This map contains NYC administrative boundaries enriched with various demographics datasets.Learn more about Esri's Enrich Layer / Geoenrichment analysis tool.Learn more about Esri's Demographics, Psychographic, and Socioeconomic datasets.Search for a specific location or site using the search bar. Toggle layer visibility with the layer list. Click on a layer to see more information about the feature.

Author: Joseph Kerski, post_secondary_educator, Esri and University of DenverGrade/Audience: high school, ap human geography, post secondary, professional developmentResource type: lessonSubject topic(s): population, maps, citiesRegion: africa, asia, australia oceania, europe, north america, south america, united states, worldStandards: All APHG population tenets. Geography for Life cultural and population geography standards. Objectives: 1. Understand how population change and demographic characteristics are evident at a variety of scales in a variety of places around the world. 2. Understand the whys of where through analysis of change over space and time. 3. Develop skills using spatial data and interactive maps. 4. Understand how population data is communicated using 2D and 3D maps, visualizations, and symbology. Summary: Teaching and learning about demographics and population change in an effective, engaging manner is enriched and enlivened through the use of web mapping tools and spatial data. These tools, enabled by the advent of cloud-based geographic information systems (GIS) technology, bring problem solving, critical thinking, and spatial analysis to every classroom instructor and student (Kerski 2003; Jo, Hong, and Verma 2016).

The demographic data displayed in this theme of Florida’s Roadmap to Living Healthy are quantitative measures that exhibit the socioeconomic state of Florida’s communities. The data sets comprising this themed map include topics such as population, race, income level, age, education, housing, and lifestyle data for all of Florida’s 67 counties, and other basic demographic characteristics. The Florida Department of Agriculture and Consumer Services has utilized the most current demographic statistical data from trusted sources such as the U.S. Census Bureau, U.S. Department of Housing and Urban Development, U.S. Department of Labor Bureau of Labor Statistics, Florida Department of Children and Families, and Esri to craft this custom visualization. Demographics provide profound perspective to your data analytics and will help you recognize the distinctive characteristics of a population based on its location. This demographic-themed mapping tool will simplify your ability to identify the specific socioeconomic needs of every community in Florida.

These high-resolution maps estimate not only the number of people living within 30-meter grid tiles, but also provide insights on demographics at unprecedentedly high resolutions. These maps aren’t built using Facebook data and instead rely on combining the power of machine vision AI with satellite imagery and census information.

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

This map is designed to work in the new ArcGIS Online Map Viewer. Open in Map Viewer to view map. What does this map show?This map shows the population in the US by race. The map shows this pattern nationwide for states, counties, and tracts. Open the map in the new ArcGIS Online Map Viewer Beta to see the dot density pattern. What is dot density?The density is visualized by randomly placing one dot per a given value for the desired attribute. Unlike choropleth visualizations, dot density can be mapped using total counts since the size of the polygon plays a significant role in the perceived density of the attribute.Where is the data from?The data in this map comes from the most current American Community Survey (ACS) from the U.S. Census Bureau. Table B03002. The layer being used if updated with the most current data each year when the Census releases new estimates. The layer can be found in ArcGIS Living Atlas of the World: ACS Race and Hispanic Origin Variables - Boundaries.What questions does this map answer?Where do people of different races live?Do people of a similar race live close to people of their own race?Which cities have a diverse range of different races? Less diverse?

Population data for a selection of countries, allocated to 1 arcsecond blocks and provided in a combination of CSV and Cloud-optimized GeoTIFF files. This refines CIESIN’s Gridded Population of the World using machine learning models on high-resolution worldwide Maxar satellite imagery. CIESIN population counts aggregated from worldwide census data are allocated to blocks where imagery appears to contain buildings.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Armenia: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Lithuania: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

This map shows population density of the United States. Areas in darker magenta have much higher population per square mile than areas in orange or yellow. Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. The map's layers contain total population counts by sex, age, and race groups for Nation, State, County, Census Tract, and Block Group in the United States and Puerto Rico. From the Census:"Population density allows for broad comparison of settlement intensity across geographic areas. In the U.S., population density is typically expressed as the number of people per square mile of land area. The U.S. value is calculated by dividing the total U.S. population (316 million in 2013) by the total U.S. land area (3.5 million square miles).When comparing population density values for different geographic areas, then, it is helpful to keep in mind that the values are most useful for small areas, such as neighborhoods. For larger areas (especially at the state or country scale), overall population density values are less likely to provide a meaningful measure of the density levels at which people actually live, but can be useful for comparing settlement intensity across geographies of similar scale." SourceAbout the dataYou can use this map as is and you can also modify it to use other attributes included in its layers. This map's layers contain total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, State, County, Census Tract, Block Group boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, State, County, Census Tract, Block GroupNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This map is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, 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 and block group 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 unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

