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Use of Kriging Estimation to Enhance the Integrity of Geospatial Climate Data for Infrastructure Management

Roadway pavements, one of the largest transportation infrastructure asset classes in terms of total value, deteriorate over time because of load (traffic) and nonload (climate) factors. Research studies over the years have shown that the nonload share of pavement damage can be as high as 60%. Historically, deterioration modeling has used coarse climate data extracted from regional or national climate maps because of insufficient local data or lack of efficient processes to refine the data. Many national and state databases contain significantly coarse climate data, and when such data are used in deterioration and cost models, the potential exists for significant misspecification. To address the problem, this paper implements kriging estimation, a geostatistical method that uses the spatial distance and autocorrelation of data collection sites to impute unobserved data values within a random field. Kriging estimation can produce gradient maps of the geospatial variable as well as point predictions of the variable at locations along a linear path such as a roadway centerline. This paper presents a case study of I-65 in Indiana, which used data from 59 statewide weather stations of the National Oceanic and Atmospheric Administration. The use of kriging estimation yielded a continuous prediction curve along the roadway centerline, which was an improvement over the discrete and coarse steplike nature of traditionally reported climate data.