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?

This map shows the 2001–2010 average rate of hospitalizations classified as “heat-related” by medical professionals in 23 states that participate in CDC’s hospitalization tracking program. Rates are based on hospital discharge records for May 1 to September 30 of every year. Rates have been age-adjusted to account for differences in the population distribution over time and between states—for example, if one state has a higher proportion of older adults than another. For more information: www.epa.gov/climatechange/science/indicators

This layer shows total population count by sex and age group. 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. This layer is symbolized to show the percentage of the population that are considered dependent (ages 65+ and <18). 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): B01001Data 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.

Supermarkets are one of the most popular and convenient ways in which Americans gain access to healthy food, such as fresh meat and fish, or fresh fruits and vegetables. There are various ways in which people gain access to supermarkets. People in the suburbs drive to supermarkets and load up the car with many bags of food. People in cities depend much more on walking to the local store, or taking a bus or train.This map came about after asking a simple question: how many Americans live within a reasonable walk or drive to a supermarket?In this case, "reasonable" was defined as a 10 minute drive, or a 1 mile walk. The ArcGIS Network Analyst extension performed the calculations on NAVTEQ streets, and the ArcGIS Spatial Analyst extension created a heat map of the walkable access and drivable access to supermarkets.The green dots represent populations in poverty who live within one mile of a supermarket. The red dots represent populations in poverty who live beyond a one mile walk to a supermarket, but may live within a 10 minute drive...assuming they have access to a car. The grey dots represent the total population in a given area.This is an excellent map to use as backdrop to show how people are improving access to healthy food in their community. Open this map in ArcGIS Explorer to add your favorite farmers' market, CSA, or transit line -- then share that map via Facebook, Twitter or email.This map shows data for the entire U.S. The supermarkets included in the analysis have annual sales of $1 million or more. Populations in poverty are represented by taking the block group poverty rate (e.g. 10%) from the Census and symbolizing each block in that block group based on that percentage. Demographic data from U.S. Census 2010 and Esri Business location from infoUSAData sources: see this map package.

National Risk Index Version: March 2023 (1.19.0)The National Risk Index Census Tracts feature layer contains Census tract-level data for the Risk Index, Expected Annual Loss, Social Vulnerability, and Community Resilience.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

What is heat vulnerability? Vulnerability to heat is how likely a person is to be injured or harmed during periods of hot weather. Heat vulnerability has been linked to individuals’ characteristics (health status, age, race, income, language spoken, etc.) as well as certain aspects of the community where one lives (environment, community demographics). These characteristics or “heat vulnerability factors” can play an important role in one’s ability to adapt to heat. What is the Heat Vulnerability Index? The effects of extreme heat on health can often be prevented. Heat-related deaths and illness are more common during the summer, especially in vulnerable populations. Since vulnerability and adaptability to extreme heat in New York State (NYS) is a growing concern, the New York State Department of Health (NYSDOH) created the Heat Vulnerability Index (HVI) to help local and state public health officials identify and map heatvulnerable areas and populations in NYS (excluding New York City which has its own HVI). The HVI can assist in directing adaptation resources based on characteristics of vulnerable populations in that community and can inform long-term heat-mitigation planning efforts in the community. The HVI can help local agencies make decisions to: set up cooling centers in rural and vulnerable areas where many do not have access to air-conditioning at home provide transportation to and from cooling centers in low income neighborhoods where there may not be public transportation or few people own vehicles include risk communication and alert messaging in multiple languages especially among communities with high proportions of people who do not understand English wellarrange home visits of people in high risk groups like the elderly living alone How was the HVI developed? The HVI was developed to identify census tracts with populations that may have increased heat vulnerability. It is based on thirteen environmental and socio-demographic heat vulnerability factors that were identified from previous studies. Census tracts are subdivisions of counties and are defined by the US Census Bureau to collect, provide and present statistical data. Census tract level information for these heat vulnerability factors was obtained from the 2006-2010 and 2008-2012 US Census Bureau American Community Surveys (ACS) and 2011 National Land Cover Database (NLCD) for 2,723 census tracts in NYS (excluding New York City). Census tracts with zero population or missing census tract data were excluded. The 13 factors were grouped into four categories that represent different aspects of heat vulnerability, which in turn were used to estimate the overall HVI for each census tract. The four heat vulnerability categories include 1) language vulnerability; 2) socio-economic vulnerability; 3) environmental and urban vulnerability; and 4) elderly isolation and elderly vulnerability. The HVI and four heat vulnerability categories were mapped to display populations in NYS that are most vulnerable to heat. More Information on HVI:Heat Vulnerability Index: Statewide and County HVI maps can be found at https://www.health.ny.gov/environmental/weather/vulnerability_index/index.htm For more information on the HVI: Nayak SG et al. Development of the heat Vulnerability Index. Public Health 2017. Open access at https://www.sciencedirect.com/science/article/pii/S003335061730327X

