ANJOS, L.

HEUVELINK, G.

SAMUEL-ROSA, A.

VASQUES, G.

English

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Geophysical Research AbstractsVol. 17, EGU2015-7780, 2015EGU General Assembly 2015© Author(s) 2015. CC Attribution 3.0 License.spsann - optimization of sample patterns using spatial simulated annealingAlessandro Samuel-Rosa (1), Gerard Heuvelink (), Gustavo Vasques (), and Lúcia Anjos ()(1) Federal Rural University of Rio de Janeiro, Institude of Agronomy, Graduate Course in Agronomy-Soil Science,Seropédica, Brazil (alessandrosamuelrosa@gmail.com), (2) Soil Geography and Landscape Group, Wageningen University,and ISRIC-World Soil Information, Wageningen, the Netherlands, (3) National Center for Soil Research, BrazilianAgricultural Research Corporation, Rio de Janeiro, Brazil, (4) Department of Soil Science, Federal Rural University of Rio deJaneiro, Seropédica, BrazilThere are many algorithms and computer programs to optimize sample patterns, some private and others publiclyavailable. A few have only been presented in scientific articles and text books. This dispersion and somewhat pooravailability is holds back to their wider adoption and further development.We introduce spsann, a new R-package for the optimization of sample patterns using spatial simulated annealing.R is the most popular environment for data processing and analysis. Spatial simulated annealing is a well knownmethod with widespread use to solve optimization problems in the soil and geo-sciences. This is mainly due to itsrobustness against local optima and easiness of implementation.spsann offers many optimizing criteria for sampling for variogram estimation (number of points or point-pairsper lag distance class - PPL), trend estimation (association/correlation and marginal distribution of the covariates- ACDC), and spatial interpolation (mean squared shortest distance - MSSD). spsann also includes the mean ormaximum universal kriging variance (MUKV) as an optimizing criterion, which is used when the model of spatialvariation is known. PPL, ACDC and MSSD were combined (PAN) for sampling when we are ignorant about themodel of spatial variation. spsann solves this multi-objective optimization problem scaling the objective functionvalues using their maximum absolute value or the mean value computed over 1000 random samples. Scaled valuesare aggregated using the weighted sum method.A graphical display allows to follow how the sample pattern is being perturbed during the optimization, as wellas the evolution of its energy state. It is possible to start perturbing many points and exponentially reduce thenumber of perturbed points. The maximum perturbation distance reduces linearly with the number of iterations.The acceptance probability also reduces exponentially with the number of iterations.R is memory hungry and spatial simulated annealing is a computationally intensive method. As such, many strate-gies were used to reduce the computation time and memory usage: a) bottlenecks were implemented in C++, b) afinite set of candidate locations is used for perturbing the sample points, and c) data matrices are computed onlyonce and then updated at each iteration instead of being recomputed.spsann is available at GitHub under a licence GLP Version 2.0 and will be further developed to: a) allow the use ofa cost surface, b) implement other sensitive parts of the source code in C++, c) implement other optimizing criteria,d) allow to add or delete points to/from an existing point pattern.

Spsann - optimization of sample patterns using spatial simulated annealing.

http://www.alice.cnptia.embrapa.br/bitstream/doc/1031650/1/2015100.pdf

Spsann - optimization of sample patterns using spatial simulated annealing.

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