About this dataset

Context

The dataset presents the median household income across different racial categories in University Heights. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of University Heights population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 86.77% of the total residents in University Heights. Notably, the median household income for White households is $106,422. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $106,422.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in University Heights.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 University Heights median household income by race. You can refer the same here

College Completion Dataset

Graduation Rates, Race, Efficiency Measures and More

By Jonathan Ortiz [source]

About this dataset

This College Completion dataset provides an invaluable insight into the success and progress of college students in the United States. It contains graduation rates, race and other data to offer a comprehensive view of college completion in America. The data is sourced from two primary sources – the National Center for Education Statistics (NCES)’ Integrated Postsecondary Education System (IPEDS) and Voluntary System of Accountability’s Student Success and Progress rate.

At four-year institutions, the graduation figures come from IPEDS for first-time, full-time degree seeking students at the undergraduate level, who entered college six years earlier at four-year institutions or three years earlier at two-year institutions. Furthermore, colleges report how many students completed their program within 100 percent and 150 percent of normal time which corresponds with graduation within four years or six year respectively. Students reported as being of two or more races are included in totals but not shown separately

When analyzing race and ethnicity data NCES have classified student demographics since 2009 into seven categories; White non-Hispanic; Black non Hispanic; American Indian/ Alaskan native ; Asian/ Pacific Islander ; Unknown race or ethnicity ; Non resident with two new categorize Native Hawaiian or Other Pacific Islander combined with Asian plus students belonging to several races. Also worth noting is that different classifications for graduate data stemming from 2008 could be due to variations in time frame examined & groupings used by particular colleges – those who can’t be identified from National Student Clearinghouse records won’t be subjected to penalty by these locations .

When it comes down to efficiency measures parameters like “Awards per 100 Full Time Undergraduate Students which includes all undergraduate completions reported by a particular institution including associate degrees & certificates less than 4 year programme will assist us here while we also take into consideration measures like expenditure categories , Pell grant percentage , endowment values , average student aid amounts & full time faculty members contributing outstandingly towards instructional research / public service initiatives .

When trying to quantify outcomes back up Median Estimated SAT score metric helps us when it is derived either on 25th percentile basis / 75th percentile basis with all these factors further qualified by identifying required criteria meeting 90% threshold when incoming students are considered for relevance . Last but not least , Average Student Aid equalizes amount granted by institution dividing same over total sum received against what was allotted that particular year .

All this analysis gives an opportunity get a holistic overview about performance , potential deficits &

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For more datasets, click here.

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How to use the dataset

This dataset contains data on student success, graduation rates, race and gender demographics, an efficiency measure to compare colleges across states and more. It is a great source of information to help you better understand college completion and student success in the United States.

In this guide we’ll explain how to use the data so that you can find out the best colleges for students with certain characteristics or focus on your target completion rate. We’ll also provide some useful tips for getting the most out of this dataset when seeking guidance on which institutions offer the highest graduation rates or have a good reputation for success in terms of completing programs within normal timeframes.

Before getting into specifics about interpreting this dataset, it is important that you understand that each row represents information about a particular institution – such as its state affiliation, level (two-year vs four-year), control (public vs private), name and website. Each column contains various demographic information such as rate of awarding degrees compared to other institutions in its sector; race/ethnicity Makeup; full-time faculty percentage; median SAT score among first-time students; awards/grants comparison versus national average/state average - all applicable depending on institution location — and more!

When using this dataset, our suggestion is that you begin by forming a hypothesis or research question concerning student completion at a given school based upon observable characteristics like financ...

About this dataset

Context

The dataset tabulates the population of College Place by race. It includes the population of College Place across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of College Place across relevant racial categories.

