Abstract
In the paper an example for the application of kriging methods to estimate damage to buildings in crisis scenarios is introduced. Furthermore, the Java implementations for Ordinary and Universal Kriging on mobile GIS are presented. As variogram models an exponential, a Gaussian and a spherical variogram are tested in detail. Different test constellations are introduced with various information densities. As test data set, public data from the analysis of the 2010 Haiti earthquake by satellite images are pre-processed and visualized in a Geographic Information System. As buildings, topography and other external influences cannot be seen as being constant for the whole area under investigation, semi variograms are calculated by consulting neighboured classified buildings using the so called moving window method. The evaluation of the methods shows that the underlying variogram model is the determining factor for the quality of the interpolation rather than the choice of the kriging method or increasing the information density of a random sample. The implementation is completely realized with the programming language Java. Thereafter, the implemented software component is integrated into GeoTech Mobile, a mobile GIS Android application based on the processing of standardized spatial data representations defined by the Open Geospatial Consortium (OGC). As a result the implemented methods can be used on mobile devices, i.e. they may be transferred to other application fields. That is why we finally point out further research with new applications in the Dubai region.
Highlights
In the event of crisis scenarios – we think especially of natural hazards – it is crucial to organize the rescue measures swiftly
Due to the fact that our world gets more and more linked, the damages after a natural hazard are recorded almost instantaneous be it by Volunteered Geographic Information (VGI) (Goodchild, 2007; Goodchild and Glennon, 2010), the direct analysis of satellite images or social networks
As the analysis of the data captured by the support points delivers only an empirical variogram – which is no function, but a set of points serving as support points – a theoretical approximation to get a continuous function has to be found
Summary
In the event of crisis scenarios – we think especially of natural hazards – it is crucial to organize the rescue measures swiftly. The earth quake of Haiti in 2010 provides a convincing example for this statement, because only a few days after the catastrophe the damages were registered in the Open Street Map (OSM) data base This event delivered the initial idea to develop a tool to provide information about the condition of the building stock for rescue teams. Very soon we decided to refer to the Kriging method (Krige, 1951) as an interpolation procedure to get a continuous – if necessary – representation of the damages Continuous in this context means that an estimation of the damages for a building at any arbitrary point can be obtained. As there are various Kriging methods in use, we decided to realize two of them: Ordinary Kriging Universal Kriging The two approaches deliver a best unbiased prediction of the intermediate values The latter considers an additional trend model and is more sophisticated.
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