Physical activity is an important part of a healthy lifestyle. Regular physical activity is associated with many health benefits, including improved fitness, prevention of weight gain, and lower risk of heart disease, stroke, high blood pressure, diabetes and some cancers. In addition, regular physical activity and healthy eating can promote well-being and relieve stress. For children, regular physical activity is also associated with improved academic performance.

Physical Activity and the Environment

Neighborhood conditions and resources can encourage or discourage healthy behaviors. Aspects of the physical environment - such as the presence of well-maintained green spaces, bicycle paths, walking paths, stairs, exercise facilities and perceived safety - encourage physical activity. Healthy food choices combined with physically active lifestyle support maintenance of an optimal bodyweight and overall good health.

About the Data and Indicators

Physical activity indicators presented on this site are derived from the biennial NYC Youth Risk Behavior Survey and the annual Community Health Survey. They are estimates that depend on study participants’ accurate recollections and reporting.

Health Promotion Strategies

• Walk or bike to work and to run errands.
• Get off the subway a stop or two early and walk the rest of the way.
• Take the stairs instead of the elevator.
• Maintain good nutrition habits to support an active lifestyle, including:
  • Drink very low calorie beverages, like water and unsweetened tea. Avoid sugary drinks.
  • Eat smaller portions that include more fruits and vegetables.
  • Use healthy fats, such as canola and olive oils. Avoid unhealthy fats, such as lard, butter and trans fat-containing foods.

Key Messages

In spite of the potential benefits, many New Yorkers do not get the recommended 30 or more minutes of physical activity on five or more days of the week.

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.