Key observations

The percent distribution of College Place population by race (across all racial categories recognized by the U.S. Census Bureau): 79.33% are white, 0.57% are Black or African American, 0.28% are American Indian and Alaska Native, 2.85% are Asian, 0.08% are Native Hawaiian and other Pacific Islander, 9.11% are some other race and 7.77% are multiracial.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the College Place
• Population: The population of the racial category (excluding ethnicity) in the College Place is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of College Place 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 College Place Population by Race & Ethnicity. You can refer the same here

In 2022, about 37.7 percent of the U.S. population who were aged 25 and above had graduated from college or another higher education institution, a slight decline from 37.9 the previous year. However, this is a significant increase from 1960, when only 7.7 percent of the U.S. population had graduated from college. Demographics Educational attainment varies by gender, location, race, and age throughout the United States. Asian-American and Pacific Islanders had the highest level of education, on average, while Massachusetts and the District of Colombia are areas home to the highest rates of residents with a bachelor’s degree or higher. However, education levels are correlated with wealth. While public education is free up until the 12th grade, the cost of university is out of reach for many Americans, making social mobility increasingly difficult. Earnings White Americans with a professional degree earned the most money on average, compared to other educational levels and races. However, regardless of educational attainment, males typically earned far more on average compared to females. Despite the decreasing wage gap over the years in the country, it remains an issue to this day. Not only is there a large wage gap between males and females, but there is also a large income gap linked to race as well.

This dataset consists of a selection of variables extracted from the U.S. Department of Education's College Scorecard 2015/2016. For the original, raw data visit the College Scorecard webpage. This dataset includes variables about institution types, proportion of degree types awarded, student enrollments and demographics, and a number of price and revenue variables. For 2005-2006 data, see here.Note: Data is not uniformly available for all schools on all variables. Variables for which there is no data (NULL), or where data is suppressed for reasons of privacy, are indicated by 999999999.

ATTRIBUTE DESCRIPTION EXAMPLE

ID2 1

UNITIDUnit ID for institution 100654

OPEID 8-digit OPE ID for institution 100200

OPEID6 6-digit OPE ID for institution 1002

State FIPS

1

State

AL

Zip

35762

City

Normal

Institution Name

Alabama A & M University

Institution Type 1 Public 2 Private nonprofit 3 Private for-profit 1

Institution Level 1 4-year 2 2-year 3 Less-than-2-year 1

In Operation 1 true 0 false 1

Main Campus 1 true 0 false 1

Branches Count of the number of branches 1

Popular Degree 1 Predominantly certificate-degree granting 2 Predominantly associate's-degree granting 3 Predominantly bachelor's-degree granting 4 Entirely graduate-degree granting 3

Highest Degree 0 Non-degree-granting 1 Certificate degree 2 Associate degree 3 Bachelor's degree 4 Graduate degree 4

PCIP01 Percentage of degrees awarded in Agriculture, Agriculture Operations, And Related Sciences. 0.0446

PCIP03 Percentage of degrees awarded in Natural Resources And Conservation. 0.0023

PCIP04 Percentage of degrees awarded in Architecture And Related Services. 0.0094

PCIP05 Percentage of degrees awarded in Area, Ethnic, Cultural, Gender, And Group Studies. 0

PCIP09 Percentage of degrees awarded in Communication, Journalism, And Related Programs. 0

PCIP10 Percentage of degrees awarded in Communications Technologies/Technicians And Support Services. 0.0164

PCIP11 Percentage of degrees awarded in Computer And Information Sciences And Support Services. 0.0634

PCIP12 Percentage of degrees awarded in Personal And Culinary Services. 0

PCIP13 Percentage of degrees awarded in Education. 0.1268

PCIP14 Percentage of degrees awarded in Engineering. 0.1432

PCIP15 Percentage of degrees awarded in Engineering Technologies And Engineering-Related Fields. 0.0587

PCIP16 Percentage of degrees awarded in Foreign Languages, Literatures, And Linguistics. 0

PCIP19 Percentage of degrees awarded in Family And Consumer Sciences/Human Sciences. 0.0188

PCIP22 Percentage of degrees awarded in Legal Professions And Studies. 0

PCIP23 Percentage of degrees awarded in English Language And Literature/Letters. 0.0235

PCIP24 Percentage of degrees awarded in Liberal Arts And Sciences, General Studies And Humanities. 0.0423

PCIP25 Percentage of degrees awarded in Library Science. 0

PCIP26 Percentage of degrees awarded in Biological And Biomedical Sciences. 0.1009

