The Vehicle Inventory and Use Survey (VIUS) is conducted in partnership with the Bureau of Transportation Statistics, Federal Highway Administration, and the U.S. Department of Energy to better understand the characteristics and use of trucks on our nation's roads. The survey universe for the VIUS includes all private and commercial trucks registered (or licensed) in the United States. This includes: pickups; minivans, other light vans, and sport utility vehicles; other light single-unit trucks (GVW = 26,000 lbs.); and truck tractors. The VIUS sample excludes vehicles owned by federal, state, and local governments; ambulances; buses; motor homes; farm tractors; unpowered trailer units; and trucks reported to have been disposed of prior to January 1 of the survey year. VIUS provides data on the physical and operational characteristics of the nation's truck population. Its primary goal is to produce estimates of the total number of trucks and truck miles. This dataset provides national and state-level summary statistics for in-scope vehicles that were in use.

United States SBP: ID: Received Fin'l Assistance: Other Federal Programs data was reported at 7.500 % in 20 Sep 2020. United States SBP: ID: Received Fin'l Assistance: Other Federal Programs data is updated weekly, averaging 7.500 % from Sep 2020 (Median) to 20 Sep 2020, with 1 observations. The data reached an all-time high of 7.500 % in 20 Sep 2020 and a record low of 7.500 % in 20 Sep 2020. United States SBP: ID: Received Fin'l Assistance: Other Federal Programs data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S044: Small Business Pulse Survey: by State: West Region: Weekly, Beg Sunday (Discontinued).

This spatial data contains Surface Management Agency (SMA, also sometimes called Land Status) information for Idaho from the Idaho Bureau of Land Management (BLM). For federal government lands, this data displays the managing agency of the surface of the land, which does not mean the agency "owns" the land. SMA is sometimes referred to as "ownership", although this term is inaccurate when describing public lands. This Surface Management Agency data should not be used to depict boundaries (for example National Forest, National Park, National Wildlife Refuge, or Indian Reservation boundaries among others). Attribute information for the federal and private lands are from the BLM Master Title Plats (MTPs), the BLM case files, the BLM Legacy Rehost 2000 (LR2000) database, and corresponding federal Orders and official documents. Please note that because these official sources are strictly used, OTHER NON-BLM FEDERAL AGENCY LANDS MAY NOT BE ATTRIBUTED CORRECTLY unless the proper documents have been filed with the BLM and the land actions have been noted on the MTPs and in LR2000. Starting in the spring of 2011 a field called AGNCY_NAME is present in the data. The AGNCY_NAME field is intended to indicate the managing agency for polygons coded as OTHER in the MGMT_AGNCY field. The AGNCY_NAME field will not be used for the 100K Map Series published by the BLM for use by the public as all agencies in this field are not included in H-1553 Publication Standards Manual Handbook and, therefore, have no BLM Cartographic Standard. Except for polygons coded as OTHER in the MGMT_AGNCY field, all managing agency information in the AGNCY_NAME field should be the same as that of the MGMT_AGNCY field. The only intended difference between the AGNCY_NAME field and the MGMT_AGNCY field is where the MGMT_AGNCY is OTHER. In this case, the AGNCY_NAME will contain an abbreviation for an agency that is not represented in the H-1553 Publication Standards Manual Handbook. Examples of the agencies there are BIA (Bureau of Indian Affairs), USGS (United States Geological Survey), and FAA (Federal Aviation Administration). Attribute information for the State lands is received primarily through cooperation with the Idaho Department of Lands. This information might not reflect all State agency lands completely. A detailed analysis of State owned lands has not been done since June 2011; therefore, recent changes in ownership of State lands may not be reflected. Inclusion of State land information into this dataset is supplemental and should not be viewed as the authoritative source of State lands; please contact State agencies for questions about State lands. This data does not depict land management arrangements between government agencies such as Memorandums of Understanding or other similar agreements. When this data was originally generated in the early 2000's, the primary source of the geometry was the BLM Geographic Coordinate Database (GCDB), if it was available. In areas where GCDB was/is unavailable, the spatial features are taken from a variety of sources including the BLM Idaho Resource Base Data collection, BLM Idaho Master Title Plat AutoCad files, US Geological Survey Digital Line Graphs (DLGs), and US Forest Service Cartographic Feature Files (CFFs), among others (see Process Steps). It should be stressed that the geometry of a feature may not be GCDB-based in the first place, the geometry may shift away from GCDB due to a variety of reasons (topology procedures, automated software processes such as projections, etc.), and the GCDB-based features are not necessarily currently being edited to match improved GCDB. Therefore this data should NOT be considered actual GCDB data. For the latest Idaho GCDB spatial data, please contact the BLM Idaho State Office Cadastral Department at 208-373-4000. The BLM in Idaho creates and maintains this spatial data. This dataset is derived by dissolving based on the "MGMT_AGNCY" field from the master SMA GIS dataset (which is edited often) kept by the BLM Idaho State Office. Please get a fresh copy of this data a couple times a year as the SMA data is continually changing. Official actions that affect the managing agency happen often and changes to correct errors are always being made. Nevada SMA data was acquired from the BLM Nevada web site and clipped to the area that is managed by Idaho BLM Boise District. The data steward approved this dataset in October 2023. For more information contact us at blm_id_stateoffice@blm.gov.

