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.

Health Outcomes and Socioeconomic Factors

A Study of US County Data

By Data Exercises [source]

About this dataset

This dataset contains a wealth of health-related information and socio-economic data aggregated from multiple sources such as the American Community Survey, clinicaltrials.gov, and cancer.gov, covering a variety of US counties. Your task is to use this collection of data to build an Ordinary Least Squares (OLS) regression model that predicts the target death rate in each county. The model should incorporate variables related to population size, health insurance coverage, educational attainment levels, median incomes and poverty rates. Additionally you will need to assess linearity between your model parameters; measure serial independence among errors; test for heteroskedasticity; evaluate normality in the residual distribution; identify any outliers or missing values and determine how categories variables are handled; compare models through implementation with k=10 cross validation within linear regressions as well as assessing multicollinearity among model parameters. Examine your results by utilizing statistical agreements such as R-squared values and Root Mean Square Error (RMSE) while also interpreting implications uncovered by your analysis based on health outcomes compared to correlates among demographics surrounding those effected most closely by land structure along geographic boundaries throughout the United States

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How to use the dataset

This dataset provides data on health outcomes, demographics, and socio-economic factors for various US counties from 2010-2016. It can be used to uncover trends in health outcomes and socioeconomic factors across different counties in the US over a six year period.

The dataset contains a variety of information including statefips (a two digit code that identifies the state), countyfips (a three digit code that identifies the county), avg household size, avg annual count of cancer cases, average deaths per year, target death rate, median household income, population estimate for 2015, poverty percent study per capita binned income as well as demographic information such as median age of male and female population percent married households adults with no high school diploma adults with high school diploma percentage with some college education bachelor's degree holders among adults over 25 years old employed persons 16 and over unemployed persons 16 and over private coverage available private coverage available alone temporary private coverage available public coverage available public coverage available alone percentages of white black Asian other race married households and birth rate.

Using this dataset you can build a multivariate ordinary least squares regression model to predict “target_deathrate”. You will also need to implement k-fold (k=10) cross validation to best select your model parameters. Model diagnostics should be performed in order to assess linearity serial independence heteroskedasticity normality multicollinearity etc., while outliers missing values or categorical variables will also have an effect your model selection process. Finally it is important to interpret the resulting models within their context based upon all given factors associated with it such as outliers missing values demographic changes etc., before arriving at a meaningful conclusion which may explain trends in health outcomes and socioeconomic factors found within this dataset

Research Ideas

• Analysis of factors influencing target deathrates in different US counties.
• Prediction of the effects of varying poverty levels on health outcomes in different US counties.
• In-depth analysis of how various socio-economic factors (e.g., median income, educational attainment, etc.) contribute to overall public health outcomes in US counties

Acknowledgements

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

License

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

US County Demographics

Social, Health, and Economic Indicators

By Danny [source]

About this dataset

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

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How to use the dataset

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

Research Ideas

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

Acknowledgements

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

License

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

Columns

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

Small Area Income and Poverty Estimates: State and County

This poverty rate data shows what percentage of the measured population* falls below the poverty line. Poverty is closely related to income: different “poverty thresholds” are in place for different sizes and types of household. A family or individual is considered to be below the poverty line if that family or individual’s income falls below their relevant poverty threshold. For more information on how poverty is measured by the U.S. Census Bureau (the source for this indicator’s data), visit the U.S. Census Bureau’s poverty webpage.

The poverty rate is an important piece of information when evaluating an area’s economic health and well-being. The poverty rate can also be illustrative when considered in the contexts of other indicators and categories. As a piece of data, it is too important and too useful to omit from any indicator set.

The poverty rate for all individuals in the measured population in Champaign County has hovered around roughly 20% since 2005. However, it reached its lowest rate in 2021 at 14.9%, and its second lowest rate in 2023 at 16.3%. Although the American Community Survey (ACS) data shows fluctuations between years, given their margins of error, none of the differences between consecutive years’ estimates are statistically significant, making it impossible to identify a trend.

Poverty rate data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Poverty Status in the Past 12 Months by Age.

*According to the U.S. Census Bureau document “How Poverty is Calculated in the ACS," poverty status is calculated for everyone but those in the following groups: “people living in institutional group quarters (such as prisons or nursing homes), people in military barracks, people in college dormitories, living situations without conventional housing, and unrelated individuals under 15 years old."

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (25 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (16 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1701; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Socioeconomic indicators like the poverty rate, population change, unemployment rate, and education levels vary across the nation. ERS has compiled the latest data on these measures into a mapping and data display/download application that allows users to identify and compare States and counties on these indicators.

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.

Goal and Objective : Primary objective is to study variability in the poverty rate in the US counties by means of one or more of independent or control variable and provide best suitable model to quantify relationships in determining target value Our goal is to design various models to take into consideration the effect of various factors like employment, population and education to predict the poverty rate in all US Counties We further wish to analyze the status of a county based on whether it is metropolitan or not

List of datasets:

Socioeconomic indicators like poverty rates, population change, unemployment rates, and education levels vary geographically across U.S. States and counties 1. Unemployment 2. PovertyEstimates 3.Population Estimates 4. Education

All the four individual datasets have common unique id FIPS Code defined as State-County FIPS Code. It is unique for each county falling under the states. In our dataset, we are covering all 52 USA states including federal district DC and Puerto Rico.

Data Modelling :

Target Variable: Metro_2015 – This binary variable shows status of County as Metro or Non-Metro A decision tree model designed using Metro_2015 as target variable will efficiently determine the classification of the population into Metro and Non-metro counties. Dataset will be partitioned into training and validation datasets before implementing decision tree rules. The attributes that will be considered in selecting best model will be fit statistics, misclassification rate, and average square error.

