Description: Poverty Status in the Past 12 Months. Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: U.S. Census Bureau. (2023). Poverty Status in the Past 12 Months. American Community Survey. Retrieved from https://data.census.gov/table?q=poverty%20status&t=Employment&g=010XX00US$1400000
poverty_status_csv_s1701_c01_00
(
modelName: poverty_status_csv_s1701_c01_00, nullable: true, editable: true, defaultValue: null, alias: Estimate Total Population for whom poverty status is determined, type: esriFieldTypeInteger
)
Description: A polygon layer representing 2021 total number of households with the American Community Survey S1101 Table "Households and Families" 2021 data joined with each tract. Each column is named with the alias by data subject. All columns ending with the letter "E" are kept which are the estimated counts. All margin of error columns were removed. Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the "https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html" Technical Documentation section. Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the "https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/" Methodology section.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties.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 roughly 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 "https://www.census.gov/programs-surveys/acs/technical-documentation.html" ACS Technical Documentation. The effect of nonsampling error is not represented in these tables.Housing unit weight is used throughout this table (only exception is the average household and family size cells).Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization.Average family size is derived by dividing the number of related people in households by the number of family households.Explanation of Symbols: The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself. The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. The estimate or margin of error is not applicable or not available. The median falls in the lowest interval of an open-ended distribution (for example "2,500-"). The median falls in the highest interval of an open-ended distribution (for example "250,000+"). The margin of error could not be computed because there were an insufficient number of sample observations. The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution. A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: United States Census Bureau: https://data.census.gov/table/ACSST5Y2021.S1101?q=households&g=010XX00US$1400000&y=2021
Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates
s1101_c01_005e
(
modelName: s1101_c01_005e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Total!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years, type: esriFieldTypeInteger
)
s1101_c01_006e
(
modelName: s1101_c01_006e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Total!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!Under 6 years only, type: esriFieldTypeString
)
s1101_c01_007e
(
modelName: s1101_c01_007e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Total!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!Under 6 years and 6 to 17 years, type: esriFieldTypeString
)
s1101_c01_008e
(
modelName: s1101_c01_008e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Total!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!6 to 17 years only, type: esriFieldTypeString
)
s1101_c01_010e
(
modelName: s1101_c01_010e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Total!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people under 18 years, type: esriFieldTypeString
)
s1101_c01_011e
(
modelName: s1101_c01_011e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Total!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people 60 years and over, type: esriFieldTypeString
)
s1101_c01_012e
(
modelName: s1101_c01_012e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Total!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people 65 year and over, type: esriFieldTypeString
)
s1101_c01_017e
(
modelName: s1101_c01_017e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Total!!Total households!!UNITS IN STRUCTURE!!Mobile homes and all other types of units, type: esriFieldTypeString
)
s1101_c02_005e
(
modelName: s1101_c02_005e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Married-couple family household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years, type: esriFieldTypeInteger
)
s1101_c02_006e
(
modelName: s1101_c02_006e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Married-couple family household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!Under 6 years only, type: esriFieldTypeString
)
s1101_c02_007e
(
modelName: s1101_c02_007e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Married-couple family household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!Under 6 years and 6 to 17 years, type: esriFieldTypeString
)
s1101_c02_008e
(
modelName: s1101_c02_008e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Married-couple family household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!6 to 17 years only, type: esriFieldTypeString
)
s1101_c02_010e
(
modelName: s1101_c02_010e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Married-couple family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people under 18 years, type: esriFieldTypeString
)
s1101_c02_011e
(
modelName: s1101_c02_011e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Married-couple family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people 60 years and over, type: esriFieldTypeString
)
s1101_c02_012e
(
modelName: s1101_c02_012e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Married-couple family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people 65 year and over, type: esriFieldTypeString
)
s1101_c02_013e
(
modelName: s1101_c02_013e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Married-couple family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Householder living alone, type: esriFieldTypeString
)
s1101_c02_014e
(
modelName: s1101_c02_014e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Married-couple family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Householder living alone!!65 years and over, type: esriFieldTypeString
)
s1101_c02_017e
(
modelName: s1101_c02_017e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Married-couple family household!!Total households!!UNITS IN STRUCTURE!!Mobile homes and all other types of units, type: esriFieldTypeString
)
s1101_c03_004e
(
modelName: s1101_c03_004e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Male householder, no spouse present, family household!!FAMILIES!!Average family size, type: esriFieldTypeString
)
s1101_c03_005e
(
modelName: s1101_c03_005e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Male householder, no spouse present, family household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years, type: esriFieldTypeInteger
)
s1101_c03_006e
(
modelName: s1101_c03_006e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Male householder, no spouse present, family household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!Under 6 years only, type: esriFieldTypeString
)
s1101_c03_007e
(
modelName: s1101_c03_007e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Male householder, no spouse present, family household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!Under 6 years and 6 to 17 years, type: esriFieldTypeString
)
s1101_c03_008e
(
modelName: s1101_c03_008e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Male householder, no spouse present, family household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!6 to 17 years only, type: esriFieldTypeString
)
s1101_c03_010e
(
modelName: s1101_c03_010e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Male householder, no spouse present, family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people under 18 years, type: esriFieldTypeString
)
s1101_c03_011e
(
modelName: s1101_c03_011e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Male householder, no spouse present, family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people 60 years and over, type: esriFieldTypeString
)
s1101_c03_012e
(
modelName: s1101_c03_012e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Male householder, no spouse present, family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people 65 year and over, type: esriFieldTypeString
)
s1101_c03_013e
(
modelName: s1101_c03_013e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Male householder, no spouse present, family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Householder living alone, type: esriFieldTypeString
)
s1101_c03_014e
(
modelName: s1101_c03_014e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Male householder, no spouse present, family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Householder living alone!!65 years and over, type: esriFieldTypeString
)
s1101_c03_015e
(
modelName: s1101_c03_015e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Male householder, no spouse present, family household!!Total households!!UNITS IN STRUCTURE!!1-unit structures, type: esriFieldTypeString
)
s1101_c03_016e
(
modelName: s1101_c03_016e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Male householder, no spouse present, family household!!Total households!!UNITS IN STRUCTURE!!2-or-more-unit structures, type: esriFieldTypeString
)
s1101_c03_017e
(
modelName: s1101_c03_017e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Male householder, no spouse present, family household!!Total households!!UNITS IN STRUCTURE!!Mobile homes and all other types of units, type: esriFieldTypeString
)
s1101_c04_004e
(
modelName: s1101_c04_004e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Female householder, no spouse present, family household!!FAMILIES!!Average family size, type: esriFieldTypeString
)
s1101_c04_005e
(
modelName: s1101_c04_005e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Female householder, no spouse present, family household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years, type: esriFieldTypeInteger
)
s1101_c04_006e
(
modelName: s1101_c04_006e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Female householder, no spouse present, family household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!Under 6 years only, type: esriFieldTypeString
)
s1101_c04_007e
(
modelName: s1101_c04_007e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Female householder, no spouse present, family household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!Under 6 years and 6 to 17 years, type: esriFieldTypeString
)
s1101_c04_008e
(
modelName: s1101_c04_008e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Female householder, no spouse present, family household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!6 to 17 years only, type: esriFieldTypeString
)
s1101_c04_010e
(
modelName: s1101_c04_010e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Female householder, no spouse present, family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people under 18 years, type: esriFieldTypeString
)
s1101_c04_011e
(
modelName: s1101_c04_011e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Female householder, no spouse present, family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people 60 years and over, type: esriFieldTypeString
)
s1101_c04_012e
(
modelName: s1101_c04_012e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Female householder, no spouse present, family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people 65 year and over, type: esriFieldTypeString
)
s1101_c04_013e
(
modelName: s1101_c04_013e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Female householder, no spouse present, family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Householder living alone, type: esriFieldTypeString
)
s1101_c04_014e
(
modelName: s1101_c04_014e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Female householder, no spouse present, family household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Householder living alone!!65 years and over, type: esriFieldTypeString
)
s1101_c04_015e
(
modelName: s1101_c04_015e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Female householder, no spouse present, family household!!Total households!!UNITS IN STRUCTURE!!1-unit structures, type: esriFieldTypeString
)
s1101_c04_016e
(
modelName: s1101_c04_016e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Female householder, no spouse present, family household!!Total households!!UNITS IN STRUCTURE!!2-or-more-unit structures, type: esriFieldTypeString
)
s1101_c04_017e
(
modelName: s1101_c04_017e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Female householder, no spouse present, family household!!Total households!!UNITS IN STRUCTURE!!Mobile homes and all other types of units, type: esriFieldTypeString
)
s1101_c05_005e
(
modelName: s1101_c05_005e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Nonfamily household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years, type: esriFieldTypeString
)
s1101_c05_006e
(
modelName: s1101_c05_006e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Nonfamily household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!Under 6 years only, type: esriFieldTypeString
)
s1101_c05_007e
(
modelName: s1101_c05_007e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Nonfamily household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!Under 6 years and 6 to 17 years, type: esriFieldTypeString
)
s1101_c05_008e
(
modelName: s1101_c05_008e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Nonfamily household!!AGE OF OWN CHILDREN!!Households with own children of the householder under 18 years!!6 to 17 years only, type: esriFieldTypeString
)
s1101_c05_010e
(
modelName: s1101_c05_010e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Nonfamily household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people under 18 years, type: esriFieldTypeString
)
s1101_c05_011e
(
modelName: s1101_c05_011e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Nonfamily household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people 60 years and over, type: esriFieldTypeString
)
s1101_c05_012e
(
modelName: s1101_c05_012e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Nonfamily household!!Total households!!SELECTED HOUSEHOLDS BY TYPE!!Households with one or more people 65 year and over, type: esriFieldTypeString
)
s1101_c05_017e
(
modelName: s1101_c05_017e, nullable: true, editable: true, defaultValue: null, length: 8000, alias: Estimate!!Nonfamily household!!Total households!!UNITS IN STRUCTURE!!Mobile homes and all other types of units, type: esriFieldTypeString
)
Name: Percent Change In Houselessness by Census Tract
Display Field: namelsad
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: Percent difference in houseless population per sq. mile proportional to tract populations within Continuum of Care boundaries created by The Department of Housing and Urban Development (HUD).#1. Pulled PIC data per Coc from HUD for 2008-2022#2. Acquired and processed Coc spatial data layers using data_download.py and reproject_merge.py - pulled for all years, but will only need for 2010 and 2022#3. Reprojected all layers into equal area projection#4. Added area by sq. miles fields (double), calculated field for all Cocs#5. Joined PIC for 2010 and 2022 data to Coc spatial layers by Coc Number#6. For 2010 and 2022 Cocs -> Where count is not null and greater than 0, normalized count of houseless by sq. mile areas per Coc#7. Joined 2010 and 2022 Cocs where normalized count of houseless is not null and greater than 0#8. Added relative difference field (double)#9. Calculated relative difference as percent changed, 100.0 * ((2022 houseless per sq. mile - 2010 houseless per sq. mile) / 2010 houseless per sq. mile)#10. Pulled and reprojected Census tract data from P:\01_DataOriginals\USA\Boundaries\Census_AllCensusBoundaries\tlgdb_2021_a_us_substategeo.gdb#11. Added area field to Census tract data and calculated area in square miles#12. Added field to Census tract data for resulting calculation (proportion of relative difference of houseless per sq-mi 2022-2010)
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: The Department of Housing and Urban Development. (2023). Continuum of Care Boundaries. Continuum of Care Program. Retrieved from https://www.hudexchange.info/programs/coc/gis-tools/
Description: Tribally-controlled lands according to the U.S. Census Bureau's American Community Survey (ACS) including American Indian, Alaska Native and Native Hawaiian Areas.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: U.S. Census Bureau. (2022). Native Lands. American Community Survey. Retrieved from https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/USA_Native_Lands_View/FeatureServer