PCIP27 Percentage of degrees awarded in Mathematics And Statistics. 0.0094

PCIP29 Percentage of degrees awarded in Military Technologies And Applied Sciences. 0

PCIP30 Percentage of degrees awarded in Multi/Interdisciplinary Studies. 0

PCIP31 Percentage of degrees awarded in Parks, Recreation, Leisure, And Fitness Studies. 0

PCIP38 Percentage of degrees awarded in Philosophy And Religious Studies. 0

PCIP39 Percentage of degrees awarded in Theology And Religious Vocations. 0

PCIP40 Percentage of degrees awarded in Physical Sciences. 0.0188

PCIP41 Percentage of degrees awarded in Science Technologies/Technicians. 0

PCIP42 Percentage of degrees awarded in Psychology. 0.0282

PCIP43 Percentage of degrees awarded in Homeland Security, Law Enforcement, Firefighting And Related Protective Services. 0.0282

PCIP44 Percentage of degrees awarded in Public Administration And Social Service Professions. 0.0516

PCIP45 Percentage of degrees awarded in Social Sciences. 0.0399

PCIP46 Percentage of degrees awarded in Construction Trades. 0

PCIP47 Percentage of degrees awarded in Mechanic And Repair Technologies/Technicians. 0

PCIP48 Percentage of degrees awarded in Precision Production. 0

PCIP49 Percentage of degrees awarded in Transportation And Materials Moving. 0

PCIP50 Percentage of degrees awarded in Visual And Performing Arts. 0.0258

PCIP51 Percentage of degrees awarded in Health Professions And Related Programs. 0

PCIP52 Percentage of degrees awarded in Business, Management, Marketing, And Related Support Services. 0.1479

PCIP54 Percentage of degrees awarded in History. 0

Admission Rate

0.6538

Average RetentionRate of retention averaged between full-time and part-time students. 0.4428

Retention, Full-Time Students

0.5779

Retention, Part-Time Students

0.3077

Completion Rate

0.1104

Enrollment Number of enrolled students 4505

Male Students Percentage of the student body that is male. 0.4617

Female Students Percentage of the student body that is female. 0.5383

White Percentage of the student body that identifies as white. 0.034

Black Percentage of the student body that identifies as African American. 0.9216

Hispanic Percentage of the student body that identifies as Hispanic or Latino. 0.0058

Asian Percentage of the student body that identifies as Asian. 0.0018

American Indian and Alaskan Native Percentage of the student body that identifies as American Indian or Alaskan Native. 0.0022

Native Hawaiian and Pacific Islander Percentage of the student body that identifies as Native Hawaiian or Pacific islander. 0.0018

Two or More Races Percentage of the student body that identifies as two or more races. 0

Non-Resident Aliens Percentage of the student body that are non-resident aliens. 0.0062

Race Unknown Percentage of the student body for whom racial identity is unknown. 0.0266

Percent Parents no HS Diploma Percentage of parents of students whose highest level of education is less than high school. 0.019298937

Percent Parents HS Diploma Percentage of parents of students whose highest level of education is high school 0.369436786

Percent Parents Post-Secondary Ed. Percentage of parents of students whose highest level of education is college or above. 0.611264277

Title IV Students Percentage of student body identified as Title IV 743

HCM2 Cash Monitoring Schools identified by the Department of Ed for Higher Cash Monitoring Level 2 0

Net Price

13435

Cost of Attendance

20809

In-State Tuition and Fees

9366

Out-of-State Tuition and Fees

17136

Tuition and Fees (Program) Tuition and fees for program-year schools NULL

Tution Revenue per Full-Time Student

9657

Expenditures per Full-Time Student

7941

Average Faculty Salary

7017

Percent of Students with Federal Loan

0.8159

Share of Students with Federal Loan

0.896382157

Share of Students with Pell Grant

0.860906217

Median Loan Principal Amount upon Entering Repayment

14600

Median Debt for Completed Students Median debt for student who completed a course of study 35000

Median Debt for Incompleted Students Median debt for student who did not complete a course of study 9500

