Statistical agreement measures for spatial data

Thomas M. Menino Convention & Exhibition Center 

Measures of agreement have been extensively studied over the past three decades from multiple perspectives, reflecting both the objectives of specific studies and the nature of the data under analysis. For continuous data, beginning with the seminal concordance correlation coefficient introduced by Lin (1989), a wide range of extensions and modified coefficients has been proposed in the literature. Within the framework of spatial statistics, agreement measures have been further developed to assess concordance between two random fields. In this talk, we review recent methodological advances for agreement assessment in both geostatistical and lattice data settings. The proposed methodology is illustrated through two applications: forest data and the analysis of poverty rates in the Metropolitan and Valparaíso regions of Chile. These applications demonstrate the practical implementation of the approaches and highlight their main advantages and limitations.

Agreement coefficients

Spatial Processes

Lattice data

Probabilíty of agreement

Poverty rates 

Section on Statistics in Imaging