Space-Time Technology for Reconstructing Exposure in Cancer Epidemiology Studies

描述（由申请人提供）：用于重建癌症流行病学研究中暴露的时空技术。该项目的总体目标是开发第一个使用时空数据集进行曝光重建的软件工具。尽管越来越多地使用地理信息系统（GIS）在暴露评估研究，时空变化的数据集，如日常活动空间，居住历史，和随时间变化的地图的环境污染物的特点很差的GIS环境。虽然目前最先进的方法允许整合包含空间或时间变异性的数据集，但到目前为止，表现出时空变异性的数据集在很大程度上是无法管理的，研究人员被迫通过减少或消除空间或时间维度来简化其数据集的复杂性质。BioMedware最近开发了时空信息系统（STIS）技术，这是一种支持时间的地理方法，可以显示和分析时空数据集。该项目的具体目标将建立在STIS技术的坚实基础上，包括：（a）进行需求分析，以确定最佳曝光重建方法和功能，纳入软件;（B）开发时空曝光重建的算法和科学方法。这些算法将包括但不限于：从环境模型和全球定位系统软件成功导入时空数据集、栅格和矢量数据集之间的时空连接、以时空分辨方式进行的接触计算以及使用验证指标量化接触估计中的不确定性;（c）开发和测试一个便于用户使用的交互式软件原型，用于时空曝光重建。该原型将使健康研究人员能够可视化，分析和整合跨时空数据集的信息，用于暴露评估和环境流行病学。这一软件将使接触科学家能够产生接触估计数，其中包括从几分钟到几十年的时间变化。此外，将使用替代时间方向计算和显示暴露量，包括受试者的年龄、日历年和诊断/访谈前的年数。这些时间上详细的暴露估计对于具有长诱导期的疾病（如癌症）特别有价值，并将使环境流行病学家和风险评估人员能够以前所未有的细节量化、建模和调查暴露与疾病之间的空间和时间关系。

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.