Population Numbers By New York City Neighborhood Tabulation Areas The data was collected from Census Bureaus' Decennial data dissemination (SF1). Neighborhood Tabulation Areas (NTAs), are aggregations of census tracts that are subsets of New York City's 55 Public Use Microdata Areas (PUMAs). Primarily due to these constraints, NTA boundaries and their associated names may not definitively represent neighborhoods. This report shows change in population from 2000 to 2010 for each NTA. Compiled by the Population Division – New York City Department of City Planning.

Table of Census Demographics represented at the NTA level. NTAs are aggregations of census tracts that are subsets of New York City's 55 Public Use Micro data Areas (PUMAs)

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Census Tracts from the 2020 US Census for New York City clipped to the shoreline. These boundary files are derived from the US Census Bureau's TIGER project and have been geographically modified to fit the New York City base map. Because some census tracts are under water not all census tracts are contained in this file, only census tracts that are partially or totally located on land have been mapped in this file. All previously released versions of this data are available at the DCP Website: BYTES of the BIG APPLE.

The annual gentrification map for NYC was produced using the classification method proposed in this experiment to highlight the spatial and temporal distribution of gentrification. The time-series gentrification maps illustrate areas experienced gentrification since 2000. The data was aggregated to Census tract level for displaying a visually friendly result. The raw building level data is also provided but not presented in the paper since it is not readable using the static map.

This file contains population estimates by age and sex and single year for census tracts in New York State, from 1990-2016.Iterative proportional fitting was used to develop populations that are consistent with official Census Bureau tract-level populations from 1990, 2000, and 2010 and single-year county-level population estimates published by the SEER program of the National Cancer Institute (https://seer.cancer.gov/popdata/). The Longitudinal Tract Database (LTDB) (https://s4.ad.brown.edu/projects/diversity/researcher/bridging.htm) was used to report populations using 2010 census tract boundaries.In effect, the approach assumes that population growth or reduction at the tract level mirrors what is happening at the county level. This is an improvement over linear or geometric interpolation between census years, but is still far from perfect. Census tracts can undergo rapid year-to-year population change, such as when new housing is constructed or, less frequently, demolished. An extreme example is census tract 1.04 in Westchester County, New York, which had a population of 0 in all 3 census years, as it was located entirely within an industrial area. Since 2010, multiple large high-rise condominiums have been constructed here, so that the population in 2018 is probably now in the thousands, though any estimation or projection method tied to the 2010 census will still count 0 people here. It is conceivable that address files from the United States Postal Service or other sources could be used to capture these kinds of changes; I am unaware of any attempts to do this.The file contains data for 4893 census tracts. It has been restricted to census tracts with nonzero populations in at least one of the census years. There are other census tracts consisting entirely of water, parkland, or non-residential areas as in the example above, which have been omitted.These data are used for the calculation of small-area cancer rates in New York State.

The New York City Department of Health and Mental Hygiene (NYC DOHMH) has shared vital statistics data (birth and mortality data) online. Birth data includes demographic information on the mother, including age, race, and education. Mortality data includes demographic information on the deceased, such as age, sex, race, and education. The publicly-available birth and death micro-SAS datasets provide aggregate data on the community district, zip code, and census tract levels. Researchers may also complete an application process to request line-listed and de-identified vital statistics data from NYC DOHMH.

