This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

This application was put together at the request of the Emergency Rental Assistance Program team. They wanted an easy way to quickly look up whether an address fell within a low to moderate income census tract. This data is provided by HUD and more information can be found here. The layer used is a nation wide data set with a filter put on it to focus on census tracts in Orange County, Florida with >50% low to moderate income population.

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.

This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. 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. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data 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. For more information about ACS layers, visit the FAQ. 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 is 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, 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 2023 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 Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet that need. With respect to activities that principally benefit low- and moderate-income persons, at least 51 percent of the activity's beneficiaries must be low and moderate income. For CDBG, a person is considered to be of low income only if he or she is a member of a household whose income would qualify as "very low income" under the Section 8 Housing Assistance Payments program. Generally, these Section 8 limits are based on 50% of area median. Similarly, CDBG moderate income relies on Section 8 "lower income" limits, which are generally tied to 80% of area median. These data are derived from the 2011-2015 American Community Survey (ACS) and based on Census 2010 geography.

To learn more about the Low to Moderate Income Populations visit: https://www.hudexchange.info/programs/acs-low-mod-summary-data/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low to Moderate Income Populations by Tract

This annual study provides selected income and tax items classified by State, ZIP Code, and the size of adjusted gross income. These data include the number of returns, which approximates the number of households; the number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based who reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, ZIP Code Data.

For source data: https://data.census.gov/table/ACSST5Y2023.S1903For HUD income limits: https://www.huduser.gov/portal/datasets/il.htmlFor more information about this dataset, please contact egis@isd.lacounty.gov

This dataset includes two sets of files: (1) the school-bg files contain the block groups assigned to each school (i.e. estimated catchment areas), as well as corresponding census tracts and enrollments, using the Census-to-School (CTS) Method described in Hashim, Laski, Kelley-Kemple (2023); (2) the school-level file contains estimates for school-level mean income and variance of income derived using publicly available data on school locations from the CCD, enrollments, and census data on the number of school aged children in each block group, and the proportion of families with school-age children in each income bin in each tract. See Hashim, Laski, Kelley-Kemple (2023) for details.

Household income is a potential predictor for a number of environmental influences, for example, application of urban pesticides. This product is a U.S. conterminous mapping of block group income derived from the 2010-2014 Census American Community Survey (ACS), adjusted by a 2013 county-level Cost-of-Living index obtained from the Council for Community and Economic Research. The resultant raster is provided at 200-m spatial resolution, in units of adjusted household income in thousands of dollars per year.

This is a map to assist Department of Housing & Community Development staff determine if properties qualify for ARPA and repair funds.Targeted Rehab Boundaries Boundaries for the West Dallas Targeted Rehab Program (Census Tracts 106.01, 160.02, 105, 205, 101.01, 101.02, 43) and Tenth Street Rehab Program (Historic Tenth Street). Home repair programs available in these areas: Housing & Neighborhood Revitalization Targeted Rehabilitation Program (TRP) (dallascityhall.com) Unserved Areas Dallas Water Utilities (DWU) 's Unserved Areas Report identified geographical areas that need water and/or wastewater services throughout the City. DWU is in the process of building out service in these areas. (2020 update) Home repair programs available in these areas: Housing & Neighborhood Revitalization ARPA Septic Tank (dallascityhall.com) QCTs This service contains a list of census tracts that qualify for the American Rescue Plan Act (ARPA).  The list was provided to EGIS by BMS.  The data used to produce this service can be found at Qualified Census Tracts and Difficult Development Areas | HUD USER. Low-Income Housing Tax Credit Qualified Census Tracts must have 50 percent of households with incomes below 60 percent of the Area Median Gross Income (AMGI) or have a poverty rate of 25 percent or more. Difficult Development Areas (DDA) are areas with high land, construction and utility costs relative to the area median income and are based on Fair Market Rents, income limits, the 2010 census counts, and 5-year American Community Survey (ACS) data. Maps of Qualified Census Tracts and Difficult Development Areas are available at: 2022 and 2023 Small DDAs and QCTs | HUD USER. Qualified Census Tracts - Generate QCT Tables for Individual Areas (Also Includes DDA Information) This data was created by the Department of Housing and Urban Development in 2023.  This data is updated on a yearly basis.  Updated ARPA boundaries ARPA Home Repair Program boundaries for qualified neighborhoods. Home repair programs available in these areas: American Rescue Plan Act Neighborhood Revitalization Program (dallascityhall.com) (Limited availability, applications accepted based on funding available) Equity Strategy Target AreasThe Department of Housing & Neighborhood Revitalization (Housing) with the assistance of TDA Consultants selected three Equity Strategy Target Areas (ESTAs) for the implementation of the Dallas Housing Policy 2033 (DHP33). This layer contains boundaries as of January 2024. Housing will be collaborating with other City of Dallas departments and development and preservation partners to target housing and neighborhood revitalization projects in these areas. The Equity Strategy Target Areas (ESTAs) were selected using an Equity Index created by TDA consultants and the Housing Department. The Equity Index is based upon the 2023 Market Value Analysis, the City of Dallas OEI Equity Impact Assessment Tool (EIA), and the potential investment from the Dallas Water Utility Unserved Areas Program.Housing Opportunity Fund TIF District AreasThis is the Housing Opportunity Fund TIF District map for Housing & Community Development and Economic Development in the City of Dallas. The three TIF districts in this map are areas within the City of Dallas with select TIF funds for homeowner stabilization programs that may include Home Improvement and Preservation Programs (HIPP) and the Dallas Homebuyer Assistance Program (DHAP). The three Housing Opportunity Fund TIF districts are: the Oak Cliff Housing TIF, the Fort Worth Avenue Housing TIF, and the Deep Ellum Housing TIF. Housing & Community Development is starting to implement these areas in 2025.