Description: A polygon layer representing 2021 Census Tracts with the American Community Survey DP05 Table "Demographic and Housing Estimates" 2021 data joined with each tract. Each column is named with the alias by data subject. The field names are numeric with the table name "DP05" then a numeric code for the subject. If they end with the letter "E" they are an estimated count, and if the end with "PE" than it is a percentage from the total population in each section. 0001-0032 are SEX AND AGE0033 - 0069 are RACE and Race Alone0070 - 0085 are HISPANIC OR LATINO AND RACE0086 - 0089 are Housing units and Voting Age. The original data are text files (.csv) downloaded directly from https://data.census.gov/table. These contain other columns concerning Annotation and Margin of Error. please see the the following file ACSDP5Y2021.DP05-Column-Metadata.csv for all column names in the original data. Processing Steps:1. Deleted the 2nd row2. Pull the last 10 digits of the GEOID into a new field to match the Census 2021 Tract Layer ID3. Converted from .csv to geodatabase table but make sure to set all columns that end with "E" only to long and all columns that end with "PE" only to double. Deleteother columns that end with anything other than "E" or "PE" 4. Created the Demographic_Tract_Census_2021 feature class by exporting the 2021 Census Tract Layer. Placed inside the "Demographic_Layers_Census_2021 Geodatabase.5. Ran the join tool to merge the ACS data to the new feature class. Following fields were calculated:1. CALC_Total_Minority_count(Total_Minority_Count) - Estimate_Race alone or in combination with one or more other races_Total population - Estimate_Race alone or in combination with one or more other races_Total population_White (DP05_0063E - DP05_0064E)2.CALC_Total_Minority_Percent(Total_Minority_Percent) - Percent_Race alone or in combination with one or more other races_Total population(this field is a count and equals Estimate_Race alone or in combination with one or more other races_Total population) convert to 100% or 1.0 and then subtract Percent_Race alone or in combination with one or more other races_Total population_White. Equation for python: (DP05_0064E/DP05_0063E *100)3. CALC_Total_Population_25_and_over(Total_Population_25_and_over) - To get 25 and over, sum SEX AND AGE_Total population_25 to 34 years DP05_0010E to SEX AND AGE_Total population_85 years and over DP05_0017E (DP05_0010E +DP05_0011E+DP05_0012E+DP05_0013E+DP05_0014E+DP05_0015E+DP05_0016E+DP05_0017E)4. CALC_Total_Population_Under_25(Total_Population_Under_25) - Tract level polygon layer linked to Census Demographic population 2021 data. Sum SEX AND AGE_Total population_Under 5 years, 5 to 9 years, 10 to 14 years, 15 to 19 years, 20 to 24 years (DP05_0005E+DP05_0006E+DP05_0007E+DP05_0008E+DP05_0009E)5. CALC_Total_Pop_Under_25_Prcnt(Total_Pop_Under_25_Prcnt) - Tract level polygon layer linked to Census Demographic population 2021 data. Sum SEX AND AGE_Total population_Under 5 years, 5 to 9 years, 10 to 14 years, 15 to 19 years, 20 to 24 years (DP05_0005PE+DP05_0006PE+DP05_0007PE+DP05_0008PE+DP05_0009PE)6. CALC_Total_Pop_Over_64_Count(Total_Pop_Over_64_Count) - Tract level polygon layer linked to Census Demographic population 2021 data. sum: SEX AND AGE Total Population 65 to 74, 75 to 84, 85 and over (DP05_0015E+ DP05_0016E+DP05_0017E)7. CALC_Total_Pop_Over_64_Prcnt(Total_Pop_Over_64_Prcnt) - Tract level polygon layer linked to Census Demographic population 2021 data. sum: SEX AND AGE Total Population 65 to 74, 75 to 84, 85 and over (DP05_0015PE+ DP05_0016PE+DP05_0017PE)
dp05_0001e
(
modelName: dp05_0001e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population, type: esriFieldTypeInteger
)
dp05_0002e
(
modelName: dp05_0002e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_Male, type: esriFieldTypeInteger
)
dp05_0003e
(
modelName: dp05_0003e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_Female, type: esriFieldTypeInteger
)
dp05_0004e
(
modelName: dp05_0004e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_Sex ratio (males per 100 females), type: esriFieldTypeInteger
)
dp05_0005e
(
modelName: dp05_0005e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_Under 5 years, type: esriFieldTypeInteger
)
dp05_0006e
(
modelName: dp05_0006e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_5 to 9 years, type: esriFieldTypeInteger
)
dp05_0007e
(
modelName: dp05_0007e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_10 to 14 years, type: esriFieldTypeInteger
)
dp05_0008e
(
modelName: dp05_0008e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_15 to 19 years, type: esriFieldTypeInteger
)
dp05_0009e
(
modelName: dp05_0009e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_20 to 24 years, type: esriFieldTypeInteger
)
dp05_0010e
(
modelName: dp05_0010e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_25 to 34 years, type: esriFieldTypeInteger
)
dp05_0011e
(
modelName: dp05_0011e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_35 to 44 years, type: esriFieldTypeInteger
)
dp05_0012e
(
modelName: dp05_0012e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_45 to 54 years, type: esriFieldTypeInteger
)
dp05_0013e
(
modelName: dp05_0013e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_55 to 59 years, type: esriFieldTypeInteger
)
dp05_0014e
(
modelName: dp05_0014e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_60 to 64 years, type: esriFieldTypeInteger
)
dp05_0015e
(
modelName: dp05_0015e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_65 to 74 years, type: esriFieldTypeInteger
)
dp05_0016e
(
modelName: dp05_0016e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_75 to 84 years, type: esriFieldTypeInteger
)
dp05_0017e
(
modelName: dp05_0017e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_85 years and over, type: esriFieldTypeInteger
)
dp05_0018e
(
modelName: dp05_0018e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_Median age (years), type: esriFieldTypeInteger
)
dp05_0019e
(
modelName: dp05_0019e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_Under 18 years, type: esriFieldTypeInteger
)
dp05_0020e
(
modelName: dp05_0020e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_16 years and over, type: esriFieldTypeInteger
)
dp05_0021e
(
modelName: dp05_0021e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_18 years and over, type: esriFieldTypeInteger
)
dp05_0022e
(
modelName: dp05_0022e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_21 years and over, type: esriFieldTypeInteger
)
dp05_0023e
(
modelName: dp05_0023e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_62 years and over, type: esriFieldTypeInteger
)
dp05_0024e
(
modelName: dp05_0024e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_65 years and over, type: esriFieldTypeInteger
)
dp05_0025e
(
modelName: dp05_0025e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_18 years and over, type: esriFieldTypeInteger
)
dp05_0026e
(
modelName: dp05_0026e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_18 years and over_Male, type: esriFieldTypeInteger
)
dp05_0027e
(
modelName: dp05_0027e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_18 years and over_Female, type: esriFieldTypeInteger
)
dp05_0028e
(
modelName: dp05_0028e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_18 years and over_Sex ratio (males per 100 females), type: esriFieldTypeInteger
)
dp05_0029e
(
modelName: dp05_0029e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_65 years and over, type: esriFieldTypeInteger
)
dp05_0030e
(
modelName: dp05_0030e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_65 years and over_Male, type: esriFieldTypeInteger
)
dp05_0031e
(
modelName: dp05_0031e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_65 years and over_Female, type: esriFieldTypeInteger
)
dp05_0032e
(
modelName: dp05_0032e, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_65 years and over_Sex ratio (males per 100 females), type: esriFieldTypeInteger
)
dp05_0058e
(
modelName: dp05_0058e, nullable: true, editable: true, defaultValue: null, alias: RACE_Total population_Two or more races, type: esriFieldTypeInteger
)
dp05_0059e
(
modelName: dp05_0059e, nullable: true, editable: true, defaultValue: null, alias: RACE_Total population_Two or more races_White and Black or African American, type: esriFieldTypeInteger
)
dp05_0060e
(
modelName: dp05_0060e, nullable: true, editable: true, defaultValue: null, alias: RACE_Total population_Two or more races_White and American Indian and Alaska Native, type: esriFieldTypeInteger
)
dp05_0061e
(
modelName: dp05_0061e, nullable: true, editable: true, defaultValue: null, alias: RACE_Total population_Two or more races_White and Asian, type: esriFieldTypeInteger
)
dp05_0062e
(
modelName: dp05_0062e, nullable: true, editable: true, defaultValue: null, alias: RACE_Total population_Two or more races_Black or African American and American Indian and Alaska Native, type: esriFieldTypeInteger
)
dp05_0063e
(
modelName: dp05_0063e, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population, type: esriFieldTypeInteger
)
dp05_0064e
(
modelName: dp05_0064e, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population_White, type: esriFieldTypeInteger
)
dp05_0065e
(
modelName: dp05_0065e, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population_Black or African American, type: esriFieldTypeInteger
)
dp05_0066e
(
modelName: dp05_0066e, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population_American Indian and Alaska Native, type: esriFieldTypeInteger
)
dp05_0067e
(
modelName: dp05_0067e, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population_Asian, type: esriFieldTypeInteger
)
dp05_0068e
(
modelName: dp05_0068e, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population_Native Hawaiian and Other Pacific Islander, type: esriFieldTypeInteger
)
dp05_0069e
(
modelName: dp05_0069e, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population_Some other race, type: esriFieldTypeInteger
)
dp05_0070e
(
modelName: dp05_0070e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population, type: esriFieldTypeInteger
)
dp05_0071e
(
modelName: dp05_0071e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Hispanic or Latino (of any race), type: esriFieldTypeInteger
)
dp05_0072e
(
modelName: dp05_0072e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Hispanic or Latino (of any race)_Mexican, type: esriFieldTypeInteger
)
dp05_0073e
(
modelName: dp05_0073e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Hispanic or Latino (of any race)_Puerto Rican, type: esriFieldTypeInteger
)
dp05_0074e
(
modelName: dp05_0074e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Hispanic or Latino (of any race)_Cuban, type: esriFieldTypeInteger
)
dp05_0075e
(
modelName: dp05_0075e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Hispanic or Latino (of any race)_Other Hispanic or Latino, type: esriFieldTypeInteger
)
dp05_0076e
(
modelName: dp05_0076e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino, type: esriFieldTypeInteger
)
dp05_0077e
(
modelName: dp05_0077e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_White alone, type: esriFieldTypeInteger
)
dp05_0078e
(
modelName: dp05_0078e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Black or African American alone, type: esriFieldTypeInteger
)
dp05_0079e
(
modelName: dp05_0079e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_American Indian and Alaska Native alone, type: esriFieldTypeInteger
)
dp05_0080e
(
modelName: dp05_0080e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Asian alone, type: esriFieldTypeInteger
)
dp05_0081e
(
modelName: dp05_0081e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Native Hawaiian and Other Pacific Islander alone, type: esriFieldTypeInteger
)
dp05_0082e
(
modelName: dp05_0082e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Some other race alone, type: esriFieldTypeInteger
)
dp05_0083e
(
modelName: dp05_0083e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Two or more races, type: esriFieldTypeInteger
)
dp05_0084e
(
modelName: dp05_0084e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Two or more races_Two races including Some other race, type: esriFieldTypeInteger
)
dp05_0085e
(
modelName: dp05_0085e, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Two or more races_Two races excluding Some other race, and Three or more races, type: esriFieldTypeInteger
)
dp05_0087e
(
modelName: dp05_0087e, nullable: true, editable: true, defaultValue: null, alias: CITIZEN, VOTING AGE POPULATION_Citizen, 18 and over population, type: esriFieldTypeInteger
)
dp05_0088e
(
modelName: dp05_0088e, nullable: true, editable: true, defaultValue: null, alias: CITIZEN, VOTING AGE POPULATION_Citizen, 18 and over population_Male, type: esriFieldTypeInteger
)
dp05_0089e
(
modelName: dp05_0089e, nullable: true, editable: true, defaultValue: null, alias: CITIZEN, VOTING AGE POPULATION_Citizen, 18 and over population_Female, type: esriFieldTypeInteger
)
dp05_0001pe
(
modelName: dp05_0001pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population, type: esriFieldTypeDouble
)
dp05_0002pe
(
modelName: dp05_0002pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_Male, type: esriFieldTypeDouble
)
dp05_0003pe
(
modelName: dp05_0003pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_Female, type: esriFieldTypeDouble
)
dp05_0004pe
(
modelName: dp05_0004pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_Sex ratio (males per 100 females), type: esriFieldTypeDouble
)
dp05_0005pe
(
modelName: dp05_0005pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_Under 5 years, type: esriFieldTypeDouble
)
dp05_0006pe
(
modelName: dp05_0006pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_5 to 9 years, type: esriFieldTypeDouble
)
dp05_0007pe
(
modelName: dp05_0007pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_10 to 14 years, type: esriFieldTypeDouble
)
dp05_0008pe
(
modelName: dp05_0008pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_15 to 19 years, type: esriFieldTypeDouble
)
dp05_0009pe
(
modelName: dp05_0009pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_20 to 24 years, type: esriFieldTypeDouble
)
dp05_0010pe
(
modelName: dp05_0010pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_25 to 34 years, type: esriFieldTypeDouble
)
dp05_0011pe
(
modelName: dp05_0011pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_35 to 44 years, type: esriFieldTypeDouble
)
dp05_0012pe
(
modelName: dp05_0012pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_45 to 54 years, type: esriFieldTypeDouble
)
dp05_0013pe
(
modelName: dp05_0013pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_55 to 59 years, type: esriFieldTypeDouble
)
dp05_0014pe
(
modelName: dp05_0014pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_60 to 64 years, type: esriFieldTypeDouble
)
dp05_0015pe
(
modelName: dp05_0015pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_65 to 74 years, type: esriFieldTypeDouble
)
dp05_0016pe
(
modelName: dp05_0016pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_75 to 84 years, type: esriFieldTypeDouble
)
dp05_0017pe
(
modelName: dp05_0017pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_85 years and over, type: esriFieldTypeDouble
)
dp05_0018pe
(
modelName: dp05_0018pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_Median age (years), type: esriFieldTypeDouble
)
dp05_0019pe
(
modelName: dp05_0019pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_Under 18 years, type: esriFieldTypeDouble
)
dp05_0020pe
(
modelName: dp05_0020pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_16 years and over, type: esriFieldTypeDouble
)
dp05_0021pe
(
modelName: dp05_0021pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_18 years and over, type: esriFieldTypeDouble
)
dp05_0022pe
(
modelName: dp05_0022pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_21 years and over, type: esriFieldTypeDouble
)
dp05_0023pe
(
modelName: dp05_0023pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_62 years and over, type: esriFieldTypeDouble
)
dp05_0024pe
(
modelName: dp05_0024pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_65 years and over, type: esriFieldTypeDouble
)
dp05_0025pe
(
modelName: dp05_0025pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_18 years and over, type: esriFieldTypeDouble
)
dp05_0026pe
(
modelName: dp05_0026pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_18 years and over_Male, type: esriFieldTypeDouble
)
dp05_0027pe
(
modelName: dp05_0027pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_18 years and over_Female, type: esriFieldTypeDouble
)
dp05_0028pe
(
modelName: dp05_0028pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_18 years and over_Sex ratio (males per 100 females), type: esriFieldTypeDouble
)
dp05_0029pe
(
modelName: dp05_0029pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_65 years and over, type: esriFieldTypeDouble
)
dp05_0030pe
(
modelName: dp05_0030pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_65 years and over_Male, type: esriFieldTypeDouble
)
dp05_0031pe
(
modelName: dp05_0031pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_65 years and over_Female, type: esriFieldTypeDouble
)
dp05_0032pe
(
modelName: dp05_0032pe, nullable: true, editable: true, defaultValue: null, alias: SEX AND AGE_Total population_65 years and over_Sex ratio (males per 100 females), type: esriFieldTypeDouble
)
dp05_0058pe
(
modelName: dp05_0058pe, nullable: true, editable: true, defaultValue: null, alias: RACE_Total population_Two or more races, type: esriFieldTypeDouble
)
dp05_0059pe
(
modelName: dp05_0059pe, nullable: true, editable: true, defaultValue: null, alias: RACE_Total population_Two or more races_White and Black or African American, type: esriFieldTypeDouble
)
dp05_0060pe
(
modelName: dp05_0060pe, nullable: true, editable: true, defaultValue: null, alias: RACE_Total population_Two or more races_White and American Indian and Alaska Native, type: esriFieldTypeDouble
)
dp05_0061pe
(
modelName: dp05_0061pe, nullable: true, editable: true, defaultValue: null, alias: RACE_Total population_Two or more races_White and Asian, type: esriFieldTypeDouble
)
dp05_0062pe
(
modelName: dp05_0062pe, nullable: true, editable: true, defaultValue: null, alias: RACE_Total population_Two or more races_Black or African American and American Indian and Alaska Native, type: esriFieldTypeDouble
)
dp05_0063pe
(
modelName: dp05_0063pe, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population, type: esriFieldTypeDouble
)
dp05_0064pe
(
modelName: dp05_0064pe, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population_White, type: esriFieldTypeDouble
)
dp05_0065pe
(
modelName: dp05_0065pe, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population_Black or African American, type: esriFieldTypeDouble