These layers represent census tracts (or portions of tracts) in New York State that may qualify for New York State’s historic tax credit programs. These programs are administered by the New York State Division for Historic Preservation, also known as the New York State Historic Preservation Office (SHPO). For more information, see SHPO’s Tax Credit Programs web page: https://parks.ny.gov/shpo/tax-credit-programs/The current layers are effective April 1, 2025 through March 31, 2026. They derive from data in yearly updates to the American Community Survey 5-Year Estimates. The most recent data used in these layers are the 2019–2023 estimates.The basic qualifying criteria are based on Table B19113 of the American Community Survey 5-Year Estimates. This table represents Median Family Income in the Past 12 Months (in Inflation-adjusted Dollars). If a tract’s median family income minus its margin of error is less than or equal to the statewide median family income plus the statewide margin of error, then it qualifies for the commercial and state homeowner tax credit programs. Properties in certain cities may qualify for the state homeowner tax credit program, even if they are in census tracts that do not meet the basic qualifying criteria. The enhanced qualifying criteria are based on Table S1701 of the American Community Survey 5-Year Estimates. This table represents Poverty Status in the Past 12 Months as a percentage. If a city’s estimated percentage below poverty level plus the margin of error is greater than or equal to 15.5%, then all locations within the city boundary qualify for the state homeowner tax credit program.If a tract or city no longer meets the criteria, its qualifying status is extended for a two-year grace period.If you have questions about the tax credit programs or the information in these layers, please see SHPO’s Contact page for a list of staff who review projects in your county.

Table of ACS Demographics and profile represented at the NTA level. NTAs are aggregations of census tracts that are subsets of New York City's 55 Public Use Microdata Areas (PUMAs)

Excel table of census data created to project populations at the Neighborhood Tabulation Area, a small area level, from 2000 to 2030 for PlaNYC, the long-term sustainability plan for New York City

The Capital Planning Platform is a new resource for collaborative planning, powered by open data and open source technology.The New York City Department of City Planning pioneered open data with Bytes of the Big Apple a decade ago. With the creation of the DCP"s Capital Planning Division in 2014, we envisioned a new civic technology resource: the Capital Planning Platform - a place for planners to access the maps, data, and analytics that they need to plan for public investments in neighborhoods and collaborate with one another. The NYC Facilities Explorer (beta) is a first step in building this vision. Over the months and years to come, we plan to add more map layers, new and improved datasets, and new analysis tools to this mapping platform to help automate a broad array of planning analyses and make the capital planning process more efficient, coordinated, and strategic across the public and private sectors in New York City.The Capital Planning Platform complements other data and maps that DCP produces. We also encourage users to explore the following resources, among others, on DCP"s website.NYC Census FactFinder - An interactive tool for creating demographic, social, economic, and housing profiles for neighborhoods and user-defined groupings of Census tracts.PLUTO and MapPLUTO - Extensive land use and geographic data at the tax lot level in multiple formats.Zoning and Land use Application (ZoLA) – ZoLA provides a simple way to research zoning regulations in New York City.Waterfront Access Map - This interactive map identifies and provides information about New York City’s inventory of publicly-accessible waterfront spaces.Community Portal - The DCP Community Portal offers resources on a variety of topics related to land use, community planning, and demographic trends for each of New York City’s 59 Community Boards

Summary File 2 contains 100-percent United States decennial Census data, which is the information compiled from the questions asked of all people and about every housing unit. Population items include sex, age, race, Hispanic or Latino origin, household relationship, and group quarters occupancy. Housing items include occupancy status, vacancy status, and tenure (owner-occupied or renter- occupied). The 100-percent data are presented in 36 population tables ("PCT") and 11 housing tables ("HCT") down to the census tract level. Each table is iterated for 250 population groups: the total population, 132 race groups, 78 American Indian and Alaska Native tribe categories (reflecting 39 individual tribes), and 39 Hispanic or Latino groups. The presentation of tables for any of the 250 population groups is subject to a population threshold of 100 or more people, that is, if there were fewer than 100 people in a specific population group in a specific geographic area, their population and housing characteristics data are not available for that geographic area.

This dataset gives NYC Equitable Zoning (NYCEZ), which is a zoning system of NYC derived from census tracts and ACS data with 574 zones.

The zoning system considers data reliability of 3 minority population groups: population below poverty level, seniors above 67, and long commuters (>1 hour). Underserved groups of interest include the population above 67 years old (seniors), the population under the poverty level, the population with a commute time above one hour, and the population with one or more disabilities. Only the former three groups are considered in zoning, since populations disabilities are already highly correlated with the others.

