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?

US County Demographics

Social, Health, and Economic Indicators

By Danny [source]

About this dataset

This dataset contains US county-level demographic data from 2016, giving insight into the health and economic conditions of counties in the United States. Aggregated and filtered from various sources such as the US Census Small Area Income and Poverty Estimates (SAIPE) Program, American Community Survey, CDC National Center for Health Statistics, and more, this comprehensive dataset provides information on population as well as desert population for each county. Additionally, data is split between metropolitan and nonmetropolitan areas according to the Office of Management and Budget's 2013 classification scheme. Valuable information pertaining to infant mortality rates and total population are also included in this detailed set of data. Use this dataset to gain a better understanding of one of our nation's most essential regions

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How to use the dataset

• Look at the information within the 'About this Dataset' section to have an understanding of what data sources were used to create this dataset as well as any transformations that may have been done while creating it.
• Familiarize yourself with the columns provided in the data set to understand what information is available for each county such as total population (totpop), parental education level (educationLvl), median household income (medianIncome), etc.,
• Use a combination of filtering and sorting techniques to narrow down results and focus in on more specific county demographics that you are looking for such as total households living below poverty line by state or median household income per capita between two counties etc.,
• Keep in mind any additional transformations/simplifications/aggregations done during step 2 when using your data for analysis. For example, if certain variables were pivoted during step two from being rows into columns because it was easier to work with multiple years of income levels by having them all consolidated into one column then be aware that some states may not appear in all records due to those transformations being applied differently between regions which could result in missing values or other inconsistencies when doing downstream analysis on your selected variables.
• Utilize resources such as Wikipedia and government census estimates if you need more detailed information surrounding these demographic characteristics beyond what's available within our current dataset – these can be helpful when conducting further research outside of solely relying on our provided spreadsheet values alone!

Research Ideas

• Creating a US county-level heat map of infant mortality rates, offering insight into which areas are most at risk for poor health outcomes.
• Generating predictive models from the population data to anticipate and prepare for future population trends in different states or regions.
• Developing an interactive web-based tool for school districts to explore potential impacts of student mobility on their area's population stability and diversity

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Food Desert.csv | Column name | Description | |:--------------------|:----------------------------------------------------------------------------------| | year | The year the data was collected. (Integer) | | fips | The Federal Information Processing Standard (FIPS) code for the county. (Integer) | | state_fips | The FIPS code for the state. (Integer) | | county_fips | The FIPS code for the county. (Integer)...

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.

Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses. Source:The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.Population Definitions:Older Adults:Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.Attribute label: OlderAdultChildren: Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.Attribute label: TotChildPeople of Color: People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups aswell. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.Attribute label: POC2Limited English Proficiency: Without adequate English skills, residents can miss crucial information on how to preparefor hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more sociallyisolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.Attribute label: LEPLow to no Income: A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.Attribute label: Low_to_NoPeople with Disabilities: People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. Attribute label: TotDisMedical Illness: Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.Attribute label: MedIllnesOther attribute definitions:GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census TractAREA_SQFT: Tract area (in square feet)AREA_ACRES: Tract area (in acres)POP100_RE: Tract population countHU100_RE: Tract housing unit countName: Boston Neighborhood

The U.S. Climate Risk Projections by County, 2040-2049 data set contains a projection for 2040-2049 risk for the entire contiguous U.S. at the county level with a novel climate risk index integrating multiple hazards, exposures and vulnerabilities. Multiple hazards such as weather and climate are characterized as a frequency of heat wave, cold spells, drought, and heavy precipitation events along with anomalies of temperature and precipitation using high resolution (4 km) downscaled climate projections. Exposure is characterized by projections of population, infrastructure, and built surfaces prone to multiple hazards including sea level rise and storm surges. Vulnerability is characterized by projections of demographic groups most sensitive to climate hazards. This approach can guide planners in targeting counties at most risk and where adaptation strategies to reduce exposure or protect vulnerable populations might be best applied.

This demo shows NOAA future heat events (LOCA data of total days with max temperatures greater than 95 degrees) and CDC's Social Vulnerability Index 2018. You can select a future year and the number of heat days, and it will find and display on the map and as lists of the top 10 most vulnerable counties or tracts in the US for each SVI theme. Expanded details for the top county are broken down by contributing factors for each theme.NOAA Future Heat EventsThe LOCA climate projection data were provided by the Northeast Regional Climate Center at Cornell University with support from The National Centers for Environment Information-Asheville, the National Environmental Modeling and Analysis Center (NEMAC) and David Pierce at the Scripps Institution of Oceanography.What is CDC Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and TransportationLearn more here

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.

