The Challenge of Large-Scale Data and Geovisualization

Nowadays, geospatial data are collected in unprecedented speed, and data volume also increases exponentially. We get image data with fine spectral and spatial resolution from remote sensing technologies, volunteer geospatial information from GeoWeb and mobile technologies, and historical records from different geospatial databases. Due to those factors, geospatial research is now facing of large-scale data, and how to extract information from the large-scale data for knowledge discovery becomes an important challenge for Geovisualization, as MacEarchren et al. point out in 2000.

Previously, Geovisualization has a tight relationship with Cartography, since it is often utilized to visualize geospatial data in 2D format and provide similar functionalities as maps. But the advancement of technologies, especially Web2.0, has re-formatted Geovisualization as a portal for geospatial information sharing and exchange. With the increasing large-scale data (here large scale means both large volume and high dimension), data mining and pattern recognition are necessary techniques to extract useful information for users. As Web 2.0 brings user-centric computation, how to update knowledge and visualize it with new data turns out to be an interesting topic.

The challenges are concluded as representation, visualization-computation integration, interfaces, and cognitive issues in the paper of MacEarchren et al.. Large-scale data is a common factor in the four types of challenges. Meanwhile, Web 3.0 is approaching, which transforms Internet into a large data source. As computing platform becomes diverse (cloud computing, mobile equipment, and so on), knowledge discovery process is also extended to distributed computing environment. Thus, Geovisualization should also keep pace with this change.



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