I’ve missed my once-a-month goal for writing posts several times this year. This is partially for good reasons, as I’ve been busy supporting students and faculty with coursework and projects, and have been supervising the excellent work of my own students in the lab. We’ve made great progress, releasing a spatial database for Rhode Island mapping projects, writing new tutorials, inventorying thousands of USGS topo maps, and supporting hundreds of students and faculty with their geospatial and demographic research.
But in order to effectively support the work of others, academic librarians need to have a research agenda of their own; to keep up with evolving technology and scholarship to remain effective, and to sustain your own intellectual interests as a professional. Which brings us to the bad reasons behind my posting inactivity. My professional development has come to a screeching halt since I began my new position three years ago. My employer is adverse to supporting scholarly activities for professional librarians (although they gladly share credit if you do the work on evenings, weekends, and vacation time), and a heavy workload makes it impossible to find time for professional development. There are many reasons behind this for which I can’t go into detail – I’ll generally say that bad management and an over-sized library managerial caste are the primary culprits.
Unfortunately this is all too common in academic librarianship. Some high-profile articles have discussed this recently, surveys show that morale is low, and there’s a small but budding branch of scholarship that focuses on library dysfunction. It’s a shame, because both traditional “core-research” librarians and data services-oriented librarians play vital roles within higher ed, and there is no shortage of students and professors who remind me of this on a regular basis. In my opinion, while many students and professors understand and value the work of librarians, many library administrators do not. They dismiss traditional subject librarians as legacy service providers, and they completely do not understand the work of data librarians.
I’ve heard several depressing stories from colleagues at other schools who have been undermined, shuffled around, and in some cases put out of business by incompetent leadership within their library. Within GIS and data librarianship I know several folks who have given up, leaving higher ed for the private sector or independent consulting.
Towards the end of the semester, as I was finishing an hour-long GIS consultation with a grateful undergrad, he asked me what research projects I was currently working on, and what kind of research I do. I was embarrassed to admit that I haven’t been working on anything of my own. After having written a book and publishing several well-received reports, I’m doing nothing more than the intellectual equivalent of shoveling snow. I can’t help but think that I’ve taken a wrong turn, and as the new year begins it’s time to consider the options: focus more sharply on the positive aspects of my position while minimizing the negatives? And somehow, carve out time to do work that I’m interested in? Or, consider moving on, being mindful to avoid exchanging one set of bad circumstances with another? For the latter, this may mean leaving academic librarianship behind.
I am most fortunate in that I don’t have to return to work until the second week of January, and it’s good to have this time to recuperate and reflect. Best wishes to you in the coming new year – Frank
Right before the semester began, I updated the Rhode Island maps on my census research guide so that they link to the recently released Demographic Profile tables from the 2020 Census. I feel like the release of the 2020 census has flown lower on the radar compared to 2010 – it hasn’t made it into the news or social media feeds to the same degree. It has been released much later than usual for a variety of reasons, including the COVID pandemic and political upheaval and shenanigans. At this point in Sept 2023, most of what we can expect has been released, and is available via data.census.gov and the census APIs.
Here are the different series, and what they include.
Redistricting data. Released in Aug 2021. Also known as PL 91-171 (for the law that requires it), this data is intended for redrawing congressional and legislative districts. It includes just six tables, available for several geographies down to the block level. This was our first detailed glimpse of the count. The dataset contains population counts by race, Hispanic and Latino ethnicity, the 18 and over population, group quarters, and housing unit occupancy. Here are the six US-level tables.
Demographic and Housing Characteristics File. Released in May 2023. In the past, this series was called Summary File 1. It is the “primary” decennial census dataset that most people will use, and contains the full range of summary data tables for the 2020 census for practically all census geographies. There are fewer tables overall relative to the 2010 census, and fewer that provide a geographically granular level of detail (ostensibly due to privacy and cost concerns). The Data Table Guide is an Excel spreadsheet that lists every table and the variables they include.
Demographic Profile. Released in May 2023. This is a single table, DP1, that provides a broad cross-section of the variables included in the 2020 census. If you want a summary overview, this is the table you’ll consult. It’s an easily accessible option for folks who don’t want or need to compile data from several tables in the DHC. Here is the state-level table for all 50 states plus.
Detailed Demographic and Housing Characteristics File A. Released in Sept 2023. In the past, this series was called Summary File 2. It is a subset of the data collected in the DHC that includes more detailed cross-tabulations for race and ethnicity categories, down to the census tract level. It is primarily used by researchers who are specifically studying race, and the multiracial population.
