Paper Info
Title
An integrated classification scheme for mapping estimates and errors of estimation from the American Community Survey.
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.
Year
DOI
Venue
2017
10.1016/j.compenvurbsys.2016.04.003
Computers, Environment and Urban Systems
Keywords
Field
DocType
Classification,Uncertainty,ACS
Econometrics,Data mining,Household income,Classification scheme,Choropleth Mapping,Statistics,Confidence interval,Geography,Margin of error,Census,Sample size determination,American Community Survey
Journal
Volume
ISSN
Citations 
63
0198-9715
1
PageRank 
References 
Authors
0.48
4
3
Name
Order
Citations
PageRank
Ran Wei192.34
Daoqin Tong2617.74
Jeff M. Phillips353649.83