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

Global Population of the World (GPW) translates census population data to a latitude-longitude grid so that population data may be used in cross-disciplinary studies. There are three data files with this data set for the reference years 1990 and 1995. Over 127,000 administrative units and population counts were collected and integrated from various sources to create the gridded data. In brief, GPW was created using the following steps:

* Population data were estimated for the product reference years, 1990 and 1995, either by the data source or by interpolating or extrapolating the given estimates for other years.
* Additional population estimates were created by adjusting the source population data to match UN national population estimates for the reference years.
* Borders and coastlines of the spatial data were matched to the Digital Chart of the World where appropriate and lakes from the Digital Chart of the World were added.
* The resulting data were then transformed into grids of UN-adjusted and unadjusted population counts for the reference years.
* Grids containing the area of administrative boundary data in each cell (net of lakes) were created and used with the count grids to produce population densities.

As with any global data set based on multiple data sources, the spatial and attribute precision of GPW is variable. The level of detail and accuracy, both in time and space, vary among the countries for which data were obtained.

This 1 km resolution 41-class land cover classification map of South America was produced from 1-15 km National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data over the time period 1987 through 1991.

These data were originally acquired from Woods Hole Research Center ("http://terra.whrc.org/science/tropfor/setLBA.htm") and were modified as described in documentation provided when data are ordered from EOS-WEBSTER.

Digital images of these data are also available from the EOS-WEBSTER Image Gallary. Please see the Data Tab at the following URL: "http://eos-earthdata.sr.unh.edu/". These images can be downloaded as JPEGs and used directly in a document or printed.

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, energy characteristics, and population demographics and educational attainment. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts, as well as tribal areas. The file below, "01. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "02. Data Dictionary 2022". A list of geographic regions used in the LEAD Tool can be found in files 04-11. The Low-Income Energy Affordability Data comes primarily from the 2022 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2022 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit the "10. LEAD Tool Platform" resource link below. For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), please visit the "11. CELICA Website" resource below.

Forest Ecosystem Dynamics (FED) Project Spatial Data Archive: Elevation Contours for the Northern Experimental Forest

The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem.

The Howland Forest research site lies within the Northern Experimental Forest of International Paper. The natural stands in this boreal-northern hardwood transitional forest consist of spruce-hemlock-fir, aspen-birch, and hemlock-hardwood mixtures. The topography of the region varies from flat to gently rolling, with a maximum elevation change of less than 68 m within 10 km. Due to the region's glacial history, soil drainage classes within a small area may vary widely, from well drained to poorly drained. Consequently, an elaborate patchwork of forest communities has developed, supporting exceptional local species diversity.

This data layer contains elevation contours for the 10 X 10 km area located within the Northern Experimental Forest. Contours and elevation benchmarks from the United States Geological Survey 7.5" Maine quadsheets for Howland and Lagrange were digitized, and elevation data in feet were added.

The data was revised by projecting it into NAD83 datum by L. Prihodko at NASA Goddard Space Flight Center. Although the data was received at GSFC with an undeclared datum, it was assumed to be in North American Datum of 1927 (NAD27) because the original map from which the data were digitized was in NAD27. Also, the data fit exactly within the bounds of the FED site grid (even Universal Transverse Mercator projections) in NAD27. After projecting the data into NAD83 it was checked to insure that the change was a linear translation of the coordinates.

Forest Ecosystem Dynamics (FED) Project Spatial Data Archive: Digital Elevation Model for the Northern Experimental Forest

The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem.

The Howland Forest research site lies within the Northern Experimental Forest of International Paper. The natural stands in this boreal-northern hardwood transitional forest consist of spruce-hemlock-fir, aspen-birch, and hemlock-hardwood mixtures. The topography of the region varies from flat to gently rolling, with a maximum elevation change of less than 68 m within 10 km. Due to the region's glacial history, soil drainage classes within a small area may vary widely, from well drained to poorly drained. Consequently, an elaborate patchwork of forest communities has developed, supporting exceptional local species diversity.

Howland DEM is a digital elevation model of the 10km X 10km area located within the Northern Experimental Forest. The contours and elevation benchmarks from the United States Geological Survey 7.5'quadsheets for Howland and Lagrange were digitized and then rasterized into a 10m X 10m grid.

The data was revised by projecting it into NAD83 datum by L. Prihodko at NASA Goddard Space Flight Center. Although the data was received at GSFC with an undeclared datum, it was assumed to be in North American Datum of 1927 (NAD27) because the original map from which the data were digitized was in NAD27. Also, the data fit exactly within the bounds of the FED site grid (even Universal Transverse Mercator projections) in NAD27. After projecting the data into NAD83 it was checked to insure that the change was a linear translation of the coordinates only and that the gridded values did not undergo any changes.

In English: The main activity carried out in the Antarctic campaign in the Marambio Island, consisted of the geo-positioning of relevant topographical and geologic elements of the James Ross Island for support of the geologic mapping.

