Implements models of uncertainty into the interpolation functions

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Description

This package is the main result of diploma thesis. It can provide a basic (random) data, grids, 6 models of uncertainty, 3 automatic interpolations (idw, spline, kriging), variogram and basic data visualization. All together it could make a basic tools to solve the problem of uncertainty in this world.

Details

Package: uncertaintyInterpolation2
Type: Package
Version: 2.0
Date: 2015-11-10
License: R (>= 3.0.0)
LazyLoad: yes

Author(s)

Tomas Burian <Buri777@seznam.cz>

Maintainer: Tomas Burian <Buri777@seznam.cz>

References

BURIAN, T. EXTENSION OF INTERPOLATION TOOLS IN R PROJECT BY MODELS OF UNCERTAINTY. Univerzita Palackeho v Olomouci, 2013.

CAHA, J. Fuzzy Surface Analyses: First Experiments. Olomouc : Terra Notitia, Palacky University for Department of Geoinformatics, 1st ed. edition, 2015. ISBN 978-80-244-4055-2.

CAHA, J., MAREK, L., DVORSKY, J. (in print) Predicting PM10 concentrations using fuzzy kriging. In Hybrid Artificial Intelligent Systems. Springer, 2015.

FISHER, P. F., TATE, N. J. Causes and consequences of error in digital elevation models. Progress in Physical Geography, 30, 4, p. 467 to 489, August 2006. ISSN 03091333.

CHAMBERS, J. M. Programming with Data A Guide to the S Language. Springer- Verlag, 1998.

HEUVELINK, G. B. M. Analysing Uncertainty Propagation in GIS: Why is it not that Simple? In FOODY, G. M., ATKINSON, P. M. (Ed.) Uncertainty in remote sensing and GIS, s. 307. Chichester : Wiley, 2002. ISBN 0470844086.

HIEMSTRA, P., PEBESMA, E., TWENHOFEL, C., HEUVELINK, G. Realtime automatic interpolation of ambient gamma dose rates from the dutch radioactivity monitoring network. Computers & Geosciences, 2008. DOI: http://dx.doi.org/10.1016/j.cageo.2008.10.011.

LODWICK, W., ANILE, M., SPINELLA, S. Introduction. In LODWICK, W. (Ed.) Fuzzy surfaces in GIS and geographical analysis : theory, analytical methods, algorithms, and applications, p. 1 to 46. Boca Raton : CRC Press, 2008. ISBN 9780849363955.

MATLOFF, N. THE ART OF R PROGRAMMING A Tour of Statistical Software Design. William Pollock, 2011.

OKSANEN, J. Digital Elevation Model Error in Terrain Analysis. PhD thesis, University of Helsinki, 2006.

R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2013. Dostupne z: http: //www.R-project.org.

VIERTL, R. Statistical methods for fuzzy data. Chichester, West Sussex : Wiley, 2011. ISBN 9780470699454.

WAELDER, O. An application of the fuzzy theory in surface interpolation and surface deformation analysis. Fuzzy Sets and Systems, 158, 14, p. 1535 to 1545, July 2007.

See Also

TUTORIAL http://www.r-project.org

Examples

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# dataset meuse import
data(meuse)

# S4 class object Points input data definition
points = Points(x = meuse$x, y = meuse$y, z = meuse$elev)

# building uncertainty model (based on uncertaintyError function)
uncertaintyModel = uncertaintyError(points)

# generate grid for IDW interpolation
gridDef.spat = Grid.def(points, TRUE) # SpatialPixels

# IDW interpolation process
IDW = idwUncertain(uncertaintyModel, gridDef.spat)

# visualization of results
Plot(IDW)