People who are overweight or obese are at increased risk for heart disease and diabetes. Physical activity and healthy eating can promote well-being and relieve stress.

Weight and the Environment

Neighborhood conditions and resources can encourage or discourage healthy behaviors. The density and types of food retail outlets available in neighborhoods may affect food shopping habits and nutrition choices of residents. Healthy food choices combined with physically active lifestyle support maintenance of an optimal bodyweight and overall good health.

About the Data and Indicators

Weight 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

Maintain good nutrition habits, including:

• Eat plenty of fruits and vegetables.
• Watch your portions
• Eat whole grains
• Drink very low calorie beverages, like water and unsweetened tea. Avoid sugary drinks.
• Keep to recommended calorie levels for your age, gender, weight and physical activity level.

Key Messages

More than half of adult New Yorkers are either overweight or obese. Being at an unhealthy weight increases a person’s risk of developing many conditions, including diabetes, high blood pressure, high cholesterol, cancer and heart disease.

Poor neighborhoods have higher rates of obesity and related deaths due to diabetes and heart disease than more affluent neighborhoods.

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.