A Comparison of Spatial Prediction Techniques Using Both Hard and Soft Data

• Activity Number: 184
• Type: Contributed
• Date/Time: Monday, August 1, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Megan Liedtke Tesar*+ and David Marx and Steve Kachman
• Companies: University of Nebraska at Lincoln and University of Nebraska at Lincoln and University of Nebraska at Lincoln
• Keywords: spatial ; kriging ; soft data ; prediction ; natural variables

Abstract

There is often a large amount of variability surrounding the measurements of environmental variables. It is therefore important to develop tools which account for uncertain measurements (soft data) in addition to measurements with little or no variability (hard data). Traditional methods, such as ordinary kriging, however, do not account for an attribute with more than one level of uncertainty. Thus, a new method, called weighted kriging is proposed. This method was implemented and tested against two alternative kriging procedures. The first alternative used only the hard data and the second used both the hard and soft data but treated both as hard. Simulated case studies showed that weighted kriging consistently results in more desirable model fitting statistics.