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

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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
  • View slide
  • Aim
    Identify most appropriate interpolation method.
    View slide
  • 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