Detailed Demographic and Housing Characteristics File B. Not released yet. This will be a subset of the data collected in the DHC that includes more detailed cross-tabulations on household relationships and tenure, down to the census tract level. Primarily of interest to researchers studying these characteristics.
There are a few aspects of the 2020 census data that vary from the past – I’ll link to some NPR stories that provide a good overview. Respondents were able to identify their race or ethnicity at a more granular level. In addition to checking the standard OMB race category boxes, respondents could write in additional details, which the Census Bureau standardized against a list of races, ethnicities, and national origins. This is particularly noteworthy for the Black and White populations, for whom this had not been an option in the recent past. It’s now easier to identify subgroups within these groups, such as Africans and Afro-Caribbeans within the Black population, and Middle Eastern and North Africans (MENA) within the White population. Another major change is that same-sex marriages and partnerships are now explicitly tabulated. In the past, same-sex marriages were all counted as unmarried partners, and instead of having clearly identifiable variables for same-sex partners, researchers had to impute this population from other variables.
Another major change was the implementation of the differential privacy mechanism, which is a complex statistical process to inject noise into the summary data to prevent someone from reverse engineering it to reveal information about individual people (in violation of laws to protect census respondent’s privacy). The social science community has been critical of the application of this procedure, and IPUMS has published research to study possible impacts. One big takeaway is that published block-level population data is less reliable than in the past (housing unit data on the other hand is not impacted, as it is not subjected to the mechanism).
When would you use decennial census data versus other census data? A few considerations – when you:
Want or need to work with actual counts rather than estimates
Only need basic demographic and housing characteristics
Need data that provides detailed cross-tabulations of race, which is not available elsewhere
Need a detailed breakdown of the group quarters population, which is not available elsewhere
Are explicitly working with voting and redistricting
Are making historical comparisons relative to previous 10-year censuses
In contrast, if you’re looking for detailed socio-economic characteristics of the population, you would need to look elsewhere as the decennial census does not collect this information. The annual American Community Survey or monthly Current Population Survey would be likely alternatives. If you need basic, annual population estimates or are studying the components of population change, the Population and Housing Unit Estimates Program is your best bet.
I have written a new report that’s just been released: US Census Data: Concepts and Applications for Supporting Research, was published as the May / June 2022 issue of the American Library Association’s Library and Technology Reports. It’s available for purchase digitally or in hard copy from the ALA from now through next year. It will also be available via EBSCOhost as full text, sometime this month. One year from now, the online version will transition to become a free and open publication available via the tech report archives.
The report was designed to be a concise primer (about 30 pages) for librarians who want to be knowledgeable with assisting researchers and students with finding, accessing, and using public summary census data, or who want to apply it to their own work as administrators or LIS researchers. But I also wrote it in such a way that it’s relevant for anyone who is interested in learning more about the census. In some respects it’s a good distillation of my “greatest hits”, drawing on work from my book, technical census-related blog posts, and earlier research that used census data to study the distribution of public libraries in the United States.
Chapter Outline
Introduction
Roles of the Census: in American society, the open data landscape, and library settings
Census Concepts: geography, subject categories, tables and universes
Datasets: decennial census, American Community Survey, Population Estimates, Business Establishments
Accessing Data: data.census.gov, API with python, reports and data summaries
GIS, historical research, and microdata: covers these topics plus the Current Population Survey
The Census in Library Applications: overview of the LIS literature on site selection analysis and studying library access and user populations
I’m pleased with how it turned out, and in particular I hope that it will be used by MLIS students in data services and government information courses.
Although… I must express my displeasure with the ALA. The editorial team for the Library Technology Reports was solid. But once I finished the final reviews of the copy edits, I was put on the spot to write a short article for the American Libraries magazine, primarily to promote the report. This was not part of the contract, and I was given little direction and a month at a busy time of the school year to turn it around. I submitted a draft and never heard about it again – until I saw it in the magazine last week. They cut and revised it to focus on a narrow aspect of the census that was not the original premise, and they introduced errors to boot! As a writer I have never had an experience where I haven’t been given the opportunity to review revisions. It’s thoroughly unprofessional, and makes it difficult to defend the traditional editorial process as somehow being more accurate or thorough compared to the web posting and tweeting masses. They were apologetic, and are posting corrections. I was reluctant to contribute to the magazine to begin with, as I have a low opinion of it and think it’s deteriorated in recent years, but that’s a topic for a different discussion.
