymie R. Meliker , Melissa J. Slotnick , Gillian A. AvRuskin , Andrew M. Kaufmann , Geoffrey D. Jacquez
DOI: 10.1007/978-3-642-03647-7_36
关键词: Epidemiologic research 、 Environmental epidemiology 、 Process (engineering) 、 Geography 、 Exposure assessment 、 Data science 、 Long latency 、 Occupational mobility 、 Data mining 、 Short latency 、 Risk behavior
摘要: A key component of environmental epidemiologic research is the assessment historic exposure to contaminants. The continual expansion space-time databases, coupled with recognized need incorporate mobility histories in epidemiology, has highlighted deficiencies current software visualize and process information for (Mather et al. 2004; Pickle 2005). This most pressing retrospective studies or large where collection individual biomarkers unattainable prohibitively expensive, models tools are required reconstruction. In diseases long latency such as cancer, may be reconstructed over entire life-course, taking into consideration residential mobility, occupational changes risk behaviors, time-changing maps generated from Even outcomes short asthma attacks, reconstruction involve daily mobility/activity patterns temporally-varying These types datasets, example, timechanging contaminants, almost always characterized by spatial, temporal, and, spatio-temporal variability. While state-of-theart methods can integrate datasets that contain either spatial temporal variability, exhibiting both variability have proven largely unmanageable until now, researchers been forced simplify dynamic nature their reducing eliminating dimension.