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation_**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**

Title: Location Affordability Index - NMCDC Copy

Summary: This layer contains the Location Affordability Index from U.S. Dept. of Housing and Urban Development (HUD) - standardized household, housing, and transportation cost estimates by census tract for 8 household profiles.

Notes: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas.

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

Source: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas. Check the source documentation or other details above for more information about data sources.

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=447a461f048845979f30a2478b9e65bb

UID: 73

Data Requested: Family income spent on basic need

Method of Acquisition: Search for Location Affordability Index in the Living Atlas. Make a copy of most recent map available. To update this map, copy the most recent map available. In a new tab, open the AGOL Assistant Portal tool and use the functions in the portal to copy the new maps JSON, and paste it over the old map (this map with item id

Date Acquired: Map copied on May 10, 2022

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address marlene@income-outcome.com..

The U.S. Department of Housing and Urban Development (HUD) periodically receives "custom tabulations" of Census data from the U.S. Census Bureau that are largely not available through standard Census products. These datasets, known as "CHAS" (Comprehensive Housing Affordability Strategy) data, demonstrate the extent of housing problems and housing needs, particularly for low income households. The primary purpose of CHAS data is to demonstrate the number of households in need of housing assistance. This is estimated by the number of households that have certain housing problems and have income low enough to qualify for HUD’s programs (primarily 30, 50, and 80 percent of median income). CHAS data provides counts of the numbers of households that fit these HUD-specified characteristics in a variety of geographic areas. In addition to estimating low-income housing needs, CHAS data contributes to a more comprehensive market analysis by documenting issues like lead paint risks, "affordability mismatch," and the interaction of affordability with variables like age of homes, number of bedrooms, and type of building.This dataset is a special tabulation of the 2016-2020 American Community Survey (ACS) and reflects conditions over that time period. The dataset uses custom HUD Area Median Family Income (HAMFI) figures calculated by HUD PDR staff based on 2016-2020 ACS income data. CHAS datasets are used by Federal, State, and Local governments to plan how to spend, and distribute HUD program funds. To learn more about the Comprehensive Housing Affordability Strategy (CHAS), visit: https://www.huduser.gov/portal/datasets/cp.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs Data Dictionary: DD_ACS 5-Year CHAS Estimate Data by County Date of Coverage: 2016-2020

A Qualified Census Tract (QCT) is any census tract (or equivalent geographic area defined by the Census Bureau) in which at least 50% of households have an income less than 60% of the Area Median Gross Income (AMGI). HUD has defined 60% of AMGI as 120% of HUD's Very Low Income Limits (VLILs), which are based on 50% of area median family income, adjusted for high cost and low income areas.

