Kriging

{
  "id": "MLC-T-ENV-015200",
  "term": "Kriging",
  "field": "Environment",
  "definition": "1. A weighted, moving-average estimation technique based on geostatistics that uses the spatial correlation of point measurements to estimate values at adjacent, unmeasured points (Hunsaker and Carpenter 1990, xxiii).\n\n2. A sophisticated technique for filling in missing data values, kriging is named after a South African engineer, D.G. Krige, who first developed the method. The kriging routine preserves known data values, estimates missing data values, and also \"estimates the variance at every missing data location. After kriging, the filled matrix contains the best possible estimate of the missing data values, in the sense that the variance has been minimized\" (Fortner 1992; 215, 167-176).\n\n3. A statistical interpolation method that selects the best linear unbiased estimate of the parameter in question. Often used as a geostatistical method of spatial statistics for predicting values at unobserved locations based on data from the surrounding area. Information on fate and transport of chemicals within the area lacking data can be incorporated into kriged estimates.\n\n4. An interpolation technique in which the surrounding measured values are weighted to derive a predicted value for an unmeasured location. Weights are based on the distance between the measured points, the prediction locations, and the overall spatial arrangement (or autocorrelation) among measured points. The resultant interpolated points do not necessarily have to pass exactly through the input points.\n\n5. Weighted, moving-average estimation technique based on geostatistics that uses the spatial correlation of point measurements to estimate values at adjacent, unmeasured points (Hunsaker and Carpenter 1990, xxiii).\n\n6. Sophisticated technique for filling in missing data values, kriging is named after a South African engineer, D.G. Krige, who first developed the method. The kriging routine preserves known data values, estimates missing data values, and also \"estimates the variance at every missing data location. After kriging, the filled matrix contains the best possible estimate of the missing data values, in the sense that the variance has been minimized\" (Fortner 1992; 215, 167-176).",
  "subject": "Science and research",
  "references": [
    "Environmental Modeling and Assessment Program (EMAP) Master Glossary",
    "EPA Definitions of Terms Relevant to PRA and References for Further Reading at http://www.epa.gov/oswer/riskassessment/rags3adt/pdf/appendixe.pdf",
    "EPA Guidelines to Assessing Regional Vulnerabilities at http://www.epa.gov/reva/docs/guidelines_reva_20080627.pdf",
    "EMAP Master Glossary"
  ],
  "url": "https://mlchart.com/terminology/environment/kriging/"
}