Comparing predicted with observed geologic data is a central element of many aspects of research in the geosciences, e.g., comparing numerical ice sheet models with geomorphic data to test ice sheet model parameters and accuracy. However, the ability to verify predictions using empirical data has Blouson Belstaff Robert Downey been limited by the lack of objective techniques that provide systematic comparison and statistical assessment of the goodness of correspondence between predictions Belstaff France of spatial and temporal patterns of geologic phenomena and the field evidence. Much of this problem arises from the inability to quantify the level of agreement between straight or curvilinear features, such as between the modeled extent of some geologic phenomenon and the field evidence for the extent of the phenomenon. Automated Proximity and Conformity Analysis (APCA) addresses this challenge using a system of Geographic Information System-based buffering that determines the general proximity and parallel conformity between linear features. APCA results indicate which modeled output fits empirical data, based on the distance and angle between features. As a result, various model outputs can be sorted according to overall level of agreement by comparison with one or multiple features from field evidence, based on proximity and conformity values. In an example application drawn from glacial geomorphology, APCA is integrated into an overall model verification process that includes matching modeled ice sheets to known marginal positions and ice flow directions, among Belstaff Roadmaster other parameters. APCA is not limited to ice sheet or glacier models, but can be applied to many geoscience areas where the extent or geometry of modeled results need to be compared against field observations, such as debris flows, tsunami run-out, lava flows, or flood extents.