This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows health insurance coverage sex and race by age group. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Sums may add to more than the total, as people can be in multiple race groups (for example, Hispanic and Black). Later vintages of this layer have a different age group for children that includes age 18. This layer is symbolized to show the percent of population with no health insurance coverage. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B27010, C27001B, C27001C, C27001D, C27001E, C27001F, C27001G, C27001H, C27001I (Not all lines of these tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This dataset contains census tract level and estimated data about the number of uninsured non-institutionalized civilians, the number of persons belonging to minority (from ethnicity point of view, including Hispanic/Latino population) and the number of persons aged 5 and older who speak English less than well. In this dataset could be found all US census tracts and the estimates are made using data collected from 2014 to 2018 by the American Community Survey (ACS).

Health insurance coverage rates, from the American Community Survey, 2014 5-year Average, by Zip. For the Detroit Tri-County region. Data Driven Detroit calculated the rates by dividing the total number of insured by the total number of people in each age group.

2008-2024. American Lung Association. Cessation Coverage. Medicaid data compiled by the Centers for Disease Control and Prevention’s Office on Smoking and Health were obtained from the State Tobacco Cessation Coverage Database, developed and administered by the American Lung Association. Data from 2008-2012 are reported on an annual basis; beginning in 2013 data are reported on a quarterly basis. Data include state-level information on Medicaid coverage of approved medications by the Food and Drug Administration (FDA) for tobacco cessation treatment; types of counseling recommended by the Public Health Service (PHS) and barriers to accessing cessation treatment. Note: Section 2502 of the Patient Protection and Affordable Care Act requires all state Medicaid programs to cover all FDA-approved tobacco cessation medications as of January 1, 2014. However, states are currently in the process of modifying their coverage to come into compliance with this requirement. Data in the STATE System on Medicaid coverage of tobacco cessation medications reflect evidence of coverage that is found in documentable sources, and may not yet reflect medications covered under this requirement.

The index is constructed using socioeconomic and demographic, exposure, health, and housing indicators and is intended to serve as a planning tool for health and climate adaptation. Steps for calculating the index can be found in in the "An Assessment of San Francisco’s Vulnerability to Flooding & Extreme Storms" located at https://sfclimatehealth.org/wp-content/uploads/2018/12/FloodVulnerabilityReport_v5.pdf.pdfData Dictionary: (see attachment here also: https://data.sfgov.org/Health-and-Social-Services/San-Francisco-Flood-Health-Vulnerability/cne3-h93g)

Field Name Data Type Definition Notes (optional)

Census Blockgroup Text San Francisco Census Block Groups

Children Numeric Percentage of residents under 18 years old. American Community Survey 2009 - 2014.

Chidlren_wNULLvalues Numeric Percentage of residents under 18 years old. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Elderly Numeric Percentage of residents aged 65 and older. American Community Survey 2009 - 2014.

Elderly_wNULLvalues Numeric Percentage of residents aged 65 and older. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

NonWhite Numeric Percentage of residents that do not identify as white (not Hispanic or Latino). American Community Survey 2009 - 2014.

NonWhite_wNULLvalues Numeric Percentage of residents that do not identify as white (not Hispanic or Latino). American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Poverty Numeric Percentage of all individuals below 200% of the poverty level. American Community Survey 2009 - 2014.

Poverty_wNULLvalues Numeric Percentage of all individuals below 200% of the poverty level. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Education Numeric Percent of individuals over 25 with at least a high school degree. American Community Survey 2009 - 2014.

Education_wNULLvalues Numeric Percent of individuals over 25 with at least a high school degree. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

English Numeric Percentage of households with no one age 14 and over who speaks English only or speaks English "very well". American Community Survey 2009 - 2014.

English_wNULLvalues Numeric Percentage of households with no one age 14 and over who speaks English only or speaks English "very well". American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Elevation Numeric Minimum elevation in feet. United States Geologic Survey 2011.

SeaLevelRise Numeric Percent of land area in the 100-year flood plain with 36-inches of sea level rise. San Francisco Sea Level Rise Committee, AECOM 77inch flood inundation layer, 2014.

Precipitation Numeric Percent of land area with over 6-inches of projected precipitation-related flood inundation during an 100-year storm. San Francisco Public Utilities Commission, AECOM, 2015.

Diabetes Numeric Age-adjusted hospitalization rate due to diabetes; adults 18+. California Office of Statewide Health Planning and Development, 2004-2015.

MentalHealth Numeric Age-adjusted hospitalization rate due to schizophrenia and other psychotic disorders. California Office of Statewide Health Planning and Development, 2004-2015.