Stepping off the soapbox… I’ll be attending the ALA annual conference in DC later this month, to participate on a panel that will discuss the 2020 census, and to reconnect with some old colleagues. So if you want to talk about the census, you can buy me some coffee (or beer) and check out the report.
A final research and publication related note – the map that appears at the top of my post on the distribution of US public libraries from several years back has also made it into print. It appears on page 173 of The Argument Toolbox by K.J. Peters, published by Broadview Press. It was selected as an example of using visuals for communicating research findings, making compelling arguments in academic writing, and citing underlying sources to establish credibility. I’m browsing through the complimentary copy I received and it looks excellent. If you’re an academic librarian or a writing center professional and are looking for core research method guides, I would recommend checking it out.
We recently launched our fledgling geodata portal on GitHub for the open datasets we’ll create in our new lab. In the spring we carved out a space on the 11th floor of the Sciences Library at Brown which we’ve christened GeoData@SciLi, a GIS and data consultation and work space. We’ll be doing renovations on both the webspace and workspace over the summer.
Our inaugural dataset was created by Ethan McIntosh, a senior (now graduate) who began working with me this spring. The dataset is the United Nations International Civil Service Commission’s (UN ICSC) Retail Price Indices with Details (RPID). The index measures the cost of living based on several categories of goods and services in duty stations around the world. It’s used to adjust the salaries of the UN’s international staff relative to UN headquarters in New York City (index value of 100 = cost of living in New York). The data is updated six times a year, published in an Excel spreadsheet that contains a macro that allows you to look up the value of each duty station via a dropdown menu. The UN ICSC makes the data public by request; you register and are granted access to download the data in PDF and Excel format in files that are packaged in one month / year at a time.
We were working with a PhD student in economics who wanted to construct a time-series of this data. Ethan wrote a Python script to aggregate all of the files from 2004 to present into a single CSV; the actual values for each country / duty station were stored in hidden cells that the macro pulled from, and he was able to pull them from these cells. He parsed the data into logical divisions, and added the standard 3-letter ISO 3166 country code to each duty station so that each record now has a unique place identifier. His script generates three outputs: a basic CSV of the data in separate month / year files, a “long” (aka flat) time series file where each record represents a specific duty station and retail index category or weight for a given month and year, and a “wide” time series file where the category / weight has been pivoted to a column, so each record represents all values for a duty station for a given month / year. He’s written the program to process and incorporate additional files as they’re published.
While the primary intention was to study this data as a time series in a statistical analysis, it can also be used for geospatial analysis and mapping. Using the wide file, I created the map in the header of this post, which depicts the total retail index for February 2022 for each country, where the value represents the duty station within the country (usually the capital city). I grabbed some boundaries from Natural Earth and joined the data to it using the ISO code. I classified the data using natural breaks, but manually adjusted the top level category to include all countries with a value greater than or equal to the base value of 100.
There were only five duty stations that were more expensive than New York, with values between 102 and 124: Tokyo, Ashkhabad (Turkmenistan), Singapore, Beirut, and Hong Kong. Beijing and Geneva were equivalent in price at 100. The least expensive stations with values between 52 and 69 were: Caracas (Venezuela), Tripoli, Damascus, Ankara (Turkey), Bucharest (Romania), Mbabane (Eswatini – formerly Swaziland), and Sofia (Bulgaria). There appears to be regional clustering of like values, although I didn’t run any tests. The station in the US that’s measured relative to NYC is Washington DC (index value of 89).
The final datasets and code used to generate them are available on GitHub, and we’ll update it at least once, if not a couple times, a year. We are not providing the original month / year macro spreadsheets; if you want those you should register with the UN ICSC and access them there. If you’re using our data files, you should still register with them, as they would like to be aware of how their data is being used.
We will post additional projects, datasets, and code in individual repos as we create them, linked to from our main page. I’m working on creating a basic metadata profile for our lab, so we’ll provide structured metadata for each of our datasets in the near future.
Last week, the Census Bureau released the latest 5-year estimates for the American Community Survey for 2016-2020. This latest dataset uses the new 2020 census geography, which means if you’re focused on using the latest data, you can finally move away from the 2010-based geography which had been used for the ACS from 2010 to 2019 (with some caveats: 2020 ZCTAs won’t be utilized until the 2021 ACS, and 2020 PUMAs until 2022). As always, mappers have a choice between the TIGER Line files that depict the precise boundaries, or the generalized cartographic boundary files with smoothed lines and large sections of coastal water bodies removed to depict land areas. The 2016-2020 ACS data is available via data.census.gov and the ACS API.
