Spatial model error analysis using autocorrelation indices

Spatial model error analysis using autocorrelation indices No standard techniques yet exist for assessing the predictive performance of models that simulate spatially-explicit processes. Spatial simulation models generate autocorrelation patterns that are a critical aspect of model prediction. These patterns can be quantified using spatial (Moran's I ) and spatio-temporal (Griffith's STI ) autocorrelation indices. A method of error analysis for spatial simulation models is demonstrated using autocorrelation indices to distinguish among the effects of parameter uncertainty on a stochastic spatial simulation of seed dispersal. The resulting autocorrelation patterns of state variables display a range of nonlinear, counterintuitive effects. In contrast to techniques proposed for spatial model error analysis (contagion, spatial predictability, adjacency, multiple resolution procedure, fractal dimension, interface), autocorrelation indices can be used with interval-scaled data and have well-defined sampling distributions that enable significance testing. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

Spatial model error analysis using autocorrelation indices

Ecological Modelling, Volume 82 (1) – Sep 1, 1995

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Publisher
Elsevier
Copyright
Copyright © 1995 Elsevier Ltd
ISSN
0304-3800
eISSN
1872-7026
D.O.I.
10.1016/0304-3800(94)00074-R
Publisher site
See Article on Publisher Site

Abstract

No standard techniques yet exist for assessing the predictive performance of models that simulate spatially-explicit processes. Spatial simulation models generate autocorrelation patterns that are a critical aspect of model prediction. These patterns can be quantified using spatial (Moran's I ) and spatio-temporal (Griffith's STI ) autocorrelation indices. A method of error analysis for spatial simulation models is demonstrated using autocorrelation indices to distinguish among the effects of parameter uncertainty on a stochastic spatial simulation of seed dispersal. The resulting autocorrelation patterns of state variables display a range of nonlinear, counterintuitive effects. In contrast to techniques proposed for spatial model error analysis (contagion, spatial predictability, adjacency, multiple resolution procedure, fractal dimension, interface), autocorrelation indices can be used with interval-scaled data and have well-defined sampling distributions that enable significance testing.

Journal

Ecological ModellingElsevier

Published: Sep 1, 1995

References

  • Water quality modeling: a review of the analysis of uncertainty
    Beck, M.B.
  • Comparison of first-order error analysis with Monte Carlo simulation in time-dependent lake eutrophication models
    Scavia, D.; Powers, W.F.; Canale, R.P.; Moody, J.L.
  • Spatial autocorrelation in biology 1. Methodology
    Sokal, R.R.; Oden, N.L.
  • Spatial autocorrelation in biology 2. Some biological implication and four applications of evolutionary and ecological interest
    Sokal, R.R.; Oden, N.L.
  • Landscape ecology: the effect of pattern on process
    Turner, M.G.

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