Demographic Maps page for the Roanoke County demographics website. Contains ACS Population Variables - Boundaries, ACS Median Age Variables - Boundaries, ACS Race and Hispanic Origin Variables - Boundaries, ACS Poverty Status Variables - Boundaries, ACS Median Household Income Variables - Boundaries, ACS Educational Attainment Variables - Boundaries, ACS Language Spoken at Home Variables - Boundaries, and ACS Place of Birth Variables - Boundaries. These are layers by Esri, and are available through Living Atlas.

This paper uses data on residential buildings from the nationwide vector database. Attribute information on buildings (location, volume, function, etc.) provides opportunities to estimate the number of residents. The recalculation of the population from the urban units into new spatial units was based on the area-weighted aggregation method. The location of buildings constituted a limiting variable, while the total square meterage (calculated as the area of the buildings and the number of their floors) constituted the binding variable. The introduction of additional binding variables related to the type of building and its location, as well as various methods of determining the square meterage per building type, resulted in the creation of a total of 19 maps of Cracow’s population. The results of the recalculation of population were related to demographic data compiled by the organisation Statistics Poland (GUS) relating to the 1x1 km grid. Comparison of the results with demographic data relating to other reference units allowed the reduction of subjective interpretation and the refining of input data conversion methods. As a result, correct methods for segmenting buildings were identified, useful optimisation criteria were selected, and the accuracy of population maps developed based on the database was calculated. For the input data, based solely on the amount of population in urban units, the calculated value of the mean absolute percentage error (MAPE) in the 1x1 km grid was 310.8%, and for the root mean square error (RMSE) was 1476 people. In the dasymetric method, directly associating the population with the volume of buildings, the errors fell to 21.9% and 632 people, respectively. Among the remaining 18 variants introducing the segmentation of buildings from the database, the best result was obtained for the variant based on minimizing the RMSE, associating the number of residents to single-family buildings (2.88 people/building) and associating the number of residents to the square footage in multi-family buildings (37.1m2/person) (MAPE=19.2%, RMSE=556 people).

The North Carolina State Demographer data platform houses the latest data produced by the Office of the State Demographer. The platform allows users to create visualizations, download full (or partial) datasets, and create maps. Registered users can save their visualizations and be notified of dataset updates. This new platform is a subdomain of OSBM’s Log In to North Carolina (LINC) – a service containing over 900 data items including items pertaining to population, labor force, education, transportation, etc. LINC includes topline statistics from the State Demographer’s population estimates and projections while the North Carolina State Demographer data platform includes more detailed datasets for users requiring more detailed demographic information.

Sourcing accurate and up-to-date map data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps Map Data uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent demographics data across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

GapMaps Map Data also includes the latest Point-of-Interest (POI) Data for leading retail brands across a range of categories including Fast Food/ QSR, Health & Fitness, Supermarket/Grocery and Cafe sectors which is updated monthly.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

GapMaps Map Data for Asia and MENA can be utilized in any GIS platform and includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Map Data:

1. Retail Site Selection - identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.
6. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
7. Customer Profiling
8. Target Marketing
9. Market Share Analysis

Sourcing accurate and up-to-date demographics GIS data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent geodemographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium demographics GIS data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Demographics GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

11. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery. More information.

There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery. More information.

There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail.The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts.The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate.More information about these data is available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review FAQs.Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data.Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR).1. Population Density: 2020 Population per square mile,2. Poverty Rate: Percentage of population under 100% FPL,3. Median Household income: Based on countywide median HH income of $71,538.4. Highschool Education Attainment: Percentage of 18 years and older population without high school graduation.5. English Speaking Ability: Percentage of 18 years and older population with less or none English speaking ability. 6. Household without Internet Access: Percentage of HH without internet access.7. Non-Hispanic White Population: Percentage of Non-Hispanic White population.8. Non-Hispanic African-American Population: Percentage of Non-Hispanic African-American population.9. Non-Hispanic Asian Population: Percentage of Non-Hispanic Asian population.10. Hispanic Population: Percentage of Hispanic population.