City of New York Logo

Equitable Development Data Explorer

Methods and Data Sources

Local Law 78 of 2021 outlined a set of indicators to be included in the Equitable Development Data Explorer, but directed the NYC Departments of City Planning and Housing Preservation and Development to determine the complete list of data points and specify methodologies for how the data points should be incorporated into a displacement risk map. Below, please find descriptions of the data sources and methodologies.

Data Dictionary

The data dictionary is a set of information describing the data points, sources, and methodologies included in the Explorer.

Data Dictionary

Community Data Methodology

Community Data contains demographic, housing, and quality of life data broken out by race and ethnicity (when available) at the neighborhood, borough, and citywide scales. Community Data also shows change over time between 2000 and 2020. See data sources below.

Displacement Risk Map Methodology

Local Law 78 of 2021 called for the creation of a displacement risk index or a map of the city that illustrates the level of displacement risk in different neighborhoods as compared to each other. Displacement refers to the involuntary movement of an individual or family from their home or neighborhood, whether as the result of eviction, unaffordable housing costs, or poor-quality housing. There are many factors that may contribute to residents' risk of displacement, though there is no standard methodology for how to measure it. Building on data points listed in Local Law 78 of 2021, DCP and HPD identified a complete set of data points and a methodology to combine the data points into an index based on research, precedents from other cities, and conversations with stakeholders.

The three major categories of data points in the displacement risk index include:

  • Population Vulnerability: This category refers to the demographic and socioeconomic characteristics of a neighborhood's residents that may make them more susceptible to displacement. It includes data points such as race/ethnicity, income, and the share of a household's income spent on rent.
  • Housing Conditions: This category refers to the characteristics of housing in a neighborhood that can either help stabilize households or lead to greater instability. It includes data points such as condition of the housing stock, whether a household rents or owns, and applicability of various programs or regulations limiting rent increases.
  • Market Pressure: This category refers to the broader conditions affecting neighborhoods that tend to make it harder for lower-income residents to remain or find new housing in the area. It includes data points related to changes in the housing market and demographic composition of a neighborhood, among others.

Methodology to Combine the Data Points into an Index:

When incorporating the data into an index, individual data points or categories could be emphasized over others, depending on how strongly they contribute to the likelihood of residents' ability to stay in their homes. Because research suggests that demographic data are a stronger predictor of displacement risk than housing conditions or market pressure data, DCP and HPD focused on methodologies to combine the data while prioritizing population characteristics (e.g. race, income, English language proficiency, and rent burden).

Our selected methodology creates an overall risk level based on how neighborhood and housing conditions magnify or reduce population vulnerability. This methodology assumes that population vulnerability drives displacement risk and considers how it can be exacerbated by market pressure or mitigated by housing security.

See our Data Dictionary for more information on the methodology we used to combine the data points and an analysis that was conducted to compare the model to other data associated with displacement.

Displacement Risk Data Point maps:

See here for maps of each of the data points that comprise the displacement risk map and the categories they are grouped within (e.g. population vulnerability, housing conditions, market pressure).

Explanation of the geographic scale used for Equitable Development Data Explorer

The data that makes up Community Data is displayed at the Public Use Microdata Area (PUMA) scale, a statistical area defined by the US Census. PUMAs in New York City generally approximate Community Districts. Displaying the data at the PUMA scale allows the tool to report data broken down by race and ethnicity. Data in Community Data is also reported at the borough and citywide levels.

The Displacement Risk Map, which is not broken down by race and ethnicity, is displayed at a smaller geography, Neighborhood Tabulation Areas (NTA). NTAs are groupings of census tracts that are designed to approximate neighborhoods.

Data Reliability

The data tool incorporates several data sources that are surveys, such as the American Community Survey (ACS) and the Housing and Vacancy Survey (HVS), meaning the data are based on a sample and there is a margin of error (MOE) associated with each data estimate. Estimates and associated MOEs vary greatly in size, so it helps to examine the size of an MOE in relation to its associated estimate to better understand the relative reliability using a coefficient of variation (CV). These measures of reliability help you gauge the degree to which they can trust any given statistic.

Indicators in the tool may include values in a gray font color which mean that they have poor statistical reliability. For estimates and margins of error with a coefficient of variation, the gray font color is an indication that the CV is greater than or equal to 20%. Data associated with count estimates of zero and top-coded median (estimate can be anywhere above the displayed value) and bottom-coded median (estimate can be anywhere below the displayed value) are also shown in a gray font color to alert users that data are either relatively unreliable, or of indeterminate reliability. Estimates, and associated MOEs, of change over time for a selected area may also be shown in a gray font color, indicating that the estimates have poor statistical reliability (CVs are greater than or equal to 20%).

For more information, visit NYC Population: Geographic Reference.

Data Sources


April 2022


April 2022


housing conditions, demographic conditions, public health and more



Published by

NYC Department of City Planning, NYC Housing Preservation & Development

NYC Logo

Department of City Planning

Housing Preservation & Development