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.

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.

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.

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.

Table from the American Community Survey (ACS) 5-year series on age and gender related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B01001 Sex by Age, B01002 Median Age by Sex. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B01001, B01002Data downloaded from: Census Bureau's Explore Census Data The 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 estima

Neighborhood boundaries for the City of Providence

This dataset contains measures of the urban/rural characteristics of each census tract in the United States. These include proportions of urban and rural population, population density, rural/urban commuting area (RUCA) codes, and RUCA-based four- and seven- category urbanicity scales. A curated version of this data is available through ICPSR at https://www.icpsr.umich.edu/web/ICPSR/studies/38606/versions/V1

Neighborhood notification boundaries created by the Department of City Planning. These boundaries are designed solely for the Planning Department's neighborhood notifications where neighborhood groups are notified about certain types of developments in their area. An Excel spreadsheet of Neighborhood Groups contact details can be downloaded from this page: http://sf-planning.org/index.aspx?page=1654 There are alternative neighborhood boundaries available (which include a larger number of neighborhoods) here (Mayors Office): https://data.sfgov.org/d/pty2-tcw4 and here (Realtors): https://data.sfgov.org/Geography/SF-Realtor-Neighborhoods/wwis-y924

This dataset depicts neighborhood boundaries within the Town of Mammoth Lakes, CA.

The distinction between the effective size of a population (Ne) and the effective size of its neighborhoods (Nn) has sometimes become blurred. Ne reflects the effect of random sampling on the genetic composition of a population of size N, whereas Nn is a measure of within-population spatial genetic structure and depends strongly on the dispersal characteristics of a species. Although Nn is independent of Ne, the reverse is not true. Using simulations of a population of annual plants, it was found that the effect of Nn on Ne was well approximated by Ne=N/(1−FIS), where FIS (determined by Nn) was evaluated population wide. Nn only had a notable influence of increasing Ne as it became smaller (less than or equal to16). In contrast, the effect of Nn on genetic estimates of Ne was substantial. Using the temporal method (a standard two-sample approach) based on 1000 single-nucleotide polymorphisms (SNPs), and varying sampling method, sample size (2–25% of N) and interval between samples (T=1–32 generations), estimates of Ne ranged from infinity to <0.1% of the true value (defined as Ne based on 100% sampling). Estimates were never accurate unless Nn and T were large. Three sampling techniques were tested: same-site resampling, different-site resampling and random sampling. Random sampling was the least biased method. Extremely low estimates often resulted when different-site resampling was used, especially when the population was large and the sample fraction was small, raising the possibility that this estimation bias could be a factor determining some very low Ne/N that have been published.

This dataset describes the number and density of health care services in each census tract in the United States. The data includes counts, per capita densities, and area densities per tract for many types of businesses in the health care sector, including doctors, dentists, mental health providers, nursing homes, and pharmacies.

This study includes restricted data version 2.5, for Wave 1 of the L.A.FANS data. To compare L.A.FANS restricted data, version 2.5 with other restricted data versions, see the table on the series page for the L.A.FANS data here. Data in this study are designed for use with the public use data files for L.A.FANS, Wave 1 (study 1). This file adds only a few variables to the L.A.FANS, Wave 1 public use files. Specifically, it adds the census tract and block number for the tract each respondent lives in. It also includes certain variables, thought to be sensitive, which are not available in the public use data. These variables are identified in the L.A.FANS Wave 1 Users Guide and Codebook. Finally, some distance variables and individual characteristics which are treated in the public use data to make it harder to identify individuals are provided in an untreated form in the Version 2.5 restricted data file. Please note that L.A. FANS restricted data may only be accessed within the ICPSR Virtual Data Enclave (VDE) and must be merged with the L.A. FANS public data prior to beginning any analysis. A Users' Guide which explains the design and how to use the samples are available for Wave 1 at the RAND website. Additional information on the project, survey design, sample, and variables are available from: Sastry, Narayan, Bonnie Ghosh-Dastidar, John Adams, and Anne R. Pebley (2006). The Design of a Multilevel Survey of Children, Families, and Communities: The Los Angeles Family and Neighborhood Survey, Social Science Research, Volume 35, Number 4, Pages 1000-1024 The Users' Guides (Wave 1 and Wave 2) RAND Documentation Reports page

