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# Space-time data workshop at IfGI

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Space-time regression-kriging using time series of predictors

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### Space-time data workshop at IfGI

1. 1. Space-time regression-krigingusing time series of imagesTomislav HenglISRIC World Soil Information, Wageningen University R workshop, Mar 21th 2011
2. 2. TAAC paper R workshop, Mar 21th 2011
3. 3. Space-time dataUniversal kriging model for spatio-temporal data (Heuvelink Grith, 2010): T (s, t) = m(s, t) + ε(s, t) (1)where m(s, t) is the deterministic part of the variation (i.e. a linearfunction of the auxiliary variables), ε(s, t) is the residual for every(s, t). R workshop, Mar 21th 2011
4. 4. Space-time cube R workshop, Mar 21th 2011
5. 5. Space-time semivariance γ(si , ti ; sj , tj ) = 0.5 · E ( (si , ti ) − (sj , tj ))2 (2) R workshop, Mar 21th 2011
6. 6. ResidualsResiduals ( ) consist of three stationary and independentcomponents (Heuvelink Grith, 2010): (s, t) = s (s) + t (t) + s,t (s, t) (3)where s (s) is a purely spatial process (with constant realizationsover time), t (t) is a purely temporal process, and s,t (s, t) is aspace-time process for which distance in space is made comparableto distance in time by introducing a space-time anisotropy ratio. R workshop, Mar 21th 2011
7. 7. Zonal anisotropiesThe covariance structure can be represented by (Snepvangers et al.,2003): C(h, u) = Cs (h) + Ct (u) + Cs,t ( h2 + (α + u)2 ) (4)where C(h, u) is the covariance at distance h in space, andtime-distance u, Cs (h) + Ct (u) allow the presence of zonalanisotropies (dierent variogram sills in dierent directions), andCs,t ( h2 + (α + u)2 ) allows the presence of geometric anisotropyrepresented with the ratio α. R workshop, Mar 21th 2011
8. 8. The data set R workshop, Mar 21th 2011
9. 9. In space-time cube cdays Y X R workshop, Mar 21th 2011
10. 10. Variograms (separately) 365 days 159 stations 25 50 20 40Semivariance Semivariance 15 30 10 20 10 5 0 0 0 50000 100000 150000 200000 0 10 20 30 40 50 60 Distance (m) Distance (in days) R workshop, Mar 21th 2011
11. 11. Variograms (zonal anisotropy) ● ● ● 10 10 ● ● ● ● ● ● ● 8 8 ● ● ● semivariance semivariance ● 6 6 ● 4 ● 4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2 ● 2 50000 100000 150000 200000 5 10 15 Distance (m) Distance (in days)Marginal experimental variograms for residuals and tted models: (left) space-domain only, (right) time-domain only. R workshop, Mar 21th 2011
12. 12. Final results R workshop, Mar 21th 2011
13. 13. Some experiences By adding the time component we are better o. R workshop, Mar 21th 2011
14. 14. Some experiences By adding the time component we are better o. Automation of space-time regression-kriging (overlay, regression modeling, variogram tting, predictions, visualization in Google Earth) is anticipated. R workshop, Mar 21th 2011
15. 15. Some experiences By adding the time component we are better o. Automation of space-time regression-kriging (overlay, regression modeling, variogram tting, predictions, visualization in Google Earth) is anticipated. Fitting and visualization of space-time variograms is a bottle-neck! R workshop, Mar 21th 2011
16. 16. Some experiences By adding the time component we are better o. Automation of space-time regression-kriging (overlay, regression modeling, variogram tting, predictions, visualization in Google Earth) is anticipated. Fitting and visualization of space-time variograms is a bottle-neck! Predictions need to be visualized as animations. R workshop, Mar 21th 2011
17. 17. Some experiences By adding the time component we are better o. Automation of space-time regression-kriging (overlay, regression modeling, variogram tting, predictions, visualization in Google Earth) is anticipated. Fitting and visualization of space-time variograms is a bottle-neck! Predictions need to be visualized as animations. We have ignored the one-way auto-correlation (time works only one way)? R workshop, Mar 21th 2011
18. 18. Universal space-time referenceEach observation should have by default: Longitude and latitude (WGS84) (or projected X, Y coordinates + proj4 string); Begin / end of the time interval in UTC (GMT) system; Support size (in square meters); Uncertainty or measurement error; R workshop, Mar 21th 2011
19. 19. Space-time algebra re-visited Should we (re)dene and (re)implement space-time (4D) algebra? R workshop, Mar 21th 2011
20. 20. What does this mean? Distances always on a sphere (sphere geometry); R workshop, Mar 21th 2011
21. 21. What does this mean? Distances always on a sphere (sphere geometry); Always use information about uncertainty (weighted regression); R workshop, Mar 21th 2011
22. 22. What does this mean? Distances always on a sphere (sphere geometry); Always use information about uncertainty (weighted regression); Always use information about the support size (nugget estimation, cross-validation); R workshop, Mar 21th 2011
23. 23. What does this mean? Distances always on a sphere (sphere geometry); Always use information about uncertainty (weighted regression); Always use information about the support size (nugget estimation, cross-validation); Re-implement also any raster processing (geomorphometry, resampling, ltering etc); R workshop, Mar 21th 2011
24. 24. What does this mean? Distances always on a sphere (sphere geometry); Always use information about uncertainty (weighted regression); Always use information about the support size (nugget estimation, cross-validation); Re-implement also any raster processing (geomorphometry, resampling, ltering etc); Use Google Earth to visualize any type of geographic data; R workshop, Mar 21th 2011