Median Debt for Family Income $0K-$30K Median debt for students of families with less thank $30,000 income 14457

Median Debt for Family Income $30K-$75K Median debt for students of families with $30,000-$75,000 income 15000

Median Debt for Family Income over $75K Median debt for students of families with over $75,000 income 14250

Median Debt Female Students

16000

Median Debt Male Students

13750

Median Debt 1st Gen. Students Median debt for first generation college student 14307.5

Median Debt Not 1st Gen. Students Median debt for not first generation college students 14953

Cumulative Loan Debt Greater than 90% of Students (90th Percentile)

48750

Cumulative Loan Debt Greater than 75% of Students (75th Percentile)

32704

Cumulative Loan Debt Greater than 25% of Students (25th Percentile)

5500

Cumulative Loan Debt Greater than 10% of Students (10th Percentile)

3935.5

Accrediting Agency

Southern Association of Colleges and Schools Commission on Colleges

Website

www.aamu.edu/

Price Calculator

www2.aamu.edu/scripts/netpricecalc/npcalc.htm

Latitude

34.783368

Longitude

-86.568502

This layer contains a Vermont-only subset of state level 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau for all states plus DC and Puerto Rico. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, BLOCK, BLKGRP, TRACT, COUNTY, COUNTYCC, COUNTYNS, COUSUB, COUSUBCC, COUSUBNS, SUBMCD, SUBMCDCC, SUBMCDNS, ESTATE, ESTATECC, ESTATENS, CONCIT, CONCITCC, CONCITNS, PLACE, PLACECC, PLACENS, AIANHH, AIHHTLI, AIANHHFP, AIANHHCC, AIANHHNS, AITS, AITSFP, AITSCC, AITSNS, TTRACT, TBLKGRP, ANRC, ANRCCC, ANRCNS, NECTA, NMEMI, CNECTA, NECTADIV, CBSAPCI, NECTAPCI, UA, UATYPE, UR, CD116, CD118, CD119, CD120, CD121, SLDU18, SLDU22, SLDU24, SLDU26, SLDU28, SLDL18, SLDL22, SLDL24, SLDL26, SLDL28, VTD, VTDI, ZCTA, SDELM, SDSEC, SDUNI, and PUMA.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantVCGI exported a Vermont-only subset of the nation-wide layer to produce this layer--with fields limited to this popular subset: OBJECTID: OBJECTID GEOID: Geographic Record Identifier NAME: Area Name-Legal/Statistical Area Description (LSAD) Term-Part Indicator P0010001: Total Population P0010003: Population of one race: White alone P0010004: Population of one race: Black or African American alone P0010005: Population of one race: American Indian and Alaska Native alone P0010006: Population of one race: Asian alone P0010007: Population of one race: Native Hawaiian and Other Pacific Islander alone P0010008: Population of one race: Some Other Race alone P0020002: Hispanic or Latino Population P0020003: Non-Hispanic or Latino Population P0030001: Total population 18 years and over H0010001: Total housing units H0010002: Total occupied housing units H0010003: Total vacant housing units P0050001: Total group quarters population PCT_P0030001: Percent of Population 18 Years and Over PCT_P0020002: Percent Hispanic or Latino PCT_P0020005: Percent White alone, not Hispanic or Latino PCT_P0020006: Percent Black or African American alone, not Hispanic or Latino PCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or Latino PCT_P0020008: Percent Asian alone, not Hispanic or Latino PCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or Latino PCT_P0020010: Percent Some Other Race alone, not Hispanic or Latino PCT_P0020011: Percent Population of two or more races, not Hispanic or Latino PCT_H0010002: Percent of Housing Units that are Occupied PCT_H0010003: Percent of Housing Units that are Vacant SUMLEV: Summary Level REGION: Region DIVISION: Division STATE: State (FIPS) AREALAND: Area (Land) AREAWATR: Area (Water) INTPTLAT: Internal Point (Latitude) INTPTLON: Internal Point (Longitude) BASENAME: Area Base Name POP100: Total Population Count HU100: Total Housing Count Download Census redistricting data in this layer as a file geodatabase.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

