This study conducted linear and change-point analyses of historical trends since 1942 in the length and number of days suitable for skating on backyard rinks in the “Original Six” National Hockey League cities of Boston, Chicago, Detroit, Montreal, New York, and Toronto. Analysis is based on the relationship between ambient air temperatures and the probability of skating, using thresholds identified through the RinkWatch citizen science project. In all cities, coefficient estimates suggest the number of high-probability skating days per winter is declining, with easternmost cities displaying notable declines and growing inter-annual variability in skating days in recent decades. Linear analysis shows a statistically significant decline in Toronto, with a step-change emerging in 1980, after which there is on average one-third fewer skating days compared with preceding decades. The outdoor skating season trends towards later start dates in Boston, Montreal, New York, and Toronto. Future monitoring of outdoor rinks provides an opportunity for engaging the public in identification of winter warming trends that might otherwise be imperceptible, and for raising awareness of the impacts of climate change.

Detection of changes in spatial processes has long been of interest to quantitative geographers seeking to test models, validate theories, and anticipate change. Given the current “data-rich” environment of today, it may be time to reconsider the methodological approaches used for quantifying change in spatial processes. New tools emerging from computer vision research may hold particular potential to make significant advances in quantifying changes in spatial processes. In this article, two comparative indices from computer vision, the structural similarity (SSIM) index, and the complex wavelet structural similarity (CWSSIM) index were examined for their utility in the comparison of real and simulated spatial data sets. Gaussian Markov random fields were simulated and compared with both metrics. A case study into comparison of snow water equivalent spatial patterns over northern Canada was used to explore the properties of these indices on real-world data. CWSSIM was found to be less sensitive than SSIM to changing window dimension. The CWSSIM appears to have significant potential in characterizing change and/or similarity; distinguishing between map pairs that possess subtle structural differences. Further research is required to explore the utility of these approaches for empirical comparison cases of different forms of landscape change and in comparison to human judgments of spatial pattern differences.