Asthma Numeric Age-adjusted hospitalization rate due to asthma; adults 18+. California Office of Statewide Health Planning and Development, 2004 - 2015.

Disability Numeric Percentage of total civilian noninstitutionalized population with a disability. American Community Survey 2009 - 2014.

Disability_wNULLvalues

Percentage of total civilian noninstitutionalized population with a disability. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

HousingQuality Numeric Annual housing violations, per 1000 residents. San Francisco Department of Public Health, San Francisco Department of Building Inspections, San Francisco Fire Department, 2010 - 2012.

Homeless Numeric Homeless population, per 1000 residents. San Francisco Homeless Count 2015.

LivAlone Numeric Households with a householder living alone. American Community Surevey 2009 - 2014.

LivAlone_wNULLvalues Numeric Households with a householder living alone. American Community Surevey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

FloodHealthIndex Numeric Comparative ranking of flood health vulnerability, by block group. The Flood Health Index weights the six socioeconomic and demographic indicators (Children, Elderly, NonWhite, Poverty, Education, English) as 20% of the final score, the three exposure indicators (Sea Level Rise, Precipitation, Elevation) as 40% of the final score, the four health indicators (Diabetes, MentalHealth, Asthma, Disability) as 20% of the final score, and the three housing indicators (HousingQuality, Homeless, LivAlone) as 20% of the final score. For methodology used to develop the final Flood Health Index, please read the San Francisco Flood Vulnerability Assessment Methodology Section.

FloodHealthIndex_Quintiles Numeric Comparative ranking of flood health vulnerability, by block group. The Flood Health Index weights the six socioeconomic and demographic indicators (Children, Elderly, NonWhite, Poverty, Education, English) as 20% of the final score, the three exposure indicators (Sea Level Rise, Precipitation, Elevation) as 40% of the final score, the four health indicators (Diabetes, MentalHealth, Asthma, Disability) as 20% of the final score, and the three housing indicators (HousingQuality, Homeless, LivAlone) as 20% of the final score. For methodology used to develop the final Flood Health Index, please read the San Francisco Flood

