Much of the U.S. receives significant snowfall each year (FHWA, 2018). With winter weather, crash rates increase dramatically. To mitigate the adverse effects of winter weather on safety and mobility, agencies deploy winter maintenance operations. To validate the use of such techniques, past research has been done to evaluate the effectiveness of snowplow operations. Early reports suggested that winter maintenance activities decrease the crash rate (Hanbali,- a, Hanbali et al. - b ). With technological advances, many agencies have deployed Automatic Vehicle Location (AVL) systems on snowplows to track location and material spreading information. It was hoped that gathering such information would lead to optimizing material spreading and reducing crashes. Recent works attempting to work with this data have grappled with how to best analyze and present the data. However, a methodological framework for analyzing snowplow AVL data is lacking. This research aims to use visual analytics to better understand the data, and present methods for creating a reliable crash frequency model. The results showed that spreading material before a winter storm will reduce the crash rate. Additionally, the ratio of material spread to total precipitation during winter events was also shown to decrease crash rates. These results were obtained by implementing a feature selection method that is crucial to the model building process. Using this research as a springboard, agencies can better use the AVL data in their decision making process in the future.