Measuring similarity between geospatial lifelines in studies of environmental health

Abstract

Many epidemiological studies involve analysis of clusters of diseases to infer locations of environmental hazards that could be responsible for the disease. This approach is however only suitable for sedentary populations or diseases with small latency periods. For migratory populations and diseases with long latency periods, people may change their residential location between time of exposure and onset of ill health. For such situations, clusters are diffused and diluted by in- and out-migration and may become very difficult to detect. One way to address the problem of diffused clusters is to include in analyses not only current residential locations, but all past locations at which cases might have been exposed to environmental hazardous. In this paper, we assume that a person’s residential history provides such information and represent it through a discrete geospatial lifeline data model. Clusters of similar geospatial lifelines represent individuals who have similar residential histories—and therefore represent people who are more likely to have had similar environmental exposure histories. We therefore introduce a lifeline distance (dissimilarity) measure to detect clusters of cases, providing a basis for revealing possible regions in space-time where environmental hazards might have existed in the past. The ability of the measure to distinguish cases from controls is tested using two sets of synthetically generated cases and controls. Results indicate that the measure is able to consistently distinguish between populations of cases and controls with statistically significant results. The lifeline distance measure consistently outperforms another measure which uses only the distance between subjects’ residences at time of diagnosis. However, the advantages of using the entire residential history are only partly realized, since the ability to distinguish between cases and controls is only moderately better for the lifeline distance function. Future work is needed to investigate modifications to the inter-lifeline distance measure in order to enhance the potential of this approach to detect locations of environmental hazards over the lifespan. Copyright Springer-Verlag Berlin Heidelberg 2005Geospatial lifelines, Mobile objects, Spatio-temporal clustering,

Space–time clustering of case–control data with residential histories: insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects

“…Thus, a potential cluster could be missed if several temporal orientations are not explored. This is an important finding because only one temporal orientation is considered in most clustering analyses (Han et al 2004;Ozonoff et al 2005;Sinha and Mark 2005).…”

“…To characterize human mobility, Hagerstrand (1970) proposes constructs for representing the space-time paths formed as individuals move throughout their days, now known as geospatial life-lines (Sinha and Mark 2005). Several recent efforts have applied this concept of geospatial life-lines and furthered our ability to identify space-time disease clusters (Han et al 2004(Han et al , 2005Ozonoff et al 2005;Paulu et al 2002;Sinha and Mark 2005;Vieira et al 2005).…”

“…Several recent efforts have applied this concept of geospatial life-lines and furthered our ability to identify space-time disease clusters (Han et al 2004(Han et al , 2005Ozonoff et al 2005;Paulu et al 2002;Sinha and Mark 2005;Vieira et al 2005). All of these approaches, however, analyze static spatial point distributions at a few snapshots in time, and therefore do not fully account for human mobility.…”

Reporting Leaders and Followers among Trajectories of Moving Point Objects

“…One obvious analytical toolset to uncover proximity patterns in individual trajectories is clustering. Even though the spatio-temporal nature of movement data adds additional complexity to clustering procedures, there have been some successful approaches for clustering trajectories [10,41,55]. However, spatio-temporal co-presence does not explicitly include the idea of interactions within individuals.…”

Efficient Detection of Patterns in 2D Trajectories of Moving Points

“…There is ample research on data mining of moving objects (e.g., [13], [25], [27], [28], [30]) in particular, on the discovery of similar trajectories or clusters. Trajectories for moving points are also referred to as (geo)spatial lifelines.…”