US Census Bureau States And Counties Poverty Estimates 2015

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This dataset contains poverty estimates at county level based on US Census Bureau program, Small Area Income and Poverty Estimates (SAIPE). The estimates are for counties and states in the United States, for the entire population and for three age groups of population.

Complexity

The source of data is represented by The Economic Research Service of The United States Department Of Agriculture which published an enriched version of the dataset originally released by US Census Bureau.

The Census Bureau and other Federal agencies created the SAIPE program to provide annual income and poverty statistics for states, counties, and school districts in the United States. The SAIPE program produces yearly poverty estimates for the total population (all ages) and by selected characteristics for counties and states.
The main objective of this program is to provide updated estimates of income and poverty statistics for the administration of federal programs and the allocation of federal funds to local jurisdictions. Estimates for 2015 were released in December 2016. These estimates include the number of children under age 5 in poverty (for states only), the number of related-children aged 5 to 17 in families in poverty, the number of children under age 18 in poverty, and median household income. Due to the comprehensive geographic coverage and one year focus, SAIPE data can be used to analyze geographic variation in poverty and income, as well as changes over time.

SAIPE program estimates, for states, counties, and school districts in the United States combine data from administrative records, postcensal population estimates, and the decennial census with direct estimates from the American Community Survey to provide consistent and reliable single-year estimates. These model-based single-year estimates are more reflective of current conditions than multi-year survey estimates.

In the enriched version of the dataset was added Rural-Urban Continuum Codes and Urban Influence Codes.
The Rural-Urban Continuum Codes form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and non-metropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and non-metro categories have been subdivided into three metro and six non-metro categories. Each county in the US is assigned one of the 9 codes. This scheme allows researchers to break county data into finer residential groups, beyond metro and non-metro, particularly for the analysis of trends in non-metro areas that are related to population density and metro influence.
The 2013 Rural-Urban Continuum classification scheme of counties (which is the same as the 2003 one) is the following:

– Metro counties
– 1 – Counties in metro areas of 1 million population or more
– 2 – Counties in metro areas of 250,000 to 1 million population
– 3 – Counties in metro areas of fewer than 250,000 population
– Non-metro counties
– 4 – Urban population of 20,000 or more, adjacent to a metro area
– 5 – Urban population of 20,000 or more, not adjacent to a metro area
– 6 – Urban population of 2,500 to 19,999, adjacent to a metro area
– 7 – Urban population of 2,500 to 19,999, not adjacent to a metro area
– 8 – Completely rural or less than 2,500 urban population, adjacent to a metro area
– 9 – Completely rural or less than 2,500 urban population, not adjacent to a metro area

The Urban Influence Codes form a classification scheme that distinguishes metropolitan counties by population size of their metro area, and non-metropolitan counties by size of the largest city or town and proximity to metro and micropolitan areas. The standard Office of Management and Budget (OMB) metro and non-metro categories have been subdivided into two metro and 10 non-metro categories, resulting in a 12-part county classification. This scheme allows researchers to break county data into finer residential groups, beyond metro and non-metro, particularly for the analysis of trends in non-metro areas that are related to population density and metro influence. 
The Urban Influence classification scheme of counties (which is the same as the 2003 one) is the following:

– Metro counties
– 1 – In large metro area of 1+ million residents
– 2 – In small metro area of less than 1 million residents
– Non-metro counties
– 3 – Micropolitan area adjacent to large metro area
– 4 – Noncore adjacent to large metro area
– 5 – Micropolitan area adjacent to small metro area
– 6 – Noncore adjacent to small metro area and contains a town of at least 2,500 residents
– 7 – Noncore adjacent to small metro area and does not contain a town of at least 2,500 residents
– 8 – Micropolitan area not adjacent to a metro area
– 9 – Noncore adjacent to micro area and contains a town of at least 2,500 residents
– 10 – Noncore adjacent to micro area and does not contain a town of at least 2,500 residents
– 11 – Noncore not adjacent to metro or micro area and contains a town of at least 2,500 residents
– 12 – Noncore not adjacent to metro or micro area and does not contain a town of at least 2,500 residents