These are field data from Moorea, French Polynesia regarding the population dynamics of Diadema savignyi . Data were collected during the austral winters in 2009 and 2010. Four datasets are provided: (1) recruitment data of Diadema savignyi over the course of ~9 weeks during the austral winter of 2010, (2) predation data of adult Diadema using a tethering experiment, (3) urchin abundance surveys for 2009 and 2010, and (4) a 9 week weekly survey of newly recruited Diadema (test diameter). These data are part of a PhD dissertation, "Herbivore Community Dynamics and Functional Compositions in Moorea, French Polynesia", 2014, published by ProQuest.

This layer contains a Vermont-only subset of county subdivision level 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau for all states plus DC and Puerto Rico. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name derived from the technical documentation provided by the Census. The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderIn addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added: PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual tract level, since this data has been protected using differential privacy.*VCGI exported a subset of the TIGER geodatabase fields and tabular data fields to produce this layer--with fields limited to this popular subset: OBJECTID: OBJECTID GEOID: Geographic Record Identifier (from TIGER) NAME: Area Name-Legal/Statistical Area Description (LSAD) Term-Part Indicator County_Name: County Name State_Name: State Name P0010001: Total Population P0010003: Population of one race: White alone P0010004: Population of one race: Black or African American alone P0010005: Population of one race: American Indian and Alaska Native alone P0010006: Population of one race: Asian alone P0010007: Population of one race: Native Hawaiian and Other Pacific Islander alone P0010008: Population of one race: Some Other Race alone P0020002: Hispanic or Latino Population P0020003: Non-Hispanic or Latino Population P0030001: Total population 18 years and over H0010001: Total housing units H0010002: Total occupied housing units H0010003: Total vacant housing units P0050001: Total group quarters population PCT_P0030001: Percent of Population 18 Years and Over PCT_P0020002: Percent Hispanic or Latino PCT_P0020005: Percent White alone, not Hispanic or Latino PCT_P0020006: Percent Black or African American alone, not Hispanic or Latino PCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or Latino PCT_P0020008: Percent Asian alone, not Hispanic or Latino PCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or Latino PCT_P0020010: Percent Some Other Race alone, not Hispanic or Latino PCT_P0020011: Percent Population of two or more races, not Hispanic or Latino PCT_H0010002: Percent of Housing Units that are Occupied PCT_H0010003: Percent of Housing Units that are Vacant SUMLEV: Summary Level REGION: Region DIVISION: Division COUNTY: County (FIPS) COUNTYNS: County (NS) TRACT: Census Tract AREALAND: Area (Land) AREAWATR: Area (Water) INTPTLON: Internal Point (Longitude) INTPTLAT: Internal Point (Latitude) BASENAME: Area Base Name POP100: Total Population Count HU100: Total Housing Count *To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual tracts will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. Download Census redistricting data in this layer as a file geodatabase.Additional links:U.S. Census BureauU.S. Census Bureau Decennial CensusAbout the 2020 Census2020 Census2020 Census data qualityDecennial Census P.L. 94-171 Redistricting Data Program

Native Hawaiian and Other Pacific Islander Health Insurance Coverage Statistics for 2022. This is part of a larger dataset covering consumer health insurance coverage rates in University of California-Santa Barbara, California by age, education, race, gender, work experience and more.

The 2020 Census Redistricting Summary File contains several hundred data fields spread over six different file segments. To facilitate access to more popular variables, the Tennessee State Data Center compiled a “QuickStat” reports detailing population, race/ethnicity and housing information. These fields are combined with geographic fields from the 2020 TIGER/Line Shapefiles for use with mapping software.Field names, descriptions and types selected from the two sources are detailed below.