The Travel Time to Work indicator compares the mean, or average, commute time for Champaign County residents to the mean commute time for residents of Illinois and the United States as a whole. On its own, mean travel time of all commuters on all mode types could be reflective of a number of different conditions. Congestion, mode choice, changes in residential patterns, changes in the location of major employment centers, and changes in the transit network can all impact travel time in different and often conflicting ways. Since the onset of the COVID-19 pandemic in 2020, the workplace location (office vs. home) is another factor that can impact the mean travel time of an area. We don’t recommend trying to draw any conclusions about conditions in Champaign County, or anywhere else, based on mean travel time alone.

However, when combined with other indicators in the Mobility category (and other categories), mean travel time to work is a valuable measure of transportation behaviors in Champaign County.

Champaign County’s mean travel time to work is lower than the mean travel time to work in Illinois and the United States. Based on this figure, the state of Illinois has the longest commutes of the three analyzed areas.

The year-to-year fluctuations in mean travel time have been statistically significant in the United States since 2014, and in Illinois in 2021 and 2022. Champaign County’s year-to-year fluctuations in mean travel time were statistically significant from 2021 to 2022, the first time since this data first started being tracked in 2005.

Mean travel time data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 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 Travel Time to Work.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (10 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (17 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (29 March 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (29 March 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0801; 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 S0801; 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 S0801; 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 S0801; 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 S0801; 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 S0801; 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 S0801; 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 S0801; 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 S0801; 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 S0801; 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 S0801; 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 S0801; 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 S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).

About this dataset

Context

The dataset tabulates the State Line population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for State Line. The dataset can be utilized to understand the population distribution of State Line by age. For example, using this dataset, we can identify the largest age group in State Line.

Key observations

The largest age group in State Line, MS was for the group of age 40 to 44 years years with a population of 160 (18.76%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in State Line, MS was the 85 years and over years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the State Line is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of State Line total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for State Line Population by Age. You can refer the same here

The U.S. Department of Housing and Urban Development (HUD) periodically receives "custom tabulations" of Census data from the U.S. Census Bureau that are largely not available through standard Census products. These datasets, known as "CHAS" (Comprehensive Housing Affordability Strategy) data, demonstrate the extent of housing problems and housing needs, particularly for low income households. The primary purpose of CHAS data is to demonstrate the number of households in need of housing assistance. This is estimated by the number of households that have certain housing problems and have income low enough to qualify for HUD’s programs (primarily 30, 50, and 80 percent of median income). CHAS data provides counts of the numbers of households that fit these HUD-specified characteristics in a variety of geographic areas. In addition to estimating low-income housing needs, CHAS data contributes to a more comprehensive market analysis by documenting issues like lead paint risks, "affordability mismatch," and the interaction of affordability with variables like age of homes, number of bedrooms, and type of building. This dataset is a special tabulation of the 2016-2020 American Community Survey (ACS) and reflects conditions over that time period. The dataset uses custom HUD Area Median Family Income (HAMFI) figures calculated by HUD PDR staff based on 2016-2020 ACS income data. CHAS datasets are used by Federal, State, and Local governments to plan how to spend, and distribute HUD program funds. To learn more about the Comprehensive Housing Affordability Strategy (CHAS), visit: https://www.huduser.gov/portal/datasets/cp.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs Data Dictionary: DD_ACS 5-Year CHAS Estimate Data by State Date of Coverage: 2016-2020

Due to the change in the survey instrument regarding intention to vaccinate, our estimates for “hesitant or unsure” or “hesitant” derived from April 14-26, 2021, are not directly comparable with prior Household Pulse Survey data and should not be used to examine trends in hesitancy.