Clustering can be performed to create the collection of objects similar to each other which will give insight into data distribution. Variables will be standardized before performing clustering to avoid noisy data and outliers. Euclidean distance will be the measure to determine stability and separation.

Recommendation :

The regression equation determines % Poverty rate in a particular county based on significant factors. This model can be This model can be used by education boards to increase or decrease the funds spent on the education system in different counties in order to lower the poverty rate. Census board can use this model in identifying poverty line index based on a population estimate an average household income. By estimating the poverty rate and considering factors like unemployment and education, an analysis can be done to set up employment opportunities in targeted counties.

Poverty rate estimates (and confidence intervals) for different subpopulations at the county level for the US in 2020. Source is USDA (United States Department of Agriculture)

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.

The US Family Budget Dataset provides insights into the cost of living in different US counties based on the Family Budget Calculator by the Economic Policy Institute (EPI).

This dataset offers community-specific estimates for ten family types, including one or two adults with zero to four children, in all 1877 counties and metro areas across the United States.

Interesting Task Ideas:

1. See how family budgets compare to the federal poverty line and the Supplemental Poverty Measure in different counties.
2. Look into the money challenges faced by different types of families using the budgets provided.
3. Find out which counties have the most affordable places to live, food, transportation, healthcare, childcare, and other things people need.
4. Explore how the average income of families relates to the overall cost of living in different counties.
5. Investigate how family size affects the estimated budget and find counties where bigger families have higher costs.
6. Create visuals showing how the cost of living varies across different states and big cities.
7. Check whether specific counties are affordable for families of different sizes and types.
8. Use the dataset to compare living standards and economic security in different US counties.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

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VITAL SIGNS INDICATOR Poverty (EQ5)

FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED December 2018

DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.

This data product provides poverty area measures for counties and census tracts across the 50 States and Washington DC. The measures include indicators of high poverty areas, extreme poverty areas, persistent poverty areas, and enduring poverty areas for Decennial Census years 1960–2000 and for American Community Survey (ACS) 5-year periods spanning 2007–11, 2015–19, and 2017–21. This product uses county-level data from the U.S. Department of Commerce, Bureau of the Census, 1960, 1970, 1980, 1990, and 2000 Decennial Censuses and American Community Survey (ACS) 5-year period estimates for 2007–11, 2015–19, and 2017–21. Data for census tracts include the same data sources and years except for 1960 (because of the limited number of defined census tracts in that year of data collection) and 2017–21. Poverty rates for census tracts in the years before 2007–11 are calculated using data from Geolytics’ Neighborhood Change Database. The Neighborhood Change Database normalizes Decennial Census data to 2010 census-tract geographies allowing for better comparisons of census tract poverty rates over time. USDA, Economic Research Service (ERS) relies on poverty/population counts from these sources to derive poverty rates that are used to create the poverty area measures in this data product.

This layer shows Population and Poverty Status. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of people whose income in the past 12 months is below poverty level. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right.Current Vintage: 2018-2022ACS Table(s): B17017, C17002, DP02, DP03Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. 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:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. 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 Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. 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.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

This dataset was utilized a join from enriched tables from ESRI which was curated from the 2020 Census from the United States Census Bureau, American Community Survey (ACS) and for county boundaries created by Office of Information Technology Services Next Generation 9-1-1 team in collaboration with all 44 counties of Idaho. This layer has information for all cities within Idaho regarding the county population common behaviors for 2024.For more information on how the data is curated for the Enrich tool please go the link below. 2024/2029 Esri Updated Demographics

The table State and County Estimates for 2017 is part of the dataset Small Area Income and Poverty Estimates (SAIPE) ***, available at https://redivis.com/datasets/8wjh-9vkgzhkc0. It contains 3194 rows across 31 variables.

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries.

This shapefile represents the current State Senate Districts for New Mexico as posted on the Census Bureau website for 2006.

This layer is part of source data for the State of Poverty 2018-2024 Los Angeles County Dashboard.Layers include estimates of total population and population in poverty by demographics at each geography level in LA County.Source: Annual Population and Poverty Estimation, Los Angeles County ISD-Demography.Datasets for all years available in the State of Poverty dashboard:PAI Poverty Map Data 2024PAI Poverty Map Data 2023PAI Poverty Map Data 2022PAI Poverty Map Data 2021PAI Poverty Map Data 2020PAI Poverty Map Data 2019PAI Poverty Map Data 2018 Included Geography LevelsSplit Census TractsCensus TractsCountywide Statistical Areas (CSA)Public Use Microdata Areas (PUMA)Service Planning Area (SPA)Supervisor District (SD)Los Angeles County Split Census Tract and CSA boundaries correspond to the year of the population and poverty estimates. Census Tract, PUMA, SPA, SD, and county boundaries are current as of 2020 US Census. Field NamesPlease see Field Aliases for detailed field names.Field name logic:1st character Race/Ethnicityt = Totala = Asianb = Black or African Americanh = Hispanic or Latinoi = American Indian and Alaska Native (AIAN)p = Pacific Islanderw = White2nd character Gendert = Totalf = Femalem = Male3-4th characters Year2-digit year (2018-22)Possible 5th character Poverty Level (%FPL)a = Below 100% FPLd = Below 200% FPLg = Below 266% FPLRemaining characters after underscoret = Total (all ages)

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

The table State and County Estimates for 2020 is part of the dataset Small Area Income and Poverty Estimates (SAIPE) ***, available at https://redivis.com/datasets/8wjh-9vkgzhkc0. It contains 3195 rows across 31 variables.