This study is a restricted data file of data from the L.A.FANS Neighborhood Observation Study, an in-person observational by trained L.A.FANS interviewers of the census blocks on which L.A.FANS respondents lived during L.A.FANS Wave 1. Interviewers were trained to walk each block face and record social and physical observations on precoded check sheets. Each block face was observed by several different interviewers working independently at different times of the day and week. These data are designed to be used with L.A.FANS Wave-1 survey interview data restricted versions 2.5 or 3 to provide data on the census block and census tract in which individual respondents lived. Users who apply for these restricted data must also be approved for using restricted version 2.5 or 3. Please note that L.A. FANS restricted data may only be accessed within the ICPSR Virtual Data Enclave (VDE) and must be merged with the L.A. FANS public data prior to beginning any analysis. The study is described in detail in the L.A.FANS Neighborhood Observations Codebook. Further information is available in: Jones, M., Pebley, A. R., and Sastry, N. (2011). Eyes on the block: Measuring urban physical disorder through in-person observation. Social Science Research, 40(2), 523-537.

Selected variables from the most recent ACS Community Survey (Released 2023) aggregated by Community Area. Additional years will be added as they become available. The underlying algorithm to create the dataset calculates the % of a census tract that falls within the boundaries of a given community area. Given that census tracts and community area boundaries are not aligned, these figures should be considered an estimate. Total population in this dataset: 2,647,621 Total Chicago Population Per ACS 2023: 2,664,452 % Difference: -0.632% There are different approaches in common use for displaying Hispanic or Latino population counts. In this dataset, following the approach taken by the Census Bureau, a person who identifies as Hispanic or Latino will also be counted in the race category with which they identify. However, again following the Census Bureau data, there is also a column for White Not Hispanic or Latino. Code can be found here: https://github.com/Chicago/5-Year-ACS-Survey-Data Community Area Shapefile: https://data.cityofchicago.org/Facilities-Geographic-Boundaries/Boundaries-Community-Areas-current-/cauq-8yn6 Census Area Python Package Documentation: https://census-area.readthedocs.io/en/latest/index.html

This map symbolizes the relative percentages of adults living below the poverty level for the City's 12 Data Divisions, aggregating the tract-level estimates from the the Census Bureau's American Community Survey 2018 five-year samples. Please refer to the map's legend for context to the color shading -- darker hues indicate a higher level of adults living below the poverty level.If you click on each Data Division, you can view other Census demographic information about that Data Division in addition to the population count.About the Census Data:The data comes from the U.S. Census Bureau's American Community Survey's 2014-2018 five-year samples. The American Community Survey (ACS) is an ongoing survey conducted by the federal government that provides vital information annually about America and its population. Information from the survey generates data that help determine how more than $675 billion in federal and state funds are distributed each year.For more information about the Census Bureau's ACS data and process of constructing the survey, visit the ACS's About page.About the City's Data Divisions:As a planning analytic tool, an interdepartmental working group divided Rochester into 12 “data divisions.” These divisions are well-defined and static so they are positioned to be used by the City of Rochester for statistical and planning purposes. Census data is tied to these divisions and serves as the basis for analyses over time. As such, the data divisions are designed to follow census boundaries, while also recognizing natural and human-made boundaries, such as the River, rail lines, and highways. Historical neighborhood boundaries, while informative in the division process, did not drive the boundaries. Data divisions are distinct from the numerous neighborhoods in Rochester. Neighborhood boundaries, like quadrant boundaries, police precincts, and legislative districts often change, which makes statistical analysis challenging when looking at data over time. The data division boundaries, however, are intended to remain unchanged. It is hoped that over time, all City data analysts will adopt the data divisions for the purpose of measuring change over time throughout the city.

Boundaries of the Neighborhood Quadrants. These outline the areas of responsibility of the Planning Department's neighborhood planning teams. All building permits and cases are assigned on a geographic basis to the neighborhood planning teams. For example, permits and cases located in the NW neighborhood quadrant are assigned to planners in the NW planning team.

How does one's place of residence affect individual behavior and long-run outcomes? Understanding neighborhood and place effects has been a leading question for social scientists during the past half-century. Recent empirical studies using experimental and quasi-experimental research designs have generated new insights on the importance of residential neighborhoods in childhood and adulthood. This paper summarizes the recent neighborhood effects literature and interprets the findings. Childhood neighborhoods affect long-run economic and educational outcomes in a manner consistent with exposure models of neighborhood effects. For adults, neighborhood environments matter for their health and well-being but have more ambiguous impacts on labor market outcomes. We discuss the evidence on the mechanisms behind the observed patterns and conclude by highlighting directions for future research.

Data from: American Community Survey, 5-year Series

This dataset includes neighborhood boundaries for 150+ neighborhoods in Philadelphia. The data was gathered from a mix of publicly available maps, including from the City of Philadelphia, the City Archives, the Philadelphia Inquirer, and user feedback.

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.