Diansheng Guo

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  • Diansheng Guo
  • IEEE Transactions on Visualization and Computer…
  • 2009
Spatial interactions (or flows), such as population migration and disease spread, naturally form a weighted location-to-location network (graph). Such geographically embedded networks (graphs) are usually very large. For example, the county-to-county migration data in the U.S. has thousands of counties and about a million migration paths. Moreover, many(More)
Received: KK Revised: KK Accepted: KK Abstract Unknown (and unexpected) multivariate patterns lurking in high-dimensional datasets are often very hard to find. This paper describes a human-centered exploration environment, which incorporates a coordinated suite of computational and visualization methods to explore high-dimensional data for uncovering(More)
Voluminous geographic data have been, and continue to be, collected with modern data acquisition techniques such as global positioning systems (GPS), high-resolution remote sensing, location-aware services and surveys, and internet-based volunteered geographic information. There is an urgent need for effective and efficient methods to extract unknown and(More)
The research reported here integrates computational, visual and cartographic methods to develop a geovisual analytic approach for exploring and understanding spatio-temporal and multivariate patterns. The developed methodology and tools can help analysts investigate complex patterns across multivariate, spatial and temporal dimensions via clustering,(More)
The discovery, interpretation, and presentation of multivariate spatial patterns are important for scientific understanding of complex geographic problems. This research integrates computational, visual, and cartographic methods together to detect and visualize multivariate spatial patterns. The integrated approach is able to: (1) perform multivariate(More)
Geographic information (e.g., locations, networks, and nearest neighbors) are unique and different from other aspatial attributes (e.g., population, sales, or income). It is a challenging problem in spatial data mining and visualization to take into account both the geographic information and multiple aspatial variables in the detection of patterns. To(More)
  • Diansheng Guo
  • International Journal of Geographical Information…
  • 2010
The relationship between two or more variables may change over the geographic space. The change can be in parameter values (e.g., regression coefficients) or even in relation forms (e.g., linear, quadratic, or exponential). Existing local spatial analysis methods often assume a relationship form (e.g., a linear regression model) for all regions and focus(More)
The unprecedented large size and high dimensionality of existing geographic datasets make the complex patterns that potentially lurk in the data hard to ®nd. Clustering is one of the most important techniques for geographic knowledge discovery. However, existing clustering methods have two severe drawbacks for this purpose. First, spatial clustering methods(More)