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

metadata for Civic Life / PSU Neighborhood Profiles-- Additional Information Category: Miscellaneous Purpose: metadata for Civic Life / PSU Neighborhood Profiles Update Frequency: Annually-- Metadata Link: https://www.portlandmaps.com/metadata/index.cfm?&action=DisplayLayer&LayerID=61020

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.

Table from the American Community Survey (ACS) 5-year series on race and ethnicity related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B03002 Hispanic or Latino Origin by Race, B02008-B02013 Race Alone or in Combination with One or More Other. 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): B03002, B02008, B02009, B02010, B02011, B02012, B02013Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACS</

Interactive visualization of Newton County facts and figures, including population, demographics, community involvement, economy, employment, education, housing, health, crime and transportation. Data sources include AARP, U.S. Census/ACS, ARC, BLS, Center for Neighborhood Technology and County Health Rankings.

Interactive visualization of Cherokee County facts and figures, including population, demographics, community involvement, economy, employment, education, housing, health, crime and transportation. Data sources include AARP, U.S. Census/ACS, ARC, BLS, Center for Neighborhood Technology and County Health Rankings.

After over two years of public reporting, the State Profile Report will no longer be produced and distributed after February 2023. The final release was on February 23, 2023. We want to thank everyone who contributed to the design, production, and review of this report and we hope that it provided insight into the data trends throughout the COVID-19 pandemic. Data about COVID-19 will continue to be updated at CDC’s COVID Data Tracker.

The State Profile Report (SPR) is generated by the Data Strategy and Execution Workgroup in the Joint Coordination Cell, in collaboration with the White House. It is managed by an interagency team with representatives from multiple agencies and offices (including the United States Department of Health and Human Services (HHS), the Centers for Disease Control and Prevention, the HHS Assistant Secretary for Preparedness and Response, and the Indian Health Service). The SPR provides easily interpretable information on key indicators for each state, down to the county level.

It is a weekly snapshot in time that:

• Focuses on recent outcomes in the last seven days and changes relative to the month prior
• Provides additional contextual information at the county level for each state, and includes national level information
• Supports rapid visual interpretation of results with color thresholds

The median age indicates the age separating the population group into two halves of equal size.

Analysis of ‘Urban Village Demographic Area Profile ACS 5-year 2006-2010’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9885376b-e265-4325-a88b-e7125f8937fa on 27 January 2022.

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

Data from: American Community Survey, 5-year Series 2006-2010

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

This file contains demographic, social, economic, and housing information from the "100-percent" and unweighted sample counts from the 1980 census for locally defined neighborhoods. The Neighborhood Publication Area (NPA) is the total area within which neighborhoods were defined by each participant in the Neighborhood Statistics Program (NSP), which was developed by the Census Bureau. Population items include age, race, sex, marital status, Spanish origin, employment status, and language spoken at home. Housing items include occupancy/vacancy status, tenure, contract rent, value, condominium status, number of rooms, and plumbing facilities.

The neighbourhood profiles are 22 data profiles that, collectively, cover the whole borough of Camden. The profiles contain comprehensive, verifiable and up-to-date statistics from a variety of sources about the community characteristics, assets and strengths, challenges and needs in each neighbourhood. They are designed to help the Council, other statutory partners and the VCS understand what is being delivered in small areas and the resources that already exist in each area, and identify any gaps. The neighbourhoods are composites of lower super output areas (LSOAs), which are smaller than wards. The neighbourhoods do not conform to administrative boundaries or electoral wards. Their borders are instead based on the actual way that residents identify with particular areas and how they really move about within certain localities.

The neighbourhood profiles are 22 data profiles that, collectively, cover the whole borough of Camden. The profiles contain comprehensive, verifiable and up-to-date statistics from a variety of sources about the community characteristics, assets and strengths, challenges and needs in each neighbourhood. They are designed to help the Council, other statutory partners and the VCS understand what is being delivered in small areas and the resources that already exist in each area, and identify any gaps.