)
dp05_0066pe
(
modelName: dp05_0066pe, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population_American Indian and Alaska Native, type: esriFieldTypeDouble
)
dp05_0067pe
(
modelName: dp05_0067pe, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population_Asian, type: esriFieldTypeDouble
)
dp05_0068pe
(
modelName: dp05_0068pe, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population_Native Hawaiian and Other Pacific Islander, type: esriFieldTypeDouble
)
dp05_0069pe
(
modelName: dp05_0069pe, nullable: true, editable: true, defaultValue: null, alias: Race alone or in combination with one or more other races_Total population_Some other race, type: esriFieldTypeDouble
)
dp05_0070pe
(
modelName: dp05_0070pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population, type: esriFieldTypeDouble
)
dp05_0071pe
(
modelName: dp05_0071pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Hispanic or Latino (of any race), type: esriFieldTypeDouble
)
dp05_0072pe
(
modelName: dp05_0072pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Hispanic or Latino (of any race)_Mexican, type: esriFieldTypeDouble
)
dp05_0073pe
(
modelName: dp05_0073pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Hispanic or Latino (of any race)_Puerto Rican, type: esriFieldTypeDouble
)
dp05_0074pe
(
modelName: dp05_0074pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Hispanic or Latino (of any race)_Cuban, type: esriFieldTypeDouble
)
dp05_0075pe
(
modelName: dp05_0075pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Hispanic or Latino (of any race)_Other Hispanic or Latino, type: esriFieldTypeDouble
)
dp05_0076pe
(
modelName: dp05_0076pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino, type: esriFieldTypeDouble
)
dp05_0077pe
(
modelName: dp05_0077pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_White alone, type: esriFieldTypeDouble
)
dp05_0078pe
(
modelName: dp05_0078pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Black or African American alone, type: esriFieldTypeDouble
)
dp05_0079pe
(
modelName: dp05_0079pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_American Indian and Alaska Native alone, type: esriFieldTypeDouble
)
dp05_0080pe
(
modelName: dp05_0080pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Asian alone, type: esriFieldTypeDouble
)
dp05_0081pe
(
modelName: dp05_0081pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Native Hawaiian and Other Pacific Islander alone, type: esriFieldTypeDouble
)
dp05_0082pe
(
modelName: dp05_0082pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Some other race alone, type: esriFieldTypeDouble
)
dp05_0083pe
(
modelName: dp05_0083pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Two or more races, type: esriFieldTypeDouble
)
dp05_0084pe
(
modelName: dp05_0084pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Two or more races_Two races including Some other race, type: esriFieldTypeDouble
)
dp05_0085pe
(
modelName: dp05_0085pe, nullable: true, editable: true, defaultValue: null, alias: HISPANIC OR LATINO AND RACE_Total population_Not Hispanic or Latino_Two or more races_Two races excluding Some other race, and Three or more races, type: esriFieldTypeDouble
)
Description: Built-up area Definition: An area containing a concentration of buildings and the supporting road or rail infrastructure. Distinction: building, single; road; square
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: United States Army Corps of Engineers inland Electronic Navigational Chart Program
Name: Population Projection Change by County (White, Non-Hispanic)(2020-2100)
Display Field: name10
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Data source: https://sedac.ciesin.columbia.edu/data/set/popdynamics-us-county-level-pop-projections-sex-race-age-ssp-2020-2100/data-download
Publication: Hauer, M.E. (2019). Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway. Scientific Data 6: 190005. Socioeconomic Data and Applications Center. Retrieved from https://doi.org/10.1038/sdata.2019.5
Name: Population Projection Change by County (Total Population)(2020-2100)
Display Field: name10
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Data source: https://sedac.ciesin.columbia.edu/data/set/popdynamics-us-county-level-pop-projections-sex-race-age-ssp-2020-2100/data-download
Publication: Hauer, M.E. (2019). Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway. Scientific Data 6: 190005. Socioeconomic Data and Applications Center. Retrieved from https://doi.org/10.1038/sdata.2019.5
Name: Population Projection Change by County (Race=Other)(2020-2100)
Display Field: name10
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Data source: https://sedac.ciesin.columbia.edu/data/set/popdynamics-us-county-level-pop-projections-sex-race-age-ssp-2020-2100/data-download
Publication: Hauer, M.E. (2019). Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway. Scientific Data 6: 190005. Socioeconomic Data and Applications Center. Retrieved from https://doi.org/10.1038/sdata.2019.5
Name: Population Projection Change by County (Male Population)(2020-2100)
Display Field: name10
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Data source: https://sedac.ciesin.columbia.edu/data/set/popdynamics-us-county-level-pop-projections-sex-race-age-ssp-2020-2100/data-download
Publication: Hauer, M.E. (2019). Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway. Scientific Data 6: 190005. Socioeconomic Data and Applications Center. Retrieved from https://doi.org/10.1038/sdata.2019.5
Name: Population Projection Change by County (Hispanic)(2020-2100)
Display Field: name10
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Data source: https://sedac.ciesin.columbia.edu/data/set/popdynamics-us-county-level-pop-projections-sex-race-age-ssp-2020-2100/data-download
Publication: Hauer, M.E. (2019). Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway. Scientific Data 6: 190005. Socioeconomic Data and Applications Center. Retrieved from https://doi.org/10.1038/sdata.2019.5
Name: Population Projection Change by County (Female Population)(2020-2100)
Display Field: name10
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Data source: https://sedac.ciesin.columbia.edu/data/set/popdynamics-us-county-level-pop-projections-sex-race-age-ssp-2020-2100/data-download
Publication: Hauer, M.E. (2019). Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway. Scientific Data 6: 190005. Socioeconomic Data and Applications Center. Retrieved from https://doi.org/10.1038/sdata.2019.5
Name: Population Projection Change by County (Black, Non-Hispanic)(2020-2100)
Display Field: name10
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100 consists of county-level population projection scenarios of total population, and by age, sex, and race in five-year intervals for all U.S. counties for the period 2020 - 2100. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States in the near, middle- and long-term.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Data source: https://sedac.ciesin.columbia.edu/data/set/popdynamics-us-county-level-pop-projections-sex-race-age-ssp-2020-2100/data-download
Publication: Hauer, M.E. (2019). Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway. Scientific Data 6: 190005. Socioeconomic Data and Applications Center. Retrieved from https://doi.org/10.1038/sdata.2019.5
Description: The Climate and Economic Justice Screening Tool (CEJST) is a critical component of the Biden-Harris Administration’s historic commitment to addressing environmental justice. In Executive Order 14008 (EO 14008) on Tackling the Climate Crisis at Home and Abroad (January 27, 2021), President Biden directed the White House Council on Environmental Quality (CEQ) to develop a geospatial mapping tool to identify disadvantaged communities. These communities have been marginalized by society, overburdened by pollution, and underserved by infrastructure and other basic services. The CEJST uses publicly-available, nationally-consistent datasets to identify disadvantaged communities. The datasets are indicators of burdens that disadvantaged communities face. These burdens are related to climate change, the environment, health, and economic opportunity. The CEJST features a userfriendly, searchable map of all 50 states, the District of Columbia, and the U.S. territories. Communities are considered disadvantaged if they are in census tracts that meet the thresholds for at least one of the tool’s categories of burden, or if they are on lands within the boundaries of Federally Recognized Tribes. Census tracts are the smallest geographic unit for which reliable, nationwide data exist to support the CEJST methodology. The tool utilizes the census tract boundaries from 2010 because many of the data sources in the tool use those boundaries. More information about the CEJST methodology, datasets, and downloadable files, can be found on the CEJST website at https://screeningtool.geoplatform.gov. The CEJST Technical Support Document (TSD), also available on the CEJST website, provides additional details about the tool. On November 22, 2022, CEQ launched version 1.0 of the CEJST. Version 1.0 incorporates feedback CEQ received on the beta version of the tool. The beta version of the tool was released on February 18, 2022, with support from the U.S. Digital Service and in collaboration with other Federal agencies and departments, in order to solicit feedback from Federal agencies, Tribal Nations, State and local credit governments, Members of Congress, environmental justice stakeholders, and the public.
Description: The CDC\ATSDR Social Vulnerability Index (SVI) is a tool, created by the Geospatial Research, Analysis and Services Program (GRASP), to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event. The tract-level SVI shows the relative vulnerability of the population of every U.S. Census tract. The county-level SVI shows the relative vulnerability of every U.S. county population. The SVI ranks tracts (or counties) on 16 social factors, described in detail in the documentation. The tract (or county) rankings for individual factors are further grouped into four related themes. Thus each enumeration unit receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking.See complete documentation here: https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html. For additional questions, contact the SVI Lead at svi_coordinator@cdc.gov.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: CDC\ATSDR\Office of Innovation and Analytics\Geospatial Research, Analysis, and Services Program (GRASP)
Description: This dataset includes operational pipeline intermodal terminals as of Feb 2, 2021. Pipeline intermodal terminals are interface between pipeline mode and other transportation modes. They have the ability to receive or deliver freight commodities via pipeline and truck/rail/water. The data consists of location information, water/truck/rail mode connections, storage capacity, and a list of commodities handled at terminal. Geographical coverage includes the United States and U.S. territories.The Intermodal Freight Facilities - Pipeline Terminals dataset was compiled on February 02, 2021 and was updated on April 21, 2021 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Pipeline terminals interface between pipeline mode and other transportation modes. They have the ability to receive or deliver freight commodities via pipeline and truck/rail/water. The data consists of location information, truck/rail/water mode connections, storage capacity, and a list of commodities handled at the terminal. Geographical coverage includes the United States and U.S. territories. This dataset is one of several layers in the Bureau of Transportation Statistics (BTS) Intermodal Freight Facility Database.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Homeland Infrastructure Foundation-Level Data (HIFLD). (2022). Intermodal Freight Facilities Pipeline. Retrieved from https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::intermodal-freight-facilities-pipelines/about
Description: The Broadband Radio Service (BRS), formerly known as the Multipoint Distribution Service (MDS)/Multichannel Multipoint Distribution Service (MMDS), is a commercial service. In the past, it was generally used for the transmission of data and video programming to subscribers using high-powered systems, also known as wireless cable. However, over the years, the uses have evolved to include digital two-way systems capable of providing high-speed, high-capacity broadband service, including two-way Internet service via cellularized communication systems. Such services provide consumers integrated access to voice, high-speed data, video-on-demand, and interactive delivery services from a wireless device. The Educational Broadband Service (EBS), formerly known as the Instructional Television Fixed Service (ITFS), is an educational service that has generally been used for the transmission of instructional material to accredited educational institutions and non-educational institutions such as hospitals, nursing homes, training centers, and rehabilitation centers using high-powered systems. Our recent revamping of the EBS spectrum will now make it possible for EBS users to continue their instructional services utilizing low-power broadband systems while also providing students with high-speed internet access.
Description: The World Port Index (Pub 150) contains the location and physical characteristics of, and the facilities and services offered by major ports and terminals world-wide. Entries are numbered geographically. In 2020, NGA undertook an effort to modernize the content and delivery of the World Port Index, resulting in additional and enhanced data fields, and a web application viewing platform. The modernization also implemented crowd-sourcing methods to validate and update the content.Specific World Port Index entries can be retrieved from the on-line database using the query form below.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Maritime Safety Information
https://msi.nga.mil/Publications/WPI
Description: The Principal Ports dataset is periodically updated by the United States Army Corp of Engineers (USACE) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Principal Ports are politically defined by port limits or Corps projects, excluding non-Corps projects not authorized for publication. Principal Ports are the top 150 U.S. ports based upon total annual tonnage. Variation in annual tonnage at a port may result in exclusion or inclusion on the Principal Port list. The Principal Port dataset contains port codes, port names, geographic locations (longitude, latitude), and commodity tonnage summaries (total tons, domestic, foreign, imports and exports).
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: US Army Corps of Engineers’ (USACE) Navigation Data Center, and Bureau of Transportation Statistics (BTS) [distributor].,Bureau of Transportation Statistics https://data-usdot.opendata.arcgis.com/datasets/usdot::principal-ports/about
Description: This map provides the locations of airports, which the FAA defines as areas on land or water intended to be used either wholly or in part for the arrival, departure, and surface movement of aircraft/helicopters. Thus, places such as hospitals with helicopter pads are depicted as airports in this dataset. The data is provided as a vector geospatial-enabled file format.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Federal Aviation Administration, Air Traffic Organization, Mission Support Services, Aeronautical Information Services.
Description: The U.S. natural gas pipeline network is a highly integrated network that moves natural gas throughout the continental United States. The pipeline network has about 3 million miles of mainline and other pipelines that link natural gas production areas and storage facilities with consumers. In 2017, this natural gas transportation network delivered about 25 trillion cubic feet (Tcf) of natural gas to 75 million customers.About half of the existing mainline natural gas transmission network and a large portion of the local distribution network were installed in the 1950s and 1960s because consumer demand for natural gas more than doubled following World War II. The distribution network has continued to expand to provide natural gas service to new commercial facilities and housing developments.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Source: EIA, hosted by Homeland Infrastructure Foundation Level data (HIFLD)
https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::natural-gas-pipelines/about
Copyright Text: U.S. Census Bureau, HIFLD https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::secondary-roads-interstates-and-us-highways/about
Name: Interstates and US Highways (Secondary Roads)
Display Field: name
Type: Feature Layer
Geometry Type: esriGeometryPolyline
Description: Secondary Roads Interstates and US Highways
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: U.S. Census Bureau, HIFLD https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::secondary-roads-interstates-and-us-highways/about
Description: This feature class/shapefile represents electric power transmission lines. Transmission Lines are the system of structures, wires, insulators and associated hardware that carry electric energy from one point to another in an electric power system. Lines are operated at relatively high voltages varying from 69 kV up to 765 kV, and are capable of transmitting large quantities of electricity over long distances. Underground transmission lines are included where sources were available. The following updates have been made since the previous release: 1,166 features added.