This release is over 3 months late (compared to normal), and there was some speculation as to whether it would be released at all. The pandemic (chief among several other disruptive events) hampered 2020 decennial census and ACS operations. The 1-year 2020 ACS numbers were released over 2 months later than usual, in late November 2021, and were labeled as an experimental release. Instead of the usual 1,500 plus tables in 40 subject areas for all geographic areas with over 65,000 people, only 54 tables were released for the 50 states plus DC. This release is only available from the experimental tables page and is not being published via data.census.gov.
What happened? The details were published in a working paper, but in summary fewer addresses were sampled and the normal mail out and follow-up procedures were disrupted (pg 8). The overall sample size fell from 3.5 to 2.9 million addresses due to reduced mailing between April and June 2020 (pg 18), and total interviews fell from 2 million to 1.4 million with most of the reductions occurring in spring and summer (pg 18). The overall housing unit response rate for 2020 was 71%, down from 86% in 2019 and 92% in 2018 (pg 20). The response rate for the group quarters population fell from 91% in 2019 to 47% in 2020 (pg 21). Responses were differential, varying by time period (with the lowest rates during the peak pandemic months) and geography. Of the 818 counties that meet the 65k threshold, response rates in some were below 50% (pg 21). The data contained a large degree of non-response bias, where people who did respond to the survey had significantly different social, economic and housing characteristics from those who didn’t. As a consequence of all of this, margins of error for the data increased by 20 to 30% over normal (pg 18).
Thus, 2020 will represent a hole in the ACS estimates series. The Bureau made adjustments to weighting mechanisms to produce the experimental 1-year estimates, but is generally advising policy makers and researchers who normally use this series to choose alternatives: either the 1-year 2019 ACS, or the 5-year 2016-2020 ACS. The Bureau was able to make adjustments to produce satisfactory 5-year estimates to reduce non-response bias, and the 5-year pool of samples is balanced somewhat by having at least 4 years of good data.
The Population Estimates Program has also released its latest series of vintage 2021 estimates for counties and metropolitan areas. This dataset gives us a pretty sharp view of how the pandemic affected the nation’s population. Approximately 73% of all counties experienced natural decrease in 2021 (between July 1st 2020 and 2021), where the number of deaths outnumbered births. In contrast, 56% of counties had natural decrease in 2020 and 46% in 2019. Declining birth rates and increasing death rates are long term trends, but COVID-19 magnified them, given the large number of excess deaths on one hand and families postponing child birth due to the virus on the other hand. Net foreign migration continued its years-long decline, but net domestic migration increased in a number of places, reflecting pandemic moves. Medium to small counties benefited most, as did large counties in the Sunbelt and Mountain West. The biggest losers in overall population were counties in California (Los Angeles, San Francisco, and Alameda), Cook County (Chicago), and the counties that constitute the boroughs of NYC.
In late summer and early fall I was hammering out the draft for an ALA Tech Report on using census data for research (slated for release early 2022). The earliest 2020 census figures have been released and there are several issues surrounding this, so I’ll provide a summary of what’s happening here. Throughout this post I link to Census Bureau data sources, news bulletins, and summaries of trends, as well as analysis on population trends from Bill Frey at Brookings and reporting from Hansi Lo Wang and his colleagues at NPR.
Count Result and Reapportionment Numbers
The re-apportionment results were released back in April 2020, which provided the population totals for the US and each of the states that are used to reallocate seats in Congress. This data is typically released at the end of December of the census year, but the COVID-19 pandemic and political interference in census operations disrupted the count and pushed all the deadlines back.
Despite these disruptions, the good news is that the self-response rate, which is the percentage of households who submit the form on their own without any prompting from the Census Bureau, was 67%, which is on par with the 2010 census. This was the first decennial census where the form could be submitted online, and of the self-responders 80% chose to submit via the internet as opposed to paper or telephone. Ultimately, the Bureau said it reached over 99% of all addresses in its master address file through self-response and non-response follow-ups.
The bad news is that the rate of non-response to individual questions was much higher in 2020 than in 2010. Non-responses ranged from a low of 0.52% for the total population count to a high of 5.95% for age or date of birth. This means that a higher percentage of data will have to be imputed, but this time around the Bureau will rely more on administrative records to fill the gaps. They have transparently posted all of the data about non-response for researchers to scrutinize.