Date Created

2017-01-27

Last Modified

2017-01-27

Version

2017-01-27

Update Frequency

Annual

Temporal Coverage

2015

Spatial Coverage

United States

Source

John Snow Labs => United States Department Of Agriculture (Economic Research Service)

Source License URL

John Snow Labs Standard License

Source License Requirements

N/A

Source Citation

N/A

Keywords

Poverty Statistics 2015, US Census Bureau, Poverty By Counties, Poverty By States, Poverty By Age Groups, Children Poverty, FIPS Codes

Other Titles

State And County Level Poverty Estimates By Age Groups In 2015, US Census Small Area Income and Poverty Estimates Data 2015

Name Description Type Constraints
FIPS_CodeThe state-county FIPS (Federal Information Processing Standard) code of 6 digitsinteger-
State_AbbreviationThe abbreviated name of state and USstringrequired : 1
Area_NameThe name of state or county and United Statesstringrequired : 1
Rural_Urban_Continuum_Code_2003The 2003 classification that distinguishes metropolitan counties by the population size of their metro area, and non-metropolitan counties by degree of urbanization and adjacency to a metro area; each county in the US is assigned one of the 9 codesintegerlevel : Nominal
Urban_Influence_Code_2003The 2003 classification scheme that distinguishes metropolitan counties by population size of their metro area, and non-metropolitan counties by size of the largest city or town and proximity to metro and micropolitan areas; each county in the US is assigned one of the 12 codesintegerlevel : Nominal
Rural_Urban_Continuum_Code_2013The 2013 classification that distinguishes metropolitan counties by the population size of their metro area, and non-metropolitan counties by degree of urbanization and adjacency to a metro area; each county in the US is assigned one of the 9 codesintegerlevel : Nominal
Urban_Influence_Code_2013The 2003 classification scheme that distinguishes metropolitan counties by population size of their metro area, and non-metropolitan counties by size of the largest city or town and proximity to metro and micropolitan areas; each county in the US is assigned one of the 12 codesintegerlevel : Nominal
All_Ages_Poverty_EstimateEstimate of people of all ages in povertyintegerlevel : Ratio
Confidence_Interval_90_Low_All_Ages_Estimate90% confidence interval lower limit of estimate of people of all ages in povertyintegerlevel : Ratio
Confidence_Interval_90_High_All_Ages_Estimate90% confidence interval upper limit of estimate of people of all ages in povertyintegerlevel : Ratio
All_Ages_Poverty_PercentEstimated percent of people of all ages in povertynumberlevel : Ratio
Confidence_Interval_90_Low_All_Ages_Percent90% confidence interval lower limit of estimate of percent of people of all ages in povertynumberlevel : Ratio
Confidence_Interval_90_High_All_Ages_Percent90% confidence interval upper limit of estimate of percent of people of all ages in povertynumberlevel : Ratio
Age_0_17_Poverty_EstimateEstimate of people age 0-17 in povertyintegerlevel : Ratio
Confidence_Interval_90_Low_Age_0_17_Estimate90% confidence interval lower limit of estimate of people age 0-17 in povertyintegerlevel : Ratio
Confidence_Interval_90_High_Age_0_17_Estimate90% confidence interval upper limit of estimate of people age 0-17 in povertyintegerlevel : Ratio
Age_0_17_Poverty_PercentEstimated percent of people of all ages in povertynumberlevel : Ratio
Confidence_Interval_90_Low_Age_0_17_Percent90% confidence interval lower limit of estimate of percent of people age 0-17 in povertynumberlevel : Ratio
Confidence_Interval_90_High_Age_0_17_Percent90% confidence interval upper limit of estimate of percent of people age 0-17 in povertynumberlevel : Ratio
Age_0_4_Poverty_EstimateEstimate of people under age 5 in povertyintegerlevel : Ratio
