mpcblog – Page 2 – Use It for Good

Celebrating 30 Years: Three Decades of IPUMS Data

July 18, 2024July 17, 2024 by mpcblog

By Diana L. Magnuson; Curator and Historian, ISRDI

"Celebrating 30 years: three decades of IPUMS data" display case with promotional materials, swag items, and historical IPUMS items — “Celebrating 30 years: three decades of IPUMS data” display case at ISRDI Headquarters

“Celebrating 30 Years: Three Decades of IPUMS Data,” the current exhibit at ISRDI Headquarters, highlights thirty years of data innovation at the University of Minnesota. In the late 1980s, the Social History Research Laboratory at the University of Minnesota’s History Department proposed “the creation of a single integrated microdata series composed of public use samples for every year … with the exception of the 1890 census, which was destroyed by fire.” The primary aim was to make the U.S. census microdata “as compatible over time as possible while losing little, if any, of the detail in the original datasets.” (Integrated Public Use Microdata Series: A Prospectus).

Steven Ruggles remembers the moment he went into the History Department lounge on the sixth floor of the Social Science Tower and said, “IPUMS! Integrated Public Use Microdata Series! Isn’t that a great idea?” The response from the graduate research assistants was not enthusiastic. “What a terrible name! You can’t call it that!” According to Ruggles, “It was universal; everyone thought it was just a horrible name … It wasn’t a bad idea to propose, just a terrible thing to call it.” After a brief quandary over pronunciation (Ī-pŭms or Ĭ-pŭms), the name has stuck and is now synonymous with social research, data innovation, and free access. And for the record, we don’t care how you pronounce it, just as long as you cite it!

Digitizing and Exploring Qatar’s Population Censuses

June 24, 2024June 24, 2024 by mpcblog

By Shine Min Thant

Qatar, a small yet influential state in the Middle East, is a very interesting case study for demographic research because of its rapid development over the past thirty years. Qatar occupies a peninsula only slightly larger than the U.S. state of Rhode Island that juts out into the Persian Gulf from its border with Saudi Arabia. The country has experienced relatively rapid economic growth since the late 20th century, mainly due to its vast reserves of natural gas and oil. This newfound wealth allowed Qatar to invest heavily in its healthcare, infrastructure, and education – therefore making the country an ideal case study for social change and development. Additionally, a recent surge in Qatar’s immigrant population (which constitutes over 78 percent of the population) also makes it an ideal country to study social mobility and social change.

As part of the ISRDI Diversity Fellowship Program, I worked with Dr. Tracy Kugler, Professor Steven Manson, Professor Evan Roberts, and undergraduate student Rawan AlGahtani on a project to examine Qatar’s change using census data from 1984, 1997, and 2004. Summary tables from all three censuses were previously only available as printed documents. As a first step, we needed to transform the data from a hard-to-get printed format to widely accessible IPUMS IHGIS format. This process included multiple steps from conducting optical character recognition (OCR) to conducting data quality checks using R scripts (Figure 1).

Figure 1: IPUMS IHGIS Workflow

New Data Release from IPUMS International – From Mexico to MOSAIC

May 9, 2024May 9, 2024 by mpcblog

By Lara Cleveland and Jane Lee

IPUMS International has released new data! Eighteen new census samples have been added to the collection, including data from Côte d’Ivoire, which is new to IPUMS International. Newly released census samples include Cambodia (2019), Côte d’Ivoire (1988, 1998), Denmark (1845, 1880, 1885), Laos (1995, 2015), Mexico (2020), Peru (2017), Puerto Rico (2015, 2020), Switzerland (2011), United Kingdom (1961, 1971), United States (2015, 2020) and Vietnam (2019). As always, we gratefully acknowledge the national statistical offices of all the countries partnering with IPUMS International to make data available for research.

New geography variables are also now available with harmonized migration variables at the second-administrative level; the codes for the newly released migration variables match existing IPUMS International geography codes and labels. As an example, the geographic units in the migration variable for Mexico at the municipo level (place of residence 5 years ago, MIG2_5_MX) are reconciled to the boundaries for place of current residence (GEO2_MX).