The apportionment results showed that the population of the US grew from approximately 309 million in 2010 to 331 million in 2020, a growth rate of 7.35%. This is the lowest rate of population growth since the 1940 census that followed the Great Depression. Three states lost population (West Virginia, Mississippi, and Illinois), which is the highest number since the 1980 census. The US territory of Puerto Rico lost almost twelve percent of its population. Population growth continues to be stronger in the West and South relative to the Northeast and Midwest, and the fastest growing states are in the Mountain West.
The first detailed population statistics were released as part of the redistricting data file, PL 94-171. Data in this series is published down to the block level, the smallest geography available, so that states can redraw congressional and other voting districts based on population change. Normally released at the end of March, this data was released in August 2021. This is a small package that contains the following six tables:
P1. Race (includes total population count)
P2. Hispanic or Latino, and Not Hispanic or Latino by Race
P3. Race for the Population 18 Years and Over
P4. Hispanic or Latino, and Not Hispanic or Latino by Race for the Population 18 Years and Over
P5. Group Quarters Population by Major Group Quarters Type
H1. Occupancy Status (includes total housing units)
The raw data files for each state can be downloaded from the 2020 PL 94-171 page and loaded into stats packages or databases. That page also provides infographics (including the maps embedded in this post) and data summaries. Data tables can be readily accessed via data.census.gov, or via IPUMS NHGIS.
The redistricting files illustrate the increasing diversity of the United States. The number of people identifying as two or more races has grown from 2.9% of the total population in 2010 to 10.2% in 2020. Hispanics and Latinos continue to be the fastest growing population group, followed by Asians. The White population actually shrank for the first time in the nation’s history, but as NPR reporter Hansi-Lo Wang and his colleagues illustrate this interpretation depends on how one measures race; as race alone (people of a single race) or persons of any race (who selected white and another race), and whether or not Hispanic-whites are included with non-Hispanic whites (as Hispanic / Latino is not a race, but is counted separately as an ethnicity, and most Hispanics identify their race as White or Other). The Census Bureau has also provided summaries using the different definitions. Other findings: the nation is becoming progressively older, and urban areas outpaced rural ones in population growth. Half of the counties in the US lost population between 2010 and 2020, mostly in rural areas.
2020 Demographic and Housing Characteristics and the ACS
There still isn’t a published timeline for the release of the full results in the Demographic and Housing Characteristics File (DHC – known as Summary File 1 in previous censuses, I’m not sure if the DHC moniker is replacing the SF1 title or not). There are hints that this file is going to be much smaller in terms of the number of tables, and more limited in geographic detail compared to the 2010 census. Over the past few years there’s been a lot of discussion about the new differential privacy mechanisms, which will be used to inject noise into the data. The Census Bureau deemed this necessary for protecting people’s privacy, as increased computing power and access to third party datasets have made it possible to reverse engineer the summary census data to generate information on individuals.
What has not been as widely discussed is that many tables will simply not be published, or will only be summarized down to the county-level, also for the purpose of protecting privacy. The Census Bureau has invited the public to provide feedback on the new products and has published a spreadsheet crosswalking products from 2010 and 2020. IPUMS also released a preliminary list of tables that could be cut or reduced in specificity (derived from the crosswalk), which I’m republishing at the bottom of this post. This is still preliminary, but if all these changes are made it would drastically reduce the scope and specificity of the decennial census.
And then… there is the 2020 American Community Survey. Due to COVID-19 the response rates to the ACS were one-third lower than normal. As such, the sample is not large or reliable enough to publish the 1-year estimate data, which is typically released in September. Instead, the Census will publish a smaller series of experimental tables for a more limited range of geographies at the end of November 2021. There is still no news regarding what will happen with the 5-year estimate series that is typically released in December.
Needless to say, there’s no shortage of uncertainty regarding census data in 2020.