      Field Name
      Alias
      Data Type
      Length



      OBJECTID
      OBJECTID
      Object ID



      Shape
      Shape
      Geometry



      STATEFP
      State FIPS code
      Text
      2


      COUNTYFP
      County FIPS code
      Text
      3


      TRACTCE
      Tract code
      Text
      6


      BLKGRPCE
      BLKGRPCE
      Text
      1


      GEOID
      Geographic identifier
      Text
      12


      NAMELSAD
      Legal/statistical area description
      Text
      13


      MTFCC
      MAF/TIGER feature class code
      Text
      5


      FUNCSTAT
      Functional status
      Text
      1


      ALAND
      Land area
      Long



      AWATER
      Water area
      Long



      INTPTLAT
      Latitude of the internal point
      Text
      11


      INTPTLON
      Longitude of the internal point
      Text
      12


      SUMLEV
      Summary Level
      Text
      255


      LOGRECNO
      Logical Record Number
      Long



      P0010001
      Total population
      Long



      P0010002
      Population of one race
      Long



      P0010003
      White alone
      Long



      P0010004
      Black or African American alone
      Long



      P0010005
      American Indian and Alaska Native alone
      Long



      P0010006
      Asian alone
      Long



      P0010007
      Native Hawaiian and Other Pacific Islander alone
      Long



      P0010008
      Some Other Race alone
      Long



      P0010009
      Population of two or more races:
      Long



      P0020002
      Hispanic or Latino
      Long



      P0020003
      Not Hispanic or Latino:
      Long



      P0020004
      Population of one race (Not Hispanic or Latino)
      Long



      P0020005
      White alone (Not Hispanic or Latino)
      Long



      P0020006
      Black or African American alone (Not Hispanic or Latino)
      Long



      P0020007
      American Indian and Alaska Native alone (Not Hispanic or Latino)
      Long



      P0020008
      Asian alone (Not Hispanic or Latino)
      Long



      P0020009
      Native Hawaiian and Other Pacific Islander alone (Not Hispanic or Latino)
      Long



      P0020010
      Some Other Race alone (Not Hispanic or Latino)
      Long



      P0020011
      Population of two or more races (Not Hispanic or Latino)
      Long



      P0030001
      Total population 18 years and over
      Long



      H0010001
      Total housing units
      Long



      H0010002
      Occupied housing units
      Long



      H0010003
      Vacant housing units
      Long



      P0050001
      Total population in group quarters
      Long



      P0050002
      Institutionalized population
      Long



      P0050003
      Correctional facilities for adults
      Long



      P0050004
      Juvenile facilities
      Long



      P0050005
      Nursing facilities/Skilled-nursing facilities
      Long



      P0050006
      Other institutional facilities
      Long



      P0050007
      Noninstitutionalized population
      Long



      P0050008
      College/University student housing
      Long



      P0050009
      Military quarters
      Long



      P0050010
      Other noninstitutional facilities
      Long



      Shape_Length

      Double



      Shape_Area

      Double

As of 2022, the prevalence of a sedentary lifestyle in the United States was highest among Hispanic adults, with a total of 29.8 percent. Hawaiian/ Pacific Islanders and Asian Americans were the most active of all races/ethnic groups at that time.

Inactive states

People from Mississippi were the least physically active Americans from 2017 to 2020. About 33 percent of adults in Mississippi stated that they did not partake in any physical activity or exercise besides their job within the last thirty days. Colorado, Utah, and Washington were among the most physically active states at that time.

Watch your diet

In 2020, approximately 100 million people in the United States stated that they watched their diet in order to lose weight. Other common reasons for watching weight included keeping cholesterol and blood sugar levels under control. Furthermore, around 46 percent of U.S. college students were trying to lose weight in 2022. A larger percentage of female students were trying to lose weight than male college students.

Demographic performance (recruitment, growth, and survival) are quantified annually for multiple individual colonies of the three most common genera (Acropora, Pocillopora, Porites) at both backreef and forereef sites. Each coral was tagged in 2011 and subsequently sampled again in 2012 to track colony growth and mortality dynamics. However, since 2013, investigators have transitioned to identifying coral through detailed mapping methodology and will continue to identify corals using this method in the subsequent years. The mapping system developed in 2013 provides data appropriate for detailed demographic study of coral on varying spatial scales around the island. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2022). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.

