Day time, night time, over time: geographic and temporal uncertainty when linking event and contextual data.
Day time, night time, over time: geographic and temporal uncertainty when linking event and contextual data.
复制标题
DOI：
10.1186/s12940-021-00734-x
复制
发表时间：
2021-05-04
期刊：
影响因子：
--
通讯作者：
Mikaelian L
中科院分区：
文献类型：
--
作者：
Folch DC;Fowler CS;Mikaelian L
地理定位数据的增长为健康领域的大量新研究机会打开了大门。人们特别感兴趣的一个途径是人们花费时间的空间与他们的健康结果之间的关系。这种研究模式通常将在特定队列中收集的个人数据与公开可用的社会经济或环境汇总数据相交。在空间方面：个人在特定时间被表示为地图上的点，而上下文被表示为包含来自采样观测的聚合或建模数据的多边形。在这种复杂的表述中，不确定性比比皆是。我们提出了四种敏感度分析方法来探询点和面数据之间的空间和时间关系的稳定性。位置精度用于评估将点指定给正确的多边形的重要性。邻域大小调查假设为相关的上下文的大小如何影响观察结果。LIFE课程考虑了上下文效应随时间变化的影响。一天中的时间认识到，大多数人在一天中占据不同的空间，曝光不仅仅是一个功能性的住宅地点。我们使用了来自宾夕法尼亚州和北卡罗来纳州农村儿童的八年纵向研究的点数据，以及以0.5英里(0.805 公里)网格单元呈现的八年空气污染和人口数据。我们首先确定了试图将个人结果与背景影响相匹配的研究所面临的挑战，然后提出了估计这种不确定性可能引入分析的影响的方法，最后将这些测量作为更大的不确定性分析框架的一部分。空间和时间的不确定性在我们队列中的儿童和一般人群中是高度可变的。对于我们的测试数据集，我们发现生命过程中的不确定性比位置精度和邻域大小更大。对于这些孩子来说，一天中的时间不确定性相对较低。研究中的每个人都应该考虑到空间和时间的不确定性，因为不同观测数据的大小可能有很大差异。驱动源数据的基本假设在测量的不确定性水平上起着重要作用。
The growth of geolocated data has opened the door to a wealth of new research opportunities in the health fields. One avenue of particular interest is the relationship between the spaces where people spend time and their health outcomes. This research model typically intersects individual data collected on a specific cohort with publicly available socioeconomic or environmental aggregate data. In spatial terms: individuals are represented as points on map at a particular time, and context is represented as polygons containing aggregated or modeled data from sampled observations. Uncertainty abounds in these kinds of complex representations. We present four sensitivity analysis approaches that interrogate the stability of spatial and temporal relationships between point and polygon data. Positional accuracy assesses the significance of assigning the point to the correct polygon. Neighborhood size investigates how the size of the context assumed to be relevant impacts observed results. Life course considers the impact of variation in contextual effects over time. Time of day recognizes that most people occupy different spaces throughout the day, and that exposure is not simply a function residential location. We use eight years of point data from a longitudinal study of children living in rural Pennsylvania and North Carolina and eight years of air pollution and population data presented at 0.5 mile (0.805 km) grid cells. We first identify the challenges faced for research attempting to match individual outcomes to contextual effects, then present methods for estimating the effect this uncertainty could introduce into an analysis and finally contextualize these measures as part of a larger framework on uncertainty analysis. Spatial and temporal uncertainty is highly variable across the children within our cohort and the population in general. For our test datasets, we find greater uncertainty over the life course than in positional accuracy and neighborhood size. Time of day uncertainty is relatively low for these children. Spatial and temporal uncertainty should be considered for each individual in a study since the magnitude can vary considerably across observations. The underlying assumptions driving the source data play an important role in the level of measured uncertainty.
登录
查看更多内容
影响因子：
7.4
作者：
Burke LE;Shiffman S;Music E;Styn MA;Kriska A;Smailagic A;Siewiorek D;Ewing LJ;Chasens E;French B;Mancino J;Mendez D;Strollo P;Rathbun SL
通讯作者：
Rathbun SL
DOI：
10.1016/j.trd.2010.03.007
发表时间：
2010-08-01
影响因子：
7.6
作者：
Hatzopoulou, M.;Miller, E. J.
通讯作者：
Miller, E. J.
影响因子：
5.5
作者：
Inagami, Sanae;Cohen, Deborah A.;Asch, Steven M.
通讯作者：
Asch, Steven M.
影响因子：
5
作者：
Lisabeth, L. D.;Roux, A. V. Diez;Morgenstern, L. B.
通讯作者：
Morgenstern, L. B.
影响因子：
4.8
作者：
Gatzke-Kopp LM;Warkentien S;Willoughby M;Fowler C;Folch DC;Blair C
通讯作者：
Blair C