Confidence_Interval_90_Low_Age_0_4_Estimate90% confidence interval lower limit of estimate of people under age 5 in povertyintegerlevel : Ratio
Confidence_Interval_90_High_Age_0_4_Estimate90% confidence interval upper limit of estimate of people under age 5 in povertyintegerlevel : Ratio
Age_0_4_Poverty_PercentEstimated percent of percent under age 5 in povertynumberlevel : Ratio
Confidence_Interval_90_Low_Age_0_4_Percent90% confidence interval lower limit of estimate of percent of people under age 5 in povertynumberlevel : Ratio
Confidence_Interval_90_High_Age_0_4_Percent90% confidence interval upper limit of estimate of percent of people under age 5 in povertynumberlevel : Ratio
Age_5_17_Poverty_EstimateEstimate of related children age 5-17 in families in povertyintegerlevel : Ratio
Confidence_Interval_90_Low_Age_5_17_Estimate90% confidence interval lower limit of estimate of related children age 5-17 in families in povertyintegerlevel : Ratio
Confidence_Interval_90_High_Age_5_17_Estimate90% confidence interval upper limit of estimate of related children age 5-17 in families in povertyintegerlevel : Ratio
Age_5_17_Poverty_PercentEstimated percent of percent of people under age 5 in povertynumberlevel : Ratio
Confidence_Interval_90_Low_Age_5_17_Percent90% confidence interval lower limit of estimate of percent of related children age 5-17 in families in povertynumberlevel : Ratio
Confidence_Interval_90_High_Age_5_17_Percent90% confidence interval upper limit of estimate of percent of related children age 5-17 in families in povertynumberlevel : Ratio
Median_Household_IncomeEstimate of median household incomeintegerlevel : Ratio
Confidence_Interval_90_Low_Median_Household90% confidence interval lower limit of estimate of median household incomeintegerlevel : Ratio
Confidence_Interval_90_High_Median_Household90% confidence interval upper limit of estimate of median household incomeintegerlevel : Ratio
FIPS_CodeState_AbbreviationArea_NameRural_Urban_Continuum_Code_2003Urban_Influence_Code_2003Rural_Urban_Continuum_Code_2013Urban_Influence_Code_2013All_Ages_Poverty_EstimateConfidence_Interval_90_Low_All_Ages_EstimateConfidence_Interval_90_High_All_Ages_EstimateAll_Ages_Poverty_PercentConfidence_Interval_90_Low_All_Ages_PercentConfidence_Interval_90_High_All_Ages_PercentAge_0_17_Poverty_EstimateConfidence_Interval_90_Low_Age_0_17_EstimateConfidence_Interval_90_High_Age_0_17_EstimateAge_0_17_Poverty_PercentConfidence_Interval_90_Low_Age_0_17_PercentConfidence_Interval_90_High_Age_0_17_PercentAge_0_4_Poverty_EstimateConfidence_Interval_90_Low_Age_0_4_EstimateConfidence_Interval_90_High_Age_0_4_EstimateAge_0_4_Poverty_PercentConfidence_Interval_90_Low_Age_0_4_PercentConfidence_Interval_90_High_Age_0_4_PercentAge_5_17_Poverty_EstimateConfidence_Interval_90_Low_Age_5_17_EstimateConfidence_Interval_90_High_Age_5_17_EstimateAge_5_17_Poverty_PercentConfidence_Interval_90_Low_Age_5_17_PercentConfidence_Interval_90_High_Age_5_17_PercentMedian_Household_IncomeConfidence_Interval_90_Low_Median_HouseholdConfidence_Interval_90_High_Median_Household
15005HIKalawao County9832
31075NEGrant County9129125946729.27.211.297116.75.18.36487.15.38.9566465070862584
38007NDBillings County989106651817.15.58.7107135.94.47.46485.43.87.0769136874585081
02230AKSkagway Municipality9123930483.72.84.697116.34.87.86486.74.98.5658786033871418
38087NDSlope County9109126953859.06.911.11612209.06.811.296127.35.19.5568335078962877
46119SDSully County9109898761206.95.38.52720348.66.410.81712227.14.99.3582305224164219
48393TXRoberts County989106147756.75.18.31915238.76.810.61310167.35.59.1718596444079278
31113NELogan County98986953858.96.811.026203213.310.216.417122212.18.815.4510984634455852
38011NDBowman County9129122451942967.66.09.26348787.75.89.63928506.64.78.5628925664069144
48235TXIrion County3232123951517.96.19.740314911.28.813.627213310.68.113.1647775822071334