To support state and local communication and outreach efforts, ASPE developed state, county, and sub-state level predictions of hesitancy rates (https://aspe.hhs.gov/pdf-report/vaccine-hesitancy) using the most recently available federal survey data.

We estimate hesitancy rates at the state level using the U.S. Census Bureau’s Household Pulse Survey (HPS) (https://www.census.gov/programs-surveys/household-pulse-survey.html) data and utilize the estimated values to predict hesitancy rates at the Public Use Microdata Areas (PUMA) level using the Census Bureau’s 2019 American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS)(https://www.census.gov/programs-surveys/acs/microdata.html). To create county-level estimates, we used a PUMA-to-county crosswalk from the Missouri Census Data Center(https://mcdc.missouri.edu/applications/geocorr2014.html). PUMAs spanning multiple counties had their estimates apportioned across those counties based on overall 2010 Census populations.

The HPS is nationally representative and includes information on U.S. residents’ intentions to receive the COVID-19 vaccine when available, as well as other sociodemographic and geographic (state, region and metropolitan statistical areas) information. The ACS is a nationally representative survey, and it provides key sociodemographic and geographic (state, region, PUMAs, county) information. We utilized data for the survey collection period May 26, 2021 – June 7, 2021, which the HPS refers to as Week 31..

PUMA COVID-19 Hesitancy Data - https://data.cdc.gov/Vaccinations/Vaccine-Hesitancy-for-COVID-19-Public-Use-Microdat/djj9-kh3p

United States SBP: NH: Received Fin'l Assistance: Other Federal Programs data was reported at 6.100 % in 22 Nov 2020. United States SBP: NH: Received Fin'l Assistance: Other Federal Programs data is updated weekly, averaging 6.100 % from Nov 2020 (Median) to 22 Nov 2020, with 1 observations. United States SBP: NH: Received Fin'l Assistance: Other Federal Programs data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.S022: Small Business Pulse Survey: by State: Northeast. [COVID-19-IMPACT]

About this dataset

Context

The dataset tabulates the data for the State Line City, IN population pyramid, which represents the State Line City population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for State Line City, IN, is 14.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for State Line City, IN, is 23.9.
• Total dependency ratio for State Line City, IN is 38.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for State Line City, IN is 4.2.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the State Line City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the State Line City for the selected age group is shown in the following column.
• Population (Female): The female population in the State Line City for the selected age group is shown in the following column.
• Total Population: The total population of the State Line City for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for State Line City Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the State Center, IA population pyramid, which represents the State Center population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for State Center, IA, is 38.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for State Center, IA, is 24.2.
• Total dependency ratio for State Center, IA is 62.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for State Center, IA is 4.1.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the State Center population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the State Center for the selected age group is shown in the following column.
• Population (Female): The female population in the State Center for the selected age group is shown in the following column.
• Total Population: The total population of the State Center for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for State Center Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of State Center by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for State Center. The dataset can be utilized to understand the population distribution of State Center by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in State Center. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for State Center.

Key observations

Largest age group (population): Male # 15-19 years (97) | Female # 45-49 years (114). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the State Center population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the State Center is shown in the following column.
• Population (Female): The female population in the State Center is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in State Center for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for State Center Population by Gender. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in State College, PA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 5,317, while the mean income for the highest quintile (20% of households with the highest income) is 213,239. This indicates that the top earners earn 40 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 350,644, which is 164.44% higher compared to the highest quintile, and 6594.77% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for State College median household income. You can refer the same here

This feature class displays the First Divisions data for the dataset that represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific 'production' or operation and maintenance. This data set includes the following: PLSS Fully Intersected (all of the PLSS feature at the atomic or smallest polygon level), PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys), and the Bureau of Census 2015 Cartographic State Boundaries. The Entity-Attribute section of this metadata describes these components in greater detail.