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Definitions:Race/Ethnicity: Race/ethnicity is categorized as: All races/ethnicities, Non-Hispanic (NH) White, NH Black, Asian/Pacific Islander, or Hispanic. "All races" includes all of the above, as well as other and unknown race/ethnicity and American Indian/Alaska Native. The latter two groups are not reported separately due to small numbers for many cancer sites.Racial/Ethnic Composition: Distribution of residents' race/ethnicity (e.g., % Hispanic, % non-Hispanic White, % non-Hispanic Black, % non-Hispanic Asian/Pacific Islander). (Source: US Census, 2010.)Rural: Percent of residents who reside in blocks that are designated as rural. (Source: US Census, 2010.)Foreign Born: Percent of residents who were born outside the United States. (Source: American Community Survey, 2008-2012.)Socioeconomic Status (Neighborhood Level): A composite measure of seven indicator variables created by principal component analysis; indicators include: education, blue-collar job, unemployment, household income, poverty, rent, and house value. Quintiles based on state distribution, with quintile 1 being the lowest SES and 5 being the highest. (Source: American Community Survey, 2008-2012.)Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2010-2014, to show various demographic data by city in the state of Georgia (including the following categories: total population, household composition, grandparents, school enrollment, educational attainment, disability, foreign born status, linguistic isolation, unemployment, commuting mode, occupation, income, health insurance, poverty, housing characteristics, vehicle availability, housing values, and housing affordability).The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number. The Census Bureau also calculates a corresponding margin of error (MOE) for ACS measures (although margins of error are not included in this dataset).The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2010-2014). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.For further explanation of ACS estimates and margin of error, refer to Census Bureau documentation.Base Attributes:NAME = Name of city or municipalityAcres = Area in acresSq_Miles = Area in square milesCounty20 = Within ARC 20-county regionCounty10 = Within ARC 10-county regionAttributes from ACS:Workers_16_years_and_over= Number, Workers, 16 years and overCar_Truck_or_Van_drove_alone= Number, Car, truck, or van – drove alonePct_Car_Truck_Van_drove_alone= Percent, Car, truck, or van – drove aloneCar_truck_or_van_carpooled= Number, Car, truck, or van – carpooledPct_Car_Truck_Van_carpooled= Percent, Car, truck, or van – carpooledPublic_Transport_excluding_Taxi= Number, Public transportation (excluding taxicab)Pct_Public_Transp_exclude_Taxi= Percent, Public transportation (excluding taxicab)Worked_at_home= Number, Worked at homePct_Worked_at_home= Percent, Worked at homeMean_Travel_Time_to_Work_min= Mean travel time to work (minutes)- - - - - -Civilian_nonInstitutional_Pop= Total Civilian Noninstitutionalized PopulationCiv_nonInstitution_Pop_wDisabil= #, Civilian Noninstitutionalized Population With a disabilityPct_Civ_nonInstitut_Pop_wDisab= %, Civilian Noninstitutionalized Population With a disabilityCiv_nonInstitut_Pop_under_18yrs= #, Civilian Noninstitutionalized Population Under 18 yearsCiv_nonInst_under18_wDisab= #, Civilian Noninstitutionalized Under 18 years With a disabilityPct_Civ_nonInst_under18_wDisab= %, Civilian Noninstitutionalized Under 18 years With a disabilityCiv_nonInst_Pop_18_to_64= #, Civilian Noninstitutionalized Population 18 to 64 yearsCiv_nonInst_18_to_64_wDisab= #, Civilian Noninstitutionalized 18 to 64 years With a disabilityPct_Civ_nonInst_18to64_wDisab= %, Civilian Noninstitutionalized 18 to 64 years With a disabilityCiv_nonInst_Pop_65years_up= #, Civilian Noninstitutionalized Population 65 years and overCiv_nonInst_65up_wDisab= #, Civilian Noninstitutionalized 65 years and over With a disabilityPct_Civ_nonInst_65up_wDisab= %, Civilian Noninstitutionalized 65 years and over With a disability- - - - - -Population_25_years_and_over= #, Population 25 years and overLess_than_HS_or_GED= #, Less than HS or GEDPercent_Less_than_HS_or_GED= %, Less than HS or GEDBA_or_Higher= #, BA or HigherPercent_BA_or_Higher= %, BA or Higher- - - - - -US_Native= #, U.S. NativePercent_US_Native= %, U.S. NativeUSnative_Born_in_US= #, U.S. Native, Born in the United StatesPct_USnative_Born_US= %, U.S. Native, Born in the United StatesUSnative_Born_State_Resid= #, U.S. Native, Born in State of ResidencePct_USnative_Born_State_Resid= %, U.S. Native, Born in State of ResidenceUS_Native_Born_Diff_State= #, U.S. Native, Born in Different StatePct_US_Natv_Born_inDiff_State= %, U.S. Native, Born in Different StateForeign_Born= #, Foreign BornPercent_Foreign_Born= %, Foreign BornForBorn_Nat_UScitizen= #, Foreign Born, Naturalized U.S. CitizenPct_ForBorn_Nat_UScitizen= %, Foreign Born, Naturalized U.S. CitizenForeignBorn_notUS_Citizen= #, Foreign Born, Not a U.S. CitizenPct_ForBorn_notUS_Citizen= %, Foreign Born, Not a U.S. Citizen- - - - - -GParents_Liv_wOwn_GChild_und18= #, Grandparents living with own grandchildren under 18 yearsGParents_RespFor_Gchildren= #, Grandparents Responsible for grandchildrenPct_GPar_RespFor_Gchildren= %, Grandparents Responsible for grandchildren- - - - - -Pop_wHealth_Insurance= #, Civilian noninstitutionalized population with health insurance coveragePct_Pop_wHealth_Ins= %, Civilian noninstitutionalized population with health