Get Advances in Spatial Data Handling: Geospatial Dynamics, PDF

By Martin Breunig, Edgar Butwilowski, Paul Vincent Kuper (auth.), Sabine Timpf, Patrick Laube (eds.)

ISBN-10: 3642323154

ISBN-13: 9783642323157

ISBN-10: 3642323162

ISBN-13: 9783642323164

This quantity is predicated at the reviewed and edited complaints of the overseas Symposium on Spatial info dealing with 2012, held in Bonn. The fifteenth SDH introduced jointly students and execs from the foreign GIScience neighborhood to provide the newest learn achievements and to percentage reviews in Geospatial dynamics, geosimulation and exploratory visualization.

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Additional info for Advances in Spatial Data Handling: Geospatial Dynamics, Geosimulation and Exploratory Visualization

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Note GUI panel on right showing all options the results of all operations can be viewed on the surface in three dimensions. All available features are displayed on the right panel at all times (unless hidden by user); they are all available through menus as well. Options that are grayed-out require a second comparison DEM (see below) for activation. Rotation of the 3D surface is accomplished through the left mouse button and translation through the right button. Zooming can be done using the panel buttons or the scroll button on a mouse.

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. Severe curvature may indicate an error in the DEM; patterns in curvature may indicate systematic errors due to interpolation. Such curvature error can be displayed in DEMView, where the threshold is chosen by the viewer. The curvature is displayed via different hues, where the green surface indicates no error (little curvature) and progressing through yellow to orange for the highest error (extreme curvature).

We see the strength of our approach in the possibility to manually describe movement states. , first passage time (Fauchald and Tveraa 2003) and using k-means clustering (van Moorter et al. 2010). In this study, we concentrated on segmentation by spatio-temporal attributes describing movement states of an animal, given the knowledge of a domain expert. If expert knowledge is not available, for instance, methods from machine learning can be employed to complement our approach. In future work, we would like to explore other types of segmentation in movement ecology.

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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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