Please note that the data on this site, although published at regular intervals, may not be the most current PLSS data that is available from the BLM. Updates to the PLSS data at the BLM State Offices may have occurred since this data was published. To ensure users have the most current data, please refer to the links provided in the PLSS CadNSDI Data Set Availability accessible here: https://gis.blm.gov/EGISDownload/Docs/PLSS_CadNSDI_Data_Set_Availability.pdf or contact the BLM PLSS Data Set Manager.

This data set consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) data set published in 2006. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The data set was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the whole map unit. This data set consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1-by 2-degree topographic quadrangle units and merged into a seamless national data set. It is distributed in state/territory and national extents. The soil map units are linked to attributes in the National Soil Information System data base which gives the proportionate extent of the component soils and their properties.Individual Metadata [XML]

Nonemployer Statistics is an annual series that provides subnational economic data for businesses that have no paid employees and are subject to federal income tax, and have receipts of $1,000 or more ($1 or more for the Construction sector). The data consist of the number of businesses and total receipts by industry. Data are published by legal form of organization (U.S. and state only) and receipts-size class of establishments (U.S. level only).

The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS (Natural Resources Conservation Service) Soil & Plant Science Division (SPSD) composite ESRI file geodatabase that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase. The gNATSGO database contains a 10-meter raster of the soil map units and 70 related tables of soil properties and interpretations. It is designed to work with the SPSD gSSURGO ArcTools. Users can create full coverage thematic maps and grids of soil properties and interpretations for large geographic areas, such as the extent of a State or the conterminous United States. SSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. The current completion status of SSURGO mapping is displayed (PDF). STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods. The first version of gNATSGO was created in 2019. It is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. Three RSSs have been published as of 2019. These were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years. Resources in this dataset:Resource Title: Website Pointer for Gridded National Soil Survey Geographic Database (gNATSGO). File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625 The gNATSGO website provides an Overview slide presentation, Download links for gNATSGO databases (CONUS or States), ArcTools, Metadata, Technical Information, and Recommended Data Citations.

Investigator(s): Bureau of Justice Statistics These surveys provide a broad-based, systematic examination of the nature of general civil litigation (e.g., tort, contract, and real property cases) disposed in a sample of the nation's 75 most populous counties. Data collection was carried out by the National Center for State Courts with the assistance of WESTAT. Data collected includes information about the types of civil cases litigated at trial, types of plaintiffs and defendants, trial winners, amount of total damages awarded, amount of punitive damages awarded, and case processing time. In addition, information was collected on general civil cases concluded by bench or jury trial that were subsequently appealed to a state's intermediate appellate court or court of last resort. The appellate datasets examine information on the types of civil bench and jury trials appealed, the characteristics of litigants filing an appeal, the frequency in which appellate courts affirm, reverse, or modify trial court outcomes and cases further appealed from an intermediate appellate court to a state court of last resort.

SB: MA: CH: SC: Production Delays data was reported at 13.000 % in 11 Apr 2022. This records an increase from the previous number of 11.700 % for 04 Apr 2022. SB: MA: CH: SC: Production Delays data is updated weekly, averaging 12.550 % from Nov 2021 (Median) to 11 Apr 2022, with 18 observations. The data reached an all-time high of 15.300 % in 06 Dec 2021 and a record low of 9.100 % in 27 Dec 2021. SB: MA: CH: SC: Production Delays data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S039: Small Business Pulse Survey: by State: Northeast Region: Weekly, Beg Monday (Discontinued).

In Alaska, the Bureau of Land Management has been tasked with the largest land transfer effort ever taken in the United States. For more than 30 years, the BLM has been involved with the survey and conveyance of lands in Alaska under three statutes: the Native Allotment Act of 1906; the Alaska Statehood Act, and the Alaska Native Claims Settlement Act (ANCSA). The work being done to implement these laws is collectively called the Alaska Land Transfer Program. The Alaska Land Transfer Program has three distinct phases: preliminary adjudication and application approval; cadastral survey; and conveyance of lands and entitlements.