Description: A joint venture involving the National Atlas programs in Canada (Natural Resources Canada), Mexico (Instituto Nacional de Estadística Geografía e Informática), and the United States (U.S. Geological Survey), as well as the North American Commission for Environmental Co-operation, has led to the release (June 2004) of several new products: an updated paper map of North America, and its associated geospatial data sets and their metadata. These data sets are available online from each of the partner countries both for visualization and download. The North American Atlas data are standardized geospatial data sets at 1:10,000,000 scale. A variety of basic data layers (e.g. roads, railroads, populated places, political boundaries, hydrography, bathymetry, sea ice and glaciers) have been integrated so that their relative positions are correct. This collection of data sets forms a base with which other North American thematic data may be integrated. Any data outside of Canada, Mexico, and the United States of America included in the North American Atlas data sets is strictly to complete the context of the data. The North American Atlas - Railroads data set shows the railroads of North America at 1:10,000,000 scale. The railroads selected for this data set are either rail links between major centres of population or major resource railways. There is no classification of rail lines. This data set was produced using digital files supplied by Natural Resources Canada, Instituto Nacional de Estadística Geografía e Informática, and the U.S. Geological Survey. The North American Atlas data are intended for geographic display and analysis at the national and continental level. These data should be displayed and analyzed at scales appropriate for 1:10,000,000-scale data. No responsibility is assumed by Natural Resources Canada, Instituto Nacional de Estadística Geografía e Informática, or the U.S. Geological Survey in the use of these data.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: "Canada Centre for Mapping and Earth Observation
Natural Resources Canada. (2004). National Railway Network. Retrieved from https://app.geo.ca/result/en/national-railway-network---nrwn---geobase-series?id=ac26807e-a1e8-49fa-87bf-451175a859b8&lang=en"
Description: The North American Rail Network (NARN) Rail Lines: Class I Freight Railroads View dataset is from the Federal Railroad Administration (FRA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset is a subset of the NARN Rail Lines dataset that show the ownership and trackage rights for all the Class I freight railroads: “Burlington Northern and Santa Fe (BNSF),” "Canadian National (CN) Railway," "Canadian Pacific (CP) Railway," "CSX Transportation," "Norfolk Southern (NS) Railway," "Kansas City Southern (KCS) Railway," and "Union Pacific (UP)". It is derived from the North American Rail Network (NARN) Lines dataset, and for more information please consult, https://doi.org/10.21949/1519415. The NARN Rail Lines dataset is a database that provides ownership, trackage rights, type, passenger, STRACNET, and geographic reference for North America's railway system at 1:24,000 or better within the United States. The data set covers all 50 States, the District of Columbia, Mexico, and Canada.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Acknowledgment of the Federal Railroad Administration (FRA) and the Bureau of Transportation Statistics (BTS) [distributor].
Description: The original data was spatially joined to census tract administrative boundaries using the provided Latitude and Longitude columns. The data was filtered based on the results of the spatial join and is available within GEO_VALIDITY as VALID, INVALID, or MISSING. If the data was determined to have valid geometry, the census tract it was joined to is available in CENSUS_TRACT_NAME with the accompanying tract geographic identifier in CENSUS_TRACT_GEOID. For GEO_VALIDITY values:VALID = Latitude/Longitude intersected a Census Tract boundary and was checked for matching STATE valuesINVALID = Latitude/Longitude did not intersect a Census Tract boundary OR it was joined to a Census Tract boundary but STATE values were mismatched for original data and Census TractMISSING = Latitude/Longitude values missing from data
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Original Data: Mines Data Set, Mines Safety and Health Administration
Original Data Source Weblink: [https://www.msha.gov/data-and-reports/mine-data-retrieval-system]
Retrieval Date: October 2023
Original Data Source Description: The Mine dataset lists all Coal and Metal/Non-Metal mines under MSHA's jurisdiction since 1/1/1970. It includes such information as the current status of each mine (Active, Abandoned, NonProducing, etc.), the current owner and operating company, commodity codes and physical attributes of the mine. Mine ID is the unique key for this data. (Includes Abandoned or Abandoned and Sealed Mines)
Spatial Data: U.S. Census Bureau's TIGER/Line Shapefiles
Spatial Data Source Weblink: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html
Description: CoalMine_All_County is aggregated at the County-level for all input mine points according to the county and state designated in the input data. The script starts by installing essential Python packages to handle spatial and non-spatial data. It then imports coal mine data and integrates it with spatial data from the U.S. Census Bureau. A significant feature of the script is the spatial join process, aligning coal mine data with geographical areas of census tracts and counties. The script filters the data based on mine status, identifying active, abandoned, and problematic mines (due to missing or invalid geographic coordinates). It then aggregates the data, compiling counts of mines and associated employment figures. The script distinguishes between valid and problematic data, ensuring accurate spatial analysis. The output is organized into detailed spatial layers, formatted as CSV and GeoPackage files, encompassing aggregated information on active and abandoned coal mines at the county and census tract levels.Calculations:The script computes several key metrics related to coal mines. It calculates the total count of active and abandoned coal mines, as well as the sum of employed individuals in these mines, both at the county and census tract levels. For abandoned mines, the script aggregates counts by the year of abandonment, providing a temporal dimension to the analysis. These aggregations include spatial attributes like State, County, and Census Tract names, alongside geographical identifiers such as GEOID. In processing problematic data entries, the script filters out mines with missing or invalid geographical coordinates, ensuring the accuracy of spatial joins and subsequent analyses. These calculations are pivotal in generating a comprehensive and detailed understanding of the spatial distribution and status of coal mines across different geographical areas.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Original Data: Mines Data Set, Mines Safety and Health Administration
Original Data Source Weblink: [https://www.msha.gov/data-and-reports/mine-data-retrieval-system]
Retrieval Date: October 2023
Original Data Source Description: The Mine dataset lists all Coal and Metal/Non-Metal mines under MSHA's jurisdiction since 1/1/1970. It includes such information as the current status of each mine (Active, Abandoned, NonProducing, etc.), the current owner and operating company, commodity codes and physical attributes of the mine. Mine ID is the unique key for this data. (Includes Abandoned or Abandoned and Sealed Mines)
Spatial Data: U.S. Census Bureau's TIGER/Line Shapefiles
Spatial Data Source Weblink: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html
Name: Natural Gas Powerplants by Census Tract (EIA)
Display Field: name
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The EIA-860M Excel dataset was imported and processed using Python. The script involved data fetching, cleansing, and formatting procedures to prepare the dataset for analysis. Geospatial data from the U.S. Census Bureau was utilized to enrich the EIA-860M data with spatial information. The script performed spatial joins, aggregations, and pivot operations to align the data at the census tract level. Detailed and summary outputs were generated in various formats (CSV, Excel, GeoPackage) for ease of use and analysis.For details on the methodology and potential limitations of the original EIA-860M data, refer to the U.S. Energy Information Administration's documentation.Calculations:The fields "Number of Operable Coal Generators" (COAL_OPER_CT) and "Number of Operable Natural Gas Generators" (NGAS_OPER_CT) represent the total count of operable generators within each Census Tract as of the report's date. These are calculated by aggregating the number of generators classified as 'Coal' or 'Natural Gas' respectively. The "Capacity (MW) of Operable Coal Generators" (COAL_OPER_CPCTY) and "Capacity (MW) of Operable Natural Gas Generators" (NGAS_OPER_CPCTY) fields reflect the total capacity in Megawatts of operable generators for coal and natural gas technologies within each Census Tract as of the report's date. These values are the summation of the nameplate capacity of all operable generators within each category.For fields like "Number of Coal Generators with Planned Retirement YYYY" (COAL_PR_CT_YYYY) and "Number of Natural Gas Generators with Planned Retirement YYYY" (NGAS_PR_CT_YYYY), the totals are calculated by counting the number of generators within each technology type that have a planned retirement in the specified year. Similarly, "Capacity (MW) of Coal Generators with Planned Retirement YYYY" (COAL_PR_CPCTY_YYYY) and "Capacity (MW) of Natural Gas Generators with Planned Retirement YYYY" (NGAS_PR_CPCTY_YYYY) are calculated by summing up the capacity of all generators in each technology type that are planned to retire in the specified year. The retired generator fields, such as "Number of Coal Generators Retired YYYY" (COAL_R_CT_YYYY) and "Number of Natural Gas Generators Retired YYYY" (NGAS_R_CT_YYYY), are totals of the generators that have been retired in the specified year, categorized by technology type. The corresponding capacity fields for retired generators are computed in the same manner, summing the capacity of all retired generators for each technology type in the specified year.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Original Data: U.S. Energy Information Administration's EIA-860M data
Original Data Source Weblink: https://www.eia.gov/electricity/data/eia860m/
Original Data Release Date: September 2023
Source Use Restrictions: This dataset is publicly available and can be freely used, with proper citation to the U.S. Energy Information Administration's EIA-860M as the original data source.
Spatial Data: U.S. Census Bureau's 2022 Substate Geographic Boundary Files
Spatial Data Source Weblink: [https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html]
Description: This geospatial dataset provides a detailed overview of coal and natural gas power plants, derived from the EIA-860M form data, and processed for spatial analysis in ArcGIS Pro.The dataset began as an Excel file containing multiple sheets related to coal and natural gas power plants. Each sheet was read and processed using Pandas, a data manipulation library in Python. The data was categorized based on the energy source code into coal and natural gas datasets. From these datasets, subsets focusing on operating and retired power plants were created. Key attributes, such as 'Entity ID', 'Plant Name', and 'Capacity', were selected, and their data types were adjusted for consistency. Notably, 'Planned Retirement Year' in the operating subset and equivalent fields in the retired subset were filled with 'NA' for missing values, and all column names were standardized. These subsets were then aggregated by 'Plant ID', applying specific rules like 'first' for categorical fields and 'sum' for numerical fields. The aggregated datasets were merged based on shared attributes like 'Entity ID' and 'Plant ID', forming a comprehensive dataset. This merged data was then converted into a GeoDataFrame using GeoPandas, assigning geographic point locations based on 'Latitude' and 'Longitude'. The geospatial data was exported as a GeoPackage file, suitable for importing into ArcGIS Pro as feature classes. The resulting spatial layers offer insights into the distribution, capacity, and operational status of power plants across different energy types.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Original Data: U.S. Energy Information Administration's EIA-860M data
Original Data Source Weblink: https://www.eia.gov/electricity/data/eia860m/
Original Data Release Date: September 2023
Source Use Restrictions: This dataset is publicly available and can be freely used, with proper citation to the U.S. Energy Information Administration's EIA-860M as the original data source.
Description: The EIA-860M Excel dataset was imported and processed using Python. The script involved data fetching, cleansing, and formatting procedures to prepare the dataset for analysis. Geospatial data from the U.S. Census Bureau was utilized to enrich the EIA-860M data with spatial information. The script performed spatial joins, aggregations, and pivot operations to align the data at the census tract level. Detailed and summary outputs were generated in various formats (CSV, Excel, GeoPackage) for ease of use and analysis.For details on the methodology and potential limitations of the original EIA-860M data, refer to the U.S. Energy Information Administration's documentation.Calculations:The fields "Number of Operable Coal Generators" (COAL_OPER_CT) and "Number of Operable Natural Gas Generators" (NGAS_OPER_CT) represent the total count of operable generators within each Census Tract as of the report's date. These are calculated by aggregating the number of generators classified as 'Coal' or 'Natural Gas' respectively. The "Capacity (MW) of Operable Coal Generators" (COAL_OPER_CPCTY) and "Capacity (MW) of Operable Natural Gas Generators" (NGAS_OPER_CPCTY) fields reflect the total capacity in Megawatts of operable generators for coal and natural gas technologies within each Census Tract as of the report's date. These values are the summation of the nameplate capacity of all operable generators within each category.For fields like "Number of Coal Generators with Planned Retirement YYYY" (COAL_PR_CT_YYYY) and "Number of Natural Gas Generators with Planned Retirement YYYY" (NGAS_PR_CT_YYYY), the totals are calculated by counting the number of generators within each technology type that have a planned retirement in the specified year. Similarly, "Capacity (MW) of Coal Generators with Planned Retirement YYYY" (COAL_PR_CPCTY_YYYY) and "Capacity (MW) of Natural Gas Generators with Planned Retirement YYYY" (NGAS_PR_CPCTY_YYYY) are calculated by summing up the capacity of all generators in each technology type that are planned to retire in the specified year. The retired generator fields, such as "Number of Coal Generators Retired YYYY" (COAL_R_CT_YYYY) and "Number of Natural Gas Generators Retired YYYY" (NGAS_R_CT_YYYY), are totals of the generators that have been retired in the specified year, categorized by technology type. The corresponding capacity fields for retired generators are computed in the same manner, summing the capacity of all retired generators for each technology type in the specified year.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Original Data: U.S. Energy Information Administration's EIA-860M data
Original Data Source Weblink: https://www.eia.gov/electricity/data/eia860m/
Original Data Release Date: September 2023
Source Use Restrictions: This dataset is publicly available and can be freely used, with proper citation to the U.S. Energy Information Administration's EIA-860M as the original data source.
Spatial Data: U.S. Census Bureau's 2022 Substate Geographic Boundary Files
Spatial Data Source Weblink: [https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html]
Description: The dataset began as an Excel file containing multiple sheets related to coal and natural gas power plants. Each sheet was read and processed using Pandas, a data manipulation library in Python. The data was categorized based on the energy source code into coal and natural gas datasets. From these datasets, subsets focusing on operating and retired power plants were created. Key attributes, such as 'Entity ID', 'Plant Name', and 'Capacity', were selected, and their data types were adjusted for consistency. Notably, 'Planned Retirement Year' in the operating subset and equivalent fields in the retired subset were filled with 'NA' for missing values, and all column names were standardized. These subsets were then aggregated by 'Plant ID', applying specific rules like 'first' for categorical fields and 'sum' for numerical fields. The aggregated datasets were merged based on shared attributes like 'Entity ID' and 'Plant ID', forming a comprehensive dataset. This merged data was then converted into a GeoDataFrame using GeoPandas, assigning geographic point locations based on 'Latitude' and 'Longitude'. The geospatial data was exported as a GeoPackage file, suitable for importing into ArcGIS Pro as feature classes. The resulting spatial layers offer insights into the distribution, capacity, and operational status of power plants across different energy types.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Original Data: U.S. Energy Information Administration's EIA-860M data
Original Data Source Weblink: https://www.eia.gov/electricity/data/eia860m/
Original Data Release Date: September 2023
Source Use Restrictions: This dataset is publicly available and can be freely used, with proper citation to the U.S. Energy Information Administration's EIA-860M as the original data source.
