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2020 Resident Population Map

2020 Census Updates

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.

https://www.census.gov/library/visualizations/2021/dec/2020-percent-change-map.html

Public Redistricting Data

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.

https://www.census.gov/library/visualizations/2021/dec/percent-change-county-population.html

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 NameProposed 2020 Lowest Level of Geography2010 Lowest Level of Geography
Hispanic or Latino Origin of Householder by Race of HouseholderCountyBlock
Household Size by Household Type by Presence of Own ChildrenCountyBlock
Household Type by Age of HouseholderCountyBlock
Households by Presence of People 60 Years and Over by Household TypeCountyBlock
Households by Presence of People 60 Years and Over, Household Size, and Household TypeCountyBlock
Households by Presence of People 75 Years and Over, Household Size, and Household TypeCountyBlock
Household Type by Household SizeCountyBlock
Household Type by Household Size by Race of HouseholderCountyBlock
Relationship by Age for the Population Under 18 YearsCountyBlock
Household Type by Relationship for the Population 65 Years and OverCountyBlock
Household Type by Relationship for the Population 65 Years and Over by RaceCountyBlock
Family Type by Presence and Age of Own ChildrenCountyBlock
Family Type by Presence and Age of Own Children by Race of HouseholderCountyBlock
Age of Grandchildren Under 18 Years Living with A Grandparent HouseholderCountyBlock
Household Type by Relationship by RaceCountyBlock
Average Household Size by AgeTo be determinedBlock
Household Type for the Population in HouseholdsTo be determinedBlock
Household Type by Relationship for the Population Under 18 YearsTo be determinedBlock
Population in Families by AgeTo be determinedBlock
Average Family Size by AgeTo be determinedBlock
Family Type and Age for Own Children Under 18 YearsTo be determinedBlock
Total Population in Occupied Housing Units by TenureTo be determinedBlock
Average Household Size of Occupied Housing Units by TenureTo be determinedBlock
Sex by Age for the Population in HouseholdsCountyTract
Sex by Age for the Population in Households by RaceCountyTract
Presence of Multigenerational HouseholdsCountyTract
Presence of Multigenerational Households by Race of HouseholderCountyTract
Coupled Households by TypeCountyTract
Nonfamily Households by Sex of Householder by Living Alone by Age of HouseholderCountyTract
Group Quarters Population by Sex by Age by Group Quarters TypeStateTract

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. 
Dewey Beach, Delaware

Summer Break

Upon receiving a reminder from WordPress that it’s time to renew my subscription, I looked back and realized I’ve been pretty consistent over the years. Since rebooting this blog in Sept 2017, with few exceptions I’ve fulfilled my goal to write one post per month.

Unfortunately, due to professional and personal constraints I’m going to have to break my streak and put posting on pause for a while. Hopefully I can return to writing in the fall. Until then, enjoy the rest of summer.

Best – Frank

QGIS Example

QGIS 3.16 Tutorial Workbook

I just released a new edition of my introductory QGIS manual for QGIS 3.16 Hannover (the current long term release), and as always I’m providing it under Creative Commons for classroom use and self-directed learning. I’ve also updated my QGIS FAQs handout, which is useful for new folks as a quick reference. This material will eventually move to a Brown University website, but when that happens I’ll still hold on to my page and will link to the new spot. I’m also leaving the previous version of the tutorial written for QGIS 3.10 A Coruna up alongside it, but will pull that down when the fall semester begins.

The new edition has a new title. When I first wrote Introduction to GIS Using Open Source Software, free and open source (FOSS) GIS was a novelty in higher ed. QGIS was a lot simpler, and I had to pull in several different tools to accomplish basic tasks like CRS transformations and calculating natural breaks. Ten years later, many university libraries and labs with GIS services either reference or support QGIS, and the package is infinitely more robust. So a name change to simply Introduction to GIS with QGIS seemed overdue.

My move from Baruch CUNY to Brown prompted me to make several revisions in this version. The biggest change was swapping the NYC-based business site selection example with a Rhode Island-based public policy one in chapters 2 and 3. The goal of the new hypothetical example is to identify public libraries in RI that meet certain criteria that would qualify them to receive funding for after school programs for K-12 public school students (replacing the example of finding an optimal location for a new coffee shop in NYC). In rethinking the examples I endeavored to introduce the same core concepts: attribute table joins, plotting coordinates, and geoprocessing. In this version I do a better job of illustrating and differentiating between creating subsets of features by: selecting by attributes and location, filtering (a new addition), and deleting features. I also managed to add spatial joins and calculated fields to the mix.

