Final answer:
Categorical data in the New York crash data refers to non-numeric information that classifies accidents into different groups, such as the borough, cause of accident, or type of vehicle involved.
Step-by-step explanation:
When reviewing the New York crash data, categorical data refers to variables that represent names, labels, or qualities of data that can be used to classify elements into different groups. This type of data is typically non-numeric and includes attributes such as car brand, type of collision, or descriptions of road conditions. For example, in a dataset, you might find categorical data under columns labeled 'Vehicle Make', 'Collision Type', and 'Road Surface Conditions'. Each of these columns would have non-numeric values that categorize each accident into types or groups.
An example of categorical data in the New York crash dataset could be the 'Borough' where the crash took place (e.g., Manhattan, Brooklyn, etc.), the 'Cause of Accident' (e.g., Distracted Driving, Speeding, etc.), or the 'Type of Vehicle Involved' (e.g., Passenger Vehicle, Commercial Truck, Motorcycle, etc.).
Why Categorical Data is Important in Crash Analysis
Analyzing categorical data from crash reports enables researchers and city planners to identify patterns and trends in accidents, which can inform safety improvements and policy changes. For instance, if a high frequency of accidents is categorized as 'Pedestrian Involvement', it might prompt the implementation of better crosswalks or pedestrian signs in those areas. Similarly, identifying a concentration of 'Rear-End Collisions' at certain intersections might lead to traffic flow adjustments to reduce such incidents.