Description: Downloaded the 5G coverage shapefile for each state. Merged them into single layer feature class for national level, then ran a dissolve using minimum downloads speed greater than 25 Mbps and minimum uploaded speed of 3 Mbps. The layers in the geodatabase above, are these dissolved layers at the national level and by state. This map displays where Internet services are available across the United States, as reported by Internet Service Providers (ISPs) to the FCC. The map will be updated continuously to improve its accuracy through a combination of FCC verification efforts, new data from Internet providers, updates to the location data, and—importantly—information from the public. Minimum download speed for modeled coverage inMbps.- Value is 0.2 when technology value is 300 (i.e., 3G), is5.0 when technology value is 400 (i.e., 4G LTE), and iseither 7.0 or 35.0 when technology value is 500 (i.e.,5G-NR).Minimum upload speed for modeled coverage in Mbps.- Value is 0.05 when technology value is 300 (i.e., 3G), is1.0 when technology value is 400 (i.e., 4G LTE), and iseither 1.0 or 3.0 when technology value is 500 (i.e., 5GNR).
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: FCC National Broadband Map https://broadbandmap.fcc.gov/data-download/nationwide-data?version=dec2022
For questions contact:
Maneesh Sharma: Maneesh.Sharma@netl.doe.gov
Daniel Amrine: Daniel.Amrine@netl.doe.gov
Description: Brownfields are properties potentially complicated by the presence of hazardous substances. This dataset not only includes locations of these sites but also offers a detailed perspective on their characteristics, historical use, and potential industry associations, making it valuable for environmental assessment and urban planning.AttributesPROPERTY_HIGHLIGHTS: Text extracted from each site's 'Property Highlights' section, detailing features and environmental considerations.FORMER_USE: A subset of PROPERTY_HIGHLIGHTS, focusing on the text following 'Former Use:' to specify previous site usage.PGM_SYS_ID_URL: A generated URL for each site, created using the PGM_SYS_ID value, providing direct access to more detailed information.NAICS_ALL: NAICS codes scraped from the PGM_SYS_ID_URL, reflecting the full range of industry classifications associated with each site.NAICS_INDUSTRY_CODE: The first two digits of the NAICS_ALL code, representing the broad industry category.NAICS_INDUSTRY_NAME: The name corresponding to the NAICS_INDUSTRY_CODE, providing a textual description of the industry sector.PROPOSED_INDUSTRY: Industry classification inferred from matching keywords in PROPERTY_HIGHLIGHTS to predefined industry-specific keywords.OWNER_OPERATOR: Information about the owner or operator of the site, extracted via web scraping.SIZE_IN_ACRES: Area of the site in acres.MethodologyURL Generation: Creating direct links to additional site-specific information using PGM_SYS_ID values.Web Scraping: Custom scripts to collect data from specific URLs related to each Brownfield site.Text Matching & Analysis: Utilizing keywords for industry type inference and extracting specific data points like FORMER_USE.Industry Classification: Using NAICS codes and keywords to classify and understand the potential industry background of each site.Industry Type Assignment Keywords: These keywords were used to analyze and categorize the text in the PROPERTY_HIGHLIGHTS column of the dataset, contributing to the identification of potential industry types associated with each site.Agriculture, Forestry, Fishing, and Hunting (11): agriculture, agricultural, farm, crop, livestock, harvest, timber, fishery, greenhouseMining, Quarrying, and Oil and Gas Extraction (21): mining, quarry, oil, extract, extraction, mineral extract, coal, ore, petroleum, natural gas, mineUtilities (22): electric power, natural gas, steam, sewer, treatment, sewageConstruction (23): constructionManufacturing (31-33): manufacturing, manufacture, manufacturer, plant, factories, factory, mill, processingWholesale Trade (42): wholesale, trade, wholesaling, warehouse, distribution, merchants, sales, sorting, packaging, labeling, marketing servicesRetail Trade (44-45): retail, merchandise, consumer good, shopping, mall, boutique, department store, grocery store, clothing store, storeTransportation and Warehousing (48-49): transport, warehousing, storage, cargo, airport, rail, train, ship, road, pipeline, postal, courierInformation (51): information sector, library, libraries, publishing, broadcasting, information servicesFinance and Insurance (52): finance, insuranceReal Estate and Rental and Leasing (53): real estate, rental, leasingProfessional, Scientific, and Technical Services (54): professional, scientific, technical, legal, accounting, firm, consulting, laboratory, laboratoriesManagement of Companies and Enterprises (55): management sector, companies management, enterprises managementAdministrative and Support and Waste Management and Remediation Services (56): waste disposal, waste management, waste collection, waste treatment, waste disposal, dump, septic pump, landfill, incineratorEducational Services (61): education, school, college, universities, university, academicHealth Care and Social Assistance (62): health care, social assistance, medical, health service, hospital, clinic, nursing, social service, public healthArts, Entertainment, and Recreation (71): event, exhibit, cultural, historical, museum, zoo, garden, amusement park, theme park, casino, entertainment, recreational, leisure, open space, park, landmarkAccommodation and Food Services (72): lodging, food service, restaurant, hotel, motel, inn, bed and breakfast, cafe, bar, food preparation, hospitality, dining, camping, campgroundOther Services (except Public Administration) (81): equipment repair, machinery repair, religious, church, laundromat, laundry, dry cleaning, salon, funeral, pet care, photofinish, parking garage, parking lot, barber, cemetery, crematorPublic Administration (92)* public administration, courthouse, public safety, defense, fire stations, police stations, armory, fire station, police, firestation, military, baseLimitationsAccuracy of Industry Classification: The PROPOSED_INDUSTRY is based on keyword matching and may not reflect the actual industry.Dependence on Web Sources: Changes in web page structures can affect data scraping accuracy.Scope of Data: The dataset focuses on specific attributes and might not cover all aspects of each Brownfield site.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Original Data Source:
"ACRES: Assessment, Cleanup and Redevelopment Exchange System," Environmental Protection Agency [https://www.epa.gov/frs/geospatial-data-download-service]
Description: 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.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: FEMA National Risk Index
https://hazards.fema.gov/nri/data-resources
Description: The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation?s land cover and land cover change.Processing Steps:1. converted the .img to .tiff2. resampled the image to 1km cell resolution3. Used Raster to polygon tool to convert the raster values to a polygon layer4. Used the dissolve tool to dissolve the indivdual value polygons into single larger polygons5. Created a description field to define the key values based on the NLCD land cover classification6. created a nlcd color symbology to match the classification 7.Exported as a layer file to ensure it can be displayed the same way from project to project.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: USGS National Land Cover Database https://www.usgs.gov/data/national-land-cover-database-nlcd-2019-products
Processed by: Isabelle Pfander, Daniel Amrine
Isabelle.Pfander@netl.doe.gov
Daniel.Amrine@netl.doe.gov
Description: The Environmental Justice Index uses data from the U.S. Census Bureau, the U.S. Environmental Protection Agency, the U.S. Mine Safety and Health Administration, and the U.S. Centers for Disease Control and Prevention to rank the cumulative impacts of environmental injustice on health for every block group.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: United States Environmental Protection Agency. (2023). EJScreen. Retrieved from https://www.epa.gov/ejscreen/download-ejscreen-data
p_lesshspct
(
modelName: p_lesshspct, nullable: true, editable: true, defaultValue: null, alias: Less Than High School Education, type: esriFieldTypeSmallInteger
)
p_under5pct
(
modelName: p_under5pct, nullable: true, editable: true, defaultValue: null, alias: Under Age 5, type: esriFieldTypeSmallInteger
)
p_over64pct
(
modelName: p_over64pct, nullable: true, editable: true, defaultValue: null, alias: Over Age 64, type: esriFieldTypeSmallInteger
)
Description: The Environmental Justice Index uses data from the U.S. Census Bureau, the U.S. Environmental Protection Agency, the U.S. Mine Safety and Health Administration, and the U.S. Centers for Disease Control and Prevention to rank the cumulative impacts of environmental injustice on health for every block group.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: United States Environmental Protection Agency. (2023). EJScreen. Retrieved from https://www.epa.gov/ejscreen/download-ejscreen-data
p_lesshspct
(
modelName: p_lesshspct, nullable: true, editable: true, defaultValue: null, alias: Less Than High School Education, type: esriFieldTypeSmallInteger
)
p_under5pct
(
modelName: p_under5pct, nullable: true, editable: true, defaultValue: null, alias: Under Age 5, type: esriFieldTypeSmallInteger
)
p_over64pct
(
modelName: p_over64pct, nullable: true, editable: true, defaultValue: null, alias: Over Age 64, type: esriFieldTypeSmallInteger
)
Description: The Environmental Justice Index (EJI) uses data from the U.S. Census Bureau, the U.S. Environmental Protection Agency, the U.S. Mine Safety and Health Administration, and the U.S. Centers for Disease Control and Prevention to rank the cumulative impacts of environmental injustice on health for every census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. The EJI ranks each tract on 36 environmental, social, and health factors and groups them into three overarching modules and ten different domains.
Description: Processing steps: Import original coal production data downloaded from the U.S. EIA in csv format and load cartographic boundary layers into R using the tigris package. Subset the coal production data by geographic area (state), then join the subsetted state data to the cartographic boundary layer. Export the spatial data as shapefile.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Data Source Information: U.S. Energy Information Administration
Data Source Web Link: https://www.eia.gov/coal/data/browser/#/topic/33?agg=2,0,1&rank=g&geo=nvg1qag9vvlpns&mntp=g&linechart=COAL.PRODUCTION.TOT-US-TOT.A&columnchart=COAL.PRODUCTION.TOT-US-TOT.A&map=COAL.PRODUCTION.TOT-US-TOT.A&freq=A&start=2001&end=2021&ctype=linechart<ype=pin&rtype=b&pin=COAL.AVERAGE_EMPLOYEES.US-TOT.A&rse=0&maptype=0
Data Source Description: The U.S. coal data are collected and prepared for release by the Office of Oil, Gas, and Coal Supply Statistics, U.S. Energy Information Administration (EIA). The data are compiled from the following EIA survey source: Form EIA-7A, "Coal Production and Preparation Report" and the U.S. Department of Labor, Mine Safety and Health Administration, Form 7000-2, "Quarterly Mine Employment and Coal Production Report."
Description: Climate and Economic Justice Screening Tool (CEJST) identified census tracts that are overburdened and underserved are highlighted as being disadvantaged.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Climate and Economic Justice Screening Tool. (2022). Disadvantaged Communities. Council on Environmental Quality. Retrieved from https://screeningtool.geoplatform.gov/en/
Description: Global Gas Flaring Reduction Partnership (GGFR), in partnership with the U.S. National Oceanic and Atmospheric Administration (NOAA) and the Colorado School of Mines, has developed global gas flaring estimates based upon observations from satellites launched in 2012 and 2017.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: National Energy Technology Laboratory. (2022). Flare gas volumes - NOAA, the Payne Institute at the Colorado School of Mines, World Bank/GGFR. Retrieved from https://www.worldbank.org/en/programs/gasflaringreduction/global-flaring-data
Name: USA Oil and Gas Flaring by Census Tract (2012-2021)
Display Field: name
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: Global Gas Flaring Reduction Partnership (GGFR), in partnership with the U.S. National Oceanic and Atmospheric Administration (NOAA) and the Colorado School of Mines, has developed global gas flaring estimates based upon observations from satellites launched in 2012 and 2017 summarized within census tracts in the US.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: National Energy Technology Laboratory. (2022). Flare gas volumes - NOAA, the Payne Institute at the Colorado School of Mines, World Bank/GGFR. Retrieved from https://www.worldbank.org/en/programs/gasflaringreduction/global-flaring-data
Name: Oil and Gas Flaring In Production by Census Tract (2000-2022)
Display Field: namelsad
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: Proprietary Enverus well data used to create annual production, flaring, and injection rates from 2000-2022 per county and tract in a grid.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: National Energy Technology Laboratory
