By Martin Breunig, Edgar Butwilowski, Paul Vincent Kuper (auth.), Sabine Timpf, Patrick Laube (eds.)
This quantity relies at the reviewed and edited complaints of the foreign Symposium on Spatial facts dealing with 2012, held in Bonn. The fifteenth SDH introduced jointly students and execs from the overseas GIScience neighborhood to provide the most recent learn achievements and to percentage reports in Geospatial dynamics, geosimulation and exploratory visualization.
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Extra resources for Advances in Spatial Data Handling: Geospatial Dynamics, Geosimulation and Exploratory Visualization
1 Curvature and Local Difference Error Visualization The overall smoothness of a DEM can be computed by finding the total squared curvature, Csq (Briggs 1974): Csq = (u i+1, j + u i−1, j + u i, j+1 + u i, j−1 − 4u i, j )2 (1) The total squared curvature may be biased if there are large problem areas in a DEM. To mitigate this, an indication of local smoothness can be found by averaging the local, or absolute, curvature which is found at a point i, j: Cabs = |(u i+1, j + u i−1, j + u i, j+1 + u i, j−1 − 4u i, j )| (2) The value of Cabs is the curvature at a specific point.
7 INTERCON showing far less curvature than TOPOGRID Fig. 8 DEMview displaying elevation differences between zoomed SW corner of the TOPOGRID DEM and underlying contours (shown in gray). Colors range from yellow (small difference) to red (large difference) that was a result of the TOPOGRID interpolation; furthermore, the interpolation artifacts along the contours themselves are more easily visible. Another functional aspect of DEMView is the ability to layer two DEMs on top of one another and then compute and display the local elevation differences and local curvature differences.
13 Same comparison as in Fig. 12, but with glyphs turned on. The brightness of the colors indicate the magnitude of the elevation difference Fig. 14 Comparison of two DEMs of Mt. Washington with magnifier turned on; red indicates areas with a poor match between the two In the future, additional visualizations of spatial statistics will be investigated. The magnifier is of special interest; current and new uncertainty visualizations could be rendered through the magnifier window, similar to Magic Lenses (Bier et al.
Advances in Spatial Data Handling: Geospatial Dynamics, Geosimulation and Exploratory Visualization by Martin Breunig, Edgar Butwilowski, Paul Vincent Kuper (auth.), Sabine Timpf, Patrick Laube (eds.)
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