Navigating Statistical Uncertainty: How Urban and Regional Planners Understand and Work With American Community Survey (ACS) Data for Guiding Policy

Jurjevich, Jason R.; Griffin, Amy L.; Spielman, Seth E.; Folch, David C.; Merrick, Meg; Nagle, Nicholas N.

doi:10.1080/01944363.2018.1440182

Cited by 20 publications

(18 citation statements)

References 22 publications

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“…A limitation of working with sample-derived ACS data are that statistical uncertainty increases as sample size and geographical unit decreases. In following expert recommendations ( 56 ), we report MOE values where possible. We convey statistical reliability using a statistic called the coefficient of variation (CV).…”

Section: Methodsmentioning

confidence: 99%

Geographies of insecure water access and the housing–water nexus in US cities

Meehan

Jurjevich

Chun

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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124

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Safe, reliable, and equitable water access is critical to human health and livelihoods. In the United States, an estimated 471,000 households or 1.1 million individuals lack a piped water connection and 73% of households are located in cities, close to networked supply. In this study, we undertake a nationwide analysis of urban water access in the United States, with the aim of explaining the drivers of infrastructural inequality in the 50 largest metropolitan areas. Drawing on statistical analysis and regression modeling of census microdata at the household scale, our analysis reveals spatial and sociodemographic patterns of racialized, class-based, and housing disparities that characterize plumbing poverty. Among unplumbed households, we show that households headed by people of color are almost 35% more likely to lack piped water as compared to white, non-Hispanic households. Precarious housing conditions are an equally strong predictor: Renter-occupied households in the 50 largest US metros were 1.61 times more likely than owner-occupied households to lack piped water. We argue that insecure domestic water access in the United States should be understood as a housing issue that reflects structural inequalities of race and class, particularly in cities with widening wealth gaps. The article concludes with a call for research and action at the intersection of water provision, housing, and social inequality—a paradigm we call the housing–water nexus.

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Section: Methodsmentioning

confidence: 99%

Geographies of insecure water access and the housing–water nexus in US cities

Meehan

Jurjevich

Chun

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

124

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“…However, rural researchers must be cautious when using this data because of the high margin of error that is often associated with their small sample sizes. After surveying and interviewing urban and regional planners, Jurjevich et al (2018) found that most planners do not understand the statistical uncertainty of ACS data and have a difficult time knowing how to portray that uncertainty to stakeholders. This statistical uncertainty cannot be ignored by researchers who are dealing with some of the smallest populations and must be navigated carefully and precisely to accurately represent the results for local leaders.…”

Section: Interpreting Acs Datamentioning

confidence: 99%

“…These suggestions can be summarized in four guidelines, which read similar to guidelines laid out by Jurjevich et al (2018), with slight modifications and additions. (1) Report the MOE with descriptive statistics; (2) Explore the variation in data quality; (3) Reduce uncertainty by combining estimates; and (4) Run the model with and without high MOE estimates.…”

Section: Interpreting Acs Datamentioning

confidence: 99%

“…Therefore, while Census tracts or block groups may have less uncertainty tied to their estimates and produce better results mathematically, zooming into the Main Street of a small town with Census place data allows me to better capture the vitality of the community center, conceptually. This is where the guidelines I laid out, informed by past researchers (Bazuin and Fraser 2013;Folch et al 2016;Jurjevich et al 2018;Spielman et al 2014), should be utilized. (1) Report the MOE with descriptive statistics; (2) Explore the variation in data quality; (3) Reduce uncertainty by combining estimates; and (4) Run the model with and without high MOE estimates.…”

Section: Acs Obstacle: Margin Of Errormentioning

confidence: 99%

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A Guide to the American Community Survey (ACS) for the Rural Researcher: Unpacking the Conceptual and Technical Aspects of Using Secondary Data for Rural Research^☆

LeBeau

2023

Rural Sociology

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Sparsely populated rural areas are susceptible to high levels of error in their data, making it difficult to examine patterns and trends across geographies. This article aims to advance research methods for rural researchers by offering guidelines for navigating high levels of error associated with the American Community Survey (ACS). The ACS presents a useful source of U.S. community level data for rural researchers to utilize in school–community research but not without its difficulties. The small population sizes of rural communities often translate to large margins of error in the data, presenting a degree of uncertainty in the actual measure. To illustrate challenges and best practice, the author conducts a case study of the relationship between the presence of schools and economic vitality of rural communities in Indiana using ACS data. The author demonstrates how to examine the error in the data, introduces options to reduce uncertainty, and ultimately, explains how to move forward with the data, working with the margin of error and acknowledging its presence in the analysis and results. This article offers suggestions and techniques to assist rural researchers in navigating ACS obstacles so that they might produce transparent results with as little uncertainty as possible.

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“…The visibility of uncertainty was problematic – should researchers and local stakeholders simply ignore the MOEs? As Jurjevich et al (2018) have shown, many users of ACS data, including local planners and stakeholders, simply do not know how to interpret the uncertainty embedded in MOEs. Practically speaking, wider margins of error mean more uncertainty, so that it can be difficult to know what the ‘true’ neighbourhood characteristics of a place actually are.…”

Section: Uncertain Datamentioning

confidence: 99%

Quantitative methods I: Reckoning with uncertainty

Franklin

2022

Progress in Human Geography

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In this report, I address the timeless topic of uncertainty. Quantitative methods are in flux and uncertainty provides a useful vantage point for assessing these changes. I draw particular attention to two forces at odds with each other: on the one hand, innovative methods and data that allow researchers to embrace uncertainty, and on the other, recent trends towards deliberate amplification of uncertainty, in the form of differential privacy methods which potentially limit our capacity for measuring and understanding the world around us. I close by highlighting some implications for users and producers of quantitative methods and data in geography.

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Navigating Statistical Uncertainty: How Urban and Regional Planners Understand and Work With American Community Survey (ACS) Data for Guiding Policy

Cited by 20 publications

References 22 publications

Geographies of insecure water access and the housing–water nexus in US cities

Geographies of insecure water access and the housing–water nexus in US cities

A Guide to the American Community Survey (ACS) for the Rural Researcher: Unpacking the Conceptual and Technical Aspects of Using Secondary Data for Rural Research^☆

Quantitative methods I: Reckoning with uncertainty

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Navigating Statistical Uncertainty: How Urban and Regional Planners Understand and Work With American Community Survey (ACS) Data for Guiding Policy

Cited by 20 publications

References 22 publications

Geographies of insecure water access and the housing–water nexus in US cities

Geographies of insecure water access and the housing–water nexus in US cities

A Guide to the American Community Survey (ACS) for the Rural Researcher: Unpacking the Conceptual and Technical Aspects of Using Secondary Data for Rural Research☆

Quantitative methods I: Reckoning with uncertainty

Contact Info

Product

Resources

About

A Guide to the American Community Survey (ACS) for the Rural Researcher: Unpacking the Conceptual and Technical Aspects of Using Secondary Data for Rural Research^☆