The text discusses the integration of new data streams and spatial analysis to improve road safety in New York City, focusing on the use of GPS and mobile event data from applications like TomTom and Waze. By examining NYC Open Data, specifically NYPD motor vehicle collision records, analysts can identify patterns in collision occurrences, such as time and location, and contributing factors like driver distraction. Spatial data science methodologies, including spatial autocorrelation and decision trees, help to pinpoint collision hotspots and common features of crash-prone streets, such as the number of parking and travel lanes. These analyses reveal that collisions are more frequent during early morning hours on weekends and that motorists are more often injured in crashes outside of traffic jams, while pedestrians are more vulnerable during jams. The findings emphasize the importance of leveraging both traditional and new data sources to inform city planners and transportation agencies on how to enhance road safety and manage traffic more effectively.