This layer shows the age statistics in Tucson by neighborhood, aggregated from block level data, between 2010-2019. For questions, contact GIS_IT@tucsonaz.gov. The data shown is from Esri's 2019 Updated Demographic estimates.Esri's U.S. Updated Demographic (2019/2024) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2019/2024 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

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 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.

Note: As of April 16, 2021, this dataset will update daily with a five-day data lag.

A. SUMMARY This dataset includes COVID-19 tests by resident neighborhood and specimen collection date (the day the test was collected). Specifically, this dataset includes tests of San Francisco residents who listed a San Francisco home address at the time of testing. These resident addresses were then geo-located and mapped to neighborhoods. The resident address associated with each test is hand-entered and susceptible to errors, therefore neighborhood data should be interpreted as an approximation, not a precise nor comprehensive total.

In recent months, about 5% of tests are missing addresses and therefore cannot be included in any neighborhood totals. In earlier months, more tests were missing address data. Because of this high percentage of tests missing resident address data, this neighborhood testing data for March, April, and May should be interpreted with caution (see below)

Percentage of tests missing address information, by month in 2020 Mar - 33.6% Apr - 25.9% May - 11.1% Jun - 7.2% Jul - 5.8% Aug - 5.4% Sep - 5.1% Oct (Oct 1-12) - 5.1%

To protect the privacy of residents, the City does not disclose the number of tests in neighborhoods with resident populations of fewer than 1,000 people. These neighborhoods are omitted from the data (they include Golden Gate Park, John McLaren Park, and Lands End).

Tests for residents that listed a Skilled Nursing Facility as their home address are not included in this neighborhood-level testing data. Skilled Nursing Facilities have required and repeated testing of residents, which would change neighborhood trends and not reflect the broader neighborhood's testing data.

This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected).

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times (which is common). To see the number of new confirmed cases by neighborhood, reference this map: https://data.sfgov.org/stories/s/Map-of-Cumulative-Cases/adm5-wq8i#new-cases-map

B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information. All testing data is then geo-coded by resident address. Then data is aggregated by "https://data.sfgov.org/Geographic-Locations-and-Boundaries/Analysis-Neighborhoods/p5b7-5n3h ">analysis neighborhood and specimen collection date.

Data are prepared by close of business Monday through Saturday for public display.

C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure.

D. HOW TO USE THIS DATASET Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a data user can analyze this data by "specimen_collection_date".

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. Percent positivity indicates how widesprea

This data layer contains the boundaries for the City of Hartford neighborhoods.

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 dataset contains Neighborhood Planning and Neighborhood Plan Amendment applications as reflected in the City of Austin's AMANDA database.

GIS data: neighborhood labels as depicted in New York City: A City of Neighborhoods.

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Neighborhood boundaries for the City of Providence

This dataset contains measures of the number and density of health care services per United States Census Tract or ZIP Code Tabulation Area (ZCTA) from 1990 through 2021. The dataset includes four separate files for four different geographic areas (GIS shapefiles from the United States Census Bureau). The four geographies include:● Census Tract 2010 ● Census Tract 2020● ZIP Code Tabulation Area (ZCTA) 2010 ● ZIP Code Tabulation Area (ZCTA) 2020Information about which dataset to use can be found in the Usage Notes section of this document.

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.

Data from: American Community Survey, 5-year Series

description: Building project data for 75,110 single-family homes upgraded between July 1, 2010, and September 30, 2013 from the Better Building Neighborhood Program. This dataset includes a documentation file and three data tables. Reported data for some elements have been transformed and data for some upgraded homes have been omitted to protect privacy. See documentation file for details.; abstract: Building project data for 75,110 single-family homes upgraded between July 1, 2010, and September 30, 2013 from the Better Building Neighborhood Program. This dataset includes a documentation file and three data tables. Reported data for some elements have been transformed and data for some upgraded homes have been omitted to protect privacy. See documentation file for details.

Analysis of ‘New York City Population By Neighborhood Tabulation Areas’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/903f4a4a-0402-4ff8-b8bd-f1d6ad856d0c on 13 February 2022.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

The American Community Survey (ACS), conducted by the U.S. Census Bureau, replaced the long form of the decennial census in 2000. The ACS allows researchers, policy makers, and others access to timely information about the U.S. population to make decisions about infrastructure and distribution of federal funds. The monthly survey is sent to a sample of approximately 3.5 million U.S. addresses, including the District of Columbia and Puerto Rico. The ACS includes questions on topics not included in the decennial census, such as those about occupations and employment, education, and key areas of infrastructure like internet access and transportation. When studying large geographic areas, such as states, researchers can use a single year's worth of ACS data to create population-level estimates. However, the study of smaller groups of the population, such as those employed in arts-related fields, requires additional data for more accurate estimation. Specifically, researchers often use 5-year increments of ACS data to draw conclusions about smaller geographies or slices of the population. Note, the Census Bureau produced 3-year estimates between 2005 and 2013 (resulting in seven files: 2005-2007, 2006-2008, 2007-2009, . . . 2011-2013), which remain available but no additional 3-year estimate files have been created. Individuals wishing to describe people working in occupations related to the arts or culture should plan to use at least five years' worth of data to generate precise estimates. When selecting data from the U.S. Census Bureau or IPUMS USA, users should select data collected over 60 months, such as 2020-2024. NADAC's Guide to Creating Artist Extracts and Special Tabulations of Artists from the American Community Survey provides information about the occupation codes used to identify artists.

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 map symbolizes the relative population counts for the City's 12 Data Divisions, aggregating the tract-level estimates from the the Census Bureau's American Community Survey 2021 five-year samples. Please refer to the map's legend for context to the color shading -- darker hues indicate more population.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 2017-2021 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.

The Home Mortgage Disclosure Act (HMDA) database (Consumer Financial Protection Bureau, 2022) has compiled mortgage lending data since 1981, but the collection and dissemination methods have changed over time (Federal Financial Institutions Examination Council, 2018), creating barriers to conducting longitudinal analyses. This HMDA Longitudinal Dataset (HLD) organizes and standardizes information across different eras of HMDA data collection between 1981 and 2021, enabling such analysis. This collection contains two types of datasets: 1) HMDA aggregated data by census tract for each decade and 2) HMDA aggregated data by census tract for individual years. Items for analysis include borrower income values, mortgages by loan type (e.g., conventional, Federal Housing Administration (FHA), Veterans Affairs (VA), refinances), and mortgages by borrower race and gender.

Comprehensive dataset of 614 Community centers in State of Paraná, Brazil as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.