insurance coveragePop_wPriv_Health_Ins= #, Civilian noninstitutionalized population with private health insurancePct_Pop_wPriv_Health_Ins= %, Civilian noninstitutionalized population with private health insurancePopulation_with_public_coverage= #, Civilian noninstitutionalized population with public coveragePct_Pop_with_public_coverage= %, Civilian noninstitutionalized population with public coveragePop_wNo_Health_Ins= #, Civilian noninstitutionalized population with no health insurance coveragePct_Pop_wNo_Health_Ins= %, Civilian noninstitutionalized population with no health insurance coveragePop_u18_wNo_Health_Ins= #, Civilian Noninstitutionalized Population Under 18 years with no health insurancePct_Pop_u18_wNo_Health_Ins= %, Civilian Noninstitutionalized Population Under 18 years with no health insurancePop_18to64_Employed= #, Civilian noninstitutionalized ages 18 to 64, employedPop_18to64_Empl_wNo_Health_Ins= #, Civilian noninstitutionalized ages 18 to 64, employed with no health insurancePct_Pop_18to64_Emp_wNo_Hlth_Ins= %, Civilian noninstitutionalized ages 18 to 64, employed with no health insurancePop_18to64_Unemployed= #, Civilian noninstitutionalized ages 18 to 64, unemployedPop_18to64_Unemp_wNo_Health_Ins= #, Civilian noninstitutionalized ages 18 to 64, unemployed with no health insurancePct_Pop_18to64_Unemp_No_HlthIns= %, Civilian noninstitutionalized ages 18 to 64, unemployed with no health insurancePop_18to64_Not_in_Labor_Force= #, Civilian noninstitutionalized ages 18 to 64, not in labor forcePop_18to64_Not_LabFor_NoHlthIns= #, Civilian noninstitutionalized ages 18 to 64, not in labor force with no health insurancePctPop_18to64_NotLFor_NoHlthIns= %, Civilian noninstitutionalized ages 18 to 64, not in labor force with no health insurance- - - - - -HousUnits_MonthOwnerCosts_toInc= #, Housing units for which Selected Monthly Owner Costs as % of income are computedSel_Mo_Own_Costs_30pct_of_Incom= #, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household incomePct_Sel_Mo_Own_Costs_30pct_Inc= %, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household incomeHousUnits_Compute_RentPctIncome= #, Housing units for which Gross rent as a percentage of income is computedRent_Pct_of_Inc_More30Pct= #, Gross rent as a percentage of household income (GRAPI) is 30% or morePctRent_PctIncome_More30Pct= %, Gross rent as a percentage of household income (GRAPI) is 30% or moreHousUnits_OwnRent_Compute= #, Housing units for which SMOCAPI or GRAPI are computedHousCosts_Units_30pctMore_Inc= #, Housing costs (GRAPI or SMOCAPI) are 30% or more of household incomePctHousCost_30pctMore_Income= %, Housing costs (GRAPI or SMOCAPI) are 30% or more of household income- - - - - -Total_housing_units = Housing Characteristics: Total housing unitsOccupied_housing_units= #, Occupied housing unitsPercent_Occupied_housing_units= %, Occupied housing unitsVacant_housing_units= #, Vacant housing unitsPercent_Vacant_housing_units= %, Vacant housing unitsHomeowner_vacancy_rate= Homeowner vacancy rateRental_vacancy_rate= Rental vacancy rateOne_unit_detatched_housing_unit= #, 1-unit detached housing unitsPercent_1Unit_Detached= %, 1-unit detached housing unitsHousing_units_built_since_2000= #, Housing units built since 2000Pct_Units_Built_Since_2000= %, Housing units built since 2000Units_Built_1980_to_1999= #, Housing units built 1980 to 1999Pct_Units_Built_1980_to_1999= %, Housing units built 1980 to 1999Units_Built_1979_or_Earlier= #, Housing units built 1979 or earlierPct_Units_Built_1979_or_Earlier= %, Housing units built 1979 or earlier- - - - - -Total_Housing_Units_Val = Housing Value: Total Housing UnitsOccupied_Housing_Units_Val = Housing Value: Occupied Housing UnitsOwnOcc_units_valued_less_100k= #, Owner occupied housing units valued less than $100,000Pct_OwnOcc_units_val_less_100k= %, Owner occupied housing units valued less than $100,000OwnOcc_units_valued_100k_300k= #, Owner occupied housing units valued $100,000-$299,999Pct_OwnOcc_units_val_100k_300k= %, Owner occupied housing units valued $100,000-$299,999OwnOcc_units_valued_300k_more= #, Owner occupied housing units valued $300,000 or morePct_OwnOcc_units_val_300k_more= %, Owner occupied housing units valued $300,000 or moreMedian_value_own_occ_units= Median value, owner occupied housing units- - - - - -Income_Total_households = Income: Total householdsHousehold_inc_less_35k= #, Household income less than $35,000Pct_Household_inc_less_35k= %, Household income less than $35,000Household_inc_35k_75k= #, Household income $35,000 to $74,999Pct_Household_inc_35k_75k= %, Household income $35,000 to $74,999Household_inc_75k_200k= #, Household income $75,000 to $200,000Pct_Household_inc_75k_200k= %, Household income $75,000 to $200,000Household_inc_200k_more= #,

The American Community Survey 5-year Data Profile (DP03) of Selected Economic Characteristics was downloaded from the U.S. Census Bureau for state, county, place, reservation, house district, senate district and tract geographies in the state of Montana.Selected economic characteristics in this data set include: EMPLOYMENT STATUS, COMMUTING TO WORK, OCCUPATION, INDUSTRY, CLASS OF WORKER, INCOME AND BENEFITS, HEALTH INSURANCE COVERAGE, POVERTY - PERCENTAGE OF FAMILIES AND PEOPLE WHOSE INCOME IN THE PAST 12 MONTHS IS BELOW THE POVERTY LEVEL. Source: U.S. Census Bureau, 2014-2018 American Community Survey 5-Year Estimates.Downloaded January 2020.Please refer to the American Community Survey section of the U.S. Census Bureau website for detailed information about this data set.