This is a map showing the 2020 census 5-year migration rates for GEO1 in Mexico, and GEO2 in Nuevo Leon state — 2020 census 5-year migration rates for GEO1 in Mexico, and GEO2 in Nuevo Leon state. Map by Quinn Heimann

New IPUMS DHS Climate Change and Health Research Hub

April 23, 2024April 23, 2024 by mpcblog

By Miriam King, Senior Research Scientist

Men wading through flood waters In October 2023, the World Health Organization stated, “3.6 billion people already live in areas highly susceptible to climate change. Between 2030 and 2050, climate change is expected to cause approximately 250,000 additional deaths per year, from undernutrition, malaria, diarrhea and heat stress alone.”

The Demographic and Health Surveys (DHS) are an ideal source for research on the health effects of climate change. Since the 1980s, the DHS has collected a broad range of nationally representative health data from over 90 countries. With supplemental funding from NICHD, harmonized DHS data from IPUMS (dhs.ipums.org) is now doing more to support research on the effects of climate change on health. We are adding new contextual variables; we are integrating data from Malaria Indicator Surveys (MIS); and we are offering guidance through the new Climate Change and Health Research Hub.

Sound research on climate change and health requires combining social science and health data with natural science data. While social scientists and public health researchers have considerable experience analyzing health survey data, few have been trained in simultaneously employing data on environmental factors. This knowledge gap is addressed by the Climate Change and Health Research Hub, under the leadership of Dr. Kathryn Grace and Senior Data Analyst Finn Roberts.

IPUMS Announces 2023 Research Award Recipients

April 29, 2024April 15, 2024 by mpcblog

IPUMS is excited to announce the winners of its annual IPUMS Research Awards. These awards honor the best published research and nominated graduate student papers from 2023 that use IPUMS data to advance or deepen our understanding of social and demographic processes.

The 2023 competition awarded prizes for the best published and best graduate student research in eight categories:

IPUMS USA, providing data from the U.S. decennial censuses, the American Community Survey, and includes full count data, from 1850 to the present.
IPUMS CPS, providing data from the monthly U.S. labor force survey, the Current Population Survey (CPS), from 1962 to the present.
IPUMS International, providing harmonized data contributed by more than 100 international statistical office partners; it currently includes information on over 1 billion people in more than 547 censuses and surveys from around the world, from 1960 forward.
IPUMS Health Surveys, which makes available the U.S. National Health Interview Survey (NHIS) and the Medical Expenditure Panel Survey (MEPS).
IPUMS Spatial, covering IPUMS NHGIS, IPUMS IHGIS, IPUMS Terra, and IPUMS CDOH. NHGIS includes U.S. census summary tables and GIS boundary files from 1790 to the present; IHGIS provides data tables from population and housing censuses as well as agricultural censuses from around the world; Terra (now decommissioned) provided data on population and the environment from 1960 to the present; CDOH provides access to measures of disparities, policies, and counts, by state and county, for historically marginalized populations in the US.
IPUMS Global Health, providing harmonized data from the Demographic and Health Surveys and the Performance Monitoring and Accountability surveys, for low and middle-income countries from the 1980s to the present.
IPUMS Time Use, providing time diary data from the U.S. and around the world from 1965 to the present.
IPUMS Excellence in Research, The IPUMS mission of democratizing data demands that we increase representation of scholars from groups that are systemically excluded in research spaces. This award is an opportunity to highlight and reward outstanding work using any of the IPUMS data collections by authors who are underrepresented in social science research*.

Over 1,200 publications based on IPUMS data appeared in journals, magazines, and newspapers worldwide last year. From these publications and from nominated graduate student papers, the award committees selected the 2023 honorees.

2020 Public Use Microdata Area (PUMA) Updates in the 2022 American Community Survey

March 25, 2024March 25, 2024 by mpcblog

By Natalie Mac Arthur, Senior Research Associate, SHADAC

Thank you to our collaborators at the State Health Access Data Assistance Center (SHADAC) for contributing this blog post; view the original blog on the SHADAC website.

A Public Use Microdata Area (PUMA) is a type of geographic unit created for statistical purposes. PUMAs represent geographic areas with a population size of 100,000–200,000 within a state (PUMAs cannot cross state lines). PUMAs are the smallest level of geography available in American Community Survey (ACS) microdata. They are designed to protect respondent confidentiality while simultaneously allowing analysts to produce estimates for small geographic areas.

