Traffic injuries are no accident. Like other public health concerns, they are preventable through data driven solutions. In NYC, traffic injuries, which include injuries among pedestrians, bicyclists, motorcyclists, and motor vehicle occupants, are a leading cause of injury-related hospitalizations and emergency department visits.

In February 2014, NYC launched “Vision Zero”, an action plan to reduce injuries and deaths on city streets. Vision Zero is a multi-agency effort, incorporating road engineering, traffic enforcement, community outreach and education, and policy work to make NYC streets safer.

Traffic Safety and the Environment

Many factors contribute to traffic injuries, including human, vehicle and environmental factors. The built environment influences traffic injury outcomes by shaping the actions of road users, which can prevent crashes from happening, as well as mitigating the effects of crashes, when they do happen. Built environment features, such as road design and traffic calming strategies include: pedestrian islands, signal timing, separated bicycle paths, speed humps and bumps, signage, and more. Environmental interventions can be tailored for a community’s specific needs to improve traffic safety and reduce injuries.

About the Data and Indicators

Rates of traffic injuries provided here are based on all live transportation-related injury discharges from a NYC hospital and include emergency department visits and hospitalizations among residents and non-residents of NYC. Neighborhood data are based on zip code of residence for NYC residents only who were discharged from a hospital located in NYC. Crash locations are not available in these data.

Visits to emergency departments and hospital stays reflect injuries that require immediate medical treatment. Minor injuries not treated at a hospital are not captured through emergency department and hospitalization data. Data were obtained from the New York State Department of Health Statewide Planning and Research Cooperative System (SPARCS). Transportation-related injuries are defined by the International Classification of Diseases, Clinical Modification (ICD-10-CM) and were recently revised.  The revisions largely pertain to injury-related diagnostic codes including those that capture transportation-related injuries, allowing for better diagnosis. The Centers for Disease Control and national epidemiologists provided guidance to DOHMH for enumerating new injury codes. Because the two ICD systems differ in how they classify injuries, current data and previously posted data are not comparable.  Data prior to 2016 are not shown because they use the older coding ICD-CM schema.

Prevention

• Drivers should pay attention while driving and yield to pedestrians crossing the street. Obey the speed limit. Never drink and drive, text and drive, or talk on a cellphone while driving.
• Pedestrians should cross at intersections with the light, listen for traffic, look both ways, and put away phones or other distracting mobile devices.
• Cyclists should wear a helmet and ride predictably, with the direction of traffic.
• Community-based organizations, schools, policy-makers and others can advocate for continued safety engineering improvements to NYC’s roadways, promote traffic safety in their education and outreach activities, and support increased enforcement for traffic safety.

Key Messages

Traffic injuries are preventable. Everyone can play a role in making our streets safer.

There are many schemes of neighborhood boundaries (or geographies) available for different datasets. These include:

• Citywide
• Borough
• United Hospital Fund 42 (UHF42) and United Hospital Fund 34 (UHF34) neighborhoods
• Community Districts (CD), and, related, Community District Tabulation Areas (CDTA)
• Public Use Microdata Areas (PUMA)
• Neighborhood Tabulation Areas (NTA)

We try to make data available at as fine a geographic resolution as we can. Read more about different geographies at this data story.

It would be convenient if each dataset could be made available at the same geography, but this is not always possible. The neighborhood boundaries available depend on the specifics of the underlying dataset.

When we have multiple years of data, we show the data as trend charts.

Trends for individual neighborhoods tend to be very unstable (or "bouncy"), due to small counts for outcomes in individual neighborhoods. To show clearer trends, we usually aggregate up and show trend data by Borough.

Options on some indicators let you compare this indicator to other similar indicators.

We try to make data available as soon as we can.

Many datasets take a long time to reach us — a "lag" of several years, during which data are gathered from different entities to try to create a complete dataset; data are processed (cleaned, deduplicated, etc), and analyzed. It can take several years to prepare a dataset — so a dataset that is several years old is sometimes the most up-to-date information available. Still, this data continues to show us valuable trends and insights.

We also try to display datasets that have been available for many years. Some datasets (like those that are asked on surveys) are not asked every year of the survey, which means that we cannot provide annual data.

For some datasets, we have to aggregate time periods in order to break down data by borough or neighborhood.

Measures are the numeric ways that a dataset (or indicator) are expressed. Examples of different measures might be: number, percent or rate.

A number tells us how much of an outcome is in a certain area. But a rate (usually expressed as, for example, 40 per 100,000) tells us how much of an outcome there is, relative to the population of the area in question. This lets us compare neighborhoods of different sizes.

On this site, we try to show how environmental factors might be connected to health outcomes. One way we do this is with Correlates. We choose potentially-related datasets and display them on a scatterplot so you can see how these two datasets are correlated.

The Disparities view displays neighborhoods by poverty category to show underlying disparities in the data.

Read more about correlation here.