Description: Pore space rights feature class has been created for most recent pore space rights by states. States Feature class has been enriched by using data from https://www.globalccsinstitute.com/wp-content/uploads/2022/05/Brief-Pore-Space-Rights-5.25.pdf.References used in the able are belowReferences1. “Understanding ‘Pore Space” Law in Oil and Gas Litigation”, https://burfordperry.com/understanding-pore-space-law-in-oil-and-gas-litigation/.2. “Who Owns Pore Space for Geologic Carbon Sequestration? Renewed Focus on Carbon Capture and Storage Likely to Bring Ownership Uncertainties on Western Split-Estate Lands Back into the Picture”, https://www.jdsupra.com/legalnews/who-owns-pore-space-for- geologic-carbon-2984045/.3. “A 2015 Analysis and Update on U.S. Pore Space Law —The Necessity of Proceeding Cautiously With Respect to the “Stick” Known as Pore Space”, https://digitalcommons.law.ou.edu/cgi/viewcontent.cgi?article=1013&context=onej.4. “Part 2: Analysis of Property Rights Issues Related to Underground Space Used for Geologic Storage of Carbon Dioxide”, https://cdrlaw.org/wp- content/uploads/2020/10/PropertyRights.pdf.5. “Horizontal Drilling and Trespass: A Challenge to the Norms of Property and Tort Law”, https://www.colorado.edu/law/sites/default/files/Kramer%2025-2.pdf.6. “Environment, Land Use & Natural Resources Advisory: Carbon Capture & Sequestration Faces Significant Permitting and Regulatory Obstacles in California”, https://www.alston.com/en/insights/publications/2021/08/pending-california-legislation- highlights-need.7. “PORE SPACE AS A PROPERTY RIGHT: WHAT IS IT, WHO OWNS IT AND WHAT IS IT WORTH?” https://www.wylr.net/2017/12/16/pore-space-as-a-property-right-what-is-it-who-owns-it-and- what-is-it-worth/.8. “Does the Federal Government Own the Pore Space Under Private Lands in the West? Implications of the Stock-raising Homestead Act OF 1916 For Geologic Storage of Carbon Dioxide”, https://law.lclark.edu/live/files/11617-422doranpdf.9. “State and Regional Control of Geological Carbon Sequestration”, https://www.osti.gov/servlets/purl/1158542.10. “Who Owns the Right to Store Gas: A Survey of Pore Space Ownership in U.S. Jurisdictions”, http://www.duqlawblogs.org/joule/wp-content/uploads/2016/07/Who-Owns-the-Right-to-Store- Gas-A-Survey-of-Pore-Space-Ownership-in-U.S.-Jurisdictions-.pdf.11. “Geographic Availability,” https://climate.law.columbia.edu/sites/default/files/content/CO2- EGU-NSPS-TSD-Geographic-Availability.pdf.12. “Testimony before the Joint Committee on Energy and Environmental Policy Ownership of underground pore space”, http://kslegislature.org/li_2012/b2011_12/committees/misc/ctte_jt_engy_envrn_plcy_1_20111017_39_other.pdf.13. “Pore Space Property”,https://dc.law.utah.edu/cgi/viewcontent.cgi?article=1277&context=ulr#:~:text=Anadarko%20E%26P%20Onshore%20LLC%20upheld,the%20subsurface%20pore%20space%20and.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Ruth Ivory-Moore (2022).Pore Space Rights – U.S. Overview
Description: The data provides locations for primary performer and the region where project will be implemented. Processing steps: Compiled data in spreadsheet, created latitude and longitude columns for each city, and plotted them in ArcGIS Pro.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: National Science Foundation. (2023). NSF Engines Development Awards. Retrieved on 09/27/2023 from https://www.nsf.gov/awardsearch/advancedSearchResult?ProgEleCode=200Y&BooleanElement=All&Keyword=%22NSF+Engines+Development+Award%22&AwardTitleOnly=true&ActiveAwards=true
Description: U.S. Department of Commerce Announces Winners of American Rescue Plan $500 Million Good Jobs Challenge to Expand Employment Opportunities32 Regional Partnerships Receive Once-In-A-Generation Funding from the American Rescue Plan to Develop Training Programs that Support Local Economies and Place an Additional 50,000 Workers in Quality JobsProcessing Steps:1. Manually Scraped the 32 awardees from the link below2. Added all key attributes to an excel sheet3. Converted Excel sheet to feature class using XY table to point tool.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: U.S. Economic Development Administration
https://www.eda.gov/news/press-release/2022/08/03/us-department-commerce-announces-winners-American-rescue-plan-500
https://www.eda.gov/funding/programs/american-rescue-plan/good-jobs-challenge/awardees
For questions contact:
Maneesh Sharma: Maneesh.Sharma@netl.doe.gov
Daniel Amrine: Daniel.Amrine@netl.doe.gov
Description: Brief Description:The $1 billion Build Back Better Regional Challenge (PDF) is the marquee of EDA’s American Rescue Plan programs that aims to boost economic recovery from the pandemic and rebuild American communities, including those grappling with decades of disinvestment. Processsing Steps:1. Scraped the winner attributes from the link below2. Copied into an Excel xlsx layer 3. Captured latitude and longitude from google earth using the addresses of the applicants
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: U.S. Economic Development Administration, Build Back Better Regional Challenge (BBBRC)
https://www.eda.gov/funding/programs/american-rescue-plan/build-back-better
For questions contact:
Maneesh Sharma: Maneesh.Sharma@netl.doe.gov
Daniel Amrine: Daniel.Amrine@netl.doe.gov
Description: The DOE Communities LEAP (Local Energy Action Program) aims to facilitate sustained community-wide economic and environmental benefits primarily through DOE’s clean energy deployment work. This opportunity is specifically open to low-income, energy-burdened communities that are also experiencing either direct environmental justice impacts, or direct economic impacts from a shift away from historical reliance on fossil fuels. Under Communities LEAP, DOE matches selected communities with technical assistance providers who assist them with bringing their clean energy planning and economic development vision to life.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Department of Energy, 2022, The DOE Communities LEAP (Local Energy Action Program). Retrieved from https://www.energy.gov/communitiesLEAP/communities-leap
Description: LPO currently manages a portfolio of loans, loan guarantees, and conditional commitments for projects that are under construction and in operations. Project types include Title 17 Clean Energy Financing Program, Advanced Technology Vehicles Manufacturing (ATVM) Loan Program, Tribal Energy Financing, and Carbon Dioxide Transportation Infrastructure Finance and Innovation (CIFIA) Program.Processing steps: Compiled data in spreadsheet, created latitude and longitude columns for each city, and plotted them in ArcGIS Pro.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Department of Energy
https://www.energy.gov/lpo/portfolio-projects
Description: Fostering innovation, coast to coast. Manufacturing USA consists of a national network of linked manufacturing institutes. Each has a unique technological concentration, but is also designed to accelerate U.S. advanced manufacturing as a whole.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Manufacturing USA
https://www.manufacturingusa.com/institutes
Questions? Contact:
Maneesh Sharma: maneesh.sharma@netl.doe.gov
Daniel Amrine: daniel.amrine@netl.doe.gov
Description: Brief Description: The U.S. Department of Energy’s (DOE) Industrial Efficiency and Decarbonization Office (IEDO) announced the selection of nine organizations—eight regional and one national—that will establish a network of Technical Assistance Partnerships (TAPs) to help industrial facilities and other large energy users increase the adoption of onsite energy technologies. Processing Steps:1. Manually scraped data from source URL2. Created a .csv table with target information3. Grabbed Latitude and longitudes from Google Earth (Approximate)4. Converted to feature class
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Department of Energy Industrial Efficiency and Decarbonization Office , 2023, Onsite Energy Technical Assistance Partnerships. Retrieved 2023 from https://www.energy.gov/eere/iedo/articles/funding-selections-onsite-energy-technical-assistance-partnerships#information
Name: EPA Technical Assistance Center Locations Award
Display Field: epa_regiona
Type: Feature Layer
Geometry Type: esriGeometryPoint
Description: The U.S. Environmental Protection Agency is announcing the selection of 17 selectees for the Environmental Justice Thriving Communities Technical Assistance Centers Program (EJ TCTACs or Program.) Seventeen organizations and their partners have been selected to receive a total of approximately $177 million to establish a network of technical assistance centers (Centers) across the nation providing direct technical assistance, training, and capacity-building support to communities and organizations to advance environmental and energy justice priorities. With this critical investment, the Centers will provide training and other assistance to build capacity of local grassroots nonprofit organizations, tribal governments, and other similar community stakeholders in navigating federal, state, and private grant application systems such as Grants.gov and SAM.gov, writing stronger grant proposals, and effectively managing grant funding. In addition, these Centers will provide guidance on community engagement, meeting facilitation, and translation and interpretation services for limited English-speaking participants, thus removing barriers and improving accessibility to resources for communities with environmental justice concerns. Each of the Centers will also create and manage communication channels to ensure all communities have direct access to resources and information. Final awards are subject to administrative and legal reviews to verify compliance with applicable requirements.EPA and the US Department of Energy (DOE) will cooperatively fund these 17 awards, which will feature 14 Regional Centers and 3 National Centers working collaboratively across the United States supporting communities. These 17 awards will be in the form of incrementally funded cooperative agreements where EPA and DOE staff will have substantial involvement in the oversight and implementation of the Program. The establishment of this Program and Centers is in direct response to feedback from communities and environmental justice leaders who have long called for technical assistance and capacity building support for communities and their partners as they work to access critical federal, state, and private resources and engage in decision-making that impacts them. The 17 Centers will provide comprehensive coverage for the entire United States through a network of over 160 partners including community-based organizations, additional academic institutions, and other stakeholders so more communities can access federal funding opportunities like those made available through President Biden’s Inflation Reduction Act and Bipartisan Infrastructure Law as well as funding from states and private foundationsProcesssing Steps:1. Each recipient information was scraped from pdf into an Excel Sheet2. Google Earth was used to gather locations for each recipient 3. XY tabl to point tool was used to convert .XLSX to a feature class.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: EPA,2022-23 ENVIRONMENTAL JUSTICE THRIVING COMMUNITIES TECHNICAL ASSISTANCE CENTERS PROGRAM (EJ TCTAC)
https://www.epa.gov/system/files/documents/2023-07/Project%20Summaries%202022-23%20ENVIRONMENTAL%20JUSTICE%20THRIVING%20COMMUNITIES%20TECHNICAL%20ASSISTANCE%20CENTERS%20PROGRAM.pdf
For questions contact:
Maneesh Sharma: Maneesh.Sharma@netl.doe.gov
Daniel Amrine: Daniel.Amrine@netl.doe.gov
Description: Focused on helping small and medium-sized manufacturers generate business results and thrive in today's technology-driven economy, the MEP National Network comprises the National Institute of Standards and Technology’s Manufacturing Extension Partnership (NIST MEP), the 51 MEP Centers located in all 50 states and Puerto Rico, the MEP Advisory Board, MEP Center boards, and the Foundation for Manufacturing Excellence, as well as over 1,400 trusted advisors and experts at approximately 450 MEP service locations, providing any U.S. manufacturer with access to resources they need to succeed.call (800) MEP-4MFG.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: National Institute of Standards and Technology(NLST), Manufacturing Extension Partnership(MEP) https://www.nist.gov/mep/centers/map?state=MA&zip%5Bvalue%5D=&zip%5Bsource_configuration%5D%5Borigin_address%5D=
Questions, Contact:
Maneesh Sharma maneesh.sharma@netl.doe.gov
Daniel Amrine daniel.amrine#netl.doe.gov
Description: Many people live very close to one of the Energy Department's 17 National Labs and don't even know it. After checking out our new National Labs map, you won’t be one of these people.The National Labs are charged with developing science and technology to further our nation's energy sector, and conducting research that spurs greater innovation. Whether it’s learning how to harness the power of a star on Earth or using particle accelerators to reveal new subatomic particles, we think that the research and innovation done at these labs is very exciting and important to share with all Americans.Our new map, released today, lets you learn more about each lab throughout the country. Click on an icon to learn more about each one. Some labs are very close to each other, so if you see a number in a circle, click on it to zoom in. Pan and zoom around the map to discover information about the labs and click through to see their websites. We hope this tool will help you understand the labs better.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Department of Energy
https://www.energy.gov/articles/map-explore-national-labs
04/09/2014
For Questions contact:
Maneesh Sharma Maneesh.Sharma@netl.doe.gov
Daniel Amrine Daniel.Amrine@netl.doe.gov
Description: The Industrial Assessment Center Program advances a clean energy and manufacturing workforce that represents the diversity of America, and a reinvigorated manufacturing base prepared to lead the global clean energy transition. Small- and medium-sized manufacturers may be eligible to receive a no-cost assessment provided by DOE Industrial Assessment Centers (IACs). Teams located at 37 universities around the country conduct the energy assessments to identify opportunities to improve productivity and competitiveness, reduce waste, and save energy. IACs typically identify more than $130,000 in potential annual savings opportunities for every manufacturer assessed, nearly $50,000 of which is implemented during the first year following the assessment. Over 20,000 IAC assessments have been conducted.Processing steps:1. Scraped the locations from source website2. Compiled into a single spreadsheet3. Used Google Earth to capture latitude and longitude in WGS844. Used XY table o point to convert spreadsheet into a feature class
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Department of Energy:Office of Manufacturing and Supply Chains, 2022, Industrial Assessment Centers. Retrieved 2023 from https://www.energy.gov/mesc/locations-industrial-assessment-centers
Description: Our mission at AMCC is to create and strengthen an alliance of communities with regional economic development initiatives underway dedicated to achieving sustainability through economic growth, improved environmental performance, and inclusive well-paid job creation supporting initiatives to create new opportunities and equity within a revitalized American manufacturing base.Processing: AMCC_Regions is a layer by county, all the communities are grouped into counties by AMCC name ( IMCP_AMCC) and Type (AMCC_Type). 2022 Census County Layer was used. These where dissolved into a single polygon for each AMCC for labeling purposes (AMCC_Regions_Dissolved).
