Starting from environmental and metereological data, I have defined an annual count variable (number of favourable events, raster type object). The purpose of the analysis is to predict the next year favourable events raster, given the time series (last 15 years). Working at the pixel level, it would be possible to make a Poisson regression, but treating pixels independently, would loose spatial effects... Which is, in your opinion, the best approach? Is there a spatio-temporal model for this kind of data that could usefully combine spatial effect with time series analysis? Thanks for any suggestions
Questions about spatio-temporal modelling
2 messages · luca candeloro, Thierry Onkelinx
Dear Luca, Have a look at Blangiarde & Cameletti (2015) Spatial and Spatio-temporal Bayesian Models with R - INLA ISBN: 978-1-118-32655-8 They describe how you can tackle this problem with mixed models with correlated random effects. Best regards, ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey 2016-04-17 14:39 GMT+02:00 Luca Candeloro <luca.candeloro at gmail.com>:
Starting from environmental and metereological data, I have defined an
annual count variable (number of favourable events, raster type object).
The purpose of the analysis is to predict the next year favourable events
raster, given the time series (last 15 years).
Working at the pixel level, it would be possible to make a Poisson
regression, but treating pixels independently, would loose spatial
effects...
Which is, in your opinion, the best approach?
Is there a spatio-temporal model for this kind of data that could usefully
combine spatial effect with time series analysis?
Thanks for any suggestions
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