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

Den Boychuk,Mike Wotton,Douglas G Woolford,Chelene C Hanes,Colin B Mcfayden,Melanie Wheatley,Jordan Evens,Aaron Stacey

doi:10.3390/fire4030050

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.

Highlights

Situational awareness and decision-making for operational wildland fire management is supported by a large amount of complex, numerical information, often covering large areas and sometimes spanning multi-day forecasts
Categorization is useful, conforming to traditional schemes, such as a four-category blue–green–yellow–red sequence from low to extreme, may be less than ideal for new information products. This issue arose during our implementation of fine-scale, spatially explicit fire occurrence prediction (FOP) models
We used a nonlinear-systematic scale with boundaries obtained using Equation (1) with parameters 1/b = 3, FOPMax = 3 and NumCat = 20

Summary

Introduction

Situational awareness and decision-making for operational wildland fire management is supported by a large amount of complex, numerical information, often covering large areas and sometimes spanning multi-day forecasts. Comprehending and interpreting that quantity of information under time-limited and stressful conditions is challenging. Among other ways, this task is commonly made faster and easier by categorizing and visualizing the numerical information. Categorization is useful, conforming to traditional schemes, such as a four-category blue–green–yellow–red sequence from low to extreme, may be less than ideal for new information products. This issue arose during our implementation of fine-scale, spatially explicit fire occurrence prediction (FOP) models

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