The County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. This feature layer contains 2020 County Health Rankings data for nation, state, and county levels. The Rankings are compiled using county-level measures from a variety of national and state data sources. Some example measures are:adult smokingphysical inactivityflu vaccinationschild povertydriving alone to workTo see a full list of variables, as well as their definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details. These measures are standardized and combined using scientifically-informed weights."By ranking the health of nearly every county in the nation, County Health Rankings & Roadmaps (CHR&R) illustrates how where we live affects how well and how long we live. CHR&R also shows what each of us can do to create healthier places to live, learn, work, and play – for everyone."Some new features of the 2020 Rankings data compared to previous versions:More race/ethnicity categories, including Asian/Pacific Islander and American Indian/Alaska NativeReliability flags that to flag an estimate as unreliable5 new variables: math scores, reading scores, juvenile arrests, suicides, and traffic volumeData Processing Notes:Data downloaded March 2020Slight modifications made to the source data are as follows:The string " raw value" was removed from field labels/aliases so that auto-generated legends and pop-ups would only have the measure's name, not "(measure's name) raw value" and strings such as "(%)", "rate", or "per 100,000" were added depending on the type of measure.Percentage and Prevalence fields were multiplied by 100 to make them easier to work with in the map.For demographic variables only, the word "numerator" was removed and the word "population" was added where appropriate.Fields dropped from analytic data file: yearall fields ending in "_cihigh" and "_cilow"and any variables that are not listed in the sources and years documentation.Analytic data file was then merged with state-specific ranking files so that all county rankings and subrankings are included in this layer.

The County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. This feature layer contains 2020 County Health Rankings data for nation, state, and county levels. The Rankings are compiled using county-level measures from a variety of national and state data sources. Some example measures are:adult smokingphysical inactivityflu vaccinationschild povertydriving alone to workTo see a full list of variables, as well as their definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details. These measures are standardized and combined using scientifically-informed weights."By ranking the health of nearly every county in the nation, County Health Rankings & Roadmaps (CHR&R) illustrates how where we live affects how well and how long we live. CHR&R also shows what each of us can do to create healthier places to live, learn, work, and play – for everyone."Some new features of the 2020 Rankings data compared to previous versions:More race/ethnicity categories, including Asian/Pacific Islander and American Indian/Alaska NativeReliability flags that to flag an estimate as unreliable5 new variables: math scores, reading scores, juvenile arrests, suicides, and traffic volumeData Processing Notes:Data downloaded March 2020Slight modifications made to the source data are as follows:The string " raw value" was removed from field labels/aliases so that auto-generated legends and pop-ups would only have the measure's name, not "(measure's name) raw value" and strings such as "(%)", "rate", or "per 100,000" were added depending on the type of measure.Percentage and Prevalence fields were multiplied by 100 to make them easier to work with in the map.For demographic variables only, the word "numerator" was removed and the word "population" was added where appropriate.Fields dropped from analytic data file: yearall fields ending in "_cihigh" and "_cilow"and any variables that are not listed in the sources and years documentation.Analytic data file was then merged with state-specific ranking files so that all county rankings and subrankings are included in this layer.

About this dataset

Context

The dataset tabulates the population of University Heights by race. It includes the population of University Heights across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of University Heights across relevant racial categories.

Key observations

The percent distribution of University Heights population by race (across all racial categories recognized by the U.S. Census Bureau): 70.51% are white, 22.45% are Black or African American, 0.13% are American Indian and Alaska Native, 1.72% are Asian, 1.56% are some other race and 3.63% are multiracial.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the University Heights
• Population: The population of the racial category (excluding ethnicity) in the University Heights is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of University Heights 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 University Heights Population by Race & Ethnicity. You can refer the same here

Regression models of COVID-19-related racial discrimination and depression symptoms among the full sample.

About this dataset

Context

The dataset presents the median household income across different racial categories in University Park. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of University Park population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 85.65% of the total residents in University Park. Notably, the median household income for White households is $67,283. Interestingly, despite the White population being the most populous, it is worth noting that Two or More Races households actually reports the highest median household income, with a median income of $160,375. This reveals that, while Whites may be the most numerous in University Park, Two or More Races households experience greater economic prosperity in terms of median household income.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in University Park.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 University Park median household income by race. You can refer the same here

About this dataset

Context

The dataset tabulates the population of University Park by race. It includes the population of University Park across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of University Park across relevant racial categories.

