In the past few decades, there has been an explosion of spatial data, analysis methodologies and vehicular technology’s that have influenced policy and safety technology to protect drivers’ lives all over the world. This research focuses on adding to the growing body of work by creating a new methodology to detect, measure and study Deviant Driving (DD) behavior. DD behavior refers to driving actions that deviate from normal driving behavior and can include aggressive, risky, and unintentional driving actions. The new methodology is called the Deviant Driving Detector (DDD), and it unifies multiple driving aspects into a single measurable variable to aid in the understanding of how and why DD is geographically distributed. The DDD was created using data acquired from the Safety Pilot Model Deployment (SPMD) initiative which created a vast and comprehensive naturalistic driving dataset (NDD). The NDD contains data from Volunteer drivers who had their driving behavior recorded in real world circumstances. The naturalistic data allowed for normal driving to be quantitatively defined in order for DD to be detected. The main objectives are to develop a novel methodological framework to detect and measure DD from naturalistic driving data for analysis. To evaluate the DDD, the geographic distribution of DD instances was compared to crash data acquired from the National Highway Traffic Safety Administration (NHTSA). Results highlight areas of interest that are susceptible to DD such as intersections and driving patterns such as familiar roads among small groups of drivers, both which are supported by existing literature. There are many benefits to detecting and curving abnormal driving behavior in terms of economic efficiency and human safety. Understanding how DD occurs can facilitate the development of new technologies, new policies, improved infrastructure, aid in the development of automated vehicles, or driver monitoring systems. Existing methods and technologies can also benefit from a new automated approach to detect changes on the road, and reduce the time needed to detect driving events from vast amounts of naturalistic driving data.