Statistical Evaluation of Spatial Interpolation Methods for Small-Sampled Region.A Case Study of Temperature Change Phenom...
Outline<br />
Aim<br />Identify most appropriate interpolation method.<br />
Study Area - Bangladesh<br />Total Area : 1,47,570 sq.km.<br />Mean annual temperature has increased during the period of ...
Objectives<br />Describe overall and station specific Average, Maximum and Minimum temperature trend.<br />Interpolate tre...
Trend Analysis<br />y= a + bx<br />Trend Value,<br />Goodness to fit or <br />Co-efficient of Significance,<br />
Trend Analysis - Results <br />Maximum Temperature<br />Average Temperature<br />Minimum Temperature<br />
Trend Analysis - Results<br />
Variograms<br /> Lag Number = 10 <br /> Lag size = 3<br />Average Temperature<br />Range = 8<br />Maximum Temperature<br /...
Interpolation-Average Temperature Change<br />
Interpolation-Maximum Temperature Change<br />
Interpolation-Minimum Temperature Change<br />
Univariate Statistical Analysis<br />Mean Bias Error (MBE)<br />Standard Deviation of Observed (SDo)<br />Standard Deviati...
Univariate Statistical Analysis - Results<br />
Univariate Statistical Analysis - Results<br />
Univariate Statistical Analysis - Results<br />
Evaluation of Univariate Statistical Analysis<br />Estimated Temperature Change<br />Observed Temperature Change<br />Aver...
Evaluation of Univariate Statistical Analysis<br />Estimated Temperature Change<br />Observed Temperature Change<br />Maxi...
Evaluation of Univariate Statistical Analysis<br />Estimated Temperature Change<br />Observed Temperature Change<br />Mini...
Willmott (1984) Statistical Analysis<br />
Willmott (1984) Statistical Analysis - Results<br />Average Temperature Change<br />
Willmott (1984) Statistical Analysis - Results<br />Maximum Temperature Change<br />
Willmott (1984) Statistical Analysis - Results<br />Minimum Temperature Change<br />
Results<br />
Major Findings<br />Not only Mean Bias Error, but Root Mean Square Error has significant Influence in determining the best...
Discussion<br />Standard Errors<br />Measured Values<br />
Thanks for your Attention<br />Questions or Comments<br />
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Statistical Evaluation of Spatial Interpolation Methods for Small-Sampled Region. A Case Study of Temperature Change Phenomenon in Bangladesh

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Statistical Evaluation of Spatial Interpolation Methods for Small-Sampled Region. A Case Study of Temperature Change Phenomenon in Bangladesh
Avit Bhowmik, Pedro Cabral - Institute of Statistics and Information Management, New University of Lisbon

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Statistical Evaluation of Spatial Interpolation Methods for Small-Sampled Region. A Case Study of Temperature Change Phenomenon in Bangladesh

  1. 1. Statistical Evaluation of Spatial Interpolation Methods for Small-Sampled Region.A Case Study of Temperature Change Phenomenon in Bangladesh <br />Presented by: Avit Kumar Bhowmik<br />
  2. 2. Outline<br />
  3. 3.
  4. 4. Aim<br />Identify most appropriate interpolation method.<br />
  5. 5. Study Area - Bangladesh<br />Total Area : 1,47,570 sq.km.<br />Mean annual temperature has increased during the period of 1895-1980 at 0.310c and the annual mean maximum temperature will increase to 0.40c and 0.730c by the year of 2050 and 2100 respectively.<br />Small Sample Size – 34 Meteorological Stations.<br />
  6. 6. Objectives<br />Describe overall and station specific Average, Maximum and Minimum temperature trend.<br />Interpolate trend values obtained from trend analysis using Spline, IDW and Ordinary Kriging.<br />Evaluate interpolation results using Univariate and Willmott Statistical method.thus identifying the most appropriate interpolation method.<br />
  7. 7.
  8. 8. Trend Analysis<br />y= a + bx<br />Trend Value,<br />Goodness to fit or <br />Co-efficient of Significance,<br />
  9. 9. Trend Analysis - Results <br />Maximum Temperature<br />Average Temperature<br />Minimum Temperature<br />
  10. 10. Trend Analysis - Results<br />
  11. 11.
  12. 12. Variograms<br /> Lag Number = 10 <br /> Lag size = 3<br />Average Temperature<br />Range = 8<br />Maximum Temperature<br />Range = 7<br />Minimum Temperature<br />Range = 3<br />
  13. 13. Interpolation-Average Temperature Change<br />
  14. 14. Interpolation-Maximum Temperature Change<br />
  15. 15. Interpolation-Minimum Temperature Change<br />
  16. 16.
  17. 17. Univariate Statistical Analysis<br />Mean Bias Error (MBE)<br />Standard Deviation of Observed (SDo)<br />Standard Deviation of Estimated (SDe)<br />
  18. 18. Univariate Statistical Analysis - Results<br />
  19. 19. Univariate Statistical Analysis - Results<br />
  20. 20. Univariate Statistical Analysis - Results<br />
  21. 21. Evaluation of Univariate Statistical Analysis<br />Estimated Temperature Change<br />Observed Temperature Change<br />Average Temperature<br />
  22. 22. Evaluation of Univariate Statistical Analysis<br />Estimated Temperature Change<br />Observed Temperature Change<br />Maximum Temperature<br />
  23. 23. Evaluation of Univariate Statistical Analysis<br />Estimated Temperature Change<br />Observed Temperature Change<br />Minimum Temperature<br />
  24. 24. Willmott (1984) Statistical Analysis<br />
  25. 25. Willmott (1984) Statistical Analysis - Results<br />Average Temperature Change<br />
  26. 26. Willmott (1984) Statistical Analysis - Results<br />Maximum Temperature Change<br />
  27. 27. Willmott (1984) Statistical Analysis - Results<br />Minimum Temperature Change<br />
  28. 28.
  29. 29. Results<br />
  30. 30. Major Findings<br />Not only Mean Bias Error, but Root Mean Square Error has significant Influence in determining the best Spatial Interpolation Method.<br />The best approach is to look for Error in the Errors.<br />
  31. 31. Discussion<br />Standard Errors<br />Measured Values<br />
  32. 32. Thanks for your Attention<br />Questions or Comments<br />

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