Key observations

The percent distribution of University Park population by race (across all racial categories recognized by the U.S. Census Bureau): 64.38% are white, 19.04% are Black or African American, 1.40% are American Indian and Alaska Native, 1.33% are Asian, 8.95% are some other race and 4.89% are multiracial.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the University Park
• Population: The population of the racial category (excluding ethnicity) in the University Park is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of University Park 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 University Park Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Non-Hispanic population of University Place by race. It includes the distribution of the Non-Hispanic population of University Place across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of University Place across relevant racial categories.

Key observations

Of the Non-Hispanic population in University Place, the largest racial group is White alone with a population of 21,106 (68.51% of the total Non-Hispanic population).

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the University Place
• Population: The population of the racial category (for Non-Hispanic) in the University Place is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of University Place total Non-Hispanic 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 University Place Population by Race & Ethnicity. You can refer the same here

This layer contains block group level 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau for all states plus DC and Puerto Rico. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables.Data download date: August 12, 2021Census tables: P1, P2, P3, P4, H1, P5, HeaderDownloaded from: Census FTP siteProcessing Notes:Data was downloaded from the U.S. Census Bureau FTP site, imported into SAS format and joined to the 2020 TIGER boundaries. Boundaries are sourced from the 2020 TIGER/Line Geodatabases. Boundaries have been projected into Web Mercator and each attribute has been given a clear descriptive alias name. No alterations have been made to the vertices of the data.Each attribute maintains it's specified name from Census, but also has a descriptive alias name and long description derived from the technical documentation provided by the Census. For a detailed list of the attributes contained in this layer, view the Data tab and select "Fields". The following alterations have been made to the tabular data:Joined all tables to create one wide attribute table:P1 - RaceP2 - Hispanic or Latino, and not Hispanic or Latino by RaceP3 - Race for the Population 18 Years and OverP4 - Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and OverH1 - Occupancy Status (Housing)P5 - Group Quarters Population by Group Quarters Type (correctional institutions, juvenile facilities, nursing facilities/skilled nursing, college/university student housing, military quarters, etc.)HeaderAfter joining, dropped fields: FILEID, STUSAB, CHARITER, CIFSN, LOGRECNO, GEOVAR, GEOCOMP, LSADC, and BLOCK.GEOCOMP was renamed to GEOID and moved be the first column in the table, the original GEOID was dropped.Placeholder fields for future legislative districts have been dropped: CD118, CD119, CD120, CD121, SLDU22, SLDU24, SLDU26, SLDU28, SLDL22, SLDL24 SLDL26, SLDL28.P0020001 was dropped, as it is duplicative of P0010001. Similarly, P0040001 was dropped, as it is duplicative of P0030001.In addition to calculated fields, County_Name and State_Name were added.The following calculated fields have been added (see long field descriptions in the Data tab for formulas used): PCT_P0030001: Percent of Population 18 Years and OverPCT_P0020002: Percent Hispanic or LatinoPCT_P0020005: Percent White alone, not Hispanic or LatinoPCT_P0020006: Percent Black or African American alone, not Hispanic or LatinoPCT_P0020007: Percent American Indian and Alaska Native alone, not Hispanic or LatinoPCT_P0020008: Percent Asian alone, Not Hispanic or LatinoPCT_P0020009: Percent Native Hawaiian and Other Pacific Islander alone, not Hispanic or LatinoPCT_P0020010: Percent Some Other Race alone, not Hispanic or LatinoPCT_P0020011: Percent Population of Two or More Races, not Hispanic or LatinoPCT_H0010002: Percent of Housing Units that are OccupiedPCT_H0010003: Percent of Housing Units that are VacantPlease note these percentages might look strange at the individual block group level, since this data has been protected using differential privacy.* *To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual block groups will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. The pop-up on this layer uses Arcade to display aggregated values for the surrounding area rather than values for the block group itself.