The 2168 census tracts in NYC are aggregated to improve the data reliability of the 3 minority groups. Average margin of error (MOE) percentages at census tract level of population above 67, population below poverty level, and population with a commute time above 1 hour are 15.22%, 50.07%, and 18.23%, respectively. After aggregation to the NYC Equitable Zones, MOE percentages become 8.02%, 12.33%, and 9.88%, respectively. Equitable Zones shown in Figure 5 simultaneously reduces the average MOE percentage of demographic data by 48% for seniors, 75% for low-income population, and 46% for long commuters.

Files include:

• NYC census tracts shapefile with mapping to NYCEZ (“zoning” column)
  • equitable_zoning_new_dissol.cpg
  • equitable_zoning_new_dissol.dbf
  • equitable_zoning_new_dissol.prj
  • equitable_zoning_new_dissol.sbn
  • equitable_zoning_new_dissol.sbx
  • equitable_zoning_new_dissol.shp
  • equitable_zoning_new_dissol.shx
• NYCEZ shapefile
  • equitable_zoning_new.cpg
  • equitable_zoning_new.dbf
  • equitable_zoning_new.prj
  • equitable_zoning_new.shp
  • equitable_zoning_new.shx
• Data used for NYCEZ generation (from American Community Survey (ACS))
  • Number of seniors in each census tract (with 80 variance replicate estimates)
    • data_elderly.csv
  • Number of disabled in each census tract (with 80 variance replicate estimates)
    • data_disabled.csv
  • Number of long commuters in each census tract (with 80 variance replicate estimates)
    • data_commute>1h.csv
  • Number of low incomers in each census tract (with 80 variance replicate estimates)
    • data_below_poverty.csv

Variance replicate estimates from ACS are used to MOE aggregation. Information can be found here: https://www.census.gov/programs-surveys/acs/data/variance-tables.html

Note: These layers were compiled by Esri's Demographics Team using data from the Census Bureau's American Community Survey. These data sets are not owned by the City of Rochester.Overview of the map/data: This map shows the percentage of the population living below the federal poverty level over the previous 12 months, shown by tract, county, and state boundaries. Estimates are from the 2018 ACS 5-year samples. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Current Vintage: 2019-2023ACS Table(s): B17020, C17002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer will be updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico.Census tracts with no population are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

This map answers the question "What is the most common, or predominant, education level for people in this area?" The map shows predominant educational attainment in each census tract. Darker colors indicate a greater gap between the predominant group and the next largest group.The U.S. Census Bureau asks citizens to indicate how far they went in formal education. The database includes seven different columns, each representing a count of population by that education level. A simple routine in compares the seven columns of information, and finds which one has the highest value, writing that to a string field. Each tract's transparency is set by a transparency field added to the data.Predominance maps can be created in ArcGIS Online by adding two fields, calculating their values, and setting up the renderer based on those two fields. See this blog by Jim Herries for details on how to create a predominance map in ArcGIS Online from any feature layer.See this GitHub repo by Jennifer Bell for a script you can run in ArcMap as a script tool, to calculate predominance for any columns of data you have.

Neighborhood Tabulation Areas (NTAs) were created to project populations at a small area level, from 2000 to 2030 for PlaNYC, the long-term sustainability plan for New York City. Since population size affects the error associated with population projections, these geographic units needed to have a minimum population, which we determined to be 15,000. This criterion resulted in combinations of neighborhoods that probably would not occur if one were solely designating boundaries of historical neighborhoods. Moreover, the neighborhood names associated with the neighborhood tabulation areas are not intended to be definitive. Another feature of the sustainability plan, was the creation of projections for Public Use Microdata Areas (PUMAs), which are approximations of New York City's Community Districts developed for use with the Census Bureau's Public Use Microdata Samples (PUMS). In order to make the boundaries consistent with PUMAs, NTAs were created using whole census tracts, from the 2010 census, within PUMAs. Since NTAs were not permitted to cross PUMA boundaries, this further restricted our ability to identify what may be thought of as historical neighborhood boundaries. Thus, users need to be cognizant of the reason why NTAs were created and the demographic/geographic constraints inherent in how they were configured. Despite these limitations, NTAs are a valuable summary level for use with both the 2010 Census and the American Community Survey (ACS). Regarding the decennial census, these geographic areas offer a good compromise between the very detailed data for census tracts (2,168) and the broad strokes provided by community districts (59). For the ACS, NTAs offer a statistically reliable alternative to the high sampling error that renders data for most individual census tracts unusable.

