This document discusses using geostatistical analysis tools to perform spatial interpolation of temperature and solar radiation data from multiple locations. It compares different interpolation methods, including inverse distance weighted (IDW), ordinary kriging with the default settings and with trend removed, and ordinary cokriging with trend removed. Cross-validation results showed that ordinary cokriging with trend removed produced the lowest root mean square error and best fit the data.

1. Application of Geostatistical
Analyst in Spatial Interpolation
Fei Yan
Center for Research in Water Resources
Project for GIS in Water Resources

2. Outline
 Introduction
 Data source
 Spatial interpolation
 Inverse Distance Weighted
 Ordinary kriging (default setting)
 Ordinary kriging (trend removed)
 Ordinary cokriging (trend removed)
 Automating the use of Geostatistical
tools

3. Introduction
 Point measurement ->Spatial
distribution ?
 Spatial interpolation
 Deterministic (e.g. Inverse Distance
Weighted)
 Statistical (Kriging)
 Which is better ?

4. Data source
 Average temperature
and solar radiation on
01/01/2000 from
Daymet

5. Inverse Distance Weighted
 Closer points, greater weight

6. Ordinary Kriging

7. Ordinary kriging (trend removed)

8. Trend Removed

9. Ordinary cokriging (trend removed)
330 335 340 345 350
17
17.5
18
18.5
19
19.5
20
Solar Radiation
Temprature
correlation coefficient R=0.728

10. Model Comparison (Cross Validation)
RMSE
0.2524
0.1871 0.181 0.1656
0
0.05
0.1
0.15
0.2
0.25
0.3
IDW Ordinary Kriging
(default setting)
Ordinary Kriging
(trend removed)
Ordinary Cokriging
(trend removed)

11. Where? Highest
Temperature
Where? Greatest
Uncertainty

12. ModelBuilder