Challenges in data integration and interoperability in geovisual analytics

Abstract

Geographic information technologies are evolving from stand-alone systems to a distributed model of independent web services. In parallel, voluminous geographic data are being collected with modern data acquisition techniques such as remote sensing and personal navigation devices. There is an urgent need for effective and efficient methods to integrate and explore relationships between remote sensing and trajectory datasets. When it comes to integration, one would commonly rely on a conventional chain of GIS operations to match trajectory locations to grid values: download grid data, georeference, match each trajectory record to a corresponding image cell, perform overlay, extract cell values for a given location and time and compose values into a resulting table. If one has to deal with large and dynamic spatio-temporal data sets, this approach is clearly unmanageable. We propose an alternative approach: a four-layered system architecture that utilises web services for the integration of trajectory and remote sensing data. We demonstrate how this integration service can be embedded into distributed components for manipulation, analysis and visualisation of geospatial trajectories, using Antarctic iceberg trajectories and wind data as a case study. The prototype can be accessed on the web. Future research will include optimisation and integration of more variables extracted from grid data sets, but the main focus will be on extension of the analytical component by implementing more mechanisms to discover patterns in the integrated data set, and on better visualisation.