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Proceedings Paper

Predicting spatio-temporal failure in large scale observational and micro scale experimental systems
Author(s): Alejandro de las Heras; Yong Hu
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Paper Abstract

Forecasting has become an essential part of modern thought, but the practical limitations still are manifold. We addressed future rates of change by comparing models that take into account time, and models that focus more on space. Cox regression confirmed that linear change can be safely assumed in the short-term. Spatially explicit Poisson regression, provided a ceiling value for the number of deforestation spots. With several observed and estimated rates, it was decided to forecast using the more robust assumptions. A Markov-chain cellular automaton thus projected 5-year deforestation in the Amazonian Arc of Deforestation, showing that even a stable rate of change would largely deplete the forest area. More generally, resolution and implementation of the existing models could explain many of the modelling difficulties still affecting forecasting.

Paper Details

Date Published: 28 October 2006
PDF: 8 pages
Proc. SPIE 6420, Geoinformatics 2006: Geospatial Information Science, 642020 (28 October 2006); doi: 10.1117/12.712994
Show Author Affiliations
Alejandro de las Heras, Univ. of East Anglia, Norwich (United Kingdom)
Yong Hu, Univ. of East Anglia, Norwich (United Kingdom)
Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 6420:
Geoinformatics 2006: Geospatial Information Science
Jianya Gong; Jingxiong Zhang, Editor(s)

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