An integrated classification scheme for mapping estimates and errors of estimation from the American Community Survey

Ran Wei, Daoqin Tong, Jeff M. Phillips

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Demographic and socio-economic information provided by the American Community Survey (ACS) have been increasingly relied upon in many planning and decision making contexts due to its timely and current estimates. However, ACS estimates are well known to be subject to larger sampling errors with a much smaller sample size compared with the decennial census data. To support the assessment of the reliability of ACS estimates, the US Census Bureau publishes a margin of error at the 90% confidence level alongside each estimate. While data error or uncertainty in ACS estimates has been widely acknowledged, little has been done to devise methods accounting for such error or uncertainty. This article focuses on addressing ACS data uncertainty issues in choropleth mapping, one of the most widely used methods to visually explore spatial distributions of demographic and socio-economic data. A new classification method is developed to explicitly integrate errors of estimation in the assessment of within-class variation and the associated groupings. The proposed method is applied to mapping the 2009–2013 ACS estimates of median household income at various scales. Results are compared with those generated using existing classification methods to demonstrate the effectiveness of the new classification scheme.

Original languageEnglish (US)
Pages (from-to)95-103
Number of pages9
JournalComputers, Environment and Urban Systems
Volume63
DOIs
StatePublished - May 1 2017
Externally publishedYes

Keywords

  • ACS
  • Classification
  • Uncertainty

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Ecological Modeling
  • Environmental Science(all)
  • Urban Studies

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