A revised automated proximity and conformity analysis method to compare predicted and observed spatial boundaries of geologic phenomena

Quantitative assessment of the level of agreement between model-predicted and field-observed geologic data is crucial to calibrate and validate numerical landscape models. Application of Geographic Information Systems (GIS) provides an opportunity to integrate model and field data and quantify their levels of correspondence. Napieralski et al. [Comparing predicted and observed spatial boundaries of geologic phenomena: Automated Proximity and Conformity Analysis (APCA) applied to ice sheet reconstructions. Computers and Geosciences 32, 124–134] introduced an Automated Proximity and Conformity Analysis (APCA) method to compare model-predicted and field-observed spatial boundaries and used it to quantify the level of correspondence between predicted ice margins from ice sheet models and field observations from end moraines. However, as originally formulated, APCA involves a relatively large amount of user intervention during the analysis and results in an index to quantify the level of correspondence that lacks direct statistical meaning. Here, we propose a revised APCA approach and a more automated and statistically robust way to quantify the level of correspondence between model predictions and field observations. Specifically, the mean and standard deviation of distances between model and field boundaries are used to quantify proximity and conformity, respectively. An illustration of the revised method comparing modeled ice margins of the Fennoscandian Ice Sheet with observed end moraines of the Last Glacial Maximum shows that this approach provides a more automated and statistically robust means to quantify correspondence than the original APCA. The revised approach can be adopted for a wide range of geoscience issues where comparisons of model-predicted and field-observed spatial boundaries are useful, including mass movement and flood extents.