The neighbourhoods are composites of lower super output areas (LSOAs), which are smaller than wards. The neighbourhoods do not conform to administrative boundaries or electoral wards. Their borders are instead based on the actual way that residents identify with particular areas and how they really move about within certain localities.

This dataset tracks annual white student percentage from 1999 to 2023 for West Somerville Neighborhood vs. Massachusetts and Somerville School District

This dataset tracks annual total students amount from 2019 to 2023 for Kepler Neighborhood

Table from the American Community Survey (ACS) 5-year series on income and earning related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B19025 Aggregate Household Income, B19013 Median Household Income, B19001 Household Income, B19113 Median Family Household Income, B19101 Family Household Income, B19202 Median Nonfamily Household Income, B19201 Nonfamily Household Income, B19301 Per Capita Income/B19313 Aggregate Income/B01001 Sex by Age, C24010 Sex by Occupation of the Civilian Employed Population 16 years and Over, B20017 Median Earnings by Sex by Work Experience for the Population 16 years and over with Earnings, B20001 Sex by Earnings for the Population 16 years and over with Earnings. 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): B19013, B19001, B19113, B19101, B19202, B19201, B19301, B19313, B01001, C24010, B20017, B20001, B19025Data 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 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:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). 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 erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. 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 RicoCensus tracts with no population that occur in areas of water, such as oceans, 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., -4444...) have been set to null, with the exception of -5555... which has been set to zero. 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.

The neighbourhood profiles are 22 data profiles that, collectively, cover the whole borough of Camden. The profiles contain comprehensive, verifiable and up-to-date statistics from a variety of sources about the community characteristics, assets and strengths, challenges and needs in each neighbourhood. They are designed to help the Council, other statutory partners and the VCS understand what is being delivered in small areas and the resources that already exist in each area, and identify any gaps. The neighbourhoods are composites of lower super output areas (LSOAs), which are smaller than wards. The neighbourhoods do not conform to administrative boundaries or electoral wards. Their borders are instead based on the actual way that residents identify with particular areas and how they really move about within certain localities.

Table from the American Community Survey (ACS) 5-year series on household types and population related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B11003 Family Type by Presence and Age of Own Children under 18 Years, B11005 Households by Presence of People Under 18 Years by Household Type, B11007 Households by Presence of People 65 Years and Over by Household Type, B11001 Household Type (Including Living Alone), B11002 Household Type by Relatives and Nonrelatives for Population in Households, B25003 Tenure, B25008 Total Population in Occupied Housing Units by Tenure, B09019 Household Type (Including Living Alone) by Relationship. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

To assist with primary health care planning, Alberta Health has developed a series of reports to provide a broad range of demographic, socio-economic and population health statistics considered relevant to primary health care for communities across the province. These community profiles provide information at the Zone and Local Geographic Area (LGA) level for each of the 132 LGAs in Alberta. Each Profile offers an overview of the current health status of residents in the LGA, indicators of the area's current and future health needs, and evidence as to which quality services are needed on a timely basis to address the area's needs. The profiles are intended to highlight areas of need and provide relevant information to support the consistent and sustainable planning of primary health services.

The neighbourhood profiles are 22 data profiles that, collectively, cover the whole borough of Camden. The profiles contain comprehensive, verifiable and up-to-date statistics from a variety of sources about the community characteristics, assets and strengths, challenges and needs in each neighbourhood. They are designed to help the Council, other statutory partners and the VCS understand what is being delivered in small areas and the resources that already exist in each area, and identify any gaps.

The neighbourhoods are composites of lower super output areas (LSOAs), which are smaller than wards. The neighbourhoods do not conform to administrative boundaries or electoral wards. Their borders are instead based on the actual way that residents identify with particular areas and how they really move about within certain localities.

In 2021, the most followed Hungarian Twitter profile in the community category belonged to the porn star, Aletta Ocean. Music Channel ranked second with 144 thousand followers.