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: The American Manufacturing Communities Collaborative (AMCC), Investing in Manufacturing Communities Partnership (IMCP) https://americanmcc.org/partner-communities/
For questions contact:
Maneesh Sharma: Maneesh.Sharma@netl.doe.gov
Daniel Amrine: Daniel.Amrine@netl.doe.gov
Description: Selected Economic Characteristics including age-categories, income-to-poverty ratio (IPR) categories, and the age universe for certain employment and education variables.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: U.S. Census Bureau. (2022). Selected Economic Characteristics. Retreived from https://data.census.gov/table/ACSDP5Y2021.DP03?t=Employment&g=010XX00US$1400000
unemployment_status_csv_dp03_04
(
modelName: unemployment_status_csv_dp03_04, nullable: true, editable: true, defaultValue: null, alias: Estimate count of employment status population 16 years and over In labor force civilian labor force unemployed., type: esriFieldTypeInteger
)
Description: A polygon layer representing 2022 mean income in the past 12 months with the American Community Survey S1902 Table "Mean Income in the Past 12 Months" 2022 data joined with each tract. Each column is named with the alias by data subject. All columns ending with the letter "E" are kept which are the estimated counts. All margin of error columns were removed. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the "https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html" Technical Documentation section of the ACS website. Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the "https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/" Methodology section.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties.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 roughly 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 "https://www.census.gov/programs-surveys/acs/technical-documentation.html" ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization.Explanation of Symbols: The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself. The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. The estimate or margin of error is not applicable or not available. The median falls in the lowest interval of an open-ended distribution (for example "2,500-"). The median falls in the highest interval of an open-ended distribution (for example "250,000+"). The margin of error could not be computed because there were an insufficient number of sample observations. The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution. A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates
United States Census Bureau: https://data.census.gov/table/ACSST5Y2022.S1902?q=median%20household%20income&g=010XX00US$1400000
s1902_c01_012e
(
modelName: s1902_c01_012e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families, type: esriFieldTypeDouble
)
s1902_c01_013e
(
modelName: s1902_c01_013e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!No workers, type: esriFieldTypeDouble
)
s1902_c01_014e
(
modelName: s1902_c01_014e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!1 worker, type: esriFieldTypeDouble
)
s1902_c01_015e
(
modelName: s1902_c01_015e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!2 workers, both spouses worked, type: esriFieldTypeDouble
)
s1902_c01_016e
(
modelName: s1902_c01_016e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!2 workers, other, type: esriFieldTypeDouble
)
s1902_c01_017e
(
modelName: s1902_c01_017e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!3 or more workers, both spouses worked, type: esriFieldTypeDouble
)
s1902_c01_018e
(
modelName: s1902_c01_018e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!3 or more workers, other, type: esriFieldTypeDouble
)
s1902_c01_019e
(
modelName: s1902_c01_019e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population, type: esriFieldTypeDouble
)
s1902_c01_020e
(
modelName: s1902_c01_020e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!White, type: esriFieldTypeDouble
)
s1902_c01_021e
(
modelName: s1902_c01_021e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!Black or African American, type: esriFieldTypeDouble
)
s1902_c01_022e
(
modelName: s1902_c01_022e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!American Indian and Alaska Native, type: esriFieldTypeDouble
)
s1902_c01_023e
(
modelName: s1902_c01_023e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!Asian, type: esriFieldTypeDouble
)
s1902_c01_024e
(
modelName: s1902_c01_024e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!Native Hawaiian and Other Pacific Islander, type: esriFieldTypeDouble
)
s1902_c01_025e
(
modelName: s1902_c01_025e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!Some other race, type: esriFieldTypeDouble
)
s1902_c01_026e
(
modelName: s1902_c01_026e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!Two or more races, type: esriFieldTypeDouble
)
s1902_c01_027e
(
modelName: s1902_c01_027e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!Hispanic or Latino origin (of any race), type: esriFieldTypeDouble
)
s1902_c01_028e
(
modelName: s1902_c01_028e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Number!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!White alone, not Hispanic or Latino, type: esriFieldTypeDouble
)
s1902_c02_012e
(
modelName: s1902_c02_012e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families, type: esriFieldTypeDouble
)
s1902_c02_013e
(
modelName: s1902_c02_013e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!No workers, type: esriFieldTypeDouble
)
s1902_c02_014e
(
modelName: s1902_c02_014e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!1 worker, type: esriFieldTypeDouble
)
s1902_c02_015e
(
modelName: s1902_c02_015e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!2 workers, both spouses worked, type: esriFieldTypeDouble
)
s1902_c02_016e
(
modelName: s1902_c02_016e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!2 workers, other, type: esriFieldTypeDouble
)
s1902_c02_017e
(
modelName: s1902_c02_017e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!3 or more workers, both spouses worked, type: esriFieldTypeDouble
)
s1902_c02_018e
(
modelName: s1902_c02_018e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!3 or more workers, other, type: esriFieldTypeDouble
)
s1902_c02_019e
(
modelName: s1902_c02_019e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population, type: esriFieldTypeDouble
)
s1902_c02_020e
(
modelName: s1902_c02_020e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!White, type: esriFieldTypeDouble
)
s1902_c02_021e
(
modelName: s1902_c02_021e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!Black or African American, type: esriFieldTypeDouble
)
s1902_c02_022e
(
modelName: s1902_c02_022e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!American Indian and Alaska Native, type: esriFieldTypeDouble
)
s1902_c02_023e
(
modelName: s1902_c02_023e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!Asian, type: esriFieldTypeDouble
)
s1902_c02_024e
(
modelName: s1902_c02_024e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!Native Hawaiian and Other Pacific Islander, type: esriFieldTypeDouble
)
s1902_c02_025e
(
modelName: s1902_c02_025e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!Some other race, type: esriFieldTypeDouble
)
s1902_c02_026e
(
modelName: s1902_c02_026e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!Two or more races, type: esriFieldTypeDouble
)
s1902_c02_027e
(
modelName: s1902_c02_027e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!Hispanic or Latino origin (of any race), type: esriFieldTypeDouble
)
s1902_c02_028e
(
modelName: s1902_c02_028e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Percent Distribution!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!White alone, not Hispanic or Latino, type: esriFieldTypeDouble
)
s1902_c03_011e
(
modelName: s1902_c03_011e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Mean income (dollars)!!HOUSEHOLD INCOME!!All households!!With other types of income, type: esriFieldTypeDouble
)
s1902_c03_012e
(
modelName: s1902_c03_012e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families, type: esriFieldTypeDouble
)
s1902_c03_013e
(
modelName: s1902_c03_013e, nullable: true, editable: true, defaultValue: null, length: 255, alias: Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!No workers, type: esriFieldTypeString
)
s1902_c03_014e
(
modelName: s1902_c03_014e, nullable: true, editable: true, defaultValue: null, length: 255, alias: Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!1 worker, type: esriFieldTypeString
)
s1902_c03_015e
(
modelName: s1902_c03_015e, nullable: true, editable: true, defaultValue: null, length: 255, alias: Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!2 workers, both spouses worked, type: esriFieldTypeString
)
s1902_c03_016e
(
modelName: s1902_c03_016e, nullable: true, editable: true, defaultValue: null, length: 255, alias: Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!2 workers, other, type: esriFieldTypeString
)
s1902_c03_017e
(
modelName: s1902_c03_017e, nullable: true, editable: true, defaultValue: null, length: 255, alias: Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!3 or more workers, both spouses worked, type: esriFieldTypeString
)
s1902_c03_018e
(
modelName: s1902_c03_018e, nullable: true, editable: true, defaultValue: null, length: 255, alias: Estimate!!Mean income (dollars)!!FAMILY INCOME BY NUMBER OF WORKERS IN FAMILY!!All families!!3 or more workers, other, type: esriFieldTypeString
)
s1902_c03_019e
(
modelName: s1902_c03_019e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population, type: esriFieldTypeDouble
)
s1902_c03_020e
(
modelName: s1902_c03_020e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!White, type: esriFieldTypeDouble
)
s1902_c03_021e
(
modelName: s1902_c03_021e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!Black or African American, type: esriFieldTypeDouble
)
s1902_c03_022e
(
modelName: s1902_c03_022e, nullable: true, editable: true, defaultValue: null, length: 255, alias: Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!American Indian and Alaska Native, type: esriFieldTypeString
)
s1902_c03_023e
(
modelName: s1902_c03_023e, nullable: true, editable: true, defaultValue: null, length: 255, alias: Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!Asian, type: esriFieldTypeString
)
s1902_c03_024e
(
modelName: s1902_c03_024e, nullable: true, editable: true, defaultValue: null, length: 255, alias: Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!Native Hawaiian and Other Pacific Islander, type: esriFieldTypeString
)
s1902_c03_025e
(
modelName: s1902_c03_025e, nullable: true, editable: true, defaultValue: null, length: 255, alias: Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!One race--!!Some other race, type: esriFieldTypeString
)
s1902_c03_026e
(
modelName: s1902_c03_026e, nullable: true, editable: true, defaultValue: null, length: 255, alias: Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!Two or more races, type: esriFieldTypeString
)
s1902_c03_027e
(
modelName: s1902_c03_027e, nullable: true, editable: true, defaultValue: null, length: 255, alias: Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!Hispanic or Latino origin (of any race), type: esriFieldTypeString
)
s1902_c03_028e
(
modelName: s1902_c03_028e, nullable: true, editable: true, defaultValue: null, alias: Estimate!!Mean income (dollars)!!PER CAPITA INCOME BY RACE AND HISPANIC OR LATINO ORIGIN!!Total population!!White alone, not Hispanic or Latino, type: esriFieldTypeDouble
)
Description: Fossil energy communities as defined by the U.S. Department of Energy's Interagency Working Group (IWG). Fossil energy communities are identified by the presence of brownfields, coal plants or mines, fossil energy jobs and tax revenue.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Bauer, J., Rose, K., and B. White. 2023. Datasets for IWG Report on Energy Communities. Interagency Working Group for Coal and Power Plant Communities. Doi: HTTPs://doi.org/10.18141/1787670
Description: National Energy Technology Laboratory analysis of state tax revenue from coal activities into a spatial data layer. Data contains revenue information for 19 counties.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: National Energy Technology Laboratory, Department of Energy
Name: Chained-Dollar Values of GDP by State (2001-2019)
Display Field: geoname
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: ---------- Data Description ----------Data Name: chainGDP_2001to2019_AREA.shpData Description: AREA includes individual layer for County, State, NationData Format: ESRI Shapefile - Polygon (MultiPolygon)Date Created: 05/12/2023Date Published: Date of Last Recorded Update: 05/12/2023CRS: EPSG:4269 - NAD83 - GeographicX Minimum: -179.23108600000000479Y Minimum: -14.60181299999999993X Maximum: 179.85968099999999481Y Maximum: 71.43978599999999801Brief Description: Chained GDP data obtained from the U.S. Bureau of Economic Analysis processed to be a spatial data layer. Units are in thousands of chained 2012 dollars. Data includes four industry descriptions: All industry total (A), Mining, quarrying, and oil and gas extraction (MiQOG), Manufacturing (Ma), Natural resources and mining (NRMi). Processing steps: Using R packages "sp", "tigris", "maptools", "dplyr", "sf", "tidycensus" the data obtained from the U.S. Bureau of Economic Analysis was imported into R Studio and spatial layers for county, state, and nation were called to the workspace. The GDP data was subsetted for industries of interest and abbreviated, then converted from long to wide format. The GDP tables were joined to the spatial boundary layers, cleaned for data type (converted to numeric) and exported as shapefiles. ---------- Source Information ----------Data Source Information: U.S. Bureau of Economic Analysis, Baumgardner, F., Hinson, J., & O’Connell, C., Gross Domestic Product by County, 2021 (2022). Retrieved from https://www.bea.gov/data/gdp/gdp-county-metro-and-other-areas. Data Source Web Link: https://www.bea.gov/data/gdp/gdp-county-metro-and-other-areasData Source Description: Gross domestic product (GDP) by county is the value of goods and services produced by the county's economy less the value of goods and services used up in production. GDP by county is the substate counterpart of the nation's GDP, the Bureau's featured and most comprehensive measure of U.S. economic activity. Chained-dollar values of GDP by county are derived by applying national chain-type price indexes to the current dollar values of GDP by county for 65 detailed North American Industry Classification System-based industries. The chain-type index formula that is used in the national accounts is then used to calculate the values of total real GDP by county and real GDP by county at more aggregated industry levels. Real GDP by county may reflect a substantial volume of output that is sold to other areas and countries. To the extent that a county's output is produced and sold in national markets at relatively uniform prices (or sold locally at national prices), real GDP by county captures the differences across counties that reflect the relative differences in the mix of goods and services that the areas produce. However, real GDP by county does not capture geographic differences in the prices of goods and services that are produced and sold locally.---------- Acquisition Information ----------Acquired by: Casey WhiteAcquisition date: 05/12/2023Contact Information: casey.white@NETL.DOE.GOV ---------- Additional Information ----------Units: Thousands of chained 2012 dollarsColumn format: INDUSTRY_YEARA: All industry totalMiQOG: Mining, quarrying, and oil and gas extractionMa: ManufacturingNRMi: Natural resources and mining
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: U.S. Bureau of Economic Analysis, National Energy Technology Laboratory
Name: Chained-Dollar Values of GDP by County (2001-2019)
Display Field: geoname
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: ---------- Data Description ----------Data Name: chainGDP_2001to2019_AREA.shpData Description: AREA includes individual layer for County, State, NationData Format: ESRI Shapefile - Polygon (MultiPolygon)Date Created: 05/12/2023Date Published: Date of Last Recorded Update: 05/12/2023CRS: EPSG:4269 - NAD83 - GeographicX Minimum: -179.23108600000000479Y Minimum: -14.60181299999999993X Maximum: 179.85968099999999481Y Maximum: 71.43978599999999801Brief Description: Chained GDP data obtained from the U.S. Bureau of Economic Analysis processed to be a spatial data layer. Units are in thousands of chained 2012 dollars. Data includes four industry descriptions: All industry total (A), Mining, quarrying, and oil and gas extraction (MiQOG), Manufacturing (Ma), Natural resources and mining (NRMi). Processing steps: Using R packages "sp", "tigris", "maptools", "dplyr", "sf", "tidycensus" the data obtained from the U.S. Bureau of Economic Analysis was imported into R Studio and spatial layers for county, state, and nation were called to the workspace. The GDP data was subsetted for industries of interest and abbreviated, then converted from long to wide format. The GDP tables were joined to the spatial boundary layers, cleaned for data type (converted to numeric) and exported as shapefiles. ---------- Source Information ----------Data Source Information: U.S. Bureau of Economic Analysis, Baumgardner, F., Hinson, J., & O’Connell, C., Gross Domestic Product by County, 2021 (2022). Retrieved from https://www.bea.gov/data/gdp/gdp-county-metro-and-other-areas. Data Source Web Link: https://www.bea.gov/data/gdp/gdp-county-metro-and-other-areasData Source Description: Gross domestic product (GDP) by county is the value of goods and services produced by the county's economy less the value of goods and services used up in production. GDP by county is the substate counterpart of the nation's GDP, the Bureau's featured and most comprehensive measure of U.S. economic activity. Chained-dollar values of GDP by county are derived by applying national chain-type price indexes to the current dollar values of GDP by county for 65 detailed North American Industry Classification System-based industries. The chain-type index formula that is used in the national accounts is then used to calculate the values of total real GDP by county and real GDP by county at more aggregated industry levels. Real GDP by county may reflect a substantial volume of output that is sold to other areas and countries. To the extent that a county's output is produced and sold in national markets at relatively uniform prices (or sold locally at national prices), real GDP by county captures the differences across counties that reflect the relative differences in the mix of goods and services that the areas produce. However, real GDP by county does not capture geographic differences in the prices of goods and services that are produced and sold locally.---------- Acquisition Information ----------Acquired by: Casey WhiteAcquisition date: 05/12/2023Contact Information: casey.white@NETL.DOE.GOV ---------- Additional Information ----------Units: Thousands of chained 2012 dollarsColumn format: INDUSTRY_YEARA: All industry totalMiQOG: Mining, quarrying, and oil and gas extractionMa: ManufacturingNRMi: Natural resources and mining