Every ten years, the decennial census results are used to redefine ACS PUMA boundaries to account for shifts in population and continue to maintain respondent confidentiality. This process is intended to yield geographic definitions that are meaningful to many stakeholders.

Most recently, new PUMAs were created based on the 2020 Census; these 2020 PUMAs were implemented in the ACS starting in the 2022 data year. Although Public Use Microdata Area components remain consistent to the extent possible, they are updated based on census results and revised criteria. Therefore, they are not directly comparable with PUMAs from any previous ACS data years. For example, the 2020 PUMAs used in the 2022 data year are distinct from the 2010 PUMAs, which were used in the 2012–2021 ACS data years.

The 2020 PUMAs were created based on definitions that include two substantive changes relative to the 2010 PUMAs:

1) An increase in the minimum population threshold for the minimum size of partial counties from 2,400 to 10,000. Increasing the population minimum for a PUMA-county part aims to further protect the confidentiality of respondents. However, exceptions are allowed on a case-by-case basis in order to maintain the stability of PUMA definitions (that were based on the previous minimum of 2,400) and due to unique geography.

2) Allowing noncontiguous geographic areas. Allowing PUMAs to include noncontiguous geographic areas aims to avoid unnecessarily splitting up demographic groups in order to provide more meaningful data. This change is not intended to create highly fragmented PUMAs.

Other than the two changes listed above, PUMA criteria remained consistent, such as treating 100,000 as a strict minimum population size for PUMAs. The maximum population size for PUMAs can exceed a population of 200,000 in certain instances due to expected population declines or geographic constraints.

Bivariate Proportional Symbol Maps, Part 2: Design Tips with Instructions for ArcGIS Pro

February 22, 2024February 22, 2024 by mpcblog

By Jonathan Schroeder, IPUMS Research Scientist, NHGIS Project Manager

How to make effective bivariate proportional symbol maps

A map of the share of population under age 18 in the Miami area in 2020. There is one colored circle for each census tract. There are five colors ranging from dark blue (representing less than 15% under age 18) to light green (representing 20 to 25% under age 18) to brown (representing 30% or more). The circle sizes correspond to tract populations. Most circles have similar sizes, representing around 1,000 to 10,000 people. The circles cluster together forming groups where there are more tracts and more people. The circles in central Miami and along the coast are bluer than elsewhere. — A bivariate proportional symbol map.
Click map for larger version.

In Part 1 of this blog series, I introduced bivariate proportional symbol maps and shared some examples to demonstrate their advantages. In short, when they’re well designed, they can make it easy to see multiple dimensions of a population all at once: size, composition, and spatial distribution.

A key part of that statement is, “when they’re well designed.” Standard mapping tools can make it easy to get started, but getting all the way to a good design still takes some extra effort.

In this Part 2 post, I discuss some key design considerations for bivariate proportional symbol maps, and I provide specific instructions to help you get to a good design.

Software considerations

I used Esri’s ArcGIS Pro to create the examples here and in Part 1. The design tips I share below should be relevant for any mapping tool, but my instructions are specifically for ArcGIS Pro (version 3.2). I expect there are ways to achieve similar designs with QGIS, R, Python, etc., quite possibly more easily than with ArcGIS Pro. I can only say that it’s easier to create effective bivariate proportional symbols now with ArcGIS Pro than it was with its predecessor ArcMap.

As I proceed, I’ll flag which instructions pertain specifically to ArcGIS Pro. All other tips are “tool neutral.”

General tip: Match size to “size” and color to “character”

When selecting which features to map, a framework that works consistently well is to use symbol color to represent an intensive property—e.g., the share of population under 18 years old, average household size, median household income, or the share of votes cast for a candidate—and use symbol size to represent the number of cases to which the intensive property pertains—e.g., the total population (when color corresponds to a population share) or the count of households (when color corresponds to average household size or median household income).

This framework enables the map to illustrate both the spatial distribution of the mapped characteristics and the frequency distribution of the intensive property—e.g., not only where a candidate received large or small vote shares but also how many votes were cast in each of those areas. Other frameworks can also work well (e.g., see the change maps in Part 1), but it’s generally very helpful if the two mapped characteristics relate to each other in a way that corresponds intuitively with “size” and “color.”