Census Tracts are small, relatively permanent statistical subdivisions of a county or statistically equivalent entity delineated by local participants as part of the U.S. Census Bureau's Participant Statistical Areas Program. The primary purpose of Census Tracts is to provide a stable set of geographic units for the presentation of decennial census data. In 1980 the New Orleans City Planning Commission, for planning and decision-making purposes, divided the city into Census Tract based 'neighborhoods'. Additional neighborhoods were created after the 1990 and 2000 Censuses. Following Hurricane Katrina the Greater New Orleans Community Data Center (GNOCDC) settled on these boundaries to facilitate the use of local data in decision-making. These neighborhoods underwent further change during the 2010 Census due to modifications (consolidation and/or splitting) of Census Tracts, the resulting boundaries were renamed as 'Neighborhood Statistical Areas' to reflect their actual function.

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English. The disadvantage variable was incorrectly calculated for the following datasets: DS7 Socioeconomic Status and Demographic Characteristics of Census Tracts (2020 Census), United States, 2018-2022 Data DS8 Socioeconomic Status and Demographic Characteristics of ZIP Code Tabulation Areas (2020 Census), United States, 2018-2022 Data Please refrain from downloading these datasets. The updated datasets are forthcoming and will be made available soon. Users needing these datasets can reach out to nanda-admin@umich.edu.

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.

Census 2010 population/demographic data approximated from block groups to LA Neighborhood Councils using Esri software.

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)

In 1980 the New Orleans City Planning Commission, for planning and decision-making purposes, divided the city into Census Tract based 'neighborhoods'. Additional neighborhoods were created after the 1990 and 2000 Censuses. Following Hurricane Katrina the Greater New Orleans Community Data Center (GNOCDC) settled on these boundaries to facilitate the use of local data in decision-making. These neighborhoods underwent further change during the 2010 Census due to modifications (consolidation and/or splitting) of Census Tracts. The resulting boundaries were renamed as 'Neighborhood Statistical Areas' to reflect their actual function. Census Tracts are small, relatively permanent statistical subdivisions of a county or statistically equivalent entity delineated by local participants as part of the U.S. Census Bureau's Participant Statistical Areas Program. The primary purpose of Census Tracts is to provide a stable set of geographic units for the presentation of decennial census data.

This data set describes Neighborhood Clusters that have been used for community planning and related purposes in the District of Columbia for many years. It does not represent boundaries of District of Columbia neighborhoods. Cluster boundaries were established in the early 2000s based on the professional judgment of the staff of the Office of Planning as reasonably descriptive units of the City for planning purposes. Once created, these boundaries have been maintained unchanged to facilitate comparisons over time, and have been used by many city agencies and outside analysts for this purpose. (The exception is that 7 “additional” areas were added to fill the gaps in the original dataset, which omitted areas without significant neighborhood character such as Rock Creek Park, the National Mall, and the Naval Observatory.) The District of Columbia does not have official neighborhood boundaries. The Office of Planning provides a separate data layer containing Neighborhood Labels that it uses to place neighborhood names on its maps. No formal set of standards describes which neighborhoods are included in that dataset.Whereas neighborhood boundaries can be subjective and fluid over time, these Neighborhood Clusters represent a stable set of boundaries that can be used to describe conditions within the District of Columbia over time.

Demographic Data for Boston’s Neighborhoods, 1950-2019

Boston is a city defined by the unique character of its many neighborhoods. The historical tables created by the BPDA Research Division from U.S. Census Decennial data describe demographic changes in Boston’s neighborhoods from 1950 through 2010 using consistent tract-based geographies. For more analysis of these data, please see Historical Trends in Boston's Neighborhoods. The most recent available neighborhood demographic data come from the 5-year American Community Survey (ACS). The ACS tables also present demographic data for Census-tract approximations of Boston’s neighborhoods. For pdf versions of the data presented here plus earlier versions of the analysis, please see Boston in Context.

Update frequency: Datasets are refreshed every night to ensure the most current information is available. Even if there are no changes, the data will be updated nightly.

Polygons representing City of Milwaukee neighborhoods as defined by the City of Milwaukee Department of City Development (DCD) through the "Milwaukee Neighborhood Identification Project" of 2000 - subdivisions, major streets, physical barriers, community group participation, housing styles, types, & ages, historic areas, and residents' opinions were among the factors used to define boundaries. Note: these boundaries are not intended to define or correspond to boundaries defined by individual neighborhood associations. The neighborhood boundaries identified through this initiative are not used for any official purposes by the City, and are not updated on an ongoing basis. They are not intended to define or correspond to boundaries defined by individual neighborhood organizations.

Shapefile is projected in Wisconsin State Plane South NAD27 (WKID 32054)