The NYC Department of City Planning’s (DCP) Housing Database contains all NYC Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. It includes the three primary construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. Records in the Housing Database Project-Level Files are geocoded to the greatest level of precision possible, subject to numerous quality assurance and control checks, recoded for usability, and joined to other housing data sources relevant to city planners and analysts. Data are updated semiannually, at the end of the second and fourth quarters of each year. Please see DCP’s annual Housing Production Snapshot summarizing findings from the 21Q4 data release here. Additional Housing and Economic analyses are also available. The NYC Department of City Planning’s (DCP) Housing Database Unit Change Summary Files provide the net change in Class A housing units since 2010, and the count of units pending completion for commonly used political and statistical boundaries (Census Block, Census Tract, City Council district, Community District, Community District Tabulation Area (CDTA), Neighborhood Tabulation Area (NTA). These tables are aggregated from the DCP Housing Database Project-Level Files, which is derived from Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions. These files can be used to determine the change in legal housing units across time and space.

Summary File 3 contains sample data, which is the information compiled from the questions asked of a sample of all people and housing units in the United States. Population items include basic population totals as well as counts for the following characteristics: urban and rural, households and families, marital status, grandparents as caregivers, language and ability to speak English, ancestry, place of birth, citizenship status, year of entry, migration, place of work, journey to work (commuting), school enrollment and educational attainment, veteran status, disability, employment status, industry, occupation, class of worker, income, and poverty status. Housing items include basic housing totals and counts for urban and rural, number of rooms, number of bedrooms, year moved into unit, household size and occupants per room, units in structure, year structure built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, and monthly rent and shelter costs. The Summary File 3 population tables are identified with a "P" prefix and the housing tables are identified with an "H," followed by a sequential number. The "P" and "H" tables are shown for the block group and higher level geography, while the "PCT" and "HCT" tables are shown for the census tract and higher level geography. There are 16 "P" tables, 15 "PCT" tables, and 20 "HCT" tables that bear an alphabetic suffix on the table number, indicating that they are repeated for nine major race and Hispanic or Latino groups. There are 484 population tables and 329 housing tables for a total of 813 unique tables. (Source: ICPSR, retrieved 06/15/2011)

This data collection provides detailed tabulations of 100-percent data items from the 1990 Census of Population and Housing. These tabulations are presented for states and their subareas in hierarchical sequence down to the census tract or block numbering area (BNA) level. Population items include age, race, sex, marital status, Hispanic origin, household type, and household relationship. Population items for Puerto Rico include persons, families, households, sex, age, marital status, household size, and household type. Housing items include occupancy/vacancy status, tenure, units in structure, contract rent, meals included in rent, value, and number of rooms in housing unit. Crosstabulations include variables such as single year of age by sex, tenure by age of householder, age by group quarters, aggregate value by units in structure, and tenure by number of nonrelatives. The dataset contains both "A" and "B" records. "A" records are provided for each summary level in a geographic area, and are repeated for each geographic component. "B" records repeat the same data for each summary level/geographic component combination, but are tabulated for each of ten categories of race and Hispanic origin. (Source: ICPSR, retrieved 06/15/2011)

Septic systems are used by 22% of homes in New York State, and they are important components of the wastewater infrastructure. This dataset quantifies how many septic systems are used across New York State. This dataset presents counts of septic systems at the census tract level. Data were derived from 2011 county parcel data and aggregated by census tract.View Dataset on the Gateway