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Small study areas are vastly underrepresented in the criminological literature, including the literature on the relationship between crime and weather. North Bay, ON (population 50,000) provides a useful study area in which to begin to address this lack. Using five years of police call for service data (2015–2019), negative binomial regression models were used to assess the relationships between weather variables and assaults, break and enters, domestic disputes, and thefts. For each crime type, the resulting models were compared based on their Aikake information criteria (AICs) to determine which performed the best. Significant relationships were found to differ between crime types. Temperature played a significant role in determining the temporal distribution of thefts, while for break and enters a model without weather variables performed best, even though both are property crimes. Similarly, for violent crimes, assaults were found to be positively correlated to temperature, while domestic disputes depended mainly on day of the week. [ABSTRACT FROM AUTHOR]