Spatially clustered associations in health related geospatial data

Overlaying maps using a desktop GIS is often the first step of a multivariate spatial analysis. The potential of this operation has increased considerably as data sources and Web services to manipulate them are becoming widely available via the internet. Standards from the OGC enable such geospatial mashups to be seamless and user driven, involving discovery of thematic data. The user is naturally inclined to look for spatial clusters and correlation of outcomes. Using classical cluster detection scan methods to identify multivariate associations can be problematic in this context, because of a lack of control on or knowledge about background populations. For public health and epidemiological mapping, this limiting factor can be critical but often the focus is on spatial identification of risk factors associated with health or clinical status. In this paper we point out that this association itself can ensure some control on underlying populations, and develop an exploratory scan statistic framework for multivariate associations. Inference using statistical map methodologies can be used to test the clustered associations. The approach is illustrated with a hypothetical data example and an epidemiological study on community MRSA. Scenarios of potential use for online mashups are introduced but full implementation is left for further research. Authors: Didier G. Leibovici, Lucy Bastin, Suchith Anand, Gobe Hobona and Mike JacksonJRC.DDG.H.3-Global environement monitorin

Constraints-driven automatic geospatial service composition: workflows for the analysis of sea-level rise impacts

Building applications based on the reuse of existing components or services has noticeably increased in the geospatial application domain, but researchers still face a variety of technical challenges designing workflows for their specific objectives and preferences. Hence, means for automatic service composition that provide semantics-based assistance in the workflow design process have become a frequent demand especially of end users who are not IT experts. This paper presents a method for automatic composition of workflows for analyzing the impacts of sea-level rise based on semantic domain modeling. The domain modeling comprises the design of adequate services, the definition of ontologies to provide domain-specific vocabulary for referring to types and services, and the input/output annotation of the services using the terms defined in the ontologies. We use the PROPHETS plugin of the jABC workflow framework to show how users can benefit from such a domain model when they apply its constraintsdriven synthesis methods to obtain the workflows that match their intentions