Changes to chapter 4 (coordinate reference systems and thematic mapping) were modest; I swapped out the 2016 voter participation data with 2020 data. I slimmed down Chapter 5 on data sources and tutorials, but added an appendix that lists web mapping services that you can add as base maps. Some material was shuffled between chapters, and all in all I cut seven pages from the document to slim it down a bit.

As always, there were minor modifications to be made due to changes between software versions. There were two significant changes. First, QGIS no longer supports 32 bit operating systems for Windows; it’s 64 bit or nothing, but that seems to be fairly common these days. Second, the Windows installer file is much bigger (and thus slower to download), but it helps insure that all dependencies are there. Otherwise, the differences between 3.16 and 3.10 are not that great, at least for the basic material I cover. In the past there was occasionally a lack of consistency regarding basic features and terminology that you’d think would be well settled, but thankfully things are pretty stable this time around.

If you have any feedback or spot errors feel free to let me know. I imagine I’ll be treading this ground again after the next long term release take’s 3.16’s place in Feb / Mar 2022. For the sake of stability I always stick with the long term release and forego the latest ones; if you’re going to use this tutorial I’d recommend downloading the LTR version and not the latest one.

Census Tracts

Call for Proposals: Celebrating the Census in the Journal of Maps

I’m serving as a co-editor for a special issue for the Journal of Maps entitled “Celebrating the Census“. The Journal of Maps is an open access, peer reviewed journal published by the Taylor & Francis Group. The journal is distinct in that all articles feature maps and spatial diagrams as the focal point for studying geographic phenomena from both a physical / environmental and social science perspective.

Here’s the official synopsis for this census-themed special issue:

We invite contributions to a special issue of the Journal of Maps focused upon the evolving character and cartographic opportunities offered by traditional census statistics and the impact of transitioning from these sources of population data at a range of spatial scales into a new era of big data assembly. In so doing, the special issue marks two important events taking place in the UK during 2021 in the history of British Censuses and seeks contributions that reflect the past transition of population data cartography through the digital era of the last 50 years and anticipates its transformation into the big data era of the foreseeable future.

While the issue marks the 100th anniversary of the UK census, submissions concerning census mapping from around the world are welcome and encouraged in these topic areas, including but not limited to:

  • Spatial and statistical consistency over time
  • People on the move
  • Mapping people through space and time
  • Mapping morbidity and mortality
  • Politics and population data
  • International comparison of demographic mapping
  • Before and after population mapping using censuses and administrative sources
  • Population data and mapping human-environmental interaction
  • Transition and evolution in population mapping

Visit the special issue announcement for full details. Deadlines:

  • April 30, 2021: a short draft (500-word limit) outlining themes and scope of the paper, preferably with a sample map
  • June 14, 2021: abstracts will be selected by the editorial team by this date
  • Sept 5, 2021: completed paper (4000-word limit) is due

The issue will be published sometime in 2022.

Brown University on OpenTopoMap

A New Year and a New Start

I have some news! After 13 1/2 years, January 31, 2021 will be my last day as the Geospatial Data Librarian at Baruch College, City University of New York (CUNY). On February 1, 2021, I will be the new GIS and Data Librarian at Brown University in Providence, Rhode Island!

It’s an exciting opportunity that I’m looking forward to. I will be building geospatial information and data services in the library from the ground up, in concert with many new colleagues. I will be working closely with the Population Studies Training Center (PSTC) and the Spatial Structures in Social Sciences (S4) as well as the Center for Digital Scholarship within the library. Several aspects of the position will be similar, as I will continue to provide research and consultation services, create research guides and tutorials, teach workshops, collect and create datasets, and eventually build and manage a data repository and small lab where we’ll provide services and peer mentor students.

The resources I’ve created at Baruch CUNY will remain accessible, and eventually a new person will take the reins. I have moved the latest materials for the GIS Practicum, my introductory QGIS tutorial and workshop, to this website and I hope to continue updating and maintaining this resource. There are a lot of people throughout CUNY that I’m going to miss, at: the Newman Library, the CUNY Institute for Demographic Research, the Weissman Center for International Business, the Marxe School, Baruch’s Journalism Department, the Geography Department at Lehman College, the Digital Humanities program and the CUNY Mapping Service at the CUNY Graduate Center, and many others.

I will continue writing posts and sharing tips and resources here based on my new adventures at Brown, but may need a little break as I transition… stay tuned!