Tables in 2010 Summary File 1 that Would Have Less Geographic Detail in 2020(Proposed)
Table Name
Proposed 2020 Lowest Level of Geography
2010 Lowest Level of Geography
Hispanic or Latino Origin of Householder by Race of Householder
County
Block
Household Size by Household Type by Presence of Own Children
County
Block
Household Type by Age of Householder
County
Block
Households by Presence of People 60 Years and Over by Household Type
County
Block
Households by Presence of People 60 Years and Over, Household Size, and Household Type
County
Block
Households by Presence of People 75 Years and Over, Household Size, and Household Type
County
Block
Household Type by Household Size
County
Block
Household Type by Household Size by Race of Householder
County
Block
Relationship by Age for the Population Under 18 Years
County
Block
Household Type by Relationship for the Population 65 Years and Over
County
Block
Household Type by Relationship for the Population 65 Years and Over by Race
County
Block
Family Type by Presence and Age of Own Children
County
Block
Family Type by Presence and Age of Own Children by Race of Householder
County
Block
Age of Grandchildren Under 18 Years Living with A Grandparent Householder
County
Block
Household Type by Relationship by Race
County
Block
Average Household Size by Age
To be determined
Block
Household Type for the Population in Households
To be determined
Block
Household Type by Relationship for the Population Under 18 Years
To be determined
Block
Population in Families by Age
To be determined
Block
Average Family Size by Age
To be determined
Block
Family Type and Age for Own Children Under 18 Years
To be determined
Block
Total Population in Occupied Housing Units by Tenure
To be determined
Block
Average Household Size of Occupied Housing Units by Tenure
To be determined
Block
Sex by Age for the Population in Households
County
Tract
Sex by Age for the Population in Households by Race
County
Tract
Presence of Multigenerational Households
County
Tract
Presence of Multigenerational Households by Race of Householder
County
Tract
Coupled Households by Type
County
Tract
Nonfamily Households by Sex of Householder by Living Alone by Age of Householder
County
Tract
Group Quarters Population by Sex by Age by Group Quarters Type
State
Tract
Tables in 2010 Summary File 1 That Would Be Eliminated in 2020(Proposed)
Population in Households by Age by Race of Householder
Average Household Size by Age by Race of Householder
Households by Age of Householder by Household Type by Presence of Related Children
Households by Presence of Nonrelatives
Household Type by Relationship for the Population Under 18 Years by Race
Household Type for the Population Under 18 Years in Households (Excluding Householders, Spouses, and Unmarried Partners)
Families*
Families by Race of Householder*
Population in Families by Age by Race of Householder
Average Family Size by Age by Race of Householder
Family Type by Presence and Age of Related Children
Family Type by Presence and Age of Related Children by Race of Householder
Group Quarters Population by Major Group Quarters Type*
Population Substituted
Allocation of Population Items
Allocation of Race
Allocation of Hispanic or Latino Origin
Allocation of Sex
Allocation of Age
Allocation of Relationship
Allocation of Population Items for the Population in Group Quarters
American Indian and Alaska Native Alone with One Tribe Reported for Selected Tribes
American Indian and Alaska Native Alone with One or More Tribes Reported for Selected Tribes
American Indian and Alaska Native Alone or in Combination with One or More Other Races and with One or More Tribes Reported for Selected Tribes
American Indian and Alaska Native Alone or in Combination with One or More Other Races
Asian Alone with One Asian Category for Selected Groups
Asian Alone with One or More Asian Categories for Selected Groups
Asian Alone or in Combination with One or More Other Races, and with One or More Asian Categories for Selected Groups
Native Hawaiian and Other Pacific Islander Alone with One Native Hawaiian and Other Pacific Islander Category for Selected Groups
Native Hawaiian and Other Pacific Islander Alone with One or More Native Hawaiian and Other Pacific Islander Categories for Selected Groups
Native Hawaiian and Other Pacific Islander Alone or in Combination with One or More Races, and with One or More Native Hawaiian and Other Pacific Islander Categories for Selected Groups
Hispanic or Latino by Specific Origin
Sex by Single Year of Age by Race
Household Type by Number of Children Under 18 (Excluding Householders, Spouses, and Unmarried Partners)
Presence of Unmarried Partner of Householder by Household Type for the Population Under 18 Years in Households (Excluding Householders, Spouses, and Unmarried Partners)
Nonrelatives by Household Type
Nonrelatives by Household Type by Race
Group Quarters Population by Major Group Quarters Type by Race
Group Quarters Population by Sex by Major Group Quarters Type for the Population 18 Years and Over by Race
Total Races Tallied for Householders
Hispanic or Latino Origin of Householders by Total Races Tallied
Total Population in Occupied Housing Units by Tenure by Race of Householder
Average Household Size of Occupied Housing Units by Tenure
Average Household Size of Occupied Housing Units by Tenure by Race of Householder
Occupied Housing Units Substituted
Allocation of Vacancy Status
Allocation of Tenure
Tenure by Presence and Age of Related Children
* Counts for these tables are available in other proposed DHC tables. For example, the count of families is available in the Household Type table, which will be available at the block level in the 2020 DHC.
At These Coordinates 
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