Supermarkets are one of the most popular and convenient ways in which Americans gain access to healthy food, such as fresh meat and fish, or fresh fruits and vegetables. There are various ways in which people gain access to supermarkets. People in the suburbs drive to supermarkets and load up the car with many bags of food. People in cities depend much more on walking to the local store, or taking a bus or train.This map came about after asking a simple question: how many Americans live within a reasonable walk or drive to a supermarket?In this case, "reasonable" was defined as a 10 minute drive, or a 1 mile walk. The ArcGIS Network Analyst extension performed the calculations on streets data from StreetMap Premium, and the ArcGIS Spatial Analyst extension created a heat map of the walkable access and drivable access to supermarkets.The green dots represent populations in poverty who live within one mile of a supermarket. The red dots represent populations in poverty who live beyond a one mile walk to a supermarket, but may live within a 10 minute drive...which presumes they have access to a car or public transit. The grey dots represent the total population in a given area.This is an excellent map to use as backdrop to show how people are improving access to healthy food in their community. Open this map in ArcGIS Pro or ArcGIS Online to use it as a backdrop to your local analysis work. Or open it in ArcGIS Explorer to add your favorite farmers' market, CSA, or transit line -- then share that map via Facebook, Twitter or email. See this web map for a map with a popup layer.This map shows data for the entire U.S. The supermarkets included in the analysis have annual sales of $1 million or more.Data source: see this map package.

The Home Owners’ Loan Corporation (HOLC) was a New Deal era program that graded neighborhoods based on perceived loan risk, but largely based on immigrant status and populations of color. Affluent areas were often graded as “A” or “Best” due to the low perceived risk of loan default. The riskiest grade was “D” or “Hazardous” and were predominantly communities of color and immigrant neighborhoods. These practices, while banned in 1968, have been linked to significant and increasing economic and demographic disparities through time. We are now also finding that these redlined areas are also associated with more extreme urban heat island effects, and that this is likely due to their lack of tree canopy and greater impervious surface (things like asphalt and cement roads) percentage. A recent paper by Hoffman et al. (2020) has connected these borrowing practices with the resulting impacts on local climate impacts along with human health. This map includes the following information for U.S. city neighborhoods:HOLC Grade (from the University of Richmond Digital Scholarship Lab)Average land surface temperature difference from citywide HOLC normal (reported in Hoffman et al., 2020)Tree cover percentage (from the National Land Cover Database)Impervious surface percentage (from the National Land Cover Database)Demographic information (from the American Community Survey)

The Home Owners’ Loan Corporation (HOLC) was a New Deal era program that graded neighborhoods based on perceived loan risk, but largely based on immigrant status and populations of color. Affluent areas were often graded as “A” or “Best” due to the low perceived risk of loan default. The riskiest grade was “D” or “Hazardous” and were predominantly communities of color and immigrant neighborhoods.These practices, while banned in 1968, have been linked to significant and increasing economic and demographic disparities through time. We are now also finding that these redlined areas are also associated with more extreme urban heat island effects, and that this is likely due to their lack of tree canopy and greater impervious surface (things like asphalt and cement roads) percentage.A recent paper by Hoffman et al. (2020) has connected these borrowing practices with the resulting impacts on local climate impacts along with human health. This map includes the following information for U.S. city neighborhoods:HOLC Grade (from the University of Richmond Digital Scholarship Lab)Average land surface temperature difference from citywide HOLC normal (reported in Hoffman et al., 2020)Tree cover percentage (from the National Land Cover Database)Impervious surface percentage (from the National Land Cover Database)Demographic information (from the American Community Survey)

Extreme heat events, or heat waves, are on the rise and are becoming more intense according to the U.S. Environmental Protection Agency (EPA). These events are more than just an annoyance and can lead to illness and death, particularly among vulnerable populations including seniors and young people. The EPA also states prolong exposure to heat events can lead to other impact such as damaging crops or killing livestock. Climate resilience planning is one approach to preparing for and mitigating the effects of these heat event. Climate resilience planning in local communities involves several steps including assessing vulnerability and risk.This map is one of three in a series developed to support local climate resilience planning. Intended as planning tools for policy makers, climate resilience planners, and community members, these maps highlight areas of the community that are most likely to benefit from the resilience intervention it supports. Each map focuses on one specific heat resilience intervention that is intended to help mitigate against the climate hazard.This intervention map highlights census tracts that could benefit from improving access to cooling centers.Three inputs are used to calculate the score,High summer average land surface temperature (°F),Population aged 65 years and older (%), andPopulation with no vehicle access (%).The heat resilience index (HRI) and methodology were developed in collaboration with the U.S. Centers for Disease Control and Prevention (CDC) and the UC Davis, Department of Public Health.See the Heat Health Census Tracts hosted feature layer for additional details about sources and data processing.Related HRI maps include “Where Will a Buddy Program Improve Urban Heat Health?” and “Where Will Tree Planting Improve Urban Heat Health?”.