Measuring similarity between geospatial lifelines in studies of environmental health |
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| Authors: | Gaurav Sinha David M Mark |
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| Institution: | (1) Department of Geography and National Center for Geographic Information and Analysis (NCGIA), University at Buffalo, Buffalo, NY 14261, USA |
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| 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.This project is supported by grant number 1 R01 ES09816-01 from the National Institute of Environmental Health Sciences, NIH. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIEHS or NIH. We wish to thank Peter Rogerson for helpful discussions of the migration models, and the anonymous reviewers for pointing out areas where the paper could be improved. |
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| Keywords: | Geospatial lifelines Mobile objects Spatio-temporal clustering |
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