rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks

被引:40
作者
Skoien, J. O.
Bloeschl, G. [1 ]
Laaha, G. [2 ]
Pebesma, E. [3 ]
Parajka, J. [1 ]
Viglione, A. [1 ]
机构
[1] Vienna Univ Technol, Inst Engn Hydrol & Water Resources Management, Vienna, Austria
[2] Univ Nat Resources & Life Sci, BOKU Vienna, Inst Appl Stat & Comp, Vienna, Austria
[3] Univ Munster, Inst Geoinformat, Munster, Germany
关键词
Geostatistics; Support problem; Hydrology; River networks; Interpolation; RUNOFF; GEOSTATISTICS;
D O I
10.1016/j.cageo.2014.02.009
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Geostatistical methods have been applied only to a limited extent for spatial interpolation in applications where the observations have an irregular support, such as runoff characteristics along a river network and population health data. Several studies have shown the potential of such methods, but these developments have so far not led to easily accessible, versatile, easy to apply and open source software. Based on the top-kriging approach suggested by Skoien et al. (2006), we will here present the package rtop, which has been implemented in the statistical environment R (R Core Team, 2013). Taking advantage of the existing methods in R for analysis of spatial objects (Bivand et al., 2013), and the extensive possibilities for visualizing the results, rtop makes it easy to apply geostatistical interpolation methods when observations have a non-point spatial support. The package also offers integration with the intamap package for automatic interpolation and the possibility to run rtop through a Web Service. (c) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:180 / 190
页数:11
相关论文
共 38 条
[1]  
[Anonymous], 2013, Runoff prediction in ungauged basins: synthesis across processes, places and scales
[2]  
[Anonymous], 1951, Bulletin Calcutta Math Soc.
[3]  
[Anonymous], 1991, STAT SPATIAL DATA
[4]  
[Anonymous], 1963, OBJECTIVE ANAL METEO
[5]  
[Anonymous], 1989, Applied Geostatistics
[6]   Projected sequential Gaussian processes: A C plus plus tool for interpolation of large datasets with heterogeneous noise [J].
Barillec, Remi ;
Ingram, Ben ;
Cornford, Dan ;
Csato, Lehel .
COMPUTERS & GEOSCIENCES, 2011, 37 (03) :295-309
[7]  
Bivand R.S., 2013, APPL SPATIAL DATA AN, V10
[8]  
Chiles J, 1999, GEOSTATISTICS MODELL
[9]   FITTING VARIOGRAM MODELS BY WEIGHTED LEAST-SQUARES [J].
CRESSIE, N .
JOURNAL OF THE INTERNATIONAL ASSOCIATION FOR MATHEMATICAL GEOLOGY, 1985, 17 (05) :563-586
[10]  
de Marsily G., 1986, QUANTITATIVE HYDROGE