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

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...

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

    • Statistical Evaluation of Spatial Interpolation Methods for Small-Sampled Region.A Case Study of Temperature Change Phenomenon in Bangladesh
      Presented by: Avit Kumar Bhowmik
    • Outline
    • Aim
      Identify most appropriate interpolation method.
    • Study Area - Bangladesh
      Total Area : 1,47,570 sq.km.
      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.
      Small Sample Size – 34 Meteorological Stations.
    • Objectives
      Describe overall and station specific Average, Maximum and Minimum temperature trend.
      Interpolate trend values obtained from trend analysis using Spline, IDW and Ordinary Kriging.
      Evaluate interpolation results using Univariate and Willmott Statistical method.thus identifying the most appropriate interpolation method.
    • Trend Analysis
      y= a + bx
      Trend Value,
      Goodness to fit or
      Co-efficient of Significance,
    • Trend Analysis - Results
      Maximum Temperature
      Average Temperature
      Minimum Temperature
    • Trend Analysis - Results
    • Variograms
      Lag Number = 10
      Lag size = 3
      Average Temperature
      Range = 8
      Maximum Temperature
      Range = 7
      Minimum Temperature
      Range = 3
    • Interpolation-Average Temperature Change
    • Interpolation-Maximum Temperature Change
    • Interpolation-Minimum Temperature Change
    • Univariate Statistical Analysis
      Mean Bias Error (MBE)
      Standard Deviation of Observed (SDo)
      Standard Deviation of Estimated (SDe)
    • Univariate Statistical Analysis - Results
    • Univariate Statistical Analysis - Results
    • Univariate Statistical Analysis - Results
    • Evaluation of Univariate Statistical Analysis
      Estimated Temperature Change
      Observed Temperature Change
      Average Temperature
    • Evaluation of Univariate Statistical Analysis
      Estimated Temperature Change
      Observed Temperature Change
      Maximum Temperature
    • Evaluation of Univariate Statistical Analysis
      Estimated Temperature Change
      Observed Temperature Change
      Minimum Temperature
    • Willmott (1984) Statistical Analysis
    • Willmott (1984) Statistical Analysis - Results
      Average Temperature Change
    • Willmott (1984) Statistical Analysis - Results
      Maximum Temperature Change
    • Willmott (1984) Statistical Analysis - Results
      Minimum Temperature Change
    • Results
    • Major Findings
      Not only Mean Bias Error, but Root Mean Square Error has significant Influence in determining the best Spatial Interpolation Method.
      The best approach is to look for Error in the Errors.
    • Discussion
      Standard Errors
      Measured Values
    • Thanks for your Attention
      Questions or Comments