The positioning of fourteen reference points was carried out with the help of two MAGELLAN 5000-PRO GPS outfits with external antennas and sub-metric kits, and differential GPS methods were used. The registration of the data was carried out in portable computers.

The fixed station of measure was located in the Marambio Island, in a point previously positioned with regard to a geodesic vertex settled by the U. S. Geological Survey. The mobile station was transported by two helicopters of the Argentinean Air Force to the target points in the James Ross Island.

At the same time that GPS measurements were made, a sampling of rocks of the James Ross Volcanic Group was carried out for the petrologic and geochemical study of this volcanic unit.

They were also carried out analysis of olivines, clinopyroxenes, ore minerals and zeolites by means of electronic microprobe. In this first stage of the studies, it is important to highlight the fact that the magnesium content of ilmenites is relatively high and that the contents of titanium of the clinopyroxenes is moderate and similar to the one observed in this mineral in typica] alkaline basalt series.

En Espanol: La actividad fundamental desarrollada en la campana en la Isla de Marambio consistio en la georreferenciacion de elementos topograficos y geologicos en la isla James Ross, para el apoyo de la cartografia geologica. Se realizo el posicionamiento de catorce puntos mediante tecnicas de GPS diferencial, utilizando dos equipos MAGELLAN 5000-PRO, con antenas externas y kits submetricos efectuandose registros de los datos en ordenadores portatiles.

Se establecio una estacion de medida en la Isla de Marambio, georreferenciada con respecto a un vertice geodesico situado en la isla y posicionado por el U.S. Geological Survey, mientras que la estacion movil, era desplazada a los puntos de medida en la isla James Ross en los helicopteros de las Fuerzas Aereas Argentinas de la Base de Marambio, realizandose alli las mediciones correspondientes, asi como los balizamientos y las fotografias aereas para la identificacion precisa de los puntos.

Simultaneamente a la campana de medidas y aprovechando los desplazamientos, se realizo una toma de muestras de rocas volcanicas del Grupo James Ross con objeto de efectuar estudios petrologicos y geoquimicos sobre este grupo volcanico.

Se realizaron analisis mediante microsonda electronica de olivinos, cliropiroxenos, ceolitas y minerales opacos, siendo de destacar en esta primera fase de los estudios, los contenidos relativamente elevados de magnesio en las ilmenitas de las rocas subvolcanicas y el que los contenidos de titanio de los cliropiroxenos es moderado y equivalente al de los otros piroxeno titanados de series basalticas alcalinas tipicas

This dataset provides county-level data for Nitrogen fertilizer applied to county croplands [1000 kg N/yr]. This includes only those crops used in an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. Cropland area statistics are from the National Agricultural Statistical Service (NASS) for 1990 for most crops, though some are 1992 data from the Census of Agriculture. Data represent total of irrigated and non-irrigated areas. (see NASS Crops County Data).

This is based on 'typical' nitrogen fertilization rates for each of the crops. The fertilizer application rates (see Table below) were derived from USDA NASS state agricultural statistics bulletins.

Crop Typical' N Fert. Rate (kg N/ha) Alfalfa 0 Barley 75 Corn (grain & silage) 125 Cotton 100 Edible Bean 0 Idle Cropland 0 Non-Legume Hay 25 Oats 75 Pasture 0 Peanut 0 Potatoes 250 Rice 140 Sorghum 75 Soybean 0 Spring Wheat 50 Sugarbeets 150 Sugarcane 200 Sunflower 100 Tobacco 100 Vegetables 100 Winter Wheat 75

County crop areas were multiplied by the nitrogen fertilization rates given above to determine total N-fertilization of these croplands per year. The 1990 national total N fertilizer use calculated by this method (8.5 million tonnes N/yr) is 83% of the 1990 national N-fertilizer sales (10.3 million tonnes N/yr). The sales total is expected to be larger because it will include fertilizer sold for other uses (eg. lawns, golf courses, other non-crop uses) as well as farm-use fertilizer applied to crops not included in the crop database (eg. vineyards, orchards, sod). The source for N fertilizer sales is American Assoc. of Plant Food Control Officials, 103 Regulatory Services Building; University of Kentucky; Lexington, KY 40546-0275; Phone (606)257-2668 fax (606)257-7351.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

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?”.