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address internet-income-train.com@proxy.dreamhost.com..

Dataset contains counts and measures for individuals from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 1.

​

The variables included in this dataset are for the census usually resident population count (unless otherwise stated). All data is for level 1 of the classification.

​

The variables for part 2 of the dataset are:

• Individual home ownership for the census usually resident population count aged 15 years and over
• Usual residence 1 year ago indicator
• Usual residence 5 years ago indicator
• Years at usual residence
• Average years at usual residence
• Years since arrival in New Zealand for the overseas-born census usually resident population count
• Average years since arrival in New Zealand for the overseas-born census usually resident population count
• Study participation
• Main means of travel to education, by usual residence address for the census usually resident population who are studying
• Main means of travel to education, by education address for the census usually resident population who are studying
• Highest qualification for the census usually resident population count aged 15 years and over
• Post-school qualification in New Zealand indicator for the census usually resident population count aged 15 years and over
• Highest secondary school qualification for the census usually resident population count aged 15 years and over
• Post-school qualification level of attainment for the census usually resident population count aged 15 years and over
• Sources of personal income (total responses) for the census usually resident population count aged 15 years and over
• Total personal income for the census usually resident population count aged 15 years and over
• Median ($) total personal income for the census usually resident population count aged 15 years and over
• Work and labour force status for the census usually resident population count aged 15 years and over
• Job search methods (total responses) for the unemployed census usually resident population count aged 15 years and over
• Status in employment for the employed census usually resident population count aged 15 years and over
• Unpaid activities (total responses) for the census usually resident population count aged 15 years and over
• Hours worked in employment per week for the employed census usually resident population count aged 15 years and over
• Average hours worked in employment per week for the employed census usually resident population count aged 15 years and over
• Industry, by usual residence address for the employed census usually resident population count aged 15 years and over
• Industry, by workplace address for the employed census usually resident population count aged 15 years and over
• Occupation, by usual residence address for the employed census usually resident population count aged 15 years and over
• Occupation, by workplace address for the employed census usually resident population count aged 15 years and over
• Main means of travel to work, by usual residence address for the employed census usually resident population count aged 15 years and over
• Main means of travel to work, by workplace address for the employed census usually resident population count aged 15 years and over
• Sector of ownership for the employed census usually resident population count aged 15 years and over
• Individual unit data source.

​

Download lookup file for part 2 from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

​

Footnotes

Te Whata

Under the Mana Ōrite Relationship Agreement, Te Kāhui Raraunga (TKR) will be publishing Māori descent and iwi affiliation data from the 2023 Census in partnership with Stats NZ. This will be available on Te Whata, a TKR platform.

​

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

​

Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

​

​

Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts.

​

Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

​

Study participation time series

In the 2013 Census study participation was only collected for the census usually resident population count aged 15 years and over.

​

About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

​

Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

​

Concept descriptions and quality ratings

Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.

​

Disability indicator

This data should not be used as an official measure of disability prevalence. Disability prevalence estimates are only available from the 2023 Household Disability Survey. Household Disability Survey 2023: Final content has more information about the survey.

Activity limitations are measured using the Washington Group Short Set (WGSS). The WGSS asks about six basic activities that a person might have difficulty with: seeing, hearing, walking or climbing stairs, remembering or concentrating, washing all over or dressing, and communicating. A person was classified as disabled in the 2023 Census if there was at least one of these activities that they had a lot of difficulty with or could not do at all.

​

Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

​

Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

​

Measures

Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value

Globally, the gap between the richest and poorest population is widening, and United States of America is no exception. Waldo Tobler's First Law of Geography states that near things are more related than distant things, which can sometimes be seen within a map as clustering of features. Use this map to explore the distribution of households within the income extremes.The app allows the user to explore an area by typing an area of interest into the search bar. Dot density is used to represent multiple households per dot and are contained within census tract boundaries. A pop-up appears at larger scales in order to provide a chart comparing the household count for the highest and lowest income ranges. The highest income range covers households which make $200,000 or more a year. The lowest income range shows households making less than $25,000 a year. The map is shown from 36M scale to 72K scale and is designed to be displayed on the Dark Gray Canvas Basemap.The data within this map comes from Esri's Updated Demographics. The vintage of the data and boundaries is 2015.