Neighborhood Map Atlas neighborhoods are derived from the Seattle City Clerk's Office Geographic Indexing Atlas. These are the smallest neighborhood areas and have been supplemented with alternate names from other sources in 2020. They roll up to the district areas. The sub-neighborhood field contains the most common name and the alternate name field is a comma delimited list of all the alternate names.The original atlas is designed for subject indexing of legislation, photographs, and other documents and is an unofficial delineation of neighborhood boundaries used by the City Clerks Office. Sources for this atlas and the neighborhood names used in it include a 1980 neighborhood map produced by the Department of Community Development, Seattle Public Library indexes, a 1984-1986 Neighborhood Profiles feature series in the Seattle Post-Intelligencer, numerous parks, land use and transportation planning studies, and records in the Seattle Municipal Archives. Many of the neighborhood names are traditional names whose meaning has changed over the years, and others derive from subdivision names or elementary school attendance areas.Disclaimer: The Seattle City Clerk's Office Geographic Indexing Atlas is designed for subject indexing of legislation, photographs, and other records in the City Clerk's Office and Seattle Municipal Archives according to geographic area. Neighborhoods are named and delineated in this collection of maps in order to provide consistency in the way geographic names are used in describing records of the Archives and City Clerk, thus allowing precise retrieval of records. The neighborhood names and boundaries are not intended to represent any "official" City of Seattle neighborhood map. The Office of the City Clerk makes no claims as to the completeness, accuracy, or content of any data contained in the Geographic Indexing Atlas; nor does it make any representation of any kind, including, but not limited to, warranty of the accuracy or fitness for a particular use; nor are any such warranties to be implied or inferred with respect to the representations furnished herein. The maps are subject to change for administrative purposes of the Office of the City Clerk. Information contained in the site, if used for any purpose other than as an indexing and search aid for the databases of the Office of the City Clerk, is being used at one's own risk.

This study contains measures of neighborhood-school gap for 2009-2010 and 2015-2016. Neighborhood-school gap (NS gap) refers to the discrepancy between the demographics of a public school and its surrounding community. For example, if 60 percent of a school's student body is Black, but 30 percent of the neighborhood population is Black, the school has a positive Black neighborhood-school gap. These datasets measure gaps in race and poverty between elementary school student populations and the census tracts and ZIP code tabulation areas (ZCTAs) that those elementary schools serve. Data is at the census tract and ZCTA level. Supplemental data containing component variables used to calculate NS gap at the school and block group level is also available.

This collection contains measures of land cover (e.g., low-, medium-, or high-density development, forest, wetland, open water) derived from the National Land Cover Database (NLCD) and aggregated by United States census tract and ZIP code tabulation area (ZCTA). For each land type, land cover is measured both in total square meters and as a proportion of all land of that type within the tract or the ZCTA.

2020 Neighborhood Tabulation Areas (NTAs) are medium-sized statistical geographies for reporting Decennial Census and American Community Survey (ACS). 2020 NTAs are created by aggregating 2020 census tracts and nest within Community District Tabulation Areas (CDTA). NTAs were delineated with the need for both geographic specificity and statistical reliability in mind. Consequently, each NTA contains enough population to mitigate sampling error associated with the ACS yet offers a unit of analysis that is smaller than a Community District. Though NTA boundaries and their associated names roughly correspond with many neighborhoods commonly recognized by New Yorkers, NTAs are not intended to definitively represent neighborhoods, nor are they intended to be exhaustive of all possible names and understandings of neighborhoods throughout New York City. Additionally, non-residential areas including large parks, airports, cemeteries, and other special areas are represented separately within this dataset and are assigned codes according to their type (See NTAType field). All previously released versions of this data are available on the DCP Website: BYTES of the BIG APPLE. Current version: 25c

This dataset contains measures of socioeconomic and demographic characteristics by US census tract 1990-2010. Example measures include population density; population distribution by race, ethnicity, age, and income; and proportion of population living below the poverty level, receiving public assistance, and female-headed families. The dataset also contains a set of index variables to represent neighborhood disadvantage and affluence.

Prior research has demonstrated that access to parks and greenspace can have a positive impact on many aspects of and contributors to health, including physical activity levels (Kaczynski et al., 2007), healthy aging (Finlay, 2015), and sense of well-being (Larson et al., 2016). Neighborhood parks can also contribute to sense of community (Gómez, 2015). These datasets describe the number and area of parks in each census tract or each ZIP code tabulation area (ZCTA) in the United States. Measures include the total number of parks, park area, and proportion of park area within each census tract or ZCTA.

Neighborhoods regions. Overlapping areas assigned to each neighborhood individually creating overlaps in the data.-- Additional Information: Category: Boundary Purpose: Identifies full area of each neighborhood individually with ID number to assign contact information. Update Frequency: As Needed-- Metadata Link https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=54371

Associations created to maintain the quality of life in a given neighborhood. These associations consist of both neighborhood associations (NA) and homeowner associations (HOA).Contact E-Mail: jacob_payne@tempe.govContact Phone: N/ALink: N/AData Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: Automatic Data Dictionary

This dataset contains American Community Survey (ACS) data aggregated by neighborhood. The current ACS vintage is for 2019-2023. Values are calculated by aggregating all the census tracts that fall within a given neighborhood. If a census tract falls across two or more neighborhood, the neighborhood which contains most of the census tract's blocks is assigned said tract. Click here to learn more about how this process works. Update FrequencyThis dataset is updated annually when the new ACS vintage is released. This dataset is featured on the following app(s):City Census Viewer ContactsSamuel Martinez, Urban Analytics and Innovationsmartinez2@clevelandohio.gov Data GlossaryField aliases from U.S. Census Bureau explain each column's meaning. See U.S. Census Bureau documentation for more details on their metrics using the field codes. Methodology1. Get all census tracts within Cuyahoga county. 2. Determine which census tracts are within the city of Cleveland. a. If a census tract falls over multiple city boundaries, the city that contains more of that census tract’s blocks is assigned to said census tract. 3. Filter the dataset for census tracts within Cleveland. 4. Determine which census tracts are within which neighborhoods. a. If a census tract falls across two or more neighborhoods, whichever neighborhood contains most of that tract’s blocks is assigned. 5. Aggregate counts for different ACS variables across census tracts within each neighborhood. This results in the final estimates.

Neighborhood level data summarized by demographic, social and economic profiles, and health outcomes and risk factors. Neighborhoods were defined based on the 2020 U.S. Census Bureau, Decennial Census Tracts.