Considerations for Categorizing and Visualizing Numerical Information: A Case Study of Fire Occurrence Prediction Models in the Province of Ontario, Canada

Abstract

Wildland fire management decision-makers need to quickly understand large amounts of quantitative information under stressful conditions. Categorization and visualization “schemes” have long been used to help, but how they are done affects the speed and accuracy of interpretation. Using traditional fire management schemes can unduly restrict the design of new products. Our design process for Ontario’s fine-scale, spatially explicit, daily fire occurrence prediction (FOP) models led us to develop guidance for designing new schemes. We show selected historical fire management schemes and describe our method. It includes specifying goals and requirements, exploring design options and making trade-offs. The design options include gradient continuity, hue selection, range completeness and scale linearity. We apply our method to a case study on designing the scheme for Ontario’s FOP models. We arrived at a smooth, nonlinear scale that accommodates data spanning many orders of magnitude. The colouring draws attention according to levels of concern, reveals meaningful spatial patterns and accommodates some colour vision deficiencies. Our method seems simple now but reconciles complex considerations and is useful for mapping many other datasets. Our method improved the clarity and ease of interpretation of several information products used by fire management decision-makers.

Canadian contributions to environmetrics

“…However, many machine learning classification tools require training data where the response variable is balanced and many ML models for FOP are not properly calibrated to produce true probabilities because the subsampling of nonfire events for training data leads to overprediction (Phelps & Woolford, 2021a). Canadian statistical scientists have played a key role here to ensure that properly calibrated models—whether they are model‐based or algorithm‐based methods—are employed, that they are properly assessed, and that the interpretability of the various approaches is discussed with fire management practitioners should the goal be operational implementation (Phelps & Woolford, 2021b) where visualization of model output plays a key role (Boychuk et al, 2021).…”

On the selection of an interpolation method with an application to the Fire Weather Index in Ontario, Canada

“…This type of patterning can occur as a result of thunderstorms that have localized intense rainfall. The base colors for these schemes were obtained from Boychuk et al (2021). Categories : FWI values are binned and given a single solid color per classification.…”

“…Note that the current approach taken by the Ministry when mapping FWI is a slight modification to the categories visualization schemes as described above. In addition to visualizing FWI according to four categories (Low, Moderate, High, Extreme), the numerical values of the FWI that are recorded at each weather station are plotted inside a 40 km radius circle centered at each station's location (see Boychuk et al, 2021, Figure 1b). The latter is done to visually convey to those using the map where they can be most confident in the values of the interpolated surface and provide the actual value of the FWI which are associated to different events and aid further in the interpretation (see Hanes et al, 2021, Table 3).…”

“…The general considerations used when designing our categorical gradients scheme followed those as outlined in Boychuk et al (2021). For example, to ensure consistency with existing practice at the Ministry, we use the same thresholds for the ordinal categories.…”

“…For this case study, we collaborate with experts from the Ontario Ministry of Natural Resources and Forestry (“the Ministry”). Since the Ministry requires accurate spatial approximations of FWI across Ontario throughout the fire season to communicate general wildland fire hazard to the public and to inform operational decision‐making (Boychuk et al, 2021; Hanes et al, 2021), they act as the “end user” for our illustrative case study. As this case study involved active knowledge exchange and direct collaboration with fire management practitioners at the Ministry, it illustrates how such considerations can impact the process of selecting the final desired interpolation product.…”