Buildings with maintenance issues and disrepair can harm health. Across NYC, low-income neighborhoods have a higher percentage of buildings in disrepair due to past and present disinvestment and housing discrimination. This housing disrepair harms residents’ health.

No family should have to choose between affordable and healthy housing. All tenants are entitled to safe, decent and sanitary housing, which prevents disease and injury. All building owners are required by law to provide essential services such as heat and hot water, make timely repairs, and maintain a pest-free building.

Housing and health

Housing quality impacts health directly and indirectly. Housing maintenance deficiencies are associated with:

• Increase likelihood of pest infestations, which can worsen asthma
• Exposure to lead
• Mold growth, which may cause allergic reactions
• Other health problems such as mental distress

To reduce health risks

Owners and landlords must:

• Provide homes and buildings free of pests, and employ pest control that uses preventive measures — not just pesticides.
• Install and maintain smoke and carbon monoxide detectors in residential units.

Tenants can:

• Report housing conditions such as peeling paint, water leaks, pest problems and mold to property owners.
• Inform the building’s owner if there is a child under the age of 11 living there, to get window guards installed.
• Test smoke and carbon monoxide detectors and replace batteries at least twice a year.
• Call 311 if landlords fail to fix problems.

For more information, visit the Housing Rules and Regulations page for NYC Owners and Tenants.

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.