Spatial autocorrelation and data uncertainty in the American Community Survey: a critique

ABSTRACT

We argue that the use of American Community Survey (ACS) data in spatial autocorrelation statistics without considering error margins is critically problematic. Public health and geographical research has been slow to recognize high data uncertainty of ACS estimates, even though ACS data are widely accepted data sources in neighborhood health studies and health policies. Detecting spatial autocorrelation patterns of health indicators on ACS data can be distorted to the point that scholars may have difficulty in perceiving the true pattern. We examine the statistical properties of spatial autocorrelation statistics of areal incidence rates based on ACS data. In a case study of teen birth rates in Mecklenburg County, North Carolina, in 2010, Global and Local Moran’s I statistics estimated on 5-year ACS estimates (2006–2010) are compared to ground truth rate estimates on actual counts of births certificate records and decennial-census data (2010). Detected spatial autocorrelation patterns are found to be significantly different between the two data sources so that actual spatial structures are misrepresented. We warn of the possibility of misjudgment of the reality and of policy failure and argue for new spatially explicit methods that mitigate the biasedness of statistical estimations imposed by the uncertainty of ACS data.

KEYWORDS:

• American Community Survey
• data uncertainty
• spatial autocorrelation
• small area estimates
• statistical attenuation

Acknowledgments

This research received approval from the University of North Carolina at Charlotte Institutional Research Board. We wish to acknowledge the work of guest editors for this special issue, Yongwan Chun, Daniel Griffith and Mei-Po Kwan. We appreciate comments made by the anonymous reviewers on earlier versions of the manuscript. They have contributed to enhancing the work reported here in meaningful ways. We also deeply thank Daniel Yonto for help in data acquisition. This work used R 3.3.3 and R Package spdep, ggplot2 and GISTools.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Paul H. Jung

Paul H. Jung is a Ph.D. candidate at Department of Geography and Earth Sciences, University of North Carolina at Charlotte. His research interests include spatial economic analysis of global trade systems, spatial econometrics, spatial data uncertainty, and spatial interaction modeling.

Jean-Claude Thill

Jean-Claude Thill is Knight Distinguished Professor in the Department of Geography and Earth Sciences, University of North Carolina at Charlotte. His research interests include transportation and mobility systems, geospatial analytics, urban and regional modeling, and regional public policy.

Michele Issel

Michele Issel a professor at Department of Public Health Sciences, University of North Carolina at Charlotte. Her research interests include community-based perinatal services, health program planning and evaluation, public health nursing, and public health workforce development.