The average level of the ocean has been rising since we started measuring and recording this data. According to the National Aeronautics and Space Agency (NASA), since 1900 the global mean sea level has risen more than 200 millimeters (nearly 8 inches) and nearly half of that increase has occurred since 1993 in a concerning change in rate of rise.Sea level rise is one of the many effects of global warming. Scientists attribute sea level rise to two things, melting ice and increased ocean water temperatures. Increasing air temperatures, particularly in the polar regions, has encouraged the melting of land-based ice reserves such as glaciers, ice sheets, and permafrost. Historically, warm season ice melt was balanced by replenishment during the cold season but warming temperatures have created conditions where melting exceeds the buildup of ice. This water flows through rivers and streams to the ocean in quantities sufficient to contribute to sea level rise.Oceans are also massive heat sinks. They pull large quantities of atmospheric heat and greenhouse gases such as carbon dioxide and store it in the ocean. The sea changes temperature much more slowly than the air and over time ocean temperatures have continued to build. As the ocean water warms it expands causing the sea levels to rise.Sea levels are not rising equally across Earth. Some areas are already experiencing significant impacts due to the rising water levels while others have seen minimal changes. This is due to a variety of reasons. First, despite how it is typically illustrated Earth is not perfectly round so the height of the ocean at any given point varies. This can be due to the Earth’s rotation, ocean currents, or prevailing wind speed and direction.Experts consider sea level rise and urgent climatic threat. Many low-lying places such as islands and coastal areas are already experiencing high waters. Higher waters also make storms such as hurricanes more dangerous due to higher storm surges and flooding. As coastlines could lose key infrastructure, land will become uninhabitable, and many people could lose their livelihoods. It is estimated 10 percent of the world’s population could be impacted as the waters rise. Many of the approximately 770 million people could be forced to migrate to higher ground, or in the case of island countries, such as Kiribati, to new countries once theirs sinks below the sea.This map was created with data from the National Oceanic and Atmospheric Administration (NOAA), NASA, and the United States Geological Survey. Experts used an elevation data and the NOAA model Scenarios of Future Mean Seal Level to illustrate the scale of potential coastal flooding. The mapmaker chose to remove levees from the data, so the areas flooded include places, particularly in the states of Texas and Louisiana, that are presently protected by this infrastructure. It is important to note that these are possible outcomes. This model does not include possible erosion, subsidence, or construction that may occur between 2022 when this data was created and 2030, 2050, or 2090 respectively. While models are powerful tools it is difficult to calculate every aspect that shapes our environment.Learn more about how coastal communities are impacted by sea level rise with this StoryMap by NOAA’s Office for Coastal Management, The King Tides Project: Snap the shore, See the Future.

Extreme heat events, or heat waves, are on the rise and 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 prolonged exposure to these heat events can lead to other impacts such as damaging crops or killing livestock. Climate resilience planning is one approach to preparing for and mitigating the effects of extreme heat. Climate resilience planning in local communities involves several steps including assessing vulnerability and risk.© 2024 Adobe Stock. All rights reserved.It is a fact that trees can lower the surrounding air temperature through evapotranspiration, providing shade, and taking up space that might otherwise be converted to pavement. Lots of pavement, blacktop roads, and concrete buildings absorb the sun's heat and radiate that heat into the surrounding air. This is especially evident in highly developed urban areas which can get up to 20 degrees warmer than surrounding vegetated areas. These hot zones are referred to as Urban Heat Islands. One way to reduce the warmer temperatures in urban areas is to plant trees and other vegetation. This layer displays census tracts that are ranked according to which would benefit most from tree planting. The ranking is based upon a composite index built with the following attributes:High Summer Average Surface Temperature (°F)Percent of Tract Covered by Tree Canopy (%)Population Density (ppl/km2)These attribute links take you to the original data sources. Preprocessing was needed to prepare many of these inputs for inclusion in our index. The links are provided for reference only.This layer is one of a series developed to support local climate resilience planning. Intended as planning tools for policy makers, climate resilience planners, and community members, these layers highlight areas of the community that are most likely to benefit from the resilience intervention the map supports. Each layer focuses on one specific heat resilience intervention intended to help mitigate against the climate hazard.Planting trees along streets and over dark surfaces in urban areas is proven to reduce air temperature which helps to mitigate the impacts of urban heat islands. For more resources on extreme heat visit heat.gov where you can learn about the impacts of tree planting campaigns. 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.Layers in the Extreme Heat hazard intervention series include Where Will a Buddy Program Improve Urban Heat Health?Where Will Tree Planting Improve Urban Heat Health? Where Will Cooling Centers Improve Urban Heat Health?Did you know you can build your own climate resilience index or use ours and customize it? The Customize a climate resilience index Tutorial provides more information on the index and also walks you through steps for taking our index and customizing it to your needs so you can create intervention maps better suited to your location and sourced from your own higher resolution data. For more information about how Esri enriched the census tracts with exposure, demographic, and environmental data to create composite indices called intervention indices, please read this technical reference.This feature layer was created from the Climate Resilience Planning Census Tracts hosted feature layer view and is one of 18 similar intervention layers, all of which can be found in ArcGIS Living Atlas of the World.