The ever-increasing number of spatial data sets accessible through spatial data clearinghouses continues to make geographic information retrieval and spatial data discovery major challenges. Such challenges have been addressed in the discipline of Information Retrieval through ranking of data according to inferred degrees of relevance. Spatial data, however, present an additional challenge as they are characteristically made up of geometry, attribute and, optionally, temporal components. As these components are mutually independent of one another, this paper suggests that they be ranked independently of one another. The representation of the results of the independent ranking of these three components of spatial data suggests that representation of the results of the ranking process requires an alternative approach to currently used textual ranked lists: visualisation of relevance in a three-dimensional visualisation environment. To illustrate the possible application of such an approach, a prototype browser is presented.

Reconstructing the shape and motion of unknown objects with active tactile sensors," in Algorithmic Foundations of Robotics V. , "Sensor-based hybrid position/force control of a robot manipulator in an uncalibrated environment," IEEE Trans.Abstract-The need for computational resources capable of processing geospatial data has accelerated the uptake of geospatial web services. Several academic and commercial organizations now offer geospatial web services for data provision, coordinate transformation, geocoding and several other tasks. These web services adopt specifications developed by the Open Geospatial Consortium (OGC)-the leading standardization body for Geographic Information Systems. In parallel with efforts of the OGC, the Grid computing community has published specifications for developing Grid applications. The Open Grid Forum (OGF) is the main body that promotes interoperability between Grid computing systems. This study examines the integration of Grid services and geospatial web services into workflows for Geoscientific processing. An architecture is proposed that bridges web services based on the abstract geospatial architecture (ISO19119) and the Open Grid Services Architecture (OGSA). The paper presents a work- flow management system, called SAW-GEO, that supports orchestration of Grid-enabled geospatial web services. An implementation of SAW-GEO is presented, based on both the Simple Conceptual Unified Flow Language (SCUFL) and the Business Process Execution Language for Web Services (WS-BPEL or BPEL for short).Note to Practitioners-Geoscientific workflows are used in several disciplines including for example geology, geophysics hydrology, and petroleum science. Some of the analysis carried out by geoscientists can now be offered on the World Wide Web using standardized web services. Our study examines the potential of workflow enactors to support the creation of geoscientific workflows involving web services based on standards of the Open Geospatial Consortium. An implementation of a prototype is presented and applied to the analysis of groundwater vulnerability using borehole data. A sample workflow is implemented using two different workflow enactors and their distinct languages to demonstrate that the proposed approach is independent of the workflow enactor adopted. The proposed approach could be used to support collaborative workflows that involve analytical services provided by multiple organizations

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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 article 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.