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: U.S. Bureau of Economic Analysis, National Energy Technology Laboratory
Description: This layer is exported records for NAICS code 2213 - WATER, SEWAGE and OTHER SYSTEMSThe North American Industry Classification System (NAICS) is the standard used by Federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy. For Biennial Report purposes, sites must provide their primary NAICS using either a 5- or 6-digit NAICS code and any number of secondary NAICSProcesssing Steps:1.The records were download from the source link2. the address were goecoded to produce lat and long3. The tabular data was converted to feature class4. Summarize by count was used to count all the points within each county.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: EPA 2021 Detailed NAICS Results for National (NAICS 2213)
https://rcrapublic.epa.gov/rcrainfoweb/action/modules/br/naics/searchDetail/false/ALL/2021/2213/ALL/null
For questions contact:
Maneesh Sharma: Maneesh.Sharma@netl.doe.gov
Daniel Amrine: Daniel.Amrine@netl.doe.gov
Description: Filter was used to filter out certain NCAIS error codes: NAICS NOT IN ('113310', 'N/A') " ", N/A. and 11310A Census tract layer and county layer with "Count of Points" attribute were created using the ESRI "Summarize Within" tool. These are also in the geodatabaseThese provide a count of manufacturing facilities in the census tract and county levels.This dataset includes the entire Industrial PinPointer database of manufacturing companies, which includes the 2009 D2 of 2 update. Eighteen (18) states have been updated in this delivery: Alaska, Arizona, Hawaii, Idaho, Massachusetts, Missouri, Nevada, New Hampshire, New York, Ohio, Oklahoma, Oregon, South Carolina, South Dakota, Tennessee, Utah, Wisconsin, and Wyoming. In addition to American Samoa, Guam, and the Commonwealth of the Northern Mariana Islands, two (2) US territories have been added to the dataset from the 2009 D1 of 2 update: Puerto Rico, and US Virgin Islands. This totals 48,930 companies. The database decreased by 65 companies from the 2009 D1 of 2 update. This dataset covers manufacturing locations in the 50 states, the District of Columbia, and US territories. Only those locations primarily engaged in manufacturing (SIC Codes 2000-3999) or those that are headquarters of manufacturing companies are included. SIC codes are not provided for 125 companies in the US territories. Where an employee count is available, only locations employing fifteen (15) or more people are included. Employee count is not available for the US territories; therefore, all locations primarily engaged in manufacturing are included for these territories. All text fields were set to upper case, leading and trailing spaces were trimmed from all text fields, and non-printable and diacritic characters were removed from all text fields per NGA's request.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Homeland Infrastructure Foundation-Level Data (HIFLD), DOD
https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::general-manufacturing-facilities/about
For questions contact:
Maneesh Sharma: Maneesh.Sharma@netl.doe.gov
Daniel Amrine: Daniel.Amrine@netl.doe.gov
Description: The US Hydropower Potential at National Conduits dataset provides the results of a national assessment of various conduit hydropower potential for the year 2022. Hydropower potential and generation estimates are provided for various types of municipal, agricultural, and industrial applications across all 50 states. A total of 1.41 gigawatts of hydropower potential is estimated across the US. This dataset provides conduit hydropower estimates summarized at state resolution in Shapefile (*.shp) format and at county resolution in comma separated (*.csv) and *.shp format.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Shih-Chieh Kao, Lindsay George, Carly Hansen, Scott DeNeale, Kurt Johnson, Alden Sampson, Marshall Moutenot, Kevin Altamirano, Kathryn Garcia, Jim Downing, Mary Beth Day, Kelsey Rugani. 2023. U.S. Hydropower Potential at National Conduits. HydroSource. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA. DOI: https://doi.org/10.21951/CONDUIT/1993154
Description: This dataset is a qualitative assessment of geothermal potential (Enhanced Geothermal System EGS) for the US based on Levelized Cost of Electricity, with CLASS 1 being most favorable, and CLASS 5 being least favorable. This dataset does not include shallow EGS resources located near hydrothermal sites or USGS assessment of undiscovered hydrothermal resources. The source data for deep EGS includes temperature at depth from 3 to 10 km provided by Southern Methodist University Geothermal Laboratory (Blackwell & Richards, 2009) and analyses (for regions with temperatures equal to or greater than 150°C) performed by NREL (2009). CLASS 999 regions have temperatures less than 150°C at 10 km depth and were not assessed for deep EGS potential. Temperature at depth data for deep EGS in Alaska and Hawaii not available.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: National Renewable Energy Laboratory. (2018) Geothermal Resources of the United States. Retrieved from https://www.nrel.gov/gis/geothermal.html
Description: United States Global Horizontal Irradiance (GHI). Annual average total daily solar resource from PSM v3 at a resolution of 0.038-degree latitude by 0.038 longitude (nominally 4 km x 4 km). The insolation values represent the resource available for solar energy systems.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Energy Information Administration. (2020). Solar Resources. Retrieved from https://atlas.eia.gov/datasets/eia::solar-resources/explore
Description: Multiyear average wind speeds in the US, meters per second, at 100 meters above surface level.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Energy Information Administration. (2022). On Shore Wind Speed. Retrieved from https://atlas.eia.gov/datasets/eia::on-shore-wind-speed/explore
Name: Manufacturing Facilities Employment by County (NAICS 31-33)
Display Field: name
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: Filter was used to filter out certain NCAIS error codes: NAICS NOT IN ('113310', 'N/A') " ", N/A. and 11310A Census county layer with "Count of Points" attribute were created using the ESRI "Summarize Within" tool. These are also in the geodatabaseEmployment was added to the original point layer as "EMP" Field. A Census county layer with "sum EMP" attribute was created using the ESRI "Summarize Within" tool. These are also in the geodatabase. The final layer contains both count of general manufacturing facilities (Point_Count) and sum of general manufacturing employees (sum EMP) attributes.These provide a count of manufacturing facilities in the census tract and county levels.Filter was used to filter out certain NCAIS error codes: NAICS NOT IN ('113310', 'N/A') " ", N/A. and 11310A Census tract layer and county layer with "Count of Points" attribute were created using the ESRI "Summarize Within" tool. These are also in the geodatabaseThese provide a count of manufacturing facilities in the census tract and county levels.This dataset includes the entire Industrial PinPointer database of manufacturing companies, which includes the 2009 D2 of 2 update. Eighteen (18) states have been updated in this delivery: Alaska, Arizona, Hawaii, Idaho, Massachusetts, Missouri, Nevada, New Hampshire, New York, Ohio, Oklahoma, Oregon, South Carolina, South Dakota, Tennessee, Utah, Wisconsin, and Wyoming. In addition to American Samoa, Guam, and the Commonwealth of the Northern Mariana Islands, two (2) US territories have been added to the dataset from the 2009 D1 of 2 update: Puerto Rico, and US Virgin Islands. This totals 48,930 companies. The database decreased by 65 companies from the 2009 D1 of 2 update. This dataset covers manufacturing locations in the 50 states, the District of Columbia, and US territories. Only those locations primarily engaged in manufacturing (SIC Codes 2000-3999) or those that are headquarters of manufacturing companies are included. SIC codes are not provided for 125 companies in the US territories. Where an employee count is available, only locations employing fifteen (15) or more people are included. Employee count is not available for the US territories; therefore, all locations primarily engaged in manufacturing are included for these territories. All text fields were set to upper case, leading and trailing spaces were trimmed from all text fields, and non-printable and diacritic characters were removed from all text fields per NGA's request.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Homeland Infrastructure Foundation-Level Data (HIFLD), DOD
https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::general-manufacturing-facilities/about
For questions contact:
Maneesh Sharma: Maneesh.Sharma@netl.doe.gov
Daniel Amrine: Daniel.Amrine@netl.doe.gov
Name: Manufacturing Facilities Employment by Census Tract (NAICS 31-33)
Display Field: name
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: Filter was used to filter out certain NCAIS error codes: NAICS NOT IN ('113310', 'N/A') " ", N/A. and 11310A Census tract layer with "Count of Points" attribute were created using the ESRI "Summarize Within" tool. These are also in the geodatabaseEmployment was added to the original point layer as "EMP" Field. A Census tract layer with "sum EMP" attribute was created using the ESRI "Summarize Within" tool. These are also in the geodatabase. The final tract layer contains both count of general manufacturing facilities (Point_Count) and sum of general manufacturing employees (sum EMP) attributes.Filter was used to filter out certain NCAIS error codes: NAICS NOT IN ('113310', 'N/A') " ", N/A. and 11310A Census tract layer and county layer with "Count of Points" attribute were created using the ESRI "Summarize Within" tool. These are also in the geodatabaseThese provide a count of manufacturing facilities in the census tract and county levels.This dataset includes the entire Industrial PinPointer database of manufacturing companies, which includes the 2009 D2 of 2 update. Eighteen (18) states have been updated in this delivery: Alaska, Arizona, Hawaii, Idaho, Massachusetts, Missouri, Nevada, New Hampshire, New York, Ohio, Oklahoma, Oregon, South Carolina, South Dakota, Tennessee, Utah, Wisconsin, and Wyoming. In addition to American Samoa, Guam, and the Commonwealth of the Northern Mariana Islands, two (2) US territories have been added to the dataset from the 2009 D1 of 2 update: Puerto Rico, and US Virgin Islands. This totals 48,930 companies. The database decreased by 65 companies from the 2009 D1 of 2 update. This dataset covers manufacturing locations in the 50 states, the District of Columbia, and US territories. Only those locations primarily engaged in manufacturing (SIC Codes 2000-3999) or those that are headquarters of manufacturing companies are included. SIC codes are not provided for 125 companies in the US territories. Where an employee count is available, only locations employing fifteen (15) or more people are included. Employee count is not available for the US territories; therefore, all locations primarily engaged in manufacturing are included for these territories. All text fields were set to upper case, leading and trailing spaces were trimmed from all text fields, and non-printable and diacritic characters were removed from all text fields per NGA's request.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Homeland Infrastructure Foundation-Level Data (HIFLD), DOD
https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::general-manufacturing-facilities/about
For questions contact:
Maneesh Sharma: Maneesh.Sharma@netl.doe.gov
Daniel Amrine: Daniel.Amrine@netl.doe.gov
Name: Manufacturing Facilities by County (NAICS 31-33)
Display Field: name
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: Filter was used to filter out certain NCAIS error codes: NAICS NOT IN ('113310', 'N/A') " ", N/A. and 11310A Census tract layer and county layer with "Count of Points" attribute were created using the ESRI "Summarize Within" tool. These are also in the geodatabaseThese provide a count of manufacturing facilities in the census tract and county levels.Filter was used to filter out certain NCAIS error codes: NAICS NOT IN ('113310', 'N/A') " ", N/A. and 11310A Census tract layer and county layer with "Count of Points" attribute were created using the ESRI "Summarize Within" tool. These are also in the geodatabaseThese provide a count of manufacturing facilities in the census tract and county levels.This dataset includes the entire Industrial PinPointer database of manufacturing companies, which includes the 2009 D2 of 2 update. Eighteen (18) states have been updated in this delivery: Alaska, Arizona, Hawaii, Idaho, Massachusetts, Missouri, Nevada, New Hampshire, New York, Ohio, Oklahoma, Oregon, South Carolina, South Dakota, Tennessee, Utah, Wisconsin, and Wyoming. In addition to American Samoa, Guam, and the Commonwealth of the Northern Mariana Islands, two (2) US territories have been added to the dataset from the 2009 D1 of 2 update: Puerto Rico, and US Virgin Islands. This totals 48,930 companies. The database decreased by 65 companies from the 2009 D1 of 2 update. This dataset covers manufacturing locations in the 50 states, the District of Columbia, and US territories. Only those locations primarily engaged in manufacturing (SIC Codes 2000-3999) or those that are headquarters of manufacturing companies are included. SIC codes are not provided for 125 companies in the US territories. Where an employee count is available, only locations employing fifteen (15) or more people are included. Employee count is not available for the US territories; therefore, all locations primarily engaged in manufacturing are included for these territories. All text fields were set to upper case, leading and trailing spaces were trimmed from all text fields, and non-printable and diacritic characters were removed from all text fields per NGA's request.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Homeland Infrastructure Foundation-Level Data (HIFLD), DOD
https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::general-manufacturing-facilities/about
For questions contact:
Maneesh Sharma: Maneesh.Sharma@netl.doe.gov
Daniel Amrine: Daniel.Amrine@netl.doe.gov
Name: Manufacturing Facilities by Census Tract (NAICS 31-33)
Display Field: name
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: Filter was used to filter out certain NCAIS error codes: NAICS NOT IN ('113310', 'N/A') " ", N/A. and 11310A Census tract layer and county layer with "Count of Points" attribute were created using the ESRI "Summarize Within" tool. These are also in the geodatabaseThese provide a count of manufacturing facilities in the census tract and county levels.Filter was used to filter out certain NCAIS error codes: NAICS NOT IN ('113310', 'N/A') " ", N/A. and 11310A Census tract layer and county layer with "Count of Points" attribute were created using the ESRI "Summarize Within" tool. These are also in the geodatabaseThese provide a count of manufacturing facilities in the census tract and county levels.This dataset includes the entire Industrial PinPointer database of manufacturing companies, which includes the 2009 D2 of 2 update. Eighteen (18) states have been updated in this delivery: Alaska, Arizona, Hawaii, Idaho, Massachusetts, Missouri, Nevada, New Hampshire, New York, Ohio, Oklahoma, Oregon, South Carolina, South Dakota, Tennessee, Utah, Wisconsin, and Wyoming. In addition to American Samoa, Guam, and the Commonwealth of the Northern Mariana Islands, two (2) US territories have been added to the dataset from the 2009 D1 of 2 update: Puerto Rico, and US Virgin Islands. This totals 48,930 companies. The database decreased by 65 companies from the 2009 D1 of 2 update. This dataset covers manufacturing locations in the 50 states, the District of Columbia, and US territories. Only those locations primarily engaged in manufacturing (SIC Codes 2000-3999) or those that are headquarters of manufacturing companies are included. SIC codes are not provided for 125 companies in the US territories. Where an employee count is available, only locations employing fifteen (15) or more people are included. Employee count is not available for the US territories; therefore, all locations primarily engaged in manufacturing are included for these territories. All text fields were set to upper case, leading and trailing spaces were trimmed from all text fields, and non-printable and diacritic characters were removed from all text fields per NGA's request.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: Homeland Infrastructure Foundation-Level Data (HIFLD), DOD
https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::general-manufacturing-facilities/about
For questions contact:
Maneesh Sharma: Maneesh.Sharma@netl.doe.gov
Daniel Amrine: Daniel.Amrine@netl.doe.gov
Name: Water Treatment Plants by County (NAICS 2213)
Display Field: name
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: This layer is exported records for NAICS code 2213 - WATER, SEWAGE and OTHER SYSTEMSThe North American Industry Classification System (NAICS) is the standard used by Federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy. For Biennial Report purposes, sites must provide their primary NAICS using either a 5- or 6-digit NAICS code and any number of secondary NAICSProcesssing Steps:1.The records were download from the source link2. the address were goecoded to produce lat and long3. The tabular data was converted to feature class4. Summarize by count was used to count all the points within each county.
Service Item Id: 22aaf2e337974faf8369d53ce09ae988
Copyright Text: EPA 2021 Detailed NAICS Results for National (NAICS 2213)
https://rcrapublic.epa.gov/rcrainfoweb/action/modules/br/naics/searchDetail/false/ALL/2021/2213/ALL/null
For questions contact:
Maneesh Sharma: Maneesh.Sharma@netl.doe.gov
Daniel Amrine: Daniel.Amrine@netl.doe.gov