Best – Frank

NYC and NYMA Pop Change Graph 2000 to 2019

New York’s Population and Migration Trends in the 2010s

The Weissman Center for International Business at Baruch College just published my paper, “New York’s Population and Migration Trends in the 2010s“, as part of their Occasional Paper Series. In the paper I study population trends over the last ten years for both New York City (NYC) and the greater New York Metropolitan Area (NYMA) using annual population estimates from the Census Bureau (vintage 2019), county to county migration data (2011-2018) from the IRS SOI, and the American Community Survey (2014-2018). I compare NYC to the nine counties that are home to the largest cities in the US (cities with population greater than 1 million) and the NYMA to the 13 largest metro areas (population over 4 million) to provide some context. I conclude with a brief discussion of the potential impact of COVID-19 on both the 2020 census count and future population growth. Most of the analysis was conducted using Python and Pandas in Jupyter Notebooks available on my GitHub. I discussed my method for creating rank change grids, which appear in the paper’s appendix and illustrate how the sources and destinations for migrants change each year, in my previous post.

Terminology

  • Natural increase: the difference between births and deaths
  • Domestic migration: moves between two points within the United States
  • Foreign migration: moves between the United States and a US territory or foreign country
  • Net migration: the difference between in-migration and out-migration (measured separately for domestic and foreign)
  • NYC: the five counties / boroughs that comprise New York City
  • NYMA: the New York Metropolitan Area as defined by the Office of Management and Budget in Sept 2018, consists of 10 counties in NY State (including the 5 NYC counties), 12 in New Jersey, and one in Pennsylvania
Map of the New York Metropolitan Area
The New York-Newark-Jersey City, NY-NJ-PA Metropolitan Area

Highlights

  • Population growth in both NYC and the NYMA was driven by positive net foreign migration and natural increase, which offset negative net domestic migration.
  • Population growth for both NYC and the NYMA was strong over the first half of the decade, but population growth slowed as domestic out-migration increased from 2011 to 2017.
  • NYC and the NYMA began experiencing population loss from 2017 forward, as both foreign migration and natural increase began to decelerate. Declines in foreign migration are part of a national trend; between 2016 and 2019 net foreign migration for the US fell by 43% (from 1.05 million to 595 thousand).
  • The city and metro’s experience fit within national trends. Most of the top counties in the US that are home to the largest cities and many of the largest metropolitan areas experienced slower population growth over the decade. In addition to NYC, three counties: Cook (Chicago), Los Angeles, and Santa Clara (San Jose) experienced actual population loss towards the decade’s end. The New York, Los Angeles, and Chicago metro areas also had declining populations by the latter half of the decade.
  • Most of NYC’s domestic out-migrants moved to suburban counties within the NYMA (representing 38% of outflows and 44% of net out-migration), and to Los Angeles County, Philadelphia County, and counties in Florida. Out-migrants from the NYMA moved to other large metros across the country, as well as smaller, neighboring metros like Poughkeepsie NY, Fairfield CT, and Trenton NJ. Metro Miami and Philadelphia were the largest sources of both in-migrants and out-migrants.
  • NYC and the NYMA lack any significant relationships with other counties and metro areas where they are net receivers of domestic migrants, receiving more migrants from those places than they send to those places.
  • NYC and the NYMA are similar to the cities and metros of Los Angeles and Chicago, in that they rely on high levels foreign migration and natural increase to offset high levels of negative domestic migration, and have few substantive relationships where they are net receivers of domestic migrants. Academic research suggests that the absolute largest cities and metros behave this way; attracting both low and high skilled foreign migrants while redistributing middle and working class domestic migrants to suburban areas and smaller metros. This pattern of positive foreign migration offsetting negative domestic migration has characterized population trends in NYC for many decades.
  • During the 2010s, most of the City and Metro’s foreign migrants came from Latin America and Asia. Compared to the US as a whole, NYC and the NYMA have slightly higher levels of Latin American and European migrants and slightly lower levels of Asian and African migrants.
  • Given the Census Bureau’s usual residency concept and the overlap in the onset the of COVID-19 pandemic lock down with the 2020 Census, in theory the pandemic should not alter how most New Yorkers identify their usual residence as of April 1, 2020. In practice, the pandemic has been highly disruptive to the census-taking process, which raises the risk of an under count.
  • The impact of COVID-19 on future domestic migration is difficult to gauge. Many of the pandemic destinations cited in recent cell phone (NYT and WSJ) and mail forwarding (NYT) studies mirror the destinations that New Yorkers have moved to between 2011 and 2018. Foreign migration will undoubtedly decline in the immediate future given pandemic disruptions, border closures, and restrictive immigration policies. The number of COVID-19 deaths will certainly push down natural increase for 2020.