Hi again,
As an update on this post, I did end up using logistic regression instead of OLS. This allowed me to simply mark which 100x100 m grid cells had a presence of fire (1) and which had an absence (0). The proportion of fire presence to absence was quite low (500 fires to 15,000ish non fires) so I used undersampling, that is just modifying the data to include 500 fires and 500 non fires (randomly selected from the total non fires).
The Generalized Linear Regression tool in Arc does have a binary logistic regression tool, but I ended up using R so that I could have more freedom in my analysis. All in all, this method worked well for me, yielding around 75% accuracy (as well as other reasonable evaluation metrics such as AUC, kappa, F1, etc.).
It helped me a lot to look into peer reviewed articles with similar analyses like the below (sorry for citation styles) as well as the ESRI spatial stats talks. The book 'Applied Logistic Regression' by Hosmer and Lemeshow was also very useful. I hope this helps. /Naomi
Z.X. Zhang, H.Y. Zhang, D.W. Zhou,
Using GIS spatial analysis and logistic regression to predict the probabilities of human-caused grassland fires, Journal of Arid Environments
H. Zhang, X. Han and S. Dai, "Fire Occurrence Probability Mapping of Northeast China With Binary Logistic Regression Model," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 1, pp. 121-127, Feb. 2013, doi: 10.1109/JSTARS.2012.2236680.
https://www.esri.com